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1
Sporre, Moa K., Ewan J. O'Connor, Nina Håkansson, Anke Thoss, Erik Swietlicki, and Tuukka Petäjä. "Comparison of MODIS and VIIRS cloud properties with ARM ground-based observations over Finland." Atmospheric Measurement Techniques 9, no. 7 (July 21, 2016): 3193–203. http://dx.doi.org/10.5194/amt-9-3193-2016.
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Abstract. Cloud retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the satellites Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the Suomi-NPP satellite are evaluated using a combination of ground-based instruments providing vertical profiles of clouds. The ground-based measurements are obtained from the Atmospheric Radiation Measurement (ARM) programme mobile facility, which was deployed in Hyytiälä, Finland, between February and September 2014 for the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) campaign. The satellite cloud parameters cloud top height (CTH) and liquid water path (LWP) are compared with ground-based CTH obtained from a cloud mask created using lidar and radar data and LWP acquired from a multi-channel microwave radiometer. Clouds from all altitudes in the atmosphere are investigated. The clouds are diagnosed as single or multiple layer using the ground-based cloud mask. For single-layer clouds, satellites overestimated CTH by 326 m (14 %) on average. When including multilayer clouds, satellites underestimated CTH by on average 169 m (5.8 %). MODIS collection 6 overestimated LWP by on average 13 g m−2 (11 %). Interestingly, LWP for MODIS collection 5.1 is slightly overestimated by Aqua (4.56 %) but is underestimated by Terra (14.3 %). This underestimation may be attributed to a known issue with a drift in the reflectance bands of the MODIS instrument on Terra. This evaluation indicates that the satellite cloud parameters selected show reasonable agreement with their ground-based counterparts over Finland, with minimal influence from the large solar zenith angle experienced by the satellites in this high-latitude location.
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Gopal, Banala Krishna. "Atmospheric Data Collecting Cubesat using Raspberry PI." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3880–88. http://dx.doi.org/10.22214/ijraset.2021.35848.
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As advances in technology make payloads and instruments for space missions smaller, lighter, and more power efficient, a distinct segment market is emerging for low-cost missions on very small spacecrafts such as - micro, nano, and picosatellites. Due to the fact that even after many technological advances the usage of miniature satellites the remote sensing of atmospheric is still not a widely explored aspect, to overcome this we idealized a system to build a CUBESAT which can be built with minimal efforts. We proposed this system with an objective to build a CUBESAT to detect different weather aspects of our earth at the troposphere layer which is the lowest layer of earth. We implemented our project using the Raspberry Pi due to its versatility in multi-processing and connectivity. Here the Raspberry-Pi is going to be configured with transceiver modules in the CUBESAT’s sender-end to gather atmospheric data associated with temperature, gasses present, humidity and pressure using CUBESAT sensors and after the reception of data at ground station by Arduino configured as receiver, data is going to be stored in an accessible website for viewing and further computations.
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da Silva, Áurea Aparecida, Wilson Yamaguti, Hélio Koiti Kuga, and Cláudia Celeste Celestino. "Assessment of the Ionospheric and Tropospheric Effects in Location Errors of Data Collection Platforms in Equatorial Region during High and Low Solar Activity Periods." Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/734280.
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The geographical locations of data collection platforms (DCP) in the Brazilian Environmental Data Collection System are obtained by processing Doppler shift measurements between satellites and DCP. When the signals travel from a DCP to a satellite crossing the terrestrial atmosphere, they are affected by the atmosphere layers, which generate a delay in the signal propagation, and cause errors in its final location coordinates computation. The signal propagation delay due to the atmospheric effects consists, essentially, of the ionospheric and tropospheric effects. This work provides an assessment of ionospheric effects using IRI and IONEX models and tropospheric delay compensation using climatic data provided by National Climatic Data Center. Two selected DCPs were used in this study in conjunction with SCD-2 satellite during high and low solar activity periods. Results show that the ionospheric effects on transmission delays are significant (about hundreds of meters) in equatorial region and should be considered to reduce DCP location errors, mainly in high solar activity periods, while in those due to tropospheric effects the zenith errors are about threemeters. Therefore it is shown that the platform location errors can be reduced when the ionospheric and tropospheric effects are properly considered.
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Kalukin, Andrew, Satoshi Endo, Russell Crook, Manoj Mahajan, Robert Fennimore, Alice Cialella, Laurie Gregory, Shinjae Yoo, Wei Xu, and Daniel Cisek. "Image Collection Simulation Using High-Resolution Atmospheric Modeling." Remote Sensing 12, no. 19 (October 1, 2020): 3214. http://dx.doi.org/10.3390/rs12193214.
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A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.
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Catchpole, Ivan, Peter Upton, Andrew Sinclair, and Jim Nagle. "Wide Area Differential GPS Field Study." Journal of Navigation 47, no. 2 (May 1994): 146–58. http://dx.doi.org/10.1017/s0373463300012066.
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Inmarsat commissioned the field study to be carried out by Signal Computing Ltd. in association with the Royal Greenwich Observatory. The study aimed to identify the commercial suitability of Wide Area Differential GPS (WADGPS) corrections. The corrections provided to users will be relayed via Inmarsat-3 geostationary satellites and are required to be valid over the footprint of an entire Inmarsat ocean region (approximately one third of the Earth's surface).The study has been conducted within the Inmarsat Atlantic Ocean Region East. Trials took place over a five-month period to achieve a representative set of data. Six widely-distributed collection sites were used to ensure that the maximum duration of satellite data were received and that a representative model for atmospheric effects was obtained. Analysis is restricted to the use of GPS carrier and phase data obtained using the C/A code transmitted by Block II satellites only.This paper presents the analysis of data collected during a four-day trial period. The ability to determine the GPS satellite ephemeris (independent of GPS broadcast ephemeris), Klobuchar model parameters tailored to the oceanic region, and satellite clock corrections using GPS data obtained from this limited deployment of collection sites is addressed. The primary degradation effects on stand-alone GPS positioning of Selective Availability (SA) are corrected using WADGPS, resulting in a significant improvement in position accuracy.
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Liu, Yuling, Yunyue Yu, Peng Yu, Heshun Wang, and Yuhan Rao. "Enterprise LST Algorithm Development and Its Evaluation with NOAA 20 Data." Remote Sensing 11, no. 17 (August 24, 2019): 2003. http://dx.doi.org/10.3390/rs11172003.
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Satellite land surface temperatures (LSTs) have been routinely produced for decades from a variety of polar-orbiting and geostationary satellites, which makes it possible to generate LST climate data globally. However, consistency of the satellite LSTs from different satellite missions is a concern for such purpose; an enterprise satellite LST algorithm is desired for the LST production through different satellite missions, or at the least, through series satellites of a satellite mission. The enterprise LST algorithm employs the split window technique and uses the emissivity explicitly in its formula. This research focuses on the enterprise LST algorithm design, development and its evaluations with the National Oceanic and Atmospheric Administration’s (NOAA) 20 (N20) Visible Infrared Imaging Radiometer Suite (VIIRS) data available since 5 January 2018. In this study, the enterprise LST algorithm was evaluated using simulation dataset consisting of over 2000 profiles from SeeBor collection and the results show a bias of 0.19 K and 0.34 K and standard deviation of 0.48 K and 0.69 K for nighttime and daytime, respectively. The in situ observations from seven NOAA Surface Radiation budget (SURFRAD) sites and two Baseline Surface Radiation Network (BSRN) sites were used for LST validation. The results indicate a bias of −0.3 K and a root mean square error (RMSE) of 2.06 K for SURFRAD stations and a bias of 0.2 K and a RMSE of ~2 K for BSRN sites. Further, the cross-satellite analysis presents a bias of 0.7 K and an RMSE of 1.9 K for comparisons with AQUA MODIS LST (MYD11_L2, Collection 6). The enterprise N20 VIIRS LST product reached the provisional maturity in February 2019 and is ready for users to use in their applications.
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Sutlieff, Gary, Lucy Berthoud, and Mark Stinchcombe. "Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling." Surveys in Geophysics 42, no. 3 (April 17, 2021): 727–55. http://dx.doi.org/10.1007/s10712-021-09637-5.
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Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field
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Tate, PM. "Monthly mean surface thermal structure in the Tasman Sea from satellite imagery, 1979-84." Marine and Freshwater Research 39, no. 5 (1988): 579. http://dx.doi.org/10.1071/mf9880579.
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The use of satellite data provides a far greater density and more uniform distribution of observations than the more classical modes of oceanographic data collection. By sacrificing some spatial resolution of the satellite data, it is possible to retrieve sea surface temperatures on a global basis that have absolute accuracies within 1�C of drifting buoy data. Five years of low resolution infra-red data from the United States' National Oceanic and Atmospheric Administration satellites have been analysed for the area of the Tasman and Southern Coral Seas. Monthly mean surface thermal patterns compare favourably with previous studies using Merchant Ship data. In the western Tasman Sea the East Australian Current can be clearly seen throughout the year, although the more transitory eddies often associated with the current system are not apparent. General circulation patterns off the west coast of the South Island of New Zealand show severe bending of the isotherms to the south. The nature of the surface thermal signal of the Tasman Front is quite different either side of the Lord Howe Rise and there is some doubt whether these two features are linked.
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Li, Ruibo, Hua Li, Lin Sun, Yikun Yang, Tian Hu, Zunjian Bian, Biao Cao, Yongming Du, and Qinhuo Liu. "An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data." Remote Sensing 12, no. 16 (August 13, 2020): 2613. http://dx.doi.org/10.3390/rs12162613.
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An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.
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Williams, Dean N., V. Balaji, Luca Cinquini, Sébastien Denvil, Daniel Duffy, Ben Evans, Robert Ferraro, Rose Hansen, Michael Lautenschlager, and Claire Trenham. "A Global Repository for Planet-Sized Experiments and Observations." Bulletin of the American Meteorological Society 97, no. 5 (May 1, 2016): 803–16. http://dx.doi.org/10.1175/bams-d-15-00132.1.
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Abstract Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP)—output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.
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Schulz, Hans Martin, Boris Thies, Shih-Chieh Chang, and Jörg Bendix. "Detection of ground fog in mountainous areas from MODIS (Collection 051) daytime data using a statistical approach." Atmospheric Measurement Techniques 9, no. 3 (March 18, 2016): 1135–52. http://dx.doi.org/10.5194/amt-9-1135-2016.
