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1
Zabolotskikh, E. V. "Review of methods to retrieve sea ice parameters from satellite microwave radiometer data." Известия Российской академии наук. Физика атмосферы и океана 55, no. 1 (April 16, 2019): 128–51. http://dx.doi.org/10.31857/s0002-3515551128-151.
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Sea ice monitoring using long-term data of satellite passive microwave instruments enables climate change estimates. These numerical estimates depend on the methods used for sea ice parameter retrievals. This work presents a review of methods to retrieve sea ice parameters from the data of satellite microwave radiometers. Physical modeling of the sea ice–ocean–atmosphere microwave radiation provides the means to identify the general sources of the retrieval errors and to classify the methods by used approach. The basics of the algorithms are formulated along with assumptions and approximations as well as the data used for the algorithm verification. Weather filters are considered to identify the areas of open water. A comparative analysis of method advantages and limitations is given related to sea ice concentration retrievals from such satellite instruments as the series of Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR). A review of the basic satellite sea ice products based on SSM/I, AMSR-E and AMSR2 data is complemented by the list of the essential internet resources for operational and historical sea ice data.
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Banks, Andrew Clive, Riho Vendt, Krista Alikas, Agnieszka Bialek, Joel Kuusk, Christophe Lerebourg, Kevin Ruddick, et al. "Fiducial Reference Measurements for Satellite Ocean Colour (FRM4SOC)." Remote Sensing 12, no. 8 (April 22, 2020): 1322. http://dx.doi.org/10.3390/rs12081322.
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Earth observation data can help us understand and address some of the grand challenges and threats facing us today as a species and as a planet, for example climate change and its impacts and sustainable use of the Earth’s resources. However, in order to have confidence in earth observation data, measurements made at the surface of the Earth, with the intention of providing verification or validation of satellite-mounted sensor measurements, should be trustworthy and at least of the same high quality as those taken with the satellite sensors themselves. Metrology tells us that in order to be trustworthy, measurements should include an unbroken chain of SI-traceable calibrations and comparisons and full uncertainty budgets for each of the in situ sensors. Until now, this has not been the case for most satellite validation measurements. Therefore, within this context, the European Space Agency (ESA) funded a series of Fiducial Reference Measurements (FRM) projects targeting the validation of satellite data products of the atmosphere, land, and ocean, and setting the framework, standards, and protocols for future satellite validation efforts. The FRM4SOC project was structured to provide this support for evaluating and improving the state of the art in ocean colour radiometry (OCR) and satellite ocean colour validation through a series of comparisons under the auspices of the Committee on Earth Observation Satellites (CEOS). This followed the recommendations from the International Ocean Colour Coordinating Group’s white paper and supports the CEOS ocean colour virtual constellation. The main objective was to establish and maintain SI traceable ground-based FRM for satellite ocean colour and thus make a fundamental contribution to the European system for monitoring the Earth (Copernicus). This paper outlines the FRM4SOC project structure, objectives and methodology and highlights the main results and achievements of the project: (1) An international SI-traceable comparison of irradiance and radiance sources used for OCR calibration that set measurement, calibration and uncertainty estimation protocols and indicated good agreement between the participating calibration laboratories from around the world; (2) An international SI-traceable laboratory and outdoor comparison of radiometers used for satellite ocean colour validation that set OCR calibration and comparison protocols; (3) A major review and update to the protocols for taking irradiance and radiance field measurements for satellite ocean colour validation, with particular focus on aspects of data acquisition and processing that must be considered in the estimation of measurement uncertainty and guidelines for good practice; (4) A technical comparison of the main radiometers used globally for satellite ocean colour validation bringing radiometer manufacturers together around the same table for the first time to discuss instrument characterisation and its documentation, as needed for measurement uncertainty estimation; (5) Two major international side-by-side field intercomparisons of multiple ocean colour radiometers, one on the Atlantic Meridional Transect (AMT) oceanographic cruise, and the other on the Acqua Alta oceanographic tower in the Gulf of Venice; (6) Impact and promotion of FRM within the ocean colour community, including a scientific road map for the FRM-based future of satellite ocean colour validation and vicarious calibration (based on the findings of the FRM4SOC project, the consensus from two major international FRM4SOC workshops and previous literature, including the IOCCG white paper on in situ ocean colour radiometry).
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Poulsen, C. A., P. D. Watts, G. E. Thomas, A. M. Sayer, R. Siddans, R. G. Grainger, B. N. Lawrence, E. Campmany, S. M. Dean, and C. Arnold. "Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR." Atmospheric Measurement Techniques Discussions 4, no. 2 (April 28, 2011): 2389–431. http://dx.doi.org/10.5194/amtd-4-2389-2011.
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Abstract. Clouds play an important role in balancing the Earth's radiation budget. Clouds reflect sunlight which cools the Earth, and also trap infrared radiation in the same manner as greenhouse gases. Changes in cloud cover and cloud properties over time can have important consequences for climate. The Intergovernmental Panel for Climate Change (IPCC) has identified current gaps in the understanding of clouds and related climate feedback processes as a leading cause of uncertainty in forecasting climate change. In this paper we present an algorithm that uses optimal estimation to retrieve cloud parameters from satellite multi-spectral imager data, in particular the Along-Track Scanning Radiometers ATSR-2 and AATSR. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase. Importantly, the technique also provides estimated errors along with the retrieved values and quantifies the consistency between retrieval representation of cloud and satellite radiances. This should enable the effective use of the products for comparison with climate models or for exploitation via data assimilation. The technique is evaluated by performing retrieval simulations for a variety of simulated single layer and multi-layer conditions. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed. This algorithm has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 year consistent record for climate research (Sayer et al., 2010).
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Urabe, Tomoyuki, Xiaoxiong Xiong, Taichiro Hashiguchi, Shigemasa Ando, Yoshihiko Okamura, and Kazuhiro Tanaka. "Radiometric Model and Inter-Comparison Results of the SGLI-VNR On-Board Calibration." Remote Sensing 12, no. 1 (December 23, 2019): 69. http://dx.doi.org/10.3390/rs12010069.
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The Second Generation Global Imager (SGLI) on Global Change Observation Mission–Climate (GCOM-C) satellite empowers surface and atmospheric measurements related to the carbon cycle and radiation budget, with two radiometers of Visible and Near Infrared Radiometer (SGLI-VNR) and Infrared Scanning Radiometer (SGLI-IRS) that perform a wide-band (380 nm–12 µm) optical observation not only with as wide as a 1150–1400 km field of view (FOV), but also with as high as 0.25–0.5 km resolution. Additionally, polarization and along-track slant view observations are quite characteristic of SGLI. It is important to calibrate radiometers to provide the sensor data records for more than 28 standard products and 23 research products including clouds, aerosols, ocean color, vegetation, snow and ice, and other applications. In this paper, the radiometric model and the first results of on-board calibrations on the SGLI-VNR, which include weekly solar and light-emitting diode (LED) calibration and monthly lunar calibration, will be described. Each calibration data was obtained with corrections, where beta angle correction and avoidance of reflection from multilayer insulation (MLI) were applied for solar calibration; LED temperature correction was performed for LED calibration; and the GIRO (GSICS (Global Space-based Inter-Calibration System) Implementation of the ROLO (RObotic Lunar Observatory) model) model was used for lunar calibration. Results show that the inter-comparison of the relative degradation amount between these three calibrations agreed to within 1% or less.
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De Mazière, Martine, Anne M. Thompson, Michael J. Kurylo, Jeannette D. Wild, Germar Bernhard, Thomas Blumenstock, Geir O. Braathen, et al. "The Network for the Detection of Atmospheric Composition Change (NDACC): history, status and perspectives." Atmospheric Chemistry and Physics 18, no. 7 (April 11, 2018): 4935–64. http://dx.doi.org/10.5194/acp-18-4935-2018.
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Abstract. The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 90 stations making high-quality measurements of atmospheric composition that began official operations in 1991 after 5 years of planning. Apart from sonde measurements, all measurements in the network are performed by ground-based remote-sensing techniques. Originally named the Network for the Detection of Stratospheric Change (NDSC), the name of the network was changed to NDACC in 2005 to better reflect the expanded scope of its measurements. The primary goal of NDACC is to establish long-term databases for detecting changes and trends in the chemical and physical state of the atmosphere (mesosphere, stratosphere, and troposphere) and to assess the coupling of such changes with climate and air quality. NDACC's origins, station locations, organizational structure, and data archiving are described. NDACC is structured around categories of ground-based observational techniques (sonde, lidar, microwave radiometers, Fourier-transform infrared, UV-visible DOAS (differential optical absorption spectroscopy)-type, and Dobson–Brewer spectrometers, as well as spectral UV radiometers), timely cross-cutting themes (ozone, water vapour, measurement strategies, cross-network data integration), satellite measurement systems, and theory and analyses. Participation in NDACC requires compliance with strict measurement and data protocols to ensure that the network data are of high and consistent quality. To widen its scope, NDACC has established formal collaborative agreements with eight other cooperating networks and Global Atmosphere Watch (GAW). A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.
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Sánchez-Zapero, Jorge, Fernando Camacho, Enrique Martínez-Sánchez, Roselyne Lacaze, Dominique Carrer, Florian Pinault, Iskander Benhadj, and Joaquín Muñoz-Sabater. "Quality Assessment of PROBA-V Surface Albedo V1 for the Continuity of the Copernicus Climate Change Service." Remote Sensing 12, no. 16 (August 12, 2020): 2596. http://dx.doi.org/10.3390/rs12162596.
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The Copernicus Climate Change Service (C3S) includes estimates of Essential Climate Variables (ECVs) as a series of Climate Data Records (CDRs) derived from satellite data. The C3S Surface Albedo (SA) v1.0 CDR is composed of observations from National Oceanic and Atmospheric Administration (NOAA) Very High Resolution Radiometers (AVHRR) (1981–2005), and VEGETATION sensors onboard Satellites for the Observation of the Earth (SPOT/VGT) (1998–2014) and Project for Onboard Autonomy satellite (PROBA-V) (2014–2020), and will continue with Sentinel-3 (from 2020 onwards). The goal of this study is to assess the uncertainties associated with the C3S PROBA-V SA v1.0 product, with a focus on the transition from SPOT/VGT to PROBA-V. The methodology followed the good practices recommended by the Land Product Validation sub-group (LPV) of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) for the validation of satellite-derived global albedo products. Several performance criteria were evaluated, including an intercomparison with National Aeronautics and Space Agency (NASA) MCD43A3 C6 products. C3S PROBA-V SA v1.0 and MCD43A3 C6 showed similar completeness but had higher fractions of missing data than C3S SPOT/VGT SA v1.0. C3S PROBA-V SA v1.0 showed similar precision (~1%) to MCD43A3 C6, improving the results of SPOT/VGT SA v1.0 (2–3%), but C3S PROBA-V SA v1.0 provided residual noise in the near-infrared (NIR). Good spatio-temporal continuity between C3S PROBA-V and SPOT/VGT SA v1.0 products was found with a mean bias between ±2%. The comparison with MCD43A3 C6 showed positive mean biases (5%, 8%, and 12% for visible, NIR and total shortwave, respectively). The accuracy assessment with ground measurements showed a median error of 18.4% with systematic overestimation (positive bias of 11.5%). The percentage of PROBA-V retrievals complying with the C3S target requirements was 28.6%.
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Sherman, Kenneth, John O'Reilly, Igor M. Belkin, Christopher Melrose, and Kevin D. Friedland. "The application of satellite remote sensing for assessing productivity in relation to fisheries yields of the world's large marine ecosystems." ICES Journal of Marine Science 68, no. 4 (January 11, 2011): 667–76. http://dx.doi.org/10.1093/icesjms/fsq177.
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Abstract Sherman, K., O'Reilly, J., Belkin, I. M., Melrose, C., and Friedland, K. D. 2011. The application of satellite remote sensing for assessing productivity in relation to fisheries yields of the world's large marine ecosystems. – ICES Journal of Marine Science, 68: 667–676. In 1992, world leaders at the historical UN Conference on Environment and Development (UNCED) recognized that the exploitation of resources in coastal oceans was becoming increasingly unsustainable, resulting in an international effort to assess, recover, and manage goods and services of large marine ecosystems (LMEs). More than $3 billion in support to 110 economically developing nations have been dedicated to operationalizing a five-module approach supporting LME assessment and management practices. An important component of this effort focuses on the effects of climate change on fisheries biomass yields of LMEs, using satellite remote sensing and in situ sampling of key indicators of changing ecological conditions. Warming appears to be reducing primary productivity in the lower latitudes, where stratification of the water column has intensified. Fishery biomass yields in the Subpolar LMEs of the Northeast Atlantic are also increasing as zooplankton levels increase with warming. During the current period of climate warming, it is especially important for space agency programmes in Asia, Europe, and the United States to continue to provide satellite-borne radiometry data to the global networks of LME assessment scientists.
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Iacovazzi, Robert A., and Changyong Cao. "Reducing Uncertainties of SNO-Estimated Intersatellite AMSU-A Brightness Temperature Biases for Surface-Sensitive Channels." Journal of Atmospheric and Oceanic Technology 25, no. 6 (June 1, 2008): 1048–54. http://dx.doi.org/10.1175/2007jtecha1020.1.
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Abstract In this study, a technique has been developed to improve collocation of two passive-microwave satellite instrument datasets at a simultaneous nadir overpass (SNO). The technique has been designed for the purpose of reducing uncertainties related to SNO-inferred intersatellite brightness temperature (Tb) biases, and it involves replacing the current “nearest-neighbor pixel matching” collocation technique with quality-controlled bilinear interpolation. Since the largest Tb bias estimation uncertainties of the SNO method are associated with highly variable earth scenes and window channels of microwave radiometers that have relatively large (∼50 km) separation between measurements, the authors have used Advanced Microwave Sounding Unit A (AMSU-A) data to develop the technique. It is found that using the new data collocation technique reduces SNO ensemble mean Tb bias confidence intervals in the SNO method, as applied to surface-sensitive channels of AMSU-A, by nearly 70% on average. This improvement in the SNO method enhances its ability to quantify intersatellite Tb biases at microwave radiometer channels that are sensitive to surface radiation, which is necessary to advance the sciences of numerical weather prediction and climate change detection.
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Hüsler, F., T. Jonas, M. Riffler, J. P. Musial, and S. Wunderle. "A satellite-based snow cover climatology (1985–2011) for the European Alps derived from AVHRR data." Cryosphere Discussions 7, no. 3 (June 24, 2013): 3001–42. http://dx.doi.org/10.5194/tcd-7-3001-2013.
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Abstract. Seasonal snow cover is of great environmental and socio-economic importance for the European Alps. Therefore a high priority has been assigned to quantifying its temporal and spatial variability. Complementary to land-based monitoring networks, optical satellite observations can be used to derive spatially comprehensive information on snow cover extent. For understanding long-term changes in alpine snow cover extent, the data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensors mounted onboard the National Oceanic and Atmospheric Association (NOAA) and Meteorological Operational satellite (MetOp) platforms offer a~unique source of information. In this paper, we present the first space-borne 1 km snow extent climatology for the Alpine region derived from AVHRR data over the period 1985–2011. The objective of this study is twofold: first, to generate a new set of cloud-free satellite snow products using a specific cloud gap-filling technique and second, to examine the spatiotemporal distribution of snow cover in the European Alps over the last 27 yr from the satellite perspective. For this purpose, snow parameters such as snow onset day, snow cover duration (SCD), melt-out date and the snow cover area percentage (SCA) were employed to analyze spatio-temporal variability of snow cover over the course of 3 decades. On the regional scale, significant trends were found toward a shorter SCD at lower elevations in the south-east and south-west. However, our results do not show any significant trends in the monthly mean SCA over the last 27 yr. This is in agreement with other research findings and may indicate a~deceleration of the decreasing snow trend in the Alpine region. Given the importance of mountain regions for climate change assessment, this study recommends the complementary use of remote sensing data for long-term snow applications. It bears the potential to provide spatially and temporally comprehensive snow information for use in related research fields or to serve as a reference for climate models.
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Myers, Daryl R., Thomas L. Stoffel, Ibrahim Reda, Stephen M. Wilcox, and Afshin M. Andreas. "Recent Progress in Reducing the Uncertainty in and Improving Pyranometer Calibrations." Journal of Solar Energy Engineering 124, no. 1 (April 1, 2001): 44–50. http://dx.doi.org/10.1115/1.1434262.
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The Measurements and Instrumentation Team within the Distributed Energy Resources Center at the National Renewable Energy Laboratory, NREL, calibrates pyranometers for outdoor testing solar energy conversion systems. The team also supports climate change research programs. These activities led NREL to improve pyranometer calibrations. Low thermal-offset radiometers measuring the sky diffuse component of the reference solar irradiance removes bias errors on the order of 20 Watts per square meter (W/m2) in the calibration reference irradiance. Zenith angle dependent corrections to responsivities of pyranometers removes 15 to 30 W/m2 bias errors from field measurements. Detailed uncertainty analysis of our outdoor calibration process shows a 20% reduction in the uncertainty in the responsivity of pyranometers. These improvements affect photovoltaic module and array performance characterization, assessment of solar resources for design, sizing, and deployment of solar renewable energy systems, and ground-based validation of satellite-derived solar radiation fluxes.
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Jeanneret, Fanny, Giovanni Martucci, Simon Pinnock, and Alexis Berne. "Correction of CCI cloud data over the Swiss Alps using ground-based radiation measurements." Atmospheric Measurement Techniques 11, no. 7 (July 17, 2018): 4153–70. http://dx.doi.org/10.5194/amt-11-4153-2018.
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Abstract. The validation of long-term cloud data sets retrieved from satellites is challenging due to their worldwide coverage going back as far as the 1980s. A trustworthy reference cannot be found easily at every location and every time. Mountainous regions present a particular problem since ground-based measurements are sparse. Moreover, as retrievals from passive satellite radiometers are difficult in winter due to the presence of snow on the ground, it is particularly important to develop new ways to evaluate and to correct satellite data sets over elevated areas. In winter for ground levels above 1000 m (a.s.l.) in Switzerland, the cloud occurrence of the newly released cloud property data sets of the ESA Climate Change Initiative Cloud_cci Project (Advanced Very High Resolution Radiometer afternoon series (AVHRR-PM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua series) is 132 to 217 % that of surface synoptic (SYNOP) observations, corresponding to a rate of false cloud detections between 24 and 54 %. Furthermore, the overestimations increase with the altitude of the sites and are associated with particular retrieved cloud properties. In this study, a novel post-processing approach is proposed to reduce the amount of false cloud detections in the satellite data sets. A combination of ground-based downwelling longwave and shortwave radiation and temperature measurements is used to provide independent validation of the cloud cover over 41 locations in Switzerland. An agreement of 85 % is obtained when the cloud cover is compared to surface synoptic observations (90 % within ± 1 okta difference). The validation data are then co-located with the satellite observations, and a decision tree model is trained to automatically detect the overestimations in the satellite cloud masks. Cross-validated results show that 62±13 % of these overestimations can be identified by the model, reducing the systematic error in the satellite data sets from 14.4±15.5 % to 4.3±2.8 %. The amount of errors is lower, and, importantly, their distribution is more homogeneous as well. These corrections happen at the cost of a global increase of 7±2 % of missed clouds. Using this model, it is possible to significantly improve the cloud detection reliability in elevated areas in the Cloud_cci AVHRR-PM and MODIS-Aqua products.
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Lainer, M., N. Kämpfer, B. Tschanz, G. E. Nedoluha, S. Ka, and J. J. Oh. "Trajectory mapping of middle atmospheric water vapor by a mini network of NDACC instruments." Atmospheric Chemistry and Physics Discussions 15, no. 8 (April 29, 2015): 12777–819. http://dx.doi.org/10.5194/acpd-15-12777-2015.
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Abstract. The important task to observe the global coverage of middle atmospheric trace gases like water vapor or ozone usually is accomplished by satellites. Climate and atmospheric studies rely upon the knowledge of trace gas distributions throughout the stratosphere and mesosphere. Many of these gases are currently measured from satellites, but it is not clear whether this capability will be maintained in the future. This could lead to a significant knowledge gap of the state of the atmosphere. We explore the possibilities of mapping middle atmospheric water vapor in the Northern Hemisphere by using Lagrangian trajectory calculations and water vapor profile data from a small network of five ground-based microwave radiometers. Four of them are operated within the frame of NDACC (Network for the Detection of Atmospheric Composition Change). Keeping in mind that the instruments are based on different hardware and calibration setups, a height dependent bias of the retrieved water vapor profiles has to be expected among the microwave radiometers. In order to correct and harmonize the different datasets, the Microwave Limb Sounder (MLS) on the Aura satellite is used to serve as a kind of travelling standard. A domain-averaging TM (trajectory mapping) method is applied which simplifies the subsequent validation of the quality of the trajectory mapped water vapor distribution towards direct satellite observations. Trajectories are calculated forwards and backwards in time for up to 10 days using 6 hourly meteorological wind analysis fields. Overall, a total of four case studies of trajectory mapping in different meteorological regimes are discussed. One of the case studies takes place during a major sudden stratospheric warming (SSW) accompanied by the polar vortex breakdown, a second takes place after the reformation of stable circulation system. TM cases close to the fall equinox and June solstice event from the year 2012 complete the study, showing the high potential of a network of ground-based remote sensing instruments to synthesize hemispheric maps of water vapor.