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Abstract. The mountain cloud forest of Taiwan can be delimited from other forest types using a map of the ground fog frequency. In order to create such a frequency map from remotely sensed data, an algorithm able to detect ground fog is necessary. Common techniques for ground fog detection based on weather satellite data cannot be applied to fog occurrences in Taiwan as they rely on several assumptions regarding cloud properties. Therefore a new statistical method for the detection of ground fog in mountainous terrain from MODIS Collection 051 data is presented. Due to the sharpening of input data using MODIS bands 1 and 2, the method provides fog masks in a resolution of 250 m per pixel. The new technique is based on negative correlations between optical thickness and terrain height that can be observed if a cloud that is relatively plane-parallel is truncated by the terrain. A validation of the new technique using camera data has shown that the quality of fog detection is comparable to that of another modern fog detection scheme developed and validated for the temperate zones. The method is particularly applicable to optically thinner water clouds. Beyond a cloud optical thickness of ≈ 40, classification errors significantly increase.
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Pooja, S. B., and Siva R. V. Balan. "An Investigation Study on Clustering and Classification Techniques for Weather Forecasting." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 417–21. http://dx.doi.org/10.1166/jctn.2019.7742.
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Weather forecasting is the prediction of atmosphere state for particular location by using principles of physics provided by many statistical and empirical techniques. Weather forecasts are frequently made by collecting quantitative data about current state of atmosphere through scientific understanding of atmospheric processes to illustrate how atmosphere changes in future. Current weather conditions are collected through the observation from the ground, ships, aircraft, radio sounds and satellites. The information is transmitted to the meteorological centers where the data are collected and examined for prediction. There are diverse techniques included in weather forecasting, from relatively simple observation of sky to complex computerized mathematical models. But, the existing techniques failed to predict the weather with higher accuracy and lesser time. In order to improve the prediction performance, the machine learning and ensemble techniques are introduced.
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Munchak, L. A., R. C. Levy, S. Mattoo, L. A. Remer, B. N. Holben, J. S. Schafer, C. A. Hostetler, and R. A. Ferrare. "MODIS 3 km aerosol product: applications over land in an urban/suburban region." Atmospheric Measurement Techniques Discussions 6, no. 1 (February 14, 2013): 1683–716. http://dx.doi.org/10.5194/amtd-6-1683-2013.
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Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites have provided a rich dataset of aerosol information at a 10 km spatial scale. Although originally intended for climate applications, the air quality community quickly became interested in using the MODIS aerosol data. However, 10 km resolution is not sufficient to resolve local scale aerosol features. With this in mind, MODIS Collection 6 is including a global aerosol product with a 3 km resolution. Here, we evaluate the 3 km product over the Baltimore/Washington D.C., USA, corridor during the summer of 2011, by comparing with spatially dense data collected as part of the DISCOVER-AQ campaign; these data were measured by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL) and a network of 44 sun photometers (SP) spaced approximately 10 km apart. The HSRL instrument shows that AOD can vary by up to 0.2 within a single 10 km MODIS pixel, meaning that higher resolution satellite retrievals may help to characterize aerosol spatial distributions in this region. Different techniques for validating a high-resolution aerosol product against SP measurements are considered. Although the 10 km product is more statistically reliable than the 3 km product, the 3 km product still performs acceptably, with more than two-thirds of MODIS/SP collocations falling within the expected error envelope with high correlation (R > 0.90). The 3 km product can better resolve aerosol gradients and retrieve closer to clouds and shorelines than the 10 km product, but tends to show more significant noise especially in urban areas. This urban degradation is quantified using ancillary land cover data. Overall, we show that the MODIS 3 km product adds new information to the existing set of satellite derived aerosol products and validates well over the region, but due to noise and problems in urban areas, should be treated with some degree of caution.
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Munchak, L. A., R. C. Levy, S. Mattoo, L. A. Remer, B. N. Holben, J. S. Schafer, C. A. Hostetler, and R. A. Ferrare. "MODIS 3 km aerosol product: applications over land in an urban/suburban region." Atmospheric Measurement Techniques 6, no. 7 (July 23, 2013): 1747–59. http://dx.doi.org/10.5194/amt-6-1747-2013.
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Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites have provided a rich dataset of aerosol information at a 10 km spatial scale. Although originally intended for climate applications, the air quality community quickly became interested in using the MODIS aerosol data. However, 10 km resolution is not sufficient to resolve local scale aerosol features. With this in mind, MODIS Collection 6 includes a global aerosol product with a 3 km resolution. Here, we evaluate the 3 km product over the Baltimore–Washington D.C., USA, corridor during the summer of 2011 by comparing with spatially dense aerosol data measured by airborne High Spectral Resolution Lidar (HSRL) and a network of 44 sun photometers (SP) spaced approximately 10 km apart, collected as part of the DISCOVER-AQ field campaign. The HSRL instrument shows that AOD can vary by over 0.2 within a single 10 km MODIS pixel, meaning that higher resolution satellite retrievals may help to better characterize aerosol spatial distributions in this region. Different techniques for validating a high-resolution aerosol product against SP measurements are considered. Although the 10 km product is more statistically reliable than the 3 km product, the 3 km product still performs acceptably with nearly two-thirds of MODIS/SP collocations falling within an expected error envelope with high correlation (R > 0.90), although with a high bias of ~ 0.06. The 3 km product can better resolve aerosol gradients and retrieve closer to clouds and shorelines than the 10 km product, but tends to show more noise, especially in urban areas. This urban degradation is quantified using ancillary land cover data. Overall, we show that the MODIS 3 km product adds new information to the existing set of satellite derived aerosol products and validates well over the region, but due to noise and problems in urban areas, should be treated with some degree of caution.
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Krassovski, Misha B., Glen E. Lyon, Jeffery S. Riggs, and Paul J. Hanson. "Near-real-time environmental monitoring and large-volume data collection over slow communication links." Geoscientific Instrumentation, Methods and Data Systems 7, no. 4 (October 25, 2018): 289–95. http://dx.doi.org/10.5194/gi-7-289-2018.
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Abstract. Climate change studies are one of the most important aspects of modern science and related experiments are getting bigger and more complex. One such experiment is the Spruce and Peatland Responses Under Changing Environments (SPRUCE; http://mnspruce.ornl.gov, last access: 16 October 2018) conducted in northern Minnesota. The SPRUCE experimental mission is to assess ecosystem-level biological responses of vulnerable, high-carbon terrestrial ecosystems to a range of climate warming manipulations and an elevated CO2 atmosphere. This manipulation experiment generates a lot of observational data and requires a reliable on-site data collection system, dependable methods to transfer data to a robust scientific facility, and real-time monitoring capabilities. This publication shares our experience of establishing a near-real-time data collection and monitoring system via a satellite link using the not very well-known possibilities of PakBus protocol.
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Sayer, A. M., A. Smirnov, N. C. Hsu, L. A. Munchak, and B. N. Holben. "Estimating marine aerosol particle volume and number from Maritime Aerosol Network data." Atmospheric Chemistry and Physics 12, no. 18 (September 28, 2012): 8889–909. http://dx.doi.org/10.5194/acp-12-8889-2012.
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Abstract. As well as spectral aerosol optical depth (AOD), aerosol composition and concentration (number, volume, or mass) are of interest for a variety of applications. However, remote sensing of these quantities is more difficult than for AOD, as it is more sensitive to assumptions relating to aerosol composition. This study uses spectral AOD measured on Maritime Aerosol Network (MAN) cruises, with the additional constraint of a microphysical model for unpolluted maritime aerosol based on analysis of Aerosol Robotic Network (AERONET) inversions, to estimate these quantities over open ocean. When the MAN data are subset to those likely to be comprised of maritime aerosol, number and volume concentrations obtained are physically reasonable. Attempts to estimate surface concentration from columnar abundance, however, are shown to be limited by uncertainties in vertical distribution. Columnar AOD at 550 nm and aerosol number for unpolluted maritime cases are also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) data, for both the present Collection 5.1 and forthcoming Collection 6. MODIS provides a best-fitting retrieval solution, as well as the average for several different solutions, with different aerosol microphysical models. The "average solution" MODIS dataset agrees more closely with MAN than the "best solution" dataset. Terra tends to retrieve lower aerosol number than MAN, and Aqua higher, linked with differences in the aerosol models commonly chosen. Collection 6 AOD is likely to agree more closely with MAN over open ocean than Collection 5.1. In situations where spectral AOD is measured accurately, and aerosol microphysical properties are reasonably well-constrained, estimates of aerosol number and volume using MAN or similar data would provide for a greater variety of potential comparisons with aerosol properties derived from satellite or chemistry transport model data. However, without accurate AOD data and prior knowledge of microphysical properties, such attempts are fraught with high uncertainties.
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Sharifnezhadazizi, Zahra, Hamid Norouzi, Satya Prakash, Christopher Beale, and Reza Khanbilvardi. "A Global Analysis of Land Surface Temperature Diurnal Cycle Using MODIS Observations." Journal of Applied Meteorology and Climatology 58, no. 6 (June 2019): 1279–91. http://dx.doi.org/10.1175/jamc-d-18-0256.1.
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AbstractDiurnal variations of land surface temperature (LST) play a vital role in a wide range of applications such as climate change assessment, land–atmosphere interactions, and heat-related health issues in urban regions. This study uses 15 years (2003–17) of daily observations of LST Collection 6 from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the Aqua and the Terra satellites. A spline interpolation method is used to estimate half-hourly global LST from the MODIS measurements. A preliminary assessment of interpolated LST with hourly ground-based observations over selected stations of North America shows bias and an error of less than 1 K. Results suggest that the present interpolation method is capable of capturing the diurnal variations of LST reasonably well for different land-cover types. The diurnal cycle of LST and time of occurrence of maximum temperature are computed from the spatially and temporally consistent interpolated diurnal LST data at a global scale. Regions with higher variability in the timing of maximum LST hours and diurnal amplitude are identified in this study. The global desert regions show generally small variability of the monthly mean diurnal LST range, whereas larger areas of the global land exhibit rather higher variability in the diurnal LST range during the study period. Moreover, the changes in diurnal temperature range for the study period are examined for distinct land-cover types. Analysis of the 15-yr time series of the diurnal LST record shows an overall decrease of 0.5 K in amplitude over the Northern Hemisphere. However, the diurnal LST range shows variant changes in the Southern Hemisphere.