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Lainer, M., N. Kämpfer, B. Tschanz, G. E. Nedoluha, S. Ka, and J. J. Oh. "Trajectory mapping of middle atmospheric water vapor by a mini network of NDACC instruments." Atmospheric Chemistry and Physics 15, no. 16 (August 31, 2015): 9711–30. http://dx.doi.org/10.5194/acp-15-9711-2015.
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Abstract. The important task to observe the global coverage of middle atmospheric trace gases like water vapor or ozone usually is accomplished by satellites. Climate and atmospheric studies rely upon the knowledge of trace gas distributions throughout the stratosphere and mesosphere. Many of these gases are currently measured from satellites, but it is not clear whether this capability will be maintained in the future. This could lead to a significant knowledge gap of the state of the atmosphere. We explore the possibilities of mapping middle atmospheric water vapor in the Northern Hemisphere by using Lagrangian trajectory calculations and water vapor profile data from a small network of five ground-based microwave radiometers. Four of them are operated within the frame of NDACC (Network for the Detection of Atmospheric Composition Change). Keeping in mind that the instruments are based on different hardware and calibration setups, a height-dependent bias of the retrieved water vapor profiles has to be expected among the microwave radiometers. In order to correct and harmonize the different data sets, the Microwave Limb Sounder (MLS) on the Aura satellite is used to serve as a kind of traveling standard. A domain-averaging TM (trajectory mapping) method is applied which simplifies the subsequent validation of the quality of the trajectory-mapped water vapor distribution towards direct satellite observations. Trajectories are calculated forwards and backwards in time for up to 10 days using 6 hourly meteorological wind analysis fields. Overall, a total of four case studies of trajectory mapping in different meteorological regimes are discussed. One of the case studies takes place during a major sudden stratospheric warming (SSW) accompanied by the polar vortex breakdown; a second takes place after the reformation of stable circulation system. TM cases close to the fall equinox and June solstice event from the year 2012 complete the study, showing the high potential of a network of ground-based remote sensing instruments to synthesize hemispheric maps of water vapor.
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Kern, S., K. Khvorostovsky, H. Skourup, E. Rinne, Z. S. Parsakhoo, V. Djepa, P. Wadhams, and S. Sandven. "About uncertainties in sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise." Cryosphere Discussions 8, no. 2 (March 7, 2014): 1517–61. http://dx.doi.org/10.5194/tcd-8-1517-2014.
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Abstract. One goal of the European Space Agency Climate Change Initiative sea ice Essential Climate Variable project is to provide a quality controlled 20 year long data set of Arctic Ocean winter-time sea ice thickness distribution. An important step to achieve this goal is to assess the accuracy of sea ice thickness retrieval based on satellite radar altimetry. For this purpose a data base is created comprising sea ice freeboard derived from satellite radar altimetry between 1993 and 2012 and collocated observations of snow and sea ice freeboard from Operation Ice Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) air-borne campaigns, of sea ice draft from moored and submarine Upward Looking Sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E) and the Warren Climatology (Warren et al., 1999). An inter-comparison of the snow depth data sets stresses the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. This is confirmed by a comparison of snow freeboard measured during OIB and CryoVEx and snow freeboard computed from radar altimetry. For first-year ice the agreement between OIB and AMSR-E snow depth within 0.02 m suggests AMSR-E snow depth as an appropriate alternative. Different freeboard-to-thickness and freeboard-to-draft conversion approaches are realized. The mean observed ULS sea ice draft agrees with the mean sea ice draft computed from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the realized approaches is able to reproduce the seasonal cycle in sea ice draft observed by moored ULS satisfactorily. A sensitivity analysis of the freeboard-to-thickness conversion suggests: in order to obtain sea ice thickness as accurate as 0.5 m from radar altimetry, besides a freeboard estimate with centimetre accuracy, an ice-type dependent sea ice density is as mandatory as a snow depth with centimetre accuracy.
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Moesinger, Leander, Wouter Dorigo, Richard de Jeu, Robin van der Schalie, Tracy Scanlon, Irene Teubner, and Matthias Forkel. "The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA)." Earth System Science Data 12, no. 1 (January 30, 2020): 177–96. http://dx.doi.org/10.5194/essd-12-177-2020.
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Abstract. Since the late 1970s, space-borne microwave radiometers have been providing measurements of radiation emitted by the Earth’s surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions, and coverage need to be overcome. Here, we present a new series of long-term VOD products, the VOD Climate Archive (VODCA). VODCA combines VOD retrievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, WindSat, and AMSR2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely the Ku-band (period 1987–2017), X-band (1997–2018), and C-band (2002–2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our merging approach builds on an existing approach that is used to merge satellite products of surface soil moisture: first, the data sets are co-calibrated via cumulative distribution function matching using AMSR-E as the scaling reference. To do so, we apply a new matching technique that scales outliers more robustly than ordinary piecewise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data. The characteristics of VODCA are assessed for self-consistency and against other products. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. Spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and with leaf area index observations from the MODIS instrument as well as with Vegetation Continuous Fields from the AVHRR instruments. Long-term trends in Ku-band VODCA show that since 1987 there has been a decline in VOD in the tropics and in large parts of east-central and north Asia, while a substantial increase is observed in India, large parts of Australia, southern Africa, southeastern China, and central North America. In summary, VODCA shows vast potential for monitoring spatial–temporal ecosystem changes as it is sensitive to vegetation water content and unaffected by cloud cover or high sun zenith angles. As such, it complements existing long-term optical indices of greenness and leaf area. The VODCA products (Moesinger et al., 2019) are open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.2575599.
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Lellouch, Gabriel, Dominique Carrer, Chloé Vincent, Mickael Pardé, Sandra C. Frietas, and Isabel F. Trigo. "Evaluation of Two Global Land Surface Albedo Datasets Distributed by the Copernicus Climate Change Service and the EUMETSAT LSA-SAF." Remote Sensing 12, no. 11 (June 10, 2020): 1888. http://dx.doi.org/10.3390/rs12111888.
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The present paper is devoted to the quality assessment of two global land surface albedo products developed by Meteo France in the frame of the Copernicus Climate Change Service (C3S) and the LSA-SAF (Satellite Application Facility on Land Surface Analysis), herein called, respectively, VGT (VeGeTation) (the C3Sv1 dataset, derived from VGT sensors onboard Satellites for the Observation of the Earth, also called SPOT) and ETAL (European polar system Ten-day surface ALbedo, derived from Advanced Very High Resolution Radiometers (AVHRR) onboard METeorological OPerational (METOP) satellites). The evaluation study inter-compared these products with measurements at 33 ground stations and two independent operational products, MTAL-R/NRT (Meteosat second generation Ten-day ALbedo Reprocessed/Near Real-Time) and MODIS (MODerate resolution Imaging Spectroradiometer), over two distinct four-year periods. In accordance with the prescription from the Land Product Validation group of the joint Committee on Earth Observation Satellites (LPV/CEOS), the evaluation was addressed per land cover; furthermore, two albedo regimes were considered throughout the evaluation to distinguish between high (over 0.15) and low (below 0.15) surface albedo behaviors. First, we show that both VGT and ETAL products agree well with the measurements and the other satellite products at the ground stations. Second, when inter-compared with MODIS, the results are noteworthy for ETAL as opposed to VGT, with 11 out of 13 land cover types passing the Global Climate Observing System (GCOS) requirements for more than 80% of the sites for albedo values less than 0.15 (compared with none for VGT) and 10 out of 14 land cover types passing the GCOS requirements for more than 50% of the sites for albedo values greater than 0.15 (compared with 5 for VGT). Finally, a pixel-by-pixel analysis reveals that VGT overestimates the surface albedo as compared with MODIS by about 0.02 in absolute value for albedo values less than 0.15 and by about 22% in relative value for albedo values greater than 0.15. The root-mean-square-deviation (RMSD) in absolute value is about 0.015 for albedo values less than 0.15 and 51.5% in relative value for albedo values greater than 0.15. In contrast, the bias for ETAL when compared with MODIS remains very small. Over the four-year period, ETAL overestimates the surface albedo as compared with MODIS by 0.001 in absolute value for the regime of surface albedo less than 0.15 and by about 5.8% in relative value for albedo values greater than 0.15. The RMSD in absolute value is about 0.014 for albedo values less than 0.15 and 19.4% in relative value for albedo values greater than 0.15. Assuming that the MODIS product is a good reference, a relative bias of around 6% can be judged satisfactory for ETAL surface albedo. The lower performance of the VGT (C3Sv1) product is currently the subject of investigation. Work is ongoing to upgrade it further towards the final C3S product.
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Yan, Yu, Kaiyue Huang, Dongdong Shao, Yingjun Xu, and Wei Gu. "Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data." Sustainability 11, no. 3 (February 1, 2019): 777. http://dx.doi.org/10.3390/su11030777.
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Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.
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Vey, Sibylle, Reinhard Dietrich, Axel Rülke, Mathias Fritsche, Peter Steigenberger, and Markus Rothacher. "Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade." Journal of Climate 23, no. 7 (April 1, 2010): 1675–95. http://dx.doi.org/10.1175/2009jcli2787.1.
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Abstract In contrast to previous studies validating numerical weather prediction (NWP) models using observations from the global positioning system (GPS), this paper focuses on the validation of seasonal and interannual variations in the water vapor. The main advantage of the performed validation is the independence of the GPS water vapor estimates compared to studies using water vapor datasets from radiosondes or satellite microwave radiometers that are already assimilated into the NWP models. Tropospheric parameters from a GPS reanalysis carried out in a common project of the Technical Universities in Munich and Dresden were converted into precipitable water (PW) using surface pressure observations from the WMO and mean atmospheric temperature data from ECMWF. PW time series were generated for 141 globally distributed GPS sites covering the time period from the beginning of 1994 to the end of 2004. The GPS-derived PW time series were carefully examined for their homogeneity. The validation of the NWP model from NCEP shows that the differences between the modeled and observed PW values are time dependent. In addition to establishing a long-term mean, this study also validates the seasonal cycle and interannual variations in the PW. Over Europe and large parts of North America the seasonal cycle and the interannual variations in the PW from GPS and NCEP agree very well. The results reveal a submillimeter accuracy of the GPS-derived PW anomalies. In the regions mentioned above, NCEP provides a highly accurate database for studies of long-term changes in the atmospheric water vapor. However, in the Southern Hemisphere large differences in the seasonal signals and in the PW anomalies were found between GPS and NCEP. The seasonal signal of the PW is underestimated by NCEP in the tropics and in Antarctica by up to 40% and 25%, respectively. Climate change studies based on water vapor data from NCEP should consider the large uncertainties in the analysis when interpreting these data, especially in the tropics.
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Lázaro, Clara, Maria Joana Fernandes, Telmo Vieira, and Eliana Vieira. "A coastally improved global dataset of wet tropospheric corrections for satellite altimetry." Earth System Science Data 12, no. 4 (December 8, 2020): 3205–28. http://dx.doi.org/10.5194/essd-12-3205-2020.
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Abstract. The accuracy of satellite radar altimetry (RA) is known to deteriorate towards the coastal regions due to several reasons, amongst which the improper account for the wet path delay (WPD) can be pointed out. The most accurate WPDs for RA are derived from the on-board microwave radiometer (MWR) radiance measurements, acquired simultaneously as the altimeter ranges. In the coastal zone, however, the signal coming from the surrounding land contaminates these measurements and the water vapour retrieval from the MWR fails. As meteorological models do not handle coastal atmospheric variability correctly yet, the altimeter measurements are rejected whenever MWR observations are absent or invalid. The need to solve this RA issue in the coastal zone, simultaneously responding to the growing demand for data in these regions, motivated the development of the GNSS (Global Navigation Satellite System) derived Path Delay (GPD) algorithm. GPD combines WPD from several sources through objective analysis (OA) to estimate the WPD or the corresponding RA correction accounting for this effect, the wet tropospheric correction (WTC), for all along-track altimeter points for which this correction has been set as invalid or is not defined. The current GPD version (GPD Plus, GPD+) uses as data sources WPD from coastal and island GNSS stations, from satellites carrying microwave radiometers, and from valid on-board MWR measurements. GPD+ has been tuned to be applied to all, past and operational, RA missions, with or without an on-board MWR. The long-term stability of the WTC dataset is ensured by its inter-calibration with respect to the Special Sensor Microwave Imager (SSM/I) and SSM/I Sounder (SSMIS). The dataset is available for the TOPEX/Poseidon (T/P); Jason-1 and Jason-2 (NASA and CNES); Jason-3 (NASA and EUMETSAT); ERS-1, ERS-2, Envisat and CryoSat-2 (ESA); SARAL/AltiKa (ISRO and CNES); and GFO (US Navy) RA missions. The GPD+ WTC for Sentinel-3 (ESA and EUMETSAT) shall be released soon. The present paper describes the GPD+ database and its assessment through statistical analyses of sea level anomaly (SLA) datasets, calculated with GPD+, the ECMWF Reanalysis Interim (ERA-Interim) model or MWR-derived WTCs. Global results, as well as results for three regions (the North American and European coasts and the Indonesia region), are presented for ESA's recent Envisat Full Mission Reprocessing (FMR) V3.0. Global results show that the GPD+ WTC leads to a reduction in the SLA variance of 1–2 cm2 in the coastal zones, when used instead of the ERA WTC, which is one of the WTCs available in these products and can be adopted when the MWR-derived WTC is absent or invalid. The improvement of the GPD+ WTC over the ERA WTC is maximal over the tropical oceans, particularly in the Pacific Ocean, showing that the model-derived WTC is not able to capture the full variability in the WPD field yet. The statistical assessment of GPD+ for the North American coast shows a reduction in SLA variance, when compared to the use of the ERA-derived WTC, of 1.2 cm2, on average, for the whole range of distances from the coast considered (0–200 km). Similar results are obtained for the European coasts. For the Indonesia region, the use of the GPD+ WTC instead of that from ERA leads to an improvement, on average, on the order of 2.2 cm2 for distances from the coast of up to 100 km. Similar results have been obtained for the remaining missions, particularly for those from ESA. Additionally, GPD+ recovers the WTC for a significant number of along-track altimeter points with missing or invalid MWR-derived WTCs, due to land, rain and ice contamination and instrument malfunctioning, which otherwise would be rejected. Consequently, the GPD+ database has been chosen as the reference WTC in the Sea Level Climate Change Initiative (CCI) products; GPD+ has also been adopted as the reference in CryoSat-2 Level-2 Geophysical Ocean Products (GOP). Strategies to further improve the methodology, therefore enhancing the quality of the database, are also discussed. The GPD+ dataset is archived on the home page of the Satellite Altimetry Group, University of Porto, publicly available at the repository https://doi.org/10.23831/FCUP_UPORTO_GPDPlus_v1.0 (Fernandes et al., 2019).
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Lamy, Kevin, Thierry Portafaix, Colette Brogniez, Kaisa Lakkala, Mikko R. A. Pitkänen, Antti Arola, Jean-Baptiste Forestier, Vincent Amelie, Mohamed Abdoulwahab Toihir, and Solofoarisoa Rakotoniaina. "UV-Indien network: ground-based measurements dedicated to the monitoring of UV radiation over the western Indian Ocean." Earth System Science Data 13, no. 9 (September 2, 2021): 4275–301. http://dx.doi.org/10.5194/essd-13-4275-2021.
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Abstract. Within the framework of the UV-Indien network, nine ground stations have been equipped with ultraviolet broadband radiometers, five of them have also been equipped with an all-sky camera, and the main station in Saint-Denis de la Réunion is also equipped with a spectroradiometer. These stations are spatially distributed to cover a wide range of latitudes, longitudes, altitudes, and environmental conditions in five countries of the western Indian Ocean region (Comoros, France, Madagascar, Mauritius, and Seychelles), a part of the world where almost no measurements have been made so far. The distribution of the stations is based on the scientific interest of studying ultraviolet radiation not only in relation to atmospheric processes but also in order to provide data relevant to fields such as biology, health (prevention of skin cancer), and agriculture. The main scientific objectives of this network are to study the annual and inter-annual variability in the ultraviolet (UV) radiation in this area, to validate the output of numerical models and satellite estimates of ground-based UV measurements, and to monitor UV radiation in the context of climate change and projected ozone depletion in this region. A calibration procedure including three types of calibrations responding to the various constraints of sustaining the network has been put in place, and a data processing chain has been set up to control the quality and the format of the files sent to the various data centres. A method of clear-sky filtering of the data is also applied. Here, we present an intercomparison with other datasets, as well as several daily or monthly representations of the UV index (UVI) and cloud fraction data, to discuss the quality of the data and their range of values for the older stations (Antananarivo, Anse Quitor, Mahé, and Saint-Denis). Ground-based measurements of the UVI are used to validate satellite estimates – Ozone Monitoring Instrument (OMI), the TROPOspheric Monitoring Instrument (TROPOMI), and the Global Ozone Monitoring Experiment (GOME) – and model forecasts of UVI – Tropospheric Emission Monitoring Internet Service (TEMIS) and Copernicus Atmospheric Monitoring Service (CAMS). The median relative differences between satellite or model estimates and ground-based measurements of clear-sky UVI range between −34.5 % and 15.8 %. Under clear skies, the smallest UVI median difference between the satellite or model estimates and the measurements made by ground-based instruments is found to be 0.02 (TROPOMI), 0.04 (OMI), −0.1 (CAMS), and −0.4 (CAMS) at Saint-Denis, Antananarivo, Anse Quitor, and Mahé, respectively. The diurnal variability in UVI and cloud fraction, as well as the monthly variability in UVI, is evaluated to ensure the quality of the dataset. The data used in this study are available at https://doi.org/10.5281/zenodo.4811488 (Lamy and Portafaix, 2021a).
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Chen, Wenjun, Lori White, Sylvain G. Leblanc, Rasim Latifovic, and Ian Olthof. "Elevation-Dependent Changes to Plant Phenology in Canada’s Arctic Detected Using Long-Term Satellite Observations." Atmosphere 12, no. 9 (September 3, 2021): 1133. http://dx.doi.org/10.3390/atmos12091133.
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Arctic temperatures have increased at almost twice the global average rate since the industrial revolution. Some studies also reported a further amplified rate of climate warming at high elevations; namely, the elevation dependency of climate change. This elevation-dependent climate change could have important implications for the fate of glaciers and ecosystems at high elevations under climate change. However, the lack of long-term climate data at high elevations, especially in the Arctic, has hindered the investigation of this question. Because of the linkage between climate warming and plant phenology changes and remote sensing’s ability to detect the latter, remote sensing provides an alternative way for investigating the elevation dependency of climate change over Arctic mountains. This study investigated the elevation-dependent changes to plant phenology using AVHRR (Advanced Very High Resolution Radiometer) time series from 1985 to 2013 over five study areas in Canada’s Arctic. We found that the start of the growing season (SOS) became earlier faster with an increasing elevation over mountainous study areas (i.e., Sirmilik, the Torngat Mountains, and Ivvavik National Parks). Similarly, the changes rates in the end of growing season (EOS) and the growing season length (GSL) were also higher at high elevations. One exception was SOS in the Ivvavik National Park: “no warming trend” with the May-June temperature at a nearby climate station decreased slightly during 1985–2013, and so no elevation-dependent amplification.
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Zeng, Xiping, Gail Skofronick-Jackson, Lin Tian, Amber E. Emory, William S. Olson, and Rachael A. Kroodsma. "Analysis of the Global Microwave Polarization Data of Clouds." Journal of Climate 32, no. 1 (December 4, 2018): 3–13. http://dx.doi.org/10.1175/jcli-d-18-0293.1.
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Abstract Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166 GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.
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Singh, Dharmendra, P. K. Gupta, R. Pradhan, A. K. Dubey, and R. P. Singh. "Discerning shifting irrigation practices from passive microwave radiometry over Punjab and Haryana." Journal of Water and Climate Change 8, no. 2 (December 5, 2016): 303–19. http://dx.doi.org/10.2166/wcc.2016.122.
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Overexploitation of groundwater (GW) in the recent past is a well-known fact for the Punjab and Haryana region of India, as reported by several studies using satellite-based gravity anomaly from the Gravity Recovery and Climate Experiment mission and also by using observed data. This decline in GW has enforced the Punjab Preservation of Sub-Soil Water Act 2009, and resulted in change in rice irrigation practices over the study region. In this study, a shifting pattern of irrigation practices has been detected during pre- and post-Water Act using high temporal passive microwave radiometer (Advanced Microwave Scanning Radiometer – Earth Observing System, AMSR-E) and optical data. Multi-year soil moisture data for the period May to September were analysed for the years 2002 to 2011. A shift in the early soil wetness pattern has been observed during 2002 to 2011 in most of the study region. The overall delay in irrigation practices was observed to be 10 ± 4 days over Punjab and Haryana in the pre- and post-Water Act implementation. Multi-temporal passive microwave radiometry was found to be expedient for observing the dynamic pattern of irrigation/agricultural practices over Punjab and Haryana states.