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Bouvet, Marc, Kurtis Thome, Béatrice Berthelot, Agnieszka Bialek, Jeffrey Czapla-Myers, Nigel Fox, Philippe Goryl, et al. "RadCalNet: A Radiometric Calibration Network for Earth Observing Imagers Operating in the Visible to Shortwave Infrared Spectral Range." Remote Sensing 11, no. 20 (October 16, 2019): 2401. http://dx.doi.org/10.3390/rs11202401.
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Vicarious calibration approaches using in situ measurements saw first use in the early 1980s and have since improved to keep pace with the evolution of the radiometric requirements of the sensors that are being calibrated. The advantage of in situ measurements for vicarious calibration is that they can be carried out with traceable and quantifiable accuracy, making them ideal for interconsistency studies of on-orbit sensors. The recent development of automated sites to collect the in situ data has led to an increase in the available number of datasets for sensor calibration. The current work describes the Radiometric Calibration Network (RadCalNet) that is an effort to provide automated surface and atmosphere in situ data as part of a network including multiple sites for the purpose of optical imager radiometric calibration in the visible to shortwave infrared spectral range. The key goals of RadCalNet are to standardize protocols for collecting data, process to top-of-atmosphere reflectance, and provide uncertainty budgets for automated sites traceable to the international system of units. RadCalNet is the result of efforts by the RadCalNet Working Group under the umbrella of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and the Infrared Visible Optical Sensors (IVOS). Four radiometric calibration instrumented sites located in the USA, France, China, and Namibia are presented here that were used as initial sites for prototyping and demonstrating RadCalNet. All four sites rely on collection of data for assessing the surface reflectance as well as atmospheric data over that site. The data are converted to top-of-atmosphere reflectance within RadCalNet and provided through a web portal to allow users to either radiometrically calibrate or verify the calibration of their sensors of interest. Top-of-atmosphere reflectance data with associated uncertainties are available at 10 nm intervals over the 400 nm to 1000 nm spectral range at 30 min intervals for a nadir-viewing geometry. An example is shown demonstrating how top-of-atmosphere data from RadCalNet can be used to determine the interconsistency between two sensors.
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Håkansson, Nina, Claudia Adok, Anke Thoss, Ronald Scheirer, and Sara Hörnquist. "Neural network cloud top pressure and height for MODIS." Atmospheric Measurement Techniques 11, no. 5 (June 1, 2018): 3177–96. http://dx.doi.org/10.5194/amt-11-3177-2018.
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Abstract. Cloud top height retrieval from imager instruments is important for nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS (Moderate Resolution Imaging Spectroradiometer) is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) dataset. Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 Level 2 height product and the cloud top temperature and height algorithm in the 2014 version of the NWC SAF (EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting) PPS (Polar Platform System). All three techniques are evaluated using both CALIOP and CPR (Cloud Profiling Radar for CloudSat (CLOUD SATellite)) height. Instruments like AVHRR (Advanced Very High Resolution Radiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance. Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighboring pixels are very important. The error distributions of the involved cloud top height algorithms are found to be non-Gaussian. Different descriptive statistic measures are presented and it is exemplified that bias and SD (standard deviation) can be misleading for non-Gaussian distributions. The median and mode are found to better describe the tendency of the error distributions and IQR (interquartile range) and MAE (mean absolute error) are found to give the most useful information of the spread of the errors. For all descriptive statistics presented MAE, IQR, RMSE (root mean square error), SD, mode, median, bias and percentage of absolute errors above 0.25, 0.5, 1 and 2 km the neural network perform better than the reference algorithms both validated with CALIOP and CPR (CloudSat). The neural networks using the brightness temperatures at 11 and 12 µm show at least 32 % (or 623 m) lower MAE compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25 % (or 430 m) reduction of MAE.
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de Leeuw, Gerrit, Larisa Sogacheva, Edith Rodriguez, Konstantinos Kourtidis, Aristeidis K. Georgoulias, Georgia Alexandri, Vassilis Amiridis, et al. "Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns." Atmospheric Chemistry and Physics 18, no. 3 (February 5, 2018): 1573–92. http://dx.doi.org/10.5194/acp-18-1573-2018.
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Abstract. The retrieval of aerosol properties from satellite observations provides their spatial distribution over a wide area in cloud-free conditions. As such, they complement ground-based measurements by providing information over sparsely instrumented areas, albeit that significant differences may exist in both the type of information obtained and the temporal information from satellite and ground-based observations. In this paper, information from different types of satellite-based instruments is used to provide a 3-D climatology of aerosol properties over mainland China, i.e., vertical profiles of extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a lidar flying aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the column-integrated extinction (aerosol optical depth – AOD) available from three radiometers: the European Space Agency (ESA)'s Along-Track Scanning Radiometer version 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR) (together referred to as ATSR) and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, together spanning the period 1995–2015. AOD data are retrieved from ATSR using the ATSR dual view (ADV) v2.31 algorithm, while for MODIS Collection 6 (C6) the AOD data set is used that was obtained from merging the AODs obtained from the dark target (DT) and deep blue (DB) algorithms, further referred to as the DTDB merged AOD product. These data sets are validated and differences are compared using Aerosol Robotic Network (AERONET) version 2 L2.0 AOD data as reference. The results show that, over China, ATSR slightly underestimates the AOD and MODIS slightly overestimates the AOD. Consequently, ATSR AOD is overall lower than that from MODIS, and the difference increases with increasing AOD. The comparison also shows that neither of the ATSR and MODIS AOD data sets is better than the other one everywhere. However, ATSR ADV has limitations over bright surfaces which the MODIS DB was designed for. To allow for comparison of MODIS C6 results with previous analyses where MODIS Collection 5.1 (C5.1) data were used, also the difference between the C6 and C5.1 merged DTDB data sets from MODIS/Terra over China is briefly discussed. The AOD data sets show strong seasonal differences and the seasonal features vary with latitude and longitude across China. Two-decadal AOD time series, averaged over all of mainland China, are presented and briefly discussed. Using the 17 years of ATSR data as the basis and MODIS/Terra to follow the temporal evolution in recent years when the environmental satellite Envisat was lost requires a comparison of the data sets for the overlapping period to show their complementarity. ATSR precedes the MODIS time series between 1995 and 2000 and shows a distinct increase in the AOD over this period. The two data series show similar variations during the overlapping period between 2000 and 2011, with minima and maxima in the same years. MODIS extends this time series beyond the end of the Envisat period in 2012, showing decreasing AOD.
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Hong, Gang, Ping Yang, Bo-Cai Gao, Bryan A. Baum, Yong X. Hu, Michael D. King, and Steven Platnick. "High Cloud Properties from Three Years of MODIS Terra and Aqua Collection-4 Data over the Tropics." Journal of Applied Meteorology and Climatology 46, no. 11 (November 1, 2007): 1840–56. http://dx.doi.org/10.1175/2007jamc1583.1.
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Abstract This study surveys the optical and microphysical properties of high (ice) clouds over the Tropics (30°S–30°N) over a 3-yr period from September 2002 through August 2005. The analyses are based on the gridded level-3 cloud products derived from the measurements acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard both the NASA Earth Observing System Terra and Aqua platforms. The present analysis is based on the MODIS collection-4 data products. The cloud products provide daily, weekly, and monthly mean cloud fraction, cloud optical thickness, cloud effective radius, cloud-top temperature, cloud-top pressure, and cloud effective emissivity, which is defined as the product of cloud emittance and cloud fraction. This study is focused on high-level ice clouds. The MODIS-derived high clouds are classified as cirriform and deep convective clouds using the International Satellite Cloud Climatology Project (ISCCP) classification scheme. Cirriform clouds make up more than 80% of the total high clouds, whereas deep convective clouds account for less than 20% of the total high clouds. High clouds are prevalent over the intertropical convergence zone (ITCZ), the South Pacific convergence zone (SPCZ), tropical Africa, the Indian Ocean, tropical America, and South America. Moreover, land–ocean, morning–afternoon, and summer–winter variations of high cloud properties are also observed.
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Clauer, C. R., H. Kim, K. Deshpande, Z. Xu, D. Weimer, S. Musko, G. Crowley, et al. "An autonomous adaptive low-power instrument platform (AAL-PIP) for remote high-latitude geospace data collection." Geoscientific Instrumentation, Methods and Data Systems 3, no. 2 (October 10, 2014): 211–27. http://dx.doi.org/10.5194/gi-3-211-2014.
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Abstract. We present the development considerations and design for ground-based instrumentation that is being deployed on the East Antarctic Plateau along a 40° magnetic meridian chain to investigate interhemispheric magnetically conjugate geomagnetic coupling and other space-weather-related phenomena. The stations are magnetically conjugate to geomagnetic stations along the west coast of Greenland. The autonomous adaptive low-power instrument platforms being deployed in the Antarctic are designed to operate unattended in remote locations for at least 5 years. They utilize solar power and AGM storage batteries for power, two-way Iridium satellite communication for data acquisition and program/operation modification, support fluxgate and induction magnetometers as well as a dual-frequency GPS receiver and a high-frequency (HF) radio experiment. Size and weight considerations are considered to enable deployment by a small team using small aircraft. Considerable experience has been gained in the development and deployment of remote polar instrumentation that is reflected in the present generation of instrumentation discussed here. We conclude with the lessons learned from our experience in the design, deployment and operation of remote polar instrumentation.
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Adachi, Yusuke, Ryota Kikuchi, Kenta Obata, and Hiroki Yoshioka. "Relative Azimuthal-Angle Matching (RAM): A Screening Method for GEO-LEO Reflectance Comparison in Middle Latitude Forests." Remote Sensing 11, no. 9 (May 8, 2019): 1095. http://dx.doi.org/10.3390/rs11091095.