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Anttila, Kati, Terhikki Manninen, Emmihenna Jääskeläinen, Aku Riihelä, and Panu Lahtinen. "The Role of Climate and Land Use in the Changes in Surface Albedo Prior to Snow Melt and the Timing of Melt Season of Seasonal Snow in Northern Land Areas of 40°N–80°N during 1982–2015." Remote Sensing 10, no. 10 (October 11, 2018): 1619. http://dx.doi.org/10.3390/rs10101619.
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The rapid warming of the Northern Hemisphere high latitudes and the observed changes in boreal forest areas affect the global surface albedo and climate. This study looks at the trends in the timing of the snow melt season as well as the albedo levels before and after the melt season in Northern Hemisphere land areas between 40°N and 80°N over the years 1982 to 2015. The analysis is based on optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). The results show that the changes in surface albedo already begin before the start of the melt season. These albedo changes are significant (the mean of absolute change is 4.4 albedo percentage units per 34 years). The largest absolute changes in pre-melt-season albedo are concentrated in areas of the boreal forest, while the pre-melt albedo of tundra remains unchanged. Trends in melt season timing are consistent over large areas. The mean of absolute change of start date of melt season is 11.2 days per 34 years, 10.6 days for end date of melt season and 14.8 days for length of melt season. The changes result in longer and shorter melt seasons, as well as changed timing of the melt, depending on the area. The albedo levels preceding the onset of melt and start of the melt season correlate with climatic parameters (air temperature, precipitation, wind speed). The changes in albedo are more closely linked to changes in vegetation, whereas the changes in melt season timing are linked to changes in climate.
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Dozier, Jeff, and Danny Marks. "Snow Mapping and Classification from Landsat Thematic Mapper Data." Annals of Glaciology 9 (1987): 97–103. http://dx.doi.org/10.1017/s026030550000046x.
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Use of satellite multi-spectral remote-sensing data to map snow and estimate snow characteristics over remote and inaccessible areas requires that we distinguish snow from other surface cover and from clouds, and compensate for the effects of the atmosphere and rugged terrain. Because our space-borne radiometers typically measure reflectance in a few wavelength bands, for climate modeling we must use inferences of snow grain-size and contaminant amount to estimate snow albedo throughout the solar spectrum. Although digital elevation data may be used to simulate typical conditions for a satellite image, precise registration of an elevation data set with satellite data is usually impossible. Instead, an atmospheric model simulates combinations of Thematic Mapper (TM) band radiances for snow of various grain-sizes and contaminant amounts. These can be recognized in TM images and snow can automatically be distinguished from other surfaces and classified into clean new snow, older metamorphosed snow, or snow mixed with vegetation.
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Dozier, Jeff, and Danny Marks. "Snow Mapping and Classification from Landsat Thematic Mapper Data." Annals of Glaciology 9 (1987): 97–103. http://dx.doi.org/10.3189/s026030550000046x.
Full textAbstract:
Use of satellite multi-spectral remote-sensing data to map snow and estimate snow characteristics over remote and inaccessible areas requires that we distinguish snow from other surface cover and from clouds, and compensate for the effects of the atmosphere and rugged terrain. Because our space-borne radiometers typically measure reflectance in a few wavelength bands, for climate modeling we must use inferences of snow grain-size and contaminant amount to estimate snow albedo throughout the solar spectrum. Although digital elevation data may be used to simulate typical conditions for a satellite image, precise registration of an elevation data set with satellite data is usually impossible. Instead, an atmospheric model simulates combinations of Thematic Mapper (TM) band radiances for snow of various grain-sizes and contaminant amounts. These can be recognized in TM images and snow can automatically be distinguished from other surfaces and classified into clean new snow, older metamorphosed snow, or snow mixed with vegetation.
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Ye, Xin, Xiaolong Yi, Chao Lin, Wei Fang, Kai Wang, Zhiwei Xia, Zhenhua Ji, Yuquan Zheng, De Sun, and Jia Quan. "Instrument Development: Chinese Radiometric Benchmark of Reflected Solar Band Based on Space Cryogenic Absolute Radiometer." Remote Sensing 12, no. 17 (September 3, 2020): 2856. http://dx.doi.org/10.3390/rs12172856.
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Low uncertainty and long-term stability remote data are urgently needed for researching climate and meteorology variability and trends. Meeting these requirements is difficult with in-orbit calibration accuracy due to the lack of radiometric satellite benchmark. The radiometric benchmark on the reflected solar band has been under development since 2015 to overcome the on-board traceability problem of hyperspectral remote sensing satellites. This paper introduces the development progress of the Chinese radiometric benchmark of the reflected solar band based on the Space Cryogenic Absolute Radiometer (SCAR). The goal of the SCAR is to calibrate the Earth–Moon Imaging Spectrometer (EMIS) on-satellite using the benchmark transfer chain (BTC) and to transfer the traceable radiometric scale to other remote sensors via cross-calibration. The SCAR, which is an electrical substitution absolute radiometer and works at 20 K, is used to realize highly accurate radiometry with an uncertainty level that is lower than 0.03%. The EMIS, which is used to measure the spectrum radiance on the reflected solar band, is designed to optimize the signal-to-noise ratio and polarization. The radiometric scale of the SCAR is converted and transferred to the EMIS by the BTC to improve the measurement accuracy and long-term stability. The payload of the radiometric benchmark on the reflected solar band has been under development since 2018. The investigation results provide the theoretical and experimental basis for the development of the reflected solar band benchmark payload. It is important to improve the measurement accuracy and long-term stability of space remote sensing and provide key data for climate change and earth radiation studies.
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Steck, T., T. von Clarmann, H. Fischer, B. Funke, N. Glatthor, U. Grabowski, M. Höpfner, et al. "Bias determination and precision validation of ozone profiles from MIPAS-Envisat retrieved with the IMK-IAA processor." Atmospheric Chemistry and Physics 7, no. 13 (July 11, 2007): 3639–62. http://dx.doi.org/10.5194/acp-7-3639-2007.
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Abstract. This paper characterizes vertical ozone profiles retrieved with the IMK-IAA (Institute for Meteorology and Climate Research, Karlsruhe – Instituto de Astrofisica de Andalucia) science-oriented processor from high spectral resolution data (until March 2004) measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) aboard the environmental satellite Envisat. Bias determination and precision validation is performed on the basis of correlative measurements by ground-based lidars, Fourier transform infrared spectrometers, and microwave radiometers as well as balloon-borne ozonesondes, the balloon-borne version of MIPAS, and two satellite instruments (Halogen Occultation Experiment and Polar Ozone and Aerosol Measurement III). Percentage mean differences between MIPAS and the comparison instruments for stratospheric ozone are generally within ±10%. The precision in this altitude region is estimated at values between 5 and 10% which gives an accuracy of 15 to 20%. Below 18 km, the spread of the percentage mean differences is larger and the precision degrades to values of more than 20% depending on altitude and latitude. The main reason for the degraded precision at low altitudes is attributed to undetected thin clouds which affect MIPAS retrievals, and to the influence of uncertainties in the water vapor concentration.
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Steck, T., T. von Clarmann, H. Fischer, B. Funke, N. Glatthor, U. Grabowski, M. Höpfner, et al. "Bias determination and precision validation of ozone profiles from MIPAS-Envisat retrieved with the IMK-IAA processor." Atmospheric Chemistry and Physics Discussions 7, no. 2 (March 30, 2007): 4427–80. http://dx.doi.org/10.5194/acpd-7-4427-2007.
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Abstract. This paper characterizes vertical ozone profiles retrieved with the IMK-IAA (Institute for Meteorology and Climate Research, Karlsruhe – Instituto de Astrofisica de Andalucia) science-oriented processor from spectra measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) aboard the environmental satellite Envisat. Bias determination and precision validation is performed on the basis of correlative measurements by ground-based lidars, Fourier transform infrared spectrometers, and microwave radiometers as well as balloon-borne ozonesondes, the balloon-borne version of MIPAS, and two satellite instruments (Halogen Occultation Experiment and Polar Ozone and Aerosol Measurement III). Percentage mean differences between MIPAS and the comparison instruments for stratospheric ozone are within ±10%. The precision in this altitude region is estimated at values between 5 and 10% which gives an accuracy of 15 to 20%. Below 18 km, the spread of the percentage mean differences is larger and the precision increases to values of more than 20% depending on altitude and latitude. The main reason for the degraded precision at low altitudes is attributed to undetected thin clouds which affect MIPAS retrievals, and to the influence of uncertainties in the water vapor concentration.
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Malinka, Aleksey, Luc Blarel, Ludmila Chaikovskaya, Anatoli Chaikovsky, Natalia Denishchik-Nelubina, Sergei Denisov, Vladimir Dick, et al. "Ground-based and satellite optical investigation of the atmosphere and surface of Antarctica." EPJ Web of Conferences 176 (2018): 10006. http://dx.doi.org/10.1051/epjconf/201817610006.
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This presentation contains the results of the 10-year research of Belarusian Antarctic expeditions. The set of instruments consists of a lidar, an albedometer, and a scanning sky radiometer CIMEL. Besides, the data from satellite radiometer MODIS were used to characterize the snow cover. The works focus on the study of aerosol, cloud and snow characteristics in the Antarctic, and their links with the long range transport of atmospheric pollutants and climate changes.
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Bojanowski, Jędrzej S., and Jan P. Musiał. "Dissecting effects of orbital drift of polar-orbiting satellites on accuracy and trends of climate data records of cloud fractional cover." Atmospheric Measurement Techniques 13, no. 12 (December 15, 2020): 6771–88. http://dx.doi.org/10.5194/amt-13-6771-2020.