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This study introduced a data screening method for comparing the reflectances in middle latitude forest regions collected by a Geostationary Earth Observing (GEO) satellite and a Low Earth Orbit (LEO) satellite. This method attempts to reduce the differences between the relative azimuth angles of the GEO and LEO observations. The method, called relative azimuthal-angle matching (RAM), takes advantage of the high temporal resolution of the GEO satellites, which enables collection of a wide range of relative azimuth angles within a day. The performance of the RAM method was evaluated using data in the visible and near-infrared bands collected by the Himawari-8/Advanced Himawari Imager (AHI) and the Terra/Moderate Resolution Imaging Spectroradiometer (MODIS). The consistency of the reflectance pairs of MODIS and AHI selected by the RAM method was compared with the consistency of the reflectance pairs acquired simultaneously by the two sensors. The data were matched pixel-by-pixel after applying atmospheric corrections and cloud screening. The results show that RAM improved the reflectance ratio by approximately 10% for the red and NIR bands on average relative to the simultaneous observations. Significant improvements in the two bands were observed (20%), especially among data collected in the fall and winter. Performance of RAM depends largely on season. Especially in summer, the reflectance pair chosen by RAM showed less consistency than solar zenith-angle matching (SZM). The results also indicated the relatively large influence of the spectral response functions on the green and red bands of the two sensors.
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Wang, Chenxi, Ping Yang, Bryan A. Baum, Steven Platnick, Andrew K. Heidinger, Yongxiang Hu, and Robert E. Holz. "Retrieval of Ice Cloud Optical Thickness and Effective Particle Size Using a Fast Infrared Radiative Transfer Model." Journal of Applied Meteorology and Climatology 50, no. 11 (November 2011): 2283–97. http://dx.doi.org/10.1175/jamc-d-11-067.1.
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AbstractA computationally efficient radiative transfer model (RTM) is developed for the inference of ice cloud optical thickness and effective particle size from satellite-based infrared (IR) measurements and is aimed at potential use in operational cloud-property retrievals from multispectral satellite imagery. The RTM employs precomputed lookup tables to simulate the top-of-the-atmosphere (TOA) radiances (or brightness temperatures) at 8.5-, 11-, and 12-μm bands. For the clear-sky atmosphere, the optical thickness of each atmospheric layer resulting from gaseous absorption is derived from the correlated-k-distribution method. The cloud reflectance, transmittance, emissivity, and effective temperature are precomputed using the Discrete Ordinate Radiative Transfer model (DISORT). For an atmosphere containing a semitransparent ice cloud layer with a visible optical thickness τ smaller than 5, the TOA brightness temperature differences (BTDs) between the fast model and the more rigorous DISORT results are less than 0.1 K, whereas the BTDs are less than 0.01 K if τ is larger than 10. With the proposed RTM, the cloud optical and microphysical properties are retrieved from collocated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) in conjunction with the Modern Era Retrospective-Analysis for Research and Applications (MERRA) data. Comparisons between the retrieved ice cloud properties (optical thickness and effective particle size) based on the present IR fast model and those from the Aqua/MODIS operational collection-5 cloud products indicate that the IR retrievals are smaller. A comparison between the IR-retrieved ice water path (IWP) and CALIOP-retrieved IWP shows robust agreement over most of the IWP range.
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Redemann, J., Q. Zhang, J. Livingston, P. Russell, Y. Shinozuka, A. Clarke, R. Johnson, and R. Levy. "Testing aerosol properties in MODIS Collection 4 and 5 using airborne sunphotometer observations in INTEX-B/MILAGRO." Atmospheric Chemistry and Physics 9, no. 21 (November 2, 2009): 8159–72. http://dx.doi.org/10.5194/acp-9-8159-2009.
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Abstract. The 14-channel Ames Airborne Tracking Sunphotometer (AATS) was operated on a Jetstream 31 (J31) aircraft in March 2006 during MILAGRO/INTEX-B (Megacity Initiative-Local And Global Research Observations/Phase B of the Intercontinental Chemical Transport Experiment). We compare AATS retrievals of spectral aerosol optical depth (AOD) and related aerosol properties with corresponding spatially coincident and temporally near-coincident measurements acquired by the MODIS-Aqua and MODIS-Terra satellite sensors. These comparisons are carried out for the older MODIS Collection 4 (C4) and the new Collection 5 (C5) data set, the latter representing a reprocessing of the entire MODIS data set completed during 2006 with updated calibration and aerosol retrieval algorithm. Our analysis yields a direct, validated assessment of the differences between select MODIS C4 and C5 aerosol retrievals. Our analyses of 37 coincident observations by AATS and MODIS-Terra and 18 coincident observations between AATS and MODIS-Aqua indicate notable differences between MODIS C4 and C5 and between the two sensors. For MODIS-Terra, we find an average increase in AOD of 0.02 at 553 nm and 0.01 or less at the shortwave infrared (SWIR) wavelengths. The change from C4 to C5 results in less good agreement with the AATS derived spectral AOD, with average differences at 553 nm increasing from 0.03 to 0.05. For MODIS-Aqua, we find an average increase in AOD of 0.008 at 553 nm, but an increase of nearly 0.02 at the SWIR wavelengths. The change from C4 to C5 results in slightly less good agreement to the AATS derived visible AOD, with average differences at 553 nm increasing from 0.03 to 0.04. However, at SWIR wavelengths, the changes from C4 to C5 result in improved agreement between MODIS-Aqua and AATS, with the average differences at 2119 nm decreasing from −0.02 to −0.003. Comparing the Angstrom exponents calculated from AOD at 553nm and 855nm, we find an increased rms difference from AATS derived Angstrom exponents in going from C4 to C5 for MODIS-Terra, and a decrease in rms difference, hence an improvement, for the transition from C4 to C5 in MODIS-Aqua. Combining the AATS retrievals with in situ measurements of size-dependent aerosol extinction, we derive a suborbital measure of the aerosol submicron fraction (SMF) of AOD and compare it to MODIS retrievals of aerosol fine mode fraction (FMF). Our analysis shows a significant rms-difference between the MODIS-Terra FMF and suborbitally-derived SMF of 0.17 for both C4 and C5. For MODIS-Aqua, there is a slight improvement in the transition from C4 to C5, with the rms-difference from AATS dropping from 0.23 to 0.16. The differences in MODIS C4 and C5 AOD in this limited data set can be traced to changes in the reflectances input to the aerosol retrievals. An extension of the C4-C5 comparisons from the area along the J31 flight track to a larger study region between 18–23° N and 93–100° W on each of the J31 flight days supports the finding of significant differences between MODIS C4 and C5.
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Zhang, Shengfu. "Surface biomass monitoring of rotational grazing in alpine grassland based on satellite Multi-spectral images." E3S Web of Conferences 257 (2021): 03051. http://dx.doi.org/10.1051/e3sconf/202125703051.
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The surface of the biomass monitoring is blocked rotational grazing land prerequisite for high scores (GF - 1) satellite multispectral image and artificial biomass index test, using the ENVI software GF1 or GF6 data (radiation calibration atmospheric correction of RPC orthographical correction), and then calculate the vegetation index (NDVI), NDVI can detect vegetation growth status, vegetation coverage and eliminate part of the radiation error manual data collection and statistical analysis, then normalized processing, the conclusion is obtained.
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Jiang, Maofei, Ke Xu, and Yalong Liu. "Calibration and Validation of Reprocessed HY-2A Altimeter Wave Height Measurements Using Data from Buoys, Jason-2, Cryosat-2, and SARAL/AltiKa." Journal of Atmospheric and Oceanic Technology 35, no. 6 (June 2018): 1331–52. http://dx.doi.org/10.1175/jtech-d-17-0151.1.
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AbstractThe Haiyang-2A (HY-2A) satellite is China’s first ocean dynamic environment satellite, and the radar altimeter is one of its main payloads. In this study the HY-2A altimeter sensor interim geophysical dataset records (SIGDR) data are reprocessed to obtain better significant wave height (Hs) measurements over a period of more than four years (from 1 October 2011 to 15 March 2016). The reprocessed HY-2A Hs measurements are calibrated and validated using National Data Buoy Center (NDBC) buoys and several operating altimeters: Joint Altimetry Satellite Oceanography Network-2 (Jason-2), CryoSat-2, and Satellite with Argos Data Collection System and Ka-Band Altimeter (SARAL/ALtiKa) The final results of buoys and cross-altimeter comparisons show that the accuracy of the reprocessed HY-2A Hs measurements is significantly improved with respect to the Hs measurements in the operational HY-2A interim geophysical data record (IGDR) publicly distributed by the National Satellite Ocean Application Service (NSOAS), State Oceanic Administration (SOA) of China. Compared with the NDBC Hs measurements, the reprocessed HY-2A Hs measurements show a root-mean-square error (RMSE) of 0.215 m with a positive bias of 0.117 m. After calibrating with the two-branched corrections, the RMSE for the reprocessed HY-2A Hs measurements is reduced to 0.173 m, which is lower than those for the calibrated HY-2A IGDR, Jason-2, Cryosat-2, and SARAL measurements with an RMSE of 0.278, 0.233, 0.239, and 0.184 m, respectively. Long-term validation of the altimeter Hs measurements shows that the reprocessed HY-2A Hs measurements after calibration are stable with respect to the buoys and three other altimeters over the entire period. The reprocessed HY-2A Hs measurements are expected to improve the practical applicability of HY-2A Hs measurements significantly.
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Koukouli, M. E., D. S. Balis, D. Loyola, P. Valks, W. Zimmer, N. Hao, J. C. Lambert, M. Van Roozendael, C. Lerot, and R. J. D. Spurr. "Geophysical validation and long-term consistency between GOME-2/MetOp-A total ozone column and measurements from the sensors GOME/ERS-2, SCIAMACHY/ENVISAT and OMI/Aura." Atmospheric Measurement Techniques 5, no. 9 (September 7, 2012): 2169–81. http://dx.doi.org/10.5194/amt-5-2169-2012.
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Abstract. The main aim of the paper is to assess the consistency of five years of Global Ozone Monitoring Experiment-2/Metop-A [GOME-2] total ozone columns and the long-term total ozone satellite monitoring database already in existence through an extensive inter-comparison and validation exercise using as reference Brewer and Dobson ground-based measurements. The behaviour of the GOME-2 measurements is being weighed against that of GOME (1995–2011), Ozone Monitoring Experiment [OMI] (since 2004) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY [SCIAMACHY] (since 2002) total ozone column products. Over the background truth of the ground-based measurements, the total ozone columns are inter-evaluated using a suite of established validation techniques; the GOME-2 time series follow the same patterns as those observed by the other satellite sensors. In particular, on average, GOME-2 data underestimate GOME data by about 0.80%, and underestimate SCIAMACHY data by 0.37% with no seasonal dependence of the differences between GOME-2, GOME and SCIAMACHY. The latter is expected since the three datasets are based on similar DOAS algorithms. This underestimation of GOME-2 is within the uncertainty of the reference data used in the comparisons. Compared to the OMI sensor, on average GOME-2 data underestimate OMI_DOAS (collection 3) data by 1.28%, without any significant seasonal dependence of the differences between them. The lack of seasonality might be expected since both the GOME data processor [GDP] 4.4 and OMI_DOAS are DOAS-type algorithms and both consider the variability of the stratospheric temperatures in their retrievals. Compared to the OMI_TOMS (collection 3) data, no bias was found. We hence conclude that the GOME-2 total ozone columns are well suitable to continue the long-term global total ozone record with the accuracy needed for climate monitoring studies.
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Koukouli, M. E., D. S. Balis, D. Loyola, P. Valks, W. Zimmer, N. Hao, J. C. Lambert, M. Van Roozendael, C. Lerot, and R. J. D. Spurr. "Geophysical validation and long-term consistency between GOME-2/MetOp-A total ozone column and measurements from the sensors GOME/ERS-2, SCIAMACHY/ENVISAT and OMI/Aura." Atmospheric Measurement Techniques Discussions 5, no. 2 (April 24, 2012): 3019–45. http://dx.doi.org/10.5194/amtd-5-3019-2012.
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Abstract. The main aim of the paper is to assess the consistency of five years of Global Ozone Monitoring Experiment-2/Metop-A (GOME-2) total ozone columns and the long-term total ozone satellite monitoring database already in existence through an extensive inter-comparison and validation exercise using as reference Brewer and Dobson ground-based measurements. The behaviour of the GOME-2 measurements is being weighed against that of GOME (1995–2011), Ozone Monitoring Experiment (OMI) (since 2004) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) (since 2002) total ozone column products. Over the background truth of the ground-based measurements, the total ozone columns are inter-evaluated using a suite of established validation techniques; the GOME-2 time series follow the same patterns as those observed by the other satellite sensors and in particular, on the average, GOME-2 data underestimate GOME data by about 0.80%, and underestimate SCIAMACHY data by 0.37% with no seasonal dependence of the differences between GOME-2, GOME and SCIAMACHY. The latter is expected since the three data sets are based on similar DOAS algorithms. This underestimation of GOME-2 is within the uncertainty of the reference data used in the comparisons. Compared to the OMI sensor, on the average GOME-2 data underestimate OMI_DOAS (collection 3) data by 1.28%, without any significant seasonal dependence of the differences between them. The lack of seasonality might be expected since both GDP 4.4 and OMI_DOAS are DOAS-type algorithms and both consider the variability of the stratospheric temperatures in their retrievals. Compared to the OMI_TOMS (collection 3) data, no bias was found. We hence conclude that the GOME-2 total ozone columns are well suitable to continue the long-term global total ozone record with the accuracy needed for climate monitoring studies.
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Heymsfield, Andrew J., Carl Schmitt, Chih-Chieh-Jack Chen, Aaron Bansemer, Andrew Gettelman, Paul R. Field, and Chuntao Liu. "Contributions of the Liquid and Ice Phases to Global Surface Precipitation: Observations and Global Climate Modeling." Journal of the Atmospheric Sciences 77, no. 8 (July 10, 2020): 2629–48. http://dx.doi.org/10.1175/jas-d-19-0352.1.
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Abstract This study is the first to reach a global view of the precipitation process partitioning, using a combination of satellite and global climate modeling data. The pathways investigated are 1) precipitating ice (ice/snow/graupel) that forms above the freezing level and melts to produce rain (S) followed by additional condensation and collection as the melted precipitating ice falls to the surface (R); 2) growth completely through condensation and collection (coalescence), warm rain (W); and 3) precipitating ice (primarily snow) that falls to the surface (SS). To quantify the amounts, data from satellite-based radar measurements—CloudSat, GPM, and TRMM—are used, as well as climate model simulations from the Community Atmosphere Model (CAM) and the Met Office Unified Model (UM). Total precipitation amounts and the fraction of the total precipitation amount for each of the pathways is examined latitudinally, regionally, and globally. Carefully examining the contributions from the satellite-based products leads to the conclusion that about 57% of Earth’s precipitation follows pathway S, 15% R, 23% W, and 5% SS, each with an uncertainty of ±5%. The percentages differ significantly from the global climate model results, with the UM indicating smaller fractional S, more R, and more SS; and CAM showing appreciably greater S, negative R (indicating net evaporation below the melting layer), a much larger percentage of W and much less SS. Possible reasons for the wide differences between the satellite- and model-based results are discussed.
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Xiao, Q., H. Zhang, M. Choi, S. Li, S. Kondragunta, J. Kim, B. Holben, R. C. Levy, and Y. Liu. "Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia." Atmospheric Chemistry and Physics 16, no. 3 (February 3, 2016): 1255–69. http://dx.doi.org/10.5194/acp-16-1255-2016.
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Abstract. Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51 % of VIIRS Environmental Data Record (EDR) AOD, 37 % of GOCI AOD, 33 % of VIIRS Intermediate Product (IP) AOD, 26 % of Terra MODIS C6 3 km AOD, and 16 % of Aqua MODIS C6 3 km AOD fell within the reference expected error (EE) envelope (±0.05 ± 0.15 AOD). Comparing against AERONET AOD over the Japan–South Korea region, 64 % of EDR, 37 % of IP, 61 % of GOCI, 39 % of Terra MODIS, and 56 % of Aqua MODIS C6 3 km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases.
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Kuba, Naomi, Kentaroh Suzuki, Tempei Hashino, Tatsuya Seiki, and Masaki Satoh. "Numerical Experiments to Analyze Cloud Microphysical Processes Depicted in Vertical Profiles of Radar Reflectivity of Warm Clouds." Journal of the Atmospheric Sciences 72, no. 12 (November 19, 2015): 4509–28. http://dx.doi.org/10.1175/jas-d-15-0053.1.
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Abstract Information about microphysical processes in warm clouds embedded in satellite measurements must be untangled to be used to improve the parameterization in global models. In this paper, the relationship between vertical profiles of horizontally averaged radar reflectivity Zm and cloud optical depth from cloud top τd was investigated using a hybrid cloud microphysical model and a forward simulator of satellite measurements. The particle size distributions were explicitly simulated using a bin method in a kinematic framework. In contrast to previous interpretations of satellite-observed data, three patterns of the Zm–τd relationship related to microphysical processes were identified. The first is related to the autoconversion process, which causes Zm to increase upward with decreasing τd. Before the initiation of surface precipitation, Zm increases downward with τd in the upper part of the cloud, which is considered to be a second characteristic pattern and is caused by the accretion process. The appearance of this pattern corresponds to the initiation of efficient production of raindrops in the cloud. The third is related to the sedimentation and evaporation of raindrops causing Zm to decrease downward with τd in the lower part of the Zm–τd relationship. It was also found that the bulk collection efficiency has a partially positive correlation with the slope factor of Zm with regard to τd and that the slope factor could be a gross measure of the collection efficiency in partial cases. This study also shows that differences in the aerosol concentration modulate the duration of these three patterns and change the slope factor of Zm.
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Carminati, Fabien, Stefano Migliorini, Bruce Ingleby, William Bell, Heather Lawrence, Stuart Newman, James Hocking, and Andrew Smith. "Using reference radiosondes to characterise NWP model uncertainty for improved satellite calibration and validation." Atmospheric Measurement Techniques 12, no. 1 (January 7, 2019): 83–106. http://dx.doi.org/10.5194/amt-12-83-2019.
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Abstract. The characterisation of errors and uncertainties in numerical weather prediction (NWP) model fields is a major challenge that is addressed as part of the Horizon 2020 Gap Analysis for Integrated Atmospheric ECV CLImate Monitoring (GAIA-CLIM) project. In that regard, observations from the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) radiosondes are being used at the Met Office and European Centre for Medium-Range Weather Forecasts (ECMWF) to assess errors and uncertainties associated with model data. The software introduced in this study and referred to as the GRUAN processor has been developed to collocate GRUAN radiosonde profiles and NWP model fields, simulate top-of-atmosphere brightness temperature at frequencies used by space-borne instruments, and propagate GRUAN uncertainties in that simulation. A mathematical framework used to estimate and assess the uncertainty budget of the comparison of simulated brightness temperature is also proposed. A total of 1 year of GRUAN radiosondes and matching NWP fields from the Met Office and ECMWF have been processed and analysed for the purposes of demonstration of capability. We present preliminary results confirming the presence of known biases in the temperature and humidity profiles of both NWP centres. The night-time difference between GRUAN and Met Office (ECMWF) simulated brightness temperature at microwave frequencies predominantly sensitive to temperature is on average smaller than 0.1 K (0.4 K). Similarly, this difference is on average smaller than 0.5 K (0.4 K) at microwave frequencies predominantly sensitive to humidity. The uncertainty estimated for the Met Office–GRUAN difference ranges from 0.08 to 0.13 K for temperature-sensitive frequencies and from 1.6 to 2.5 K for humidity-sensitive frequencies. From the analysed sampling, 90 % of the comparisons are found to be in statistical agreement. This initial study has the potential to be extended to a larger collection of GRUAN profiles, covering multiple sites and years, with the aim of providing a robust estimation of both errors and uncertainties of NWP model fields in radiance space for a selection of key microwave and infrared frequencies.
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Weir, Brad, Lesley E. Ott, George J. Collatz, Stephan R. Kawa, Benjamin Poulter, Abhishek Chatterjee, Tomohiro Oda, and Steven Pawson. "Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems." Atmospheric Chemistry and Physics 21, no. 12 (June 28, 2021): 9609–28. http://dx.doi.org/10.5194/acp-21-9609-2021.