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Abstract. Radiometers such as the AVHRR (Advanced Very High Resolution Radiometer) mounted aboard a series of NOAA and MetOp (Meteorological Operational) polar-orbiting satellites provide 4-decade-long global climate data records (CDRs) of cloud fractional cover. Generation of such long datasets requires combining data from consecutive satellite platforms. A varying number of satellites operating simultaneously in the morning and afternoon orbits, together with satellite orbital drift, cause the uneven sampling of the cloudiness diurnal cycle along a course of a CDR. This in turn leads to significant biases, spurious trends, and inhomogeneities in the data records of climate variables featuring the distinct diurnal cycle (such as clouds). To quantify the uncertainty and magnitude of spurious trends in the AVHRR-based cloudiness CDRs, we sampled the 30 min reference CM SAF (European Organisation for the Exploitation of Meteorological Satellites – EUMETSAT – Satellite Application Facility on Climate Monitoring) Cloud Fractional Cover dataset derived from Meteosat First and Second Generation (COMET) at times of the NOAA and MetOp satellite overpasses. The sampled cloud fractional cover (CFC) time series were aggregated to monthly means and compared with the reference COMET dataset covering the Meteosat disc (up to 60∘ N, S, W, and E). For individual NOAA and MetOp satellites the errors in mean monthly CFC reach ±10 % (bias) and ±7 % per decade (spurious trends). For the combined data record consisting of several NOAA and MetOp satellites, the CFC bias is 3 %, and the spurious trends are 1 % per decade. This study proves that before 2002 the AVHRR-derived CFC CDRs do not comply with the GCOS (Global Climate Observing System) temporal stability requirement of 1 % CFC per decade just due to the satellite orbital-drift effect. After this date the requirement is fulfilled due to the numerous NOAA and MetOp satellites operating simultaneously. Yet, the time series starting in 2003 is shorter than 30 years, which makes it difficult to draw reliable conclusions about long-term changes in CFC. We expect that the error estimates provided in this study will allow for a correct interpretation of the AVHRR-based CFC CDRs and ultimately will contribute to the development of a novel satellite orbital-drift correction methodology widely accepted by the AVHRR-based CDR providers.
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Zhang, Peng, Naimeng Lu, Chuanrong Li, Lei Ding, Xiaobing Zheng, Xuejun Zhang, Xiuqing Hu, et al. "Development of the Chinese Space-Based Radiometric Benchmark Mission LIBRA." Remote Sensing 12, no. 14 (July 8, 2020): 2179. http://dx.doi.org/10.3390/rs12142179.
Full textAbstract:
Climate observations and their applications require measurements with high stability and low uncertainty in order to detect and assess climate variability and trends. The difficulty with space-based observations is that it is generally not possible to trace them to standard calibration references when in orbit. In order to overcome this problem, it has been proposed to deploy space-based radiometric reference systems which intercalibrate measurements from multiple satellite platforms. Such reference systems have been strongly recommended by international expert teams. This paper describes the Chinese Space-based Radiometric Benchmark (CSRB) project which has been under development since 2014. The goal of CSRB is to launch a reference-type satellite named LIBRA in around 2025. We present the roadmap for CSRB as well as requirements and specifications for LIBRA. Key technologies of the system include miniature phase-change cells providing fixed-temperature points, a cryogenic absolute radiometer, and a spontaneous parametric down-conversion detector. LIBRA will offer measurements with SI traceability for the outgoing radiation from the Earth and the incoming radiation from the Sun with high spectral resolution. The system will be realized with four payloads, i.e., the Infrared Spectrometer (IRS), the Earth-Moon Imaging Spectrometer (EMIS), the Total Solar Irradiance (TSI), and the Solar spectral Irradiance Traceable to Quantum benchmark (SITQ). An on-orbit mode for radiometric calibration traceability and a balloon-based demonstration system for LIBRA are introduced as well in the last part of this paper. As a complementary project to the Climate Absolute Radiance and Refractivity Observatory (CLARREO) and the Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS), LIBRA is expected to join the Earth observation satellite constellation and intends to contribute to space-based climate studies via publicly available data.
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Ingram, J. Carter, and Terence P. Dawson. "Climate change impacts and vegetation response on the island of Madagascar." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1826 (January 15, 2005): 55–59. http://dx.doi.org/10.1098/rsta.2004.1476.
Full textAbstract:
The island of Madagascar has been labelled the world's number one conservation ‘hot spot’ because of increasing anthropogenic degradation of its natural habitats, which support a high level of species endemism. However, climatic phenomena may also have a significant impact upon the island's flora and fauna. An analysis of 18 years of monthly satellite images from the National Oceanographic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) have demonstrated that there is a dynamic pattern in Madagascar's vegetative cover both annually and seasonally throughout 1982–1999. Over interannual time–scales, we show that this vegetation response, calculated using the normalized difference vegetation index (NDVI), has a strong negative correlation with the El Niño Southern Oscillation (ENSO), which can be attributable to drought events and associated wildfires. Global climate change is predicted to increase the frequency of the ENSO phenomenon, resulting in further decline of Madagascar's natural environment.
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Foster, Michael J., and Andrew Heidinger. "Entering the Era of +30-Year Satellite Cloud Climatologies: A North American Case Study." Journal of Climate 27, no. 17 (August 28, 2014): 6687–97. http://dx.doi.org/10.1175/jcli-d-14-00068.1.
Full textAbstract:
Abstract The emergence of satellite-based cloud records of climate length and quality hold tremendous potential for climate model development, climate monitoring, and studies on global water cycling and its subsequent energetics. This article examines the more than 30-yr Pathfinder Atmospheres–Extended (PATMOS-x) Advanced Very High Resolution Radiometer (AVHRR) cloudiness record over North America and assesses its suitability as a climate-quality data record. A loss of ~4.2% total cloudiness is observed between 1982 and 2012 over a North American domain centered over the contiguous United States. While ENSO can explain some of the observed change, a weather state clustering analysis identifies shifts in weather patterns that result in loss of water cloud over the Great Lakes and cirrus over southern portions of the United States. The radiative properties of the shifting weather states are characterized, and the results suggest that extended cloud satellite records may prove useful tools for increasing knowledge of cloud feedbacks, a long-standing issue in the climate change community.
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Hagan, Daniel Fiifi Tawia, Guojie Wang, Seokhyeon Kim, Robert M. Parinussa, Yi Liu, Waheed Ullah, Asher Samuel Bhatti, Xiaowen Ma, Tong Jiang, and Buda Su. "Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging." Remote Sensing 12, no. 13 (July 7, 2020): 2164. http://dx.doi.org/10.3390/rs12132164.
Full textAbstract:
In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.
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McFadden, E. M., J. Ramage, and D. T. Rodbell. "Landsat TM and ETM+ derived snowline altitudes in the Cordillera Huayhuash and Cordillera Raura, Peru, 1986–2005." Cryosphere Discussions 4, no. 4 (October 6, 2010): 1931–66. http://dx.doi.org/10.5194/tcd-4-1931-2010.
Full textAbstract:
Abstract. The Cordilleras Huayhuash and Raura are remote glacierized ranges in the Andes Mountains of Peru. A robust assessment of modern glacier change is important for understanding how regional change affects Andean communities, and for placing paleo-glaciers in a context relative to modern glaciation and climate. Snowline altitudes (SLAs) derived from satellite imagery are used as a proxy for modern (1986–2005) local climate change in a key transition zone in the Andes. Clear sky, dry season Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) satellite images from 1986–2005 were used to identify snowline positions, and their altitude ranges were extracted from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM. Based on satellite records from 31 glaciers, mean snowline altitudes (SLAs), an approximation for the equilibrium line altitudes (ELAs), for the Cordillera Huayhuash (13 glaciers) and Cordillera Raura (18 glaciers) were 5046 m a.s.l. and 5013 m a.s.l., respectively, from 1986–2005. The rate of SLA rise was 25 m/decade in the Cordillera Huayhuash and 62 m/decade in the Cordillera Raura.
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Wielicki, Bruce A., D. F. Young, M. G. Mlynczak, K. J. Thome, S. Leroy, J. Corliss, J. G. Anderson, et al. "Achieving Climate Change Absolute Accuracy in Orbit." Bulletin of the American Meteorological Society 94, no. 10 (October 1, 2013): 1519–39. http://dx.doi.org/10.1175/bams-d-12-00149.1.
Full textAbstract:
The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a “NIST [National Institute of Standards and Technology] in orbit.” CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.
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Fan, Xia, and Chen. "Intercomparison of Multiple Satellite Aerosol Products against AERONET over the North China Plain." Atmosphere 10, no. 9 (August 21, 2019): 480. http://dx.doi.org/10.3390/atmos10090480.
Full textAbstract:
In this study, using Aerosol Robotic Network aerosol optical depth (AOD) products at three stations in the North China Plain (NCP)—a heavily polluted region in China—the AOD products from six satellite-borne radiometers: the Moderate Resolution Imagining Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR), Ozone Mapping Imaging (OMI), the Visible Infrared Imaging Radiometer (VIIRS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Polarization and Directionality of the Earth’s Reflectances (POLDER), were thoroughly validated, shedding new light on their advantages and disadvantages. The MODIS Deep Blue (DB) products provide more accurate retrievals than the MODIS Dark Target (DT) and other satellite products at the Beijing site (BJ,a megacity), with higher correlations with AERONET (R > 0.93), lower mean absolute bias (MB < 0.012), and higher percentages (>68%) falling within the expected error (EE). All MODIS DT and DB products perform better than the other satellite products at the Xianghe site (XH, a suburb). The MODIS/Aqua DT products at both 3-km and 10-km resolutions performed better than the other space-borne AOD products at the Xinglong site (XL, a rural area at the top of a mountain). MISR, VIIRS, and SeaWiFS tend to underestimate high AOD values and overestimate AOD values under very low AOD conditions in the NCP. Both OMI and POLDER significantly underestimate the AOD. In terms of data volume, MISR with the limited swath width of 380 km has less data volume than the other satellite sensors. MODIS products have the highest sampling rate, especially the MODIS DT and DB merged products, and can be used for various climate study and air-quality monitoring.
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Wu, Bu He, Xiao Yao Wang, Hong Zhang Ma, and Shu Guang Li. "Sensitivity Analysis of Ocean Microwave Radiation Characteristics." Advanced Materials Research 726-731 (August 2013): 4718–22. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4718.
Full textAbstract:
Ocean salinity is one of the important parameters in marine ecosystem and climate system. Satellite-borne microwave radiometer observing system is the only feasible method to study the distributions and changes of the global ocean salinity. Numbers of factors make a great impact on the sea salinity inversion accuracy based on the microwave radiation technology. These factors include the sea surface temperature, distribution range of the salinity, the observation angle, polarization, as well as the microwave frequency of the microwave radiometer. The present paper analyzed the effect of microwave frequency, incidence angle and polarization mode on the sensitivity of the ocean salinity based on the microwave radiation simulated data of calm see surface. Furthermore, it studied the effect of the seawater temperature and salinity distributions on the salinity sensitivity. The results indicated that the sensitivity between the ocean surface microwave radiation and the water salinity decreased with increased microwave frequency; the influence of the incident angle on the sensitivity is related to the polarization mode. The sensitivity increased with the increasing of the incident angle under V-polarization mode, however, the sensitivity decreased under H-polarization mode. The water temperature had an effect of amplification on the sensitivity. The higher the temperature of water is, the stronger the sensitivity is. However, the changing trend of the sensitivity was not related with the polarization, frequency, and incident angle.