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Abstract. The ability to monitor and understand natural and anthropogenic variability in atmospheric carbon dioxide (CO2) is a growing need of many stakeholders across the world. Systems that assimilate satellite observations, given their short latency and dense spatial coverage, into high-resolution global models are valuable, if not essential, tools for addressing this need. A notable drawback of modern assimilation systems is the long latency of many vital input datasets; for example, inventories, in situ measurements, and reprocessed remote-sensing data can trail the current date by months to years. This paper describes techniques for bias-correcting surface fluxes derived from satellite observations of the Earth's surface to be consistent with constraints from inventories and in situ CO2 datasets. The techniques are applicable in both short-term forecasts and retrospective simulations, thus taking advantage of the coverage and short latency of satellite data while reproducing the major features of long-term inventory and in situ records. Our approach begins with a standard collection of diagnostic fluxes which incorporate a variety of remote-sensing driver data, viz. vegetation indices, fire radiative power, and nighttime lights. We then apply an empirical sink so that global budgets of the diagnostic fluxes match given atmospheric and oceanic growth rates for each year. This step removes coherent, systematic flux errors that produce biases in CO2 which mask the signals an assimilation system hopes to capture. Depending on the simulation mode, the empirical sink uses different choices of atmospheric growth rates: estimates based on observations in retrospective mode and projections based on seasonal forecasts of sea surface temperature in forecasting mode. The retrospective fluxes, when used in simulations with NASA's Goddard Earth Observing System (GEOS), reproduce marine boundary layer measurements with comparable skill to those using fluxes from a modern inversion system. The forecasted fluxes show promising accuracy in their application to the analysis of changes in the carbon cycle as they occur.
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Levy, R. C., L. A. Munchak, S. Mattoo, F. Patadia, L. A. Remer, and R. E. Holz. "Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance." Atmospheric Measurement Techniques 8, no. 10 (October 7, 2015): 4083–110. http://dx.doi.org/10.5194/amt-8-4083-2015.
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Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.
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Levy, R. C., L. A. Munchak, S. Mattoo, F. Patadia, L. A. Remer, and R. E. Holz. "Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance." Atmospheric Measurement Techniques Discussions 8, no. 7 (July 8, 2015): 6877–947. http://dx.doi.org/10.5194/amtd-8-6877-2015.
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Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångstrom Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ∼ 0.025), while reducing the differences between AE. We characterize algorithm retrievibility through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.
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Johansson, T., T. Karlsson, G. Marklund, S. Figueiredo, P. A. Lindqvist, and S. Buchert. "A statistical study of intense electric fields at 4−7 R<sub><i>E</i></sub> geocentric distance using Cluster." Annales Geophysicae 23, no. 7 (October 14, 2005): 2579–88. http://dx.doi.org/10.5194/angeo-23-2579-2005.
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Abstract. Intense high-latitude electric fields (>150 mV/m mapped to ionospheric altitude) at 4–7 RE geocentric distance have been investigated in a statistical study, using data from the Cluster satellites. The orbit of the Cluster satellites limits the data collection at these altitudes to high latitudes, including the poleward part of the auroral oval. The occurrence and distribution of the selected events have been used to characterize the intense electric fields and to investigate their dependance on parameters such as MLT, CGLat, altitude, and also Kp. Peaks in the local time distribution are found in the evening to morning sectors but also in the noon sector, corresponding to cusp events. The electric field intensities decrease with increasing latitude in the region investigated (above 60 CGLat). A dependence on geomagnetic activity is indicated since the probability of finding an event increases up to Kp=5–6. The scales sizes are in the range up to 10 km (mapped to ionospheric altitude) with a maximum around 4–5km, consistent with earlier findings at lower altitudes and Cluster event studies. The magnitudes of the electric fields are inversely proportional to the scale sizes. The type of electric field structure (convergent or divergent) is consistent with the FAC direction for a subset of events with electric field intensities in the range 500–1000 mV/m and with clear bipolar signatures. The FAC directions are also consistent with the Region 1 and NBZ current systems, the latter of which prevail only during northward IMF conditions. For scale sizes less than 2 km the majority of the events were divergent electric field structures. Both converging and diverging electric fields were found throughout the investigated altitude range (4–7 RE geocentric distance). Keywords. Magnetospheric physics (Electric fields; Auroral phenomena; Magnetosphere-ionosphere interactions)
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Kittaka, C., D. M. Winker, M. A. Vaughan, A. Omar, and L. A. Remer. "Intercomparison of column aerosol optical depths from CALIPSO and MODIS-Aqua." Atmospheric Measurement Techniques 4, no. 2 (February 1, 2011): 131–41. http://dx.doi.org/10.5194/amt-4-131-2011.
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Abstract. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is carried on the CALIPSO satellite and has acquired global aerosol profiles since June 2006. CALIPSO is flown in formation with the Aqua satellite as part of the A-train satellite constellation, so that a large number of coincident aerosol observations are available from CALIOP and the MODIS-Aqua instrument. This study compares column aerosol optical depth at 0.532 μm derived from CALIOP aerosol profiles with MODIS-Aqua 0.55 μm aerosol optical depth over the period June 2006 through August 2008. The study is based on the CALIOP Version 2 Aerosol Layer Product and MODIS Collection 5. While CALIOP is first and foremost a profiling instrument, this comparison of column aerosol optical depth provides insight into quality of CALIOP aerosol data. It is found that daytime aerosol optical depth from the CALIOP Version 2 product has only a small global mean bias relative to MODIS Collection 5. Regional biases, of both signs, are larger and biases are seen to vary somewhat with season. Good agreement between the two sensors in ocean regions with low cloudiness suggests that the selection of lidar ratios used in the CALIOP aerosol retrieval is sufficient to provide a regional mean AOD consistent with that retrieved from MODIS. Although differences over land are observed to be larger than over ocean, the bias between CALIOP and MODIS AOD on a regional-seasonal basis is found to be roughly within the envelope of the MODIS expected uncertainty over land and ocean. This work forms a basis for further comparisons using the recently released CALIOP Version 3 data.
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Levy, Robert C., Shana Mattoo, Virginia Sawyer, Yingxi Shi, Peter R. Colarco, Alexei I. Lyapustin, Yujie Wang, and Lorraine A. Remer. "Exploring systematic offsets between aerosol products from the two MODIS sensors." Atmospheric Measurement Techniques 11, no. 7 (July 13, 2018): 4073–92. http://dx.doi.org/10.5194/amt-11-4073-2018.
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Abstract. Long-term measurements of global aerosol loading and optical properties are essential for assessing climate-related questions. Using observations of spectral reflectance and radiance, the dark-target (DT) aerosol retrieval algorithm is applied to Moderate Resolution Imaging Spectroradiometer sensors on both Terra (MODIS-T) and Aqua (MODIS-A) satellites, deriving products (known as MOD04 and MYD04, respectively) of global aerosol optical depth (AOD at 0.55 µm) over both land and ocean, and an Ångström exponent (AE derived from 0.55 and 0.86 µm) over ocean. Here, we analyze the overlapping time series (since mid-2002) of the Collection 6 (C6) aerosol products. Global monthly mean AOD from MOD04 (Terra with morning overpass) is consistently higher than MYD04 (Aqua with afternoon overpass) by ∼ 13 % (∼ 0.02 over land and ∼ 0.015 over ocean), and this offset (MOD04 – MYD04) has seasonal as well as long-term variability. Focusing on 2008 and deriving yearly gridded mean AOD and AE, we find that, over ocean, the MOD04 (morning) AOD is higher and the AE is lower. Over land, there is more variability, but only biomass-burning regions tend to have AOD lower for MOD04. Using simulated aerosol fields from the Goddard Earth Observing System (GEOS-5) Earth system model and sampling separately (in time and space) along each MODIS-observed swath during 2008, the magnitudes of morning versus afternoon offsets of AOD and AE are smaller than those in the C6 products. Since the differences are not easily attributed to either aerosol diurnal cycles or sampling issues, we test additional corrections to the input reflectance data. The first, known as C6+, corrects for long-term changes to each sensor's polarization sensitivity and the response versus the scan angle and to cross-calibration from MODIS-T to MODIS-A. A second convolves the detrending and cross-calibration into scaling factors. Each method was applied upstream of the aerosol retrieval using 2008 data. While both methods reduced the overall AOD offset over land from 0.02 to 0.01, neither significantly reduced the AOD offset over ocean. The overall negative AE offset was reduced. A collection (C6.1) of all MODIS Atmosphere products was released, but we expect that the C6.1 aerosol products will maintain similar overall AOD and AE offsets. We conclude that (a) users should not interpret global differences between Terra and Aqua aerosol products as representing a true diurnal signal in the aerosol. (b) Because the MODIS-A product appears to have an overall smaller bias compared to ground-truth data, it may be more suitable for some applications. However (c), since the AOD offset is only ∼ 0.02 and within the noise level for single retrievals, both MODIS products may be adequate for most applications.
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Jones, Thomas A., Patrick Skinner, Kent Knopfmeier, Edward Mansell, Patrick Minnis, Rabindra Palikonda, and William Smith. "Comparison of Cloud Microphysics Schemes in a Warn-on-Forecast System Using Synthetic Satellite Objects." Weather and Forecasting 33, no. 6 (November 19, 2018): 1681–708. http://dx.doi.org/10.1175/waf-d-18-0112.1.
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AbstractForecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project has developed a rapid-cycling, convection-allowing, data assimilation and forecasting system known as the NSSL Experimental WoF System for ensembles (NEWS-e), which is designed to utilize advanced cloud microphysics schemes. NEWS-e currently (2017–18) uses the double-moment NSSL variable density scheme (NVD), which has been shown to generate realistic representations of convective precipitation within the system. However, very little verification on nonprecipitating cloud features has been performed with this system. During the 2017 Hazardous Weather Testbed (HWT) experiment, an overestimation of the areal coverage of convectively generated cirrus clouds was observed. Changing the cloud microphysics scheme to Thompson generated more accurate cloud fields. This research undertook the task of improving the cloud analysis generated by NVD while maintaining its skill for other variables such as reflectivity. Adjustments to cloud condensation nuclei (CCN), fall speed, and collection efficiencies were made and tested over a set of six severe weather cases occurring during May 2017. This research uses an object-based verification approach in which objects of cold infrared brightness temperatures, high cloud-top pressures, and cloud water path are generated from model output and compared against GOES-13 observations. Results show that the modified NVD scheme generated much more skillful forecasts of cloud objects than the original formulation without having a negative impact on the skill of simulated composite reflectivity forecasts.
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Souza, Juarez Dantas de, Juan Carlos Ceballos, and Bernardo Barbosa Da Silva. "SURFACE ALBEDO OBTAINED WITH MODIS IMAGES IN CASES OF LOW AND HIGH AEROSOL LOADING IN THE ATMOSPHERE." Revista Brasileira de Geofísica 32, no. 1 (March 1, 2014): 5. http://dx.doi.org/10.22564/rbgf.v32i1.421.
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ABSTRACT. The surface albedo is a parameter of vital importance for estimating the net radiation. This parameter is obtained by using data obtained from satelliteimages and by utilizing the appropriate method for atmospheric correction. The method used by NASA (National Aeronautics and Space Administration) to obtainsurface reflectance through MODIS (Moderate Resolution Imaging Spectrometer) sensor aboard the Terra and Aqua satellites (MOD09 and MYD09 products) hasproved inaccurate for the detection of burning areas in the Amazon region – for products in both collection 4 and collection 5. Other methods have been used toobtain the albedo. However, these are limited to atmospheric conditions with negligible aerosol influence. In the present work, we have established a relationshipbetween surface reflectance and the top of atmosphere, based on a radiative transfer model in an atmosphere of multiple layers. The method applied to productsLevel 1B (MOD02 and MYD02) in regions of low aerosol loading (Northeast Brazil) produces results similar to those effected by product MOD09. Furthermore, inregions of high aerosol loading (burnings in the Amazon region of Brazil), the method presents much higher performance than that rendered by the MOD09 andMYD09 products, improving, therefore, by more than 10% the accuracy of these results. The method is simple, efficient and easy to use.Keywords: reflectance, atmospheric correction, method of two streams, burning regions. RESUMO. O albedo da superfície é um parâmetro fundamental na estimativa do saldo de radiação. Ele pode ser obtido através de dados gerados por imagens desatélite, utilizando um método adequado de correção atmosférica. O método usado pela NASA (National Aeronautics and Space Administration), para obter refletânciasda superfície, através do sensor MODIS (Moderate Resolution Imaging Spectrometer) abordo dos satélites Terra e Aqua (produtos MOD09 e MYD09), apresentaproblemas em regiões de queimadas na Amazônia, tanto os produtos da coleção 4 como os da coleção 5. Outros métodos têm sido utilizados para a obtenção doalbedo. No entanto, eles são limitados à aplicação em atmosfera com pouca influência de aerossol. Neste trabalho, é estabelecida uma relação entre a refletânciada superfície e a do topo da atmosfera, com base em um modelo de transferência radiativa numa atmosfera de múltiplas camadas. O método aplicado aos produtos nível 1B (MOD02 e MYD02), em regiões de baixa carga de aerossol (Nordeste do Brasil), produz resultados semelhantes ao produto MOD09, e em regiões de altacarga de aerossol (região de queimadas na Amazônia brasileira), apresenta desempenho superior aos produtos MOD09 e MYD09, melhorando em mais de 10% aprecisão desses resultados. O método é simples, eficiente e de fácil utilização.Palavras-chave: refletância, correção atmosférica, método de dois fluxos, região de queimadas.
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van der Werf, G. R., J. T. Randerson, L. Giglio, G. J. Collatz, P. S. Kasibhatla, and A. F. Arellano. "Interannual variability in global biomass burning emissions from 1997 to 2004." Atmospheric Chemistry and Physics 6, no. 11 (August 21, 2006): 3423–41. http://dx.doi.org/10.5194/acp-6-3423-2006.
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Abstract. Biomass burning represents an important source of atmospheric aerosols and greenhouse gases, yet little is known about its interannual variability or the underlying mechanisms regulating this variability at continental to global scales. Here we investigated fire emissions during the 8 year period from 1997 to 2004 using satellite data and the CASA biogeochemical model. Burned area from 2001–2004 was derived using newly available active fire and 500 m. burned area datasets from MODIS following the approach described by Giglio et al. (2006). ATSR and VIRS satellite data were used to extend the burned area time series back in time through 1997. In our analysis we estimated fuel loads, including organic soil layer and peatland fuels, and the net flux from terrestrial ecosystems as the balance between net primary production (NPP), heterotrophic respiration (Rh), and biomass burning, using time varying inputs of precipitation (PPT), temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation (fAPAR). For the 1997–2004 period, we found that on average approximately 58 Pg C year−1 was fixed by plants as NPP, and approximately 95% of this was returned back to the atmosphere via Rh. Another 4%, or 2.5 Pg C year−1 was emitted by biomass burning; the remainder consisted of losses from fuel wood collection and subsequent burning. At a global scale, burned area and total fire emissions were largely decoupled from year to year. Total carbon emissions tracked burning in forested areas (including deforestation fires in the tropics), whereas burned area was largely controlled by savanna fires that responded to different environmental and human factors. Biomass burning emissions showed large interannual variability with a range of more than 1 Pg C year−1, with a maximum in 1998 (3.2 Pg C year−1) and a minimum in 2000 (2.0 Pg C year−1).
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Korras-Carraca, M. B., N. Hatzianastassiou, C. Matsoukas, A. Gkikas, and C. D. Papadimas. "The regime of aerosol asymmetry parameter over Europe, the Mediterranean and the Middle East based on MODIS satellite data: evaluation against surface AERONET measurements." Atmospheric Chemistry and Physics 15, no. 22 (November 26, 2015): 13113–32. http://dx.doi.org/10.5194/acp-15-13113-2015.
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Abstract. Atmospheric particulates are a significant forcing agent for the radiative energy budget of the Earth–atmosphere system. The particulates' interaction with radiation, which defines their climate effect, is strongly dependent on their optical properties. In the present work, we study one of the most important optical properties of aerosols, the asymmetry parameter (gaer), over sea surfaces of the region comprising North Africa, the Arabian Peninsula, Europe, and the Mediterranean Basin. These areas are of great interest, because of the variety of aerosol types they host, both anthropogenic and natural. Using satellite data from the collection 051 of MODIS (Moderate Resolution Imaging Spectroradiometer, Terra and Aqua), we investigate the spatiotemporal characteristics of the asymmetry parameter. We generally find significant spatial variability, with larger values over regions dominated by larger size particles, e.g., outside the Atlantic coasts of northwestern Africa, where desert-dust outflow takes place. The gaer values tend to decrease with increasing wavelength, especially over areas dominated by small particulates. The intra-annual variability is found to be small in desert-dust areas, with maximum values during summer, while in all other areas larger values are reported during the cold season and smaller during the warm. Significant intra-annual and inter-annual variability is observed around the Black Sea. However, the inter-annual trends of gaer are found to be generally small. Although satellite data have the advantage of broad geographical coverage, they have to be validated against reliable surface measurements. Therefore, we compare satellite-measured values with gaer values measured at 69 stations of the global surface AERONET (Aerosol Robotic Network), located within our region of interest. This way, we provide some insight on the quality and reliability of MODIS data. We report generally better agreement at the wavelength of 860 nm (correlation coefficient R up to 0.47), while at all wavelengths the results of the comparison were better for spring and summer.
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Puliafito, S. E., T. Bolaño Ortiz, R. Pascual, A. Lopez-Noreña, and L. Berná. "SNOW ALBEDO REDUCTION IN CENTRAL ANDES BY ATMOSPHERIC AEROSOLS: CASE STUDY ON THE TUNUYÁN BASIN (ARGENTINA)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 407–12. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-407-2020.
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Abstract. Changes in snow albedo (SA) on several basins of the central Andes of Argentina are associated with the possible deposition of light-absorbing particles (LAP) in the austral spring. To demonstrate this possibility, we correlate SA with daily data of snow cover (SC), aerosol optical depth (AOD) and land surface temperature (LST) available from the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board NASA Terra satellite during 2000–2016, and other derived parameters such as days after albedo (DAS) and snow precipitation (SP) from the Tropical Rainfall Measuring Mission (TRMM). We used satellite pixels with 100% snow cover to obtain monthly average value of SA, LST, AOD, DAS and SP performing multiple regression analysis. Further, we analysed biomass burning emissions in northern Argentina using MODIS products MCD64 collection C6 as possible source for snow pollution. Aerosol deposition and trajectories were analysed using WRF-Chem atmospheric numerical prediction model, with inventories of regional anthropogenic emissions of own elaboration (lat. 0.025° × long. 0.025°) and the estimation of open burning emissions from the FINN global inventory (Fire INventory from NCAR).
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Malbéteau, Yoann, Stephen Parkes, Bruno Aragon, Jorge Rosas, and Matthew McCabe. "Capturing the Diurnal Cycle of Land Surface Temperature Using an Unmanned Aerial Vehicle." Remote Sensing 10, no. 9 (September 5, 2018): 1407. http://dx.doi.org/10.3390/rs10091407.
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Characterizing the land surface temperature (LST) and its diurnal cycle is important in understanding a range of surface properties, including soil moisture status, evaporative response, vegetation stress and ground heat flux. While remote-sensing platforms present a number of options to retrieve this variable, there are inevitable compromises between the resolvable spatial and temporal resolution. For instance, the spatial resolution of geostationary satellites, which can provide sub-hourly LST, is often too coarse (3 km) for many applications. On the other hand, higher-resolution polar orbiting satellites are generally infrequent in time, with return intervals on the order of weeks, limiting their capacity to capture surface dynamics. With recent developments in the application of unmanned aerial vehicles (UAVs), there is now the opportunity to collect LST measurements on demand and at ultra-high spatial resolution. Here, we detail the collection and analysis of a UAV-based LST dataset, with the purpose of examining the diurnal surface temperature response: something that has not been possible from traditional satellite platforms at these scales. Two separate campaigns were conducted over a bare desert surface in combination with either Rhodes grass or a recently harvested maize field. In both cases, thermal imagery was collected between 0800 and 1700 local solar time. The UAV-based diurnal cycle was consistent with ground-based measurements, with a mean correlation coefficient and root mean square error (RMSE) of 0.99 and 0.68 °C, respectively. LST retrieved over the grass surface presented the best results, with an RMSE of 0.45 °C compared to 0.67 °C for the single desert site and 1.28 °C for the recently harvested maize surface. Even considering the orders of magnitude difference in scale, an exploratory analysis comparing retrievals of the UAV-based diurnal cycle with METEOSAT geostationary data yielded pleasing results (R = 0.98; RMSE = 1.23 °C). Overall, our analysis revealed a diurnal range over the desert and maize surfaces of ~20 °C and ~17 °C respectively, while the grass showed a reduced amplitude of ~12 °C. Considerable heterogeneity was observed over the grass surface at the peak of the diurnal cycle, which was likely indicative of the varying crop water status. To our knowledge, this study presents the first spatially varying analysis of the diurnal LST captured at ultra-high resolution, from any remote platform. Our findings highlight the considerable potential to utilize UAV-based retrievals to enhance investigations across multi-disciplinary studies in agriculture, hydrology and land-atmosphere investigations.