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McFadden, E. M., J. Ramage, and D. T. Rodbell. "Landsat TM and ETM+ derived snowline altitudes in the Cordillera Huayhuash and Cordillera Raura, Peru, 1986–2005." Cryosphere 5, no. 2 (May 23, 2011): 419–30. http://dx.doi.org/10.5194/tc-5-419-2011.
Full textAbstract:
Abstract. The Cordilleras Huayhuash and Raura are remote glacierized ranges in the Andes Mountains of Peru. A robust assessment of modern glacier change is important for understanding how regional change affects Andean communities, and for placing paleo-glaciers in a context relative to modern glaciation and climate. Snowline altitudes (SLAs) derived from satellite imagery are used as a proxy for modern (1986–2005) local climate change in a key transition zone in the Andes. Clear sky, dry season Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) satellite images from 1986–2005 were used to identify snowline positions, and their altitude ranges were extracted from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model (DEM). Based on satellite records from 31 glaciers, average snowline altitudes (SLAs), an approximation for the equilibrium line altitude (ELA), for the Cordillera Huayhuash (13 glaciers) and Cordillera Raura (18 glaciers) from 1986–2005 were 5051 m a.s.l. from 1986–2005 and 5006 m a.s.l. from 1986–2002, respectively. During the same time period, the Cordillera Huayhuash SLA experienced no significant change while the Cordillera Raura SLA rose significantly from 4947 m a.s.l. to 5044 m a.s.l.
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Wei, Jing, Yiran Peng, Rashed Mahmood, Lin Sun, and Jianping Guo. "Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products." Atmospheric Chemistry and Physics 19, no. 10 (May 29, 2019): 7183–207. http://dx.doi.org/10.5194/acp-19-7183-2019.
Full textAbstract:
Abstract. Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.
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Gao, Qidong, Sheng Wang, and Xiaofeng Yang. "Estimation of Surface Air Specific Humidity and Air–Sea Latent Heat Flux Using FY-3C Microwave Observations." Remote Sensing 11, no. 4 (February 24, 2019): 466. http://dx.doi.org/10.3390/rs11040466.
Full textAbstract:
Latent heat flux (LHF) plays an important role in the global hydrological cycle and is therefore necessary to understand global climate variability. It has been reported that the near-surface specific humidity is a major source of error for satellite-derived LHF. Here, a new empirical model relating multichannel brightness temperatures ( T B ) obtained from the Fengyun-3 (FY-3C) microwave radiometer and sea surface air specific humidity ( Q a ) is proposed. It is based on the relationship between T B , Q a , sea surface temperature (SST), and water vapor scale height. Compared with in situ data, the new satellite-derived Q a and LHF both exhibit better statistical results than previous estimates. For Q a , the bias, root mean square difference (RMSD), and the correlation coefficient (R2) between satellite and buoy in the mid-latitude region are 0.08 g/kg, 1.76 g/kg, and 0.92, respectively. For LHF, the bias, RMSD, and R2 are 2.40 W/m2, 34.24 W/m2, and 0.87, respectively. The satellite-derived Q a are also compared with National Oceanic and Atmospheric Administration (NOAA) Cooperative Institute for Research in Environmental Sciences (CIRES) humidity datasets, with a bias, RMSD, and R2 of 0.02 g/kg, 1.02 g/kg, and 0.98, respectively. The proposed method can also be extended in the future to observations from other space-borne microwave radiometers.
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Cortesi, U., J. C. Lambert, C. De Clercq, G. Bianchini, T. Blumenstock, A. Bracher, E. Castelli, et al. "Geophysical validation of MIPAS-ENVISAT operational ozone data." Atmospheric Chemistry and Physics 7, no. 18 (September 21, 2007): 4807–67. http://dx.doi.org/10.5194/acp-7-4807-2007.
Full textAbstract:
Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), on-board the European ENVIronmental SATellite (ENVISAT) launched on 1 March 2002, is a middle infrared Fourier Transform spectrometer measuring the atmospheric emission spectrum in limb sounding geometry. The instrument is capable to retrieve the vertical distribution of temperature and trace gases, aiming at the study of climate and atmospheric chemistry and dynamics, and at applications to data assimilation and weather forecasting. MIPAS operated in its standard observation mode for approximately two years, from July 2002 to March 2004, with scans performed at nominal spectral resolution of 0.025 cm−1 and covering the altitude range from the mesosphere to the upper troposphere with relatively high vertical resolution (about 3 km in the stratosphere). Only reduced spectral resolution measurements have been performed subsequently. MIPAS data were re-processed by ESA using updated versions of the Instrument Processing Facility (IPF v4.61 and v4.62) and provided a complete set of level-2 operational products (geo-located vertical profiles of temperature and volume mixing ratio of H2O, O3, HNO3, CH4, N2O and NO2) with quasi continuous and global coverage in the period of MIPAS full spectral resolution mission. In this paper, we report a detailed description of the validation of MIPAS-ENVISAT operational ozone data, that was based on the comparison between MIPAS v4.61 (and, to a lesser extent, v4.62) O3 VMR profiles and a comprehensive set of correlative data, including observations from ozone sondes, ground-based lidar, FTIR and microwave radiometers, remote-sensing and in situ instruments on-board stratospheric aircraft and balloons, concurrent satellite sensors and ozone fields assimilated by the European Center for Medium-range Weather Forecasting. A coordinated effort was carried out, using common criteria for the selection of individual validation data sets, and similar methods for the comparisons. This enabled merging the individual results from a variety of independent reference measurements of proven quality (i.e. well characterized error budget) into an overall evaluation of MIPAS O3 data quality, having both statistical strength and the widest spatial and temporal coverage. Collocated measurements from ozone sondes and ground-based lidar and microwave radiometers of the Network for the Detection Atmospheric Composition Change (NDACC) were selected to carry out comparisons with time series of MIPAS O3 partial columns and to identify groups of stations and time periods with a uniform pattern of ozone differences, that were subsequently used for a vertically resolved statistical analysis. The results of the comparison are classified according to synoptic and regional systems and to altitude intervals, showing a generally good agreement within the comparison error bars in the upper and middle stratosphere. Significant differences emerge in the lower stratosphere and are only partly explained by the larger contributions of horizontal and vertical smoothing differences and of collocation errors to the total uncertainty. Further results obtained from a purely statistical analysis of the same data set from NDACC ground-based lidar stations, as well as from additional ozone soundings at middle latitudes and from NDACC ground-based FTIR measurements, confirm the validity of MIPAS O3 profiles down to the lower stratosphere, with evidence of larger discrepancies at the lowest altitudes. The validation against O3 VMR profiles using collocated observations performed by other satellite sensors (SAGE II, POAM III, ODIN-SMR, ACE-FTS, HALOE, GOME) and ECMWF assimilated ozone fields leads to consistent results, that are to a great extent compatible with those obtained from the comparison with ground-based measurements. Excellent agreement in the full vertical range of the comparison is shown with respect to collocated ozone data from stratospheric aircraft and balloon instruments, that was mostly obtained in very good spatial and temporal coincidence with MIPAS scans. This might suggest that the larger differences observed in the upper troposphere and lowermost stratosphere with respect to collocated ground-based and satellite O3 data are only partly due to a degradation of MIPAS data quality. They should be rather largely ascribed to the natural variability of these altitude regions and to other components of the comparison errors. By combining the results of this large number of validation data sets we derived a general assessment of MIPAS v4.61 and v4.62 ozone data quality. A clear indication of the validity of MIPAS O3 vertical profiles is obtained for most of the stratosphere, where the mean relative difference with the individual correlative data sets is always lower than ±10%. Furthermore, these differences always fall within the combined systematic error (from 1 hPa to 50 hPa) and the standard deviation is fully consistent with the random error of the comparison (from 1 hPa to ~30–40 hPa). A degradation in the quality of the agreement is generally observed in the lower stratosphere and upper troposphere, with biases up to 25% at 100 hPa and standard deviation of the global mean differences up to three times larger than the combined random error in the range 50–100 hPa. The larger differences observed at the bottom end of MIPAS retrieved profiles can be associated, as already noticed, to the effects of stronger atmospheric gradients in the UTLS that are perceived differently by the various measurement techniques. However, further components that may degrade the results of the comparison at lower altitudes can be identified as potentially including cloud contamination, which is likely not to have been fully filtered using the current settings of the MIPAS cloud detection algorithm, and in the linear approximation of the forward model that was used for the a priori estimate of systematic error components. The latter, when affecting systematic contributions with a random variability over the spatial and temporal scales of global averages, might result in an underestimation of the random error of the comparison and add up to other error sources, such as the possible underestimates of the p and T error propagation based on the assumption of a 1 K and 2% uncertainties, respectively, on MIPAS temperature and pressure retrievals. At pressure lower than 1 hPa, only a small fraction of the selected validation data set provides correlative ozone data of adequate quality and it is difficult to derive quantitative conclusions about the performance of MIPAS O3 retrieval for the topmost layers.
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Cortesi, U., J. C. Lambert, C. De Clercq, G. Bianchini, T. Blumenstock, A. Bracher, E. Castelli, et al. "Geophysical validation of MIPAS-ENVISAT operational ozone data." Atmospheric Chemistry and Physics Discussions 7, no. 3 (May 7, 2007): 5805–939. http://dx.doi.org/10.5194/acpd-7-5805-2007.