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Lee, K. H., and Y. J. Kim. "Satellite remote sensing of Asian aerosols: a case study of clean, polluted, and Asian dust storm days." Atmospheric Measurement Techniques 3, no. 6 (December 20, 2010): 1771–84. http://dx.doi.org/10.5194/amt-3-1771-2010.
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Abstract. In East Asia, satellite observation is important because aerosols from natural and anthropogenic sources have been recognized as a major source of regional and global air pollution. However, retrieving aerosols properties from satellite observations over land can be difficult because of the surface reflection, complex aerosol composition, and aerosol absorption. In this study, a new aerosol retrieval method called as the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite aerosol retrieval (MSTAR) was developed and applied to three different aerosol event cases over East Asia. MSTAR uses a separation technique that can distinguish aerosol reflectance from top-of-atmosphere (TOA) reflectance. The aerosol optical thickness (AOT) was determined by comparing this aerosol reflectance with pre-calculated values. Three case studies show how the methodology identifies discrepancies between measured and calculated values to retrieve more accurate AOT. The comparison between MODIS and the Aerosol Robotic Network (AERONET) showed improvement using the suggested methodology with the cluster-based look-up-tables (LUTs) (linear slope = 0.94, R = 0.92) than using operational MODIS collection 5 aerosol products (linear slope = 0.78, R = 0.87). In conclusion, the suggested methodology is shown to work well with aerosol models acquired by statistical clustering of the observation data in East Asia.
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Antuña-Marrero, Juan Carlos, Victoria Cachorro Revilla, Frank García Parrado, Ángel de Frutos Baraja, Albeth Rodríguez Vega, David Mateos, René Estevan Arredondo, and Carlos Toledano. "Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba." Atmospheric Measurement Techniques 11, no. 4 (April 20, 2018): 2279–93. http://dx.doi.org/10.5194/amt-11-2279-2018.
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Abstract. In the present study, we report the first comparison between the aerosol optical depth (AOD) and Ångström exponent (AE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra (AODt) and Aqua (AODa) satellites and those measured using a sun photometer (AODSP) at Camagüey, Cuba, for the period 2008 to 2014. The comparison of Terra and Aqua data includes AOD derived with both deep blue (DB) and dark target (DT) algorithms from MODIS Collection 6. Combined Terra and Aqua (AODta) data were also considered. Assuming an interval of ±30 min around the overpass time and an area of 25 km around the sun photometer site, two coincidence criteria were considered: individual pairs of observations and both spatial and temporal mean values, which we call collocated daily means. The usual statistics (root mean square error, RMSE; mean absolute error, MAE; median bias, BIAS), together with linear regression analysis, are used for this comparison. Results show very similar values for both coincidence criteria: the DT algorithm generally displays better statistics and higher homogeneity than the DB algorithm in the behaviour of AODt, AODa, AODta compared to AODSP. For collocated daily means, (a) RMSEs of 0.060 and 0.062 were obtained for Terra and Aqua with the DT algorithm and 0.084 and 0.065 for the DB algorithm, (b) MAE follows the same patterns, (c) BIAS for both Terra and Aqua presents positive and negative values but its absolute values are lower for the DT algorithm; (d) combined AODta data also give lower values of these three statistical indicators for the DT algorithm; (e) both algorithms present good correlations for comparing AODt, AODa, AODta vs. AODSP, with a slight overestimation of satellite data compared to AODSP, (f). The DT algorithm yields better figures with slopes of 0.96 (Terra), 0.96 (Aqua) and 0.96 (Terra + Aqua) compared to the DB algorithm (1.07, 0.90, 0.99), which displays greater variability. Multi-annual monthly means of AODta establish a first climatology that is more comparable to that given by the sun photometer and their statistical evaluation reveals better agreement with AODSP for the DT algorithm. Results of the AE comparison showed similar results to those reported in the literature concerning the two algorithms' capacity for retrieval. A comparison between broadband aerosol optical depth (BAOD), derived from broadband pyrheliometer observations at the Camagüey site and three other meteorological stations in Cuba, and AOD observations from MODIS on board Terra and Aqua show a poor correlation with slopes below 0.4 for both algorithms. Aqua (Terra) showed RMSE values of 0.073 (0.080) and 0.088 (0.087) for the DB and DT algorithms. As expected, RMSE values are higher than those from the MODIS–sun photometer comparison, but within the same order of magnitude. Results from the BAOD derived from solar radiation measurements demonstrate its reliability in describing climatological AOD series estimates.
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Moody, Eric G., Michael D. King, Crystal B. Schaaf, and Steven Platnick. "MODIS-Derived Spatially Complete Surface Albedo Products: Spatial and Temporal Pixel Distribution and Zonal Averages." Journal of Applied Meteorology and Climatology 47, no. 11 (November 1, 2008): 2879–94. http://dx.doi.org/10.1175/2008jamc1795.1.
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Abstract Five years (2000–04) of spatially complete snow-free land surface albedo data have been produced using high-quality-flagged diffuse bihemispherical (white sky) and direct-beam directional hemispherical (black sky) land surface albedo data derived from observations taken by the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument aboard the NASA Terra satellite platform (MOD43B3, collection 4). In addition, a spatially complete snow-free aggregate albedo climatological product was generated. These spatially complete products were prepared using an ecosystem-dependent temporal interpolation technique that retrieves missing data within 3%–8% error. These datasets have already been integrated into research and operational projects that require snow-free land surface albedo. As such, this paper provides details regarding the spatial and temporal distribution of the filled versus the original MOD43B3 data. The paper also explores the intra- and interannual variation in the 5-yr data record and provides a qualitative comparison of zonal averages and annual cycles of the filled versus the original MOD43B3 data. The analyses emphasize the data’s inter- and intraannual variation and show that the filled data exhibit large- and small-scale phenological behavior that is qualitatively similar to that of the original MOD43B3. These analyses thereby serve to showcase the inherent spectral, spatial, and temporal variability in the MOD43B3 data as well as the ability of the fill technique to preserve these unique regional and pixel-level phenological characteristics.
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Le Marshall, John, Robert Norman, David Howard, Susan Rennie, Michael Moore, Jan Kaplon, Yi Xiao, et al. "Corrigendum to: Using global navigation satellite system data for real-time moisture analysis and forecasting over the Australian region I. The system." Journal of Southern Hemisphere Earth Systems Science 70, no. 1 (2020): 394. http://dx.doi.org/10.1071/es19009_co.
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The use of high spatial and temporal resolution data assimilation and forecasting around Australia’s capital cities and rural land provided an opportunity to improve moisture analysis and forecasting. To support this endeavour, RMIT University and Geoscience Australia worked with the Bureau of Meteorology (BoM) to provide real-time GNSS (global navigation satellite system) zenith total delay (ZTD) data over the Australian region, from which a high-resolution total water vapour field for SE Australia could be determined. The ZTD data could play an important role in high-resolution data assimilation by providing mesoscale moisture data coverage from existing GNSS surface stations over significant areas of the Australian continent. The data were used by the BoM’s high-resolution ACCESS-C3 capital city numerical weather prediction (NWP) systems, the ACCESS-G3 Global system and had been used by the ACCESS-R2-Regional NWP model. A description of the data collection and analysis system is provided. An example of the application of these local GNSS data for a heavy rainfall event over SE Australia/Victoria is shown using the 1.5-km resolution ACCESS-C3 model, which was being prepared for operational use. The results from the test were assessed qualitatively, synoptically and also examined quantitatively using the Fractions Skills Score which showed the reasonableness of the forecasts and demonstrated the potential for improving rainfall forecasts over south-eastern Australia by the inclusion of ZTD data in constructing the moisture field. These data have been accepted for operational use in NWP.
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Choi, M., J. Kim, J. Lee, M. Kim, Y. Je Park, U. Jeong, W. Kim, et al. "GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during DRAGON-NE Asia 2012 campaign." Atmospheric Measurement Techniques Discussions 8, no. 9 (September 15, 2015): 9565–609. http://dx.doi.org/10.5194/amtd-8-9565-2015.
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Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorology Satellites (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm over ocean and land together with validation results during the DRAGON-NE Asia 2012 campaign. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single scattering albedo (SSA) at 440 nm, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type from selected aerosol models in calculating AOD. Assumed aerosol models are compiled from global Aerosol Robotic Networks (AERONET) inversion data, and categorized according to AOD, FMF, and SSA. Nonsphericity is considered, and unified aerosol models are used over land and ocean. Different assumptions for surface reflectance are applied over ocean and land. Surface reflectance over the ocean varies with geometry and wind speed, while surface reflectance over land is obtained from the 1–3 % darkest pixels in a 6 km × 6 km area during 30 days. In the East China Sea and Yellow Sea, significant area is covered persistently by turbid waters, for which the land algorithm is used for aerosol retrieval. To detect turbid water pixels, TOA reflectance difference at 660 nm is used. GOCI YAER products are validated using other aerosol products from AERONET and the MODIS Collection 6 aerosol data from "Dark Target (DT)" and "Deep Blue (DB)" algorithms during the DRAGON-NE Asia 2012 campaign from March to May 2012. Comparison of AOD from GOCI and AERONET gives a Pearson correlation coefficient of 0.885 and a linear regression equation with GOCI AOD =1.086 × AERONET AOD – 0.041. GOCI and MODIS AODs are more highly correlated over ocean than land. Over land, especially, GOCI AOD shows better agreement with MODIS DB than MODIS DT because of the choice of surface reflectance assumptions. Other GOCI YAER products show lower correlation with AERONET than AOD, but are still qualitatively useful.