Full textAbstract:
Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), on-board the European ENVIronmental SATellite (ENVISAT) launched on 1 March 2002, is a middle infrared Fourier Transform spectrometer measuring the atmospheric emission spectrum in limb sounding geometry. The instrument is capable to retrieve the vertical distribution of temperature and trace gases, aiming at the study of climate and atmospheric chemistry and dynamics, and at applications to data assimilation and weather forecasting. MIPAS operated in its standard observation mode for approximately two years, from July 2002 to March 2004, with scans performed at nominal spectral resolution of 0.025 cm−1 and covering the altitude range from the mesosphere to the upper troposphere with relatively high vertical resolution (about 3 km in the stratosphere). Only reduced spectral resolution measurements have been performed subsequently. MIPAS data were re-processed by ESA using updated versions of the Instrument Processing Facility (IPF v4.61 and v4.62) and provided a complete set of level-2 operational products (geo-located vertical profiles of temperature and volume mixing ratio of H2O,O3, HNO3, CH4, N2O and NO2) with quasi continuous and global coverage in the period of MIPAS full spectral resolution mission. In this paper, we report a detailed description of the validation of MIPAS-ENVISAT operational ozone data, that was based on the comparison between MIPAS v4.61 (and, to a lesser extent, v4.62) O3 VMR profiles and a comprehensive set of correlative data, including observations from ozone sondes,ground-based lidar, FTIR and microwave radiometers, remote-sensing and in situ instruments on-board stratospheric aircraft and balloons, concurrent satellite sensors and ozone fields assimilated by the European Center for Medium-range Weather Forecasting. A coordinated effort was carried out, using common criteria for the selection of individual validation data sets, and similar methods for the comparisons. This enabled merging the individual results from a variety of independent reference measurements of proven quality (i.e., well characterised error budget) into an overall evaluation of MIPAS O3 data quality, having both statistical strength and the widest spatial and temporal coverage. Collocated measurements from ozone sondes and ground-based lidar and microwave radiometers of the Network for Detection Atmospheric Composition Change (NDACC) were selected to carry out comparisons with time series of MIPAS O3 partial columns and to identify groups of stations and time periods with a uniform pattern of ozone differences, that were subsequently used for a vertically resolved statistical analysis. The results of the comparison are classified according to synoptic and regional systems and to altitude intervals, showing a generally good agreement within the comparison error bars in the upper and middle stratosphere. Significant differences emerge in the lower stratosphere and are only partly explained by the larger contributions of horizontal and vertical smoothing differences and of collocation errors to the total uncertainty. Further results obtained from a purely statistical analysis of the same data set from NDACC ground-based lidar stations, as well as from additional ozone soundings at middle latitudes and from NDACC ground-based FTIR measurements, confirm the validity of MIPAS O3 profiles down to the lower stratosphere, with evidence of larger discrepancies at the lowest altitudes. The validation against O3 VMR profiles using collocated observations performed by other satellite sensors (SAGE II, POAM III, ODIN-SMR, ACE-FTS, HALOE, GOME) and ECMWF assimilated ozone fields leads to consistent results, that are to a great extent compatible with those obtained from the comparison with ground-based measurements. Excellent agreement in the full vertical range of the comparison is shown with respect to collocated ozone data from stratospheric aircraft and balloon instruments, that was mostly obtained in very good spatial and temporal coincidence with MIPAS scans. This might suggest that the larger differences observed in the upper troposphere and lowermost stratosphere with respect to collocated ground-based and satellite O3 data are only partly due to a degradation of MIPAS data quality. They should be rather largely ascribed to the natural variability of these altitude regions and to other components of the comparison errors. By combining the results of this large number of validation data sets we derived a general assessment of MIPAS v4.61 and v4.62 ozone data quality. A clear indication of the validity of MIPAS O3 vertical profiles is obtained for most of the stratosphere, where the mean relative difference with the individual correlative data sets is always lower than 10%. Furthermore, these differences always fall within the combined systematic error (from 1 hPa to 50 hPa) and the standard deviation is fully consistent with the random error of the comparison (from 1 hPa to ~30–40 hPa). A degradation in the quality of the agreement is generally observed in the lower stratosphere and upper troposphere, with biases up to 25% at 100 hPa and standard deviation of the global mean differences up to three times larger than the combined random error in the range 50–100 hPa. The larger differences observed at the bottom end of MIPAS retrieved profiles can be associated, as already noticed, to the effects of stronger atmospheric gradients in the UTLS that are perceived differently by the various measurement techniques. However, further components that may degrade the results of the comparison at lower altitudes can be identified as potentially including cloud contamination, which is likely not to have been fully filtered using the current settings of the MIPAS cloud detection algorithm, and in the linear approximation of the forward model that was used for the climatological estimate of systematic error components. The latter, when affecting systematic contributions with a random variability over the spatial and temporal scales of global averages, might result in an underestimation of the random error of the comparison and add up to other error sources, such as the possible underestimates of the p and T error propagation based on the assumption of a 1 K and 2% uncertainties, respectively, on MIPAS temperature and pressure retrievals. At pressure lower than 1 hPa, only a small fraction of the selected validation data set provides correlative ozone data of adequate quality and it is difficult to derive quantitative conclusions about the performance of MIPAS O3 retrieval for the topmost layers.
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Tikhonov, V. V., M. D. Raev, I. V. Khvostov, D. A. Boyarskii, A. N. Romanov, E. A. Sharkov, and N. Yu Komarova. "Analysis of the Seasonal Dependence of the Brightness Temperature of the Ice Sheet of Antarctica by Microwave Satellite Data." Исследования Земли из Космоса, no. 1 (March 24, 2019): 14–28. http://dx.doi.org/10.31857/s0205-96142019114-28.
Full textAbstract:
The paper presents an analysis of seasonal brightness temperature variations of different regions of Antarctica according to the data of MIRAS (SMOS satellite) and SSMIS (DMSP series satellites) radiometers. As a region of study, the Dronning Maud Land was chosen which includes the main zones of Antarctica: the dome, the zone of runoff winds and the coastal zone. The interrelation of time dynamics of brightness temperature from the change of climatic characteristics of regions is considered. The main factors influencing the change of brightness temperature in different regions of the Antarctic ice sheet are revealed.
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Prinsenberg, Simon J., and Ingrid K. Peterson. "Observing regional-scale pack-ice decay processes with helicopter-borne sensors and moored upward-looking sonars." Annals of Glaciology 52, no. 57 (2011): 35–42. http://dx.doi.org/10.3189/172756411795931688.
Full textAbstract:
AbstractThe variability of Arctic pack-ice parameters (e.g. extent and ice type) has been monitored by satellite-borne sensors since the early 1960s, and information on ice thickness is now becoming available from satellite altimeters. However, the spatial resolution of satellite-derived ice properties is too coarse to validate fine-scale ice variability generated by regional-scale interaction processes that affect the coarse-scale pack-ice albedo, strength and decay. To understand these regional processes, researchers rely on other data-monitoring platforms such as moored upward-looking sonars and helicopter-borne sensors. Backed by observations, two such regional-scale pack-ice decay processes are discussed: the break-up of large pack-ice floes by long-period waves generated by distant storms, and the spring decay of first-year-ice ridges in a diverging pack-ice environment. These two processes, although occurring on regional spatial scales, are important contributors to the evolution of the total pack ice and need to be included in global climate models, especially as the conditions for their occurrence will alter due to climate change.
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Goodison, B. E., and A. E. Walker. "Use of snow cover derived from satellite passive microwave data as an indicator of climate change." Annals of Glaciology 17 (1993): 137–42. http://dx.doi.org/10.1017/s0260305500012738.
Full textAbstract:
To assess future global change, monitoring of the climate system through observation and analysis of seasonal and interannual fluctuations of climate variables is necessary. Cryospheric elements such as snow cover are often seen as sensitive indicators and integrators of basic climate conditions and hence an indicator of regional and global change. Snow-cover elements which may serve as signals of variability and change are discussed with respect to the effective use of conventional and remotely sensed information. Conventional data are shown to be effective for assessing questions of temporal variability, but are limited for spatial variability. Passive microwave satellite data make an important contribution by providing spatial and temporal information on snow water equivalent (SWE) and the regional distribution of snowpack extent and state. Use of NIMBUS-7 SMMR (Scanning Multichannel Microwave Radiometer) data to develop a time series of SWE is assessed as a complement to conventional data. Limitations of SMMR coverage compared to DMSP SSM/I (Special Sensor Microwave/Imager) coverage for production of SWE maps for climate change analysis are discussed. Although there are limitations during early season snow cover, information derived from passive microwave data is shown to be able to map and compute the areal coverage of SWE allowing interannual comparison of the amount of water available, the date of peak accumulation and the associated spatial distribution. However, the satellite data record is still too short to establish any definitive trend in snow-cover variability.
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Goodison, B. E., and A. E. Walker. "Use of snow cover derived from satellite passive microwave data as an indicator of climate change." Annals of Glaciology 17 (1993): 137–42. http://dx.doi.org/10.3189/s0260305500012738.
Full textAbstract:
To assess future global change, monitoring of the climate system through observation and analysis of seasonal and interannual fluctuations of climate variables is necessary. Cryospheric elements such as snow cover are often seen as sensitive indicators and integrators of basic climate conditions and hence an indicator of regional and global change. Snow-cover elements which may serve as signals of variability and change are discussed with respect to the effective use of conventional and remotely sensed information. Conventional data are shown to be effective for assessing questions of temporal variability, but are limited for spatial variability. Passive microwave satellite data make an important contribution by providing spatial and temporal information on snow water equivalent (SWE) and the regional distribution of snowpack extent and state. Use of NIMBUS-7 SMMR (Scanning Multichannel Microwave Radiometer) data to develop a time series of SWE is assessed as a complement to conventional data. Limitations of SMMR coverage compared to DMSP SSM/I (Special Sensor Microwave/Imager) coverage for production of SWE maps for climate change analysis are discussed. Although there are limitations during early season snow cover, information derived from passive microwave data is shown to be able to map and compute the areal coverage of SWE allowing interannual comparison of the amount of water available, the date of peak accumulation and the associated spatial distribution. However, the satellite data record is still too short to establish any definitive trend in snow-cover variability.
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Toohey, M., B. M. Quine, K. Strong, P. F. Bernath, C. D. Boone, A. I. Jonsson, C. T. McElroy, K. A. Walker, and D. Wunch. "Balloon-borne radiometer measurement of Northern Hemisphere mid-latitude stratospheric HNO<sub>3</sub> profiles spanning 12 years." Atmospheric Chemistry and Physics Discussions 7, no. 4 (August 6, 2007): 11561–86. http://dx.doi.org/10.5194/acpd-7-11561-2007.
Full textAbstract:
Abstract. Low-resolution atmospheric thermal emission spectra collected by balloon-borne radiometers over the time span of 1990–2002 are used to retrieve vertical profiles of HNO3, CFC-11 and CFC-12 volume mixing ratios between approximately 10 and 35 km altitude. All of the data analyzed have been collected from launches from a Northern Hemisphere mid-latitude site, during late summer, when stratospheric dynamic variability is at a minimum. The retrieval technique incorporates detailed forward modeling of the instrument and the radiative properties of the atmosphere, and obtains a best fit between modeled and measured spectra through a combination of onion-peeling and global optimization steps. The retrieved HNO3 profiles are consistent over the 12-year period, and are consistent with recent measurements by the Atmospheric Chemistry Experiment-Fourier transform spectrometer satellite instrument. This suggests that, to within the errors of the 1990 measurements, there has been no significant change in the HNO3 summer mid-latitude profile.
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Beaulieu, C., S. A. Henson, J. L. Sarmiento, J. P. Dunne, S. C. Doney, R. R. Rykaczewski, and L. Bopp. "Factors challenging our ability to detect long-term trends in ocean chlorophyll." Biogeosciences Discussions 9, no. 11 (November 20, 2012): 16419–56. http://dx.doi.org/10.5194/bgd-9-16419-2012.
Full textAbstract:
Abstract. Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of the Ocean Land Colour Instrument (OLCI) in 2013 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies.