Journal articles on the topic 'Satellite estimates'
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1
Smith, Thomas M., Phillip A. Arkin, John J. Bates, and George J. Huffman. "Estimating Bias of Satellite-Based Precipitation Estimates." Journal of Hydrometeorology 7, no. 5 (October 1, 2006): 841–56. http://dx.doi.org/10.1175/jhm524.1.
Abstract:
Abstract Systematic biases in satellite-based precipitation estimates can be the dominant component of their uncertainty. These biases may not be reduced by averaging, which makes their evaluation particularly important. Described here are several methods of evaluating the biases and their characteristics. Methods are developed and tested using monthly average precipitation estimates from several satellites. Direct estimates of bias are obtained from analysis of satellite–gauge estimates, and they indicate the general bias patterns and magnitudes over land. Direct estimates cannot be computed over the oceans, so indirect-bias estimates based on ensembles of satellite and gauge estimates are also developed. These indirect estimates are consistent with direct estimates in locations where they can be compared, while giving near-global coverage. For both bias estimates computed here, the bias uncertainty is higher than nonsystematic error estimates, caused by random or sampling errors and which have been previously reported by others for satellite estimates. Because of their greater spatial coverage, indirect-bias estimates are preferable for bias adjustment of satellite-based precipitation. The adjustment methods developed reduce the bias associated with each satellite while estimating the remaining bias uncertainty for the satellite. By adjusting all satellites to a consistent base, the bias adjustments also minimize artificial climate-scale variations in analyses that could be caused by the addition or removal of satellite products as their availability changes.
2
Zhang, Hai, Zigang Wei, Barron H. Henderson, Susan C. Anenberg, Katelyn O’Dell, and Shobha Kondragunta. "Nowcasting Applications of Geostationary Satellite Hourly Surface PM2.5 Data." Weather and Forecasting 37, no. 12 (December 2022): 2313–29. http://dx.doi.org/10.1175/waf-d-22-0114.1.
Abstract:
Abstract The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 μm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States were derived from NOAA’s operational geostationary satellites’ Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of −21.4% and −15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 μg m−3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the United States, but with pronounced differences in the western United States due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (>35 μg m−3), the hourly estimates of PM2.5 used in nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality 10 times more than standard ground observations (1.8 versus 0.17 million people per hour). Significance Statement Fine particulate matter (PM2.5; diameters less than 2.5 μm) are generated from smoke, dust, and emissions from industrial, transportation, and other sectors. They are harmful to human health and even lead to premature mortality. Data from geostationary satellites can help estimate surface PM2.5 exposure by filling in gaps that are not covered by ground monitors. With this information, people can plan their outdoor activities accordingly. This study shows that availability of hourly PM2.5 observations covering the entire continental United States is more informative to the public about harmful exposure to pollution. On average, 1.8 million people per hour can be informed using satellite data compared to 0.17 million people per hour based on ground observations alone.
3
Itkin, M., and A. Loew. "Multi-satellite rainfall sampling error estimates – a comparative study." Hydrology and Earth System Sciences Discussions 9, no. 10 (October 12, 2012): 11677–706. http://dx.doi.org/10.5194/hessd-9-11677-2012.
Abstract:
Abstract. This study focus is set on quantifying sampling related uncertainty in the satellite rainfall estimates. We conduct observing system simulation experiment to estimate sampling error for various constellations of Low-Earth orbiting and geostationary satellites. There are two types of microwave instruments currently available: cross track sounders and conical scanners. We evaluate the differences in sampling uncertainty for various satellite constellations that carry instruments of the common type as well as in combination with geostationary observations. A precise orbital model is used to simulate realistic satellite overpasses with orbital shifts taken into account. With this model we resampled rain gauge timeseries to simulate satellites rainfall estimates free of retrieval and calibration errors. We concentrate on two regions, Germany and Benin, areas with different precipitation regimes. Our results show that sampling uncertainty for all satellite constellations does not differ greatly depending on the area despite the differences in local precipitation patterns. Addition of 3 hourly geostationary observations provides equal performance improvement in Germany and Benin, reducing rainfall undersampling by 20–25% of the total rainfall amount. Authors do not find a significant difference in rainfall sampling between conical imager and cross-track sounders.
4
Bowman, Kenneth P., Cameron R. Homeyer, and Dalon G. Stone. "A Comparison of Oceanic Precipitation Estimates in the Tropics and Subtropics." Journal of Applied Meteorology and Climatology 48, no. 7 (July 1, 2009): 1335–44. http://dx.doi.org/10.1175/2009jamc2149.1.
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Abstract A number of Earth remote sensing satellites are currently carrying passive microwave radiometers. A variety of different retrieval algorithms are used to estimate surface rain rates over the ocean from the microwave radiances observed by the radiometers. This study compares several different satellite algorithms with each other and with independent data from rain gauges on ocean buoys. The rain gauge data are from buoys operated by the NOAA Pacific Marine Environmental Laboratory. Potential errors and biases in the gauge data are evaluated. Satellite data are from the Tropical Rainfall Measuring Mission Microwave Imager and from the Special Sensor Microwave Imager instruments on the operational Defense Meteorological Satellite Program F13, F14, and F15 satellites. These data have been processed into rain-rate estimates by the NASA Precipitation Measurement Mission and by Remote Sensing Systems, Inc. Biases between the different datasets are estimated by computing differences between long-term time averages. Most of the satellite datasets agree with each other, and with the gauge data, to within 10% or less. The biases tend to be proportional to the mean rain rate, but the geographical patterns of bias vary depending on the choice of data source and algorithm. Some datasets, however, show biases as large as about 25%, so care should be taken when using these data for climatological studies.
5
Tian, Yudong, Christa D. Peters-Lidard, Robert F. Adler, Takuji Kubota, and Tomoo Ushio. "Evaluation of GSMaP Precipitation Estimates over the Contiguous United States." Journal of Hydrometeorology 11, no. 2 (April 1, 2010): 566–74. http://dx.doi.org/10.1175/2009jhm1190.1.
Abstract:
Abstract Precipitation estimates from the Global Satellite Mapping of Precipitation (GSMaP) project are evaluated over the contiguous United States (CONUS) for the period of 2005–06. GSMaP combines precipitation retrievals from the Tropical Rainfall Measuring Mission satellite and other polar-orbiting satellites, and interpolates them with cloud motion vectors derived from infrared images from geostationary satellites, to produce a high-resolution dataset. Four other satellite-based datasets are also evaluated concurrently with GSMaP, to provide a better perspective. The new Climate Prediction Center (CPC) unified gauge analysis is used as the reference data. The evaluation shows that GSMaP does well in capturing the spatial patterns of precipitation, especially for summer, and that it has better estimation of precipitation amount over the eastern than over the western CONUS. Meanwhile, GSMaP shares many of the challenges common to other satellite-based products, including that it underestimates in winter and overestimates in summer. In winter, GSMaP has on average one-half less precipitation over the western region and one-third less over the eastern region, whereas in summer it has about three-quarters and one-quarter more estimated precipitation over the two respective regions, respectively. Most of the summer overestimates (winter underestimates) are from an excessive (insufficient) number of strong events (>20 mm day−1). Overall, GSMaP’s performance is comparable to other satellite-based products, with slightly better probability of detection during summer, and the different satellite-based estimates as a group have better agreement among themselves during summer than during winter.
6
Konings, Alexandra G., A. Anthony Bloom, Junjie Liu, Nicholas C. Parazoo, David S. Schimel, and Kevin W. Bowman. "Global satellite-driven estimates of heterotrophic respiration." Biogeosciences 16, no. 11 (June 4, 2019): 2269–84. http://dx.doi.org/10.5194/bg-16-2269-2019.
Abstract:
Abstract. While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least-observed carbon fluxes, particularly outside the midlatitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4∘×5∘ spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System-Flux (CMS-Flux) with an optimally scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially varying carbon use efficiency retrieved in a model–data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6±19.3 Pg C yr−1. Sensitivity and uncertainty analyses showed that the top-down Rh are more sensitive to the choice of input gross primary productivity (GPP) and net ecosystem productivity (NEP) datasets than to the assumption of a static carbon use efficiency (CUE) value, with the possible exception of the wet tropics. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, using new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes and are 30 % greater overall (43.6 Pg C yr−1 vs. 33.4 Pg C yr−1). The uncertainty ranges of both methods are comparable, except poleward of 45∘ N, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.
7
Utsumi, Nobuyuki, Hyungjun Kim, F. Joseph Turk, and Ziad S. Haddad. "Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information." Journal of Hydrometeorology 20, no. 5 (May 1, 2019): 1015–26. http://dx.doi.org/10.1175/jhm-d-18-0225.1.
Abstract:
Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.
8
Gerbi, Gregory P., Emmanuel Boss, P. Jeremy Werdell, Christopher W. Proctor, Nils Haëntjens, Marlon R. Lewis, Keith Brown, et al. "Validation of Ocean Color Remote Sensing Reflectance Using Autonomous Floats." Journal of Atmospheric and Oceanic Technology 33, no. 11 (November 2016): 2331–52. http://dx.doi.org/10.1175/jtech-d-16-0067.1.
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AbstractThe use of autonomous profiling floats for observational estimates of radiometric quantities in the ocean is explored, and the use of this platform for validation of satellite-based estimates of remote sensing reflectance in the ocean is examined. This effort includes comparing quantities estimated from float and satellite data at nominal wavelengths of 412, 443, 488, and 555 nm, and examining sources and magnitudes of uncertainty in the float estimates. This study had 65 occurrences of coincident high-quality observations from floats and MODIS Aqua and 15 occurrences of coincident high-quality observations floats and Visible Infrared Imaging Radiometer Suite (VIIRS). The float estimates of remote sensing reflectance are similar to the satellite estimates, with disagreement of a few percent in most wavelengths. The variability of the float–satellite comparisons is similar to the variability of in situ–satellite comparisons using a validation dataset from the Marine Optical Buoy (MOBY). This, combined with the agreement of float-based and satellite-based quantities, suggests that floats are likely a good platform for validation of satellite-based estimates of remote sensing reflectance.
9
Dietrich, S., D. Casella, F. Di Paola, M. Formenton, A. Mugnai, and P. Sanò. "Lightning-based propagation of convective rain fields." Natural Hazards and Earth System Sciences 11, no. 5 (May 27, 2011): 1571–81. http://dx.doi.org/10.5194/nhess-11-1571-2011.
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Abstract. This paper describes a new multi-sensor approach for continuously monitoring convective rain cells. It exploits lightning data from surface networks to propagate rain fields estimated from multi-frequency brightness temperature measurements taken by the AMSU/MHS microwave radiometers onboard NOAA/EUMETSAT low Earth orbiting operational satellites. Specifically, the method allows inferring the development (movement, morphology and intensity) of convective rain cells from the spatial and temporal distribution of lightning strokes following any observation by a satellite-borne microwave radiometer. Obviously, this is particularly attractive for real-time operational purposes, due to the sporadic nature of the low Earth orbiting satellite measurements and the continuous availability of ground-based lightning measurements – as is the case in most of the Mediterranean region. A preliminary assessment of the lightning-based rainfall propagation algorithm has been successfully made by using two pairs of consecutive AMSU observations, in conjunction with lightning measurements from the ZEUS network, for two convective events. Specifically, we show that the evolving rain fields, which are estimated by applying the algorithm to the satellite-based rainfall estimates for the first AMSU overpass, show an overall agreement with the satellite-based rainfall estimates for the second AMSU overpass.
10
Li, Min, and Yunbin Yuan. "Estimation and Analysis of the Observable-Specific Code Biases Estimated Using Multi-GNSS Observations and Global Ionospheric Maps." Remote Sensing 13, no. 16 (August 5, 2021): 3096. http://dx.doi.org/10.3390/rs13163096.
Abstract:
Observable-specific bias (OSB) parameterization allows observation biases belonging to various signal types to be flexibly addressed in the estimation of ionosphere and global navigation satellite system (GNSS) clock products. In this contribution, multi-GNSS OSBs are generated by two different methods. With regard to the first method, geometry-free (GF) linear combinations of the pseudorange and carrier-phase observations of a global multi-GNSS receiver network are formed for the extraction of OSB observables, and global ionospheric maps (GIMs) are employed to correct ionospheric path delays. Concerning the second method, satellite and receiver OSBs are converted directly from external differential code bias (DCB) products. Two assumptions are employed in the two methods to distinguish satellite- and receiver-specific OSB parameters. The first assumption is a zero-mean condition for each satellite OSB type and GNSS signal. The second assumption involves ionosphere-free (IF) linear combination signal constraints for satellites and receivers between two signals, which are compatible with the International GNSS Service (IGS) clock product. Agreement between the multi-GNSS satellite OSBs estimated by the two methods and those from the Chinese Academy of Sciences (CAS) is shown at levels of 0.15 ns and 0.1 ns, respectively. The results from observations spanning 6 months show that the multi-GNSS OSB estimates for signals in the same frequency bands may have very similar code bias characteristics, and the receiver OSB estimates present larger standard deviations (STDs) than the satellite OSB estimates. Additionally, the variations in the receiver OSB estimates are shown to be related to the types of receivers and antennas and the firmware version. The results also indicate that the root mean square (RMS) of the differences between the OSBs estimated based on the CAS- and German Aerospace Center (DLR)-provided DCB products are 0.32 ns for the global positioning system (GPS), 0.45 ns for the BeiDou navigation satellite system (BDS), 0.39 ns for GLONASS and 0.22 ns for Galileo.
11
Peter, Heike, Jaime Fernández, and Pierre Féménias. "Copernicus Sentinel-1 satellites: sensitivity of antenna offset estimation to orbit and observation modelling." Advances in Geosciences 50 (March 27, 2020): 87–100. http://dx.doi.org/10.5194/adgeo-50-87-2020.
Abstract:
Abstract. The SAR (Synthetic Aperture Radar) Copernicus Sentinel-1 satellites require a high orbit accuracy of 5 cm in 3D in comparison to external processing facilities. The official orbit products delivered by the Copernicus POD (Precise Orbit Determination) Service fulfil this requirement. Nevertheless, analyses have shown discrepancies in the orbit results for the two satellites Sentinel-1A and Sentinel-1B. Since the satellites are identical in construction estimated orbit parameters like the scale factor for the radiation pressure are expected to be at the same magnitude, which is not the case. Estimation of GPS antenna offsets leads to differences between the two satellites, which might explain the discrepancies in the estimated orbit parameters. Such offset estimations are, however, very sensitive to orbit and observation modelling. It has to be assured that the results are not biased by insufficient models. First of all, stabilisation of the antenna offset estimation is achieved by improving the observation modelling by applying single receiver ambiguity resolution. The Copernicus Sentinel-1 satellites have a very complex shape with the long SAR antenna and the two large solar arrays. Antenna offset estimation based on different satellite models may give results which differ by up to 1.5 cm. The dispersion of the estimates is quite large depending also on eclipse and non-eclipse periods. Consideration of simple assumptions on satellite self-shadowing effects improves the satellite model and also the results of the antenna offset estimation. Finally, more consistent results for the two Sentinel-1 satellites are achieved by applying the antenna offset estimates.
12
Shige, Shoichi, Satoshi Kida, Hiroki Ashiwake, Takuji Kubota, and Kazumasa Aonashi. "Improvement of TMI Rain Retrievals in Mountainous Areas." Journal of Applied Meteorology and Climatology 52, no. 1 (January 2013): 242–54. http://dx.doi.org/10.1175/jamc-d-12-074.1.
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AbstractHeavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical motion and the convergence of surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates made using the revised GSMaP algorithm are in better agreement with estimates from data obtained by the radar on the TRMM satellite and by gauge-calibrated ground radars than are estimates made using the original GSMaP algorithm.
13
Kuhlmann, Gerrit, Dominik Brunner, Grégoire Broquet, and Yasjka Meijer. "Quantifying CO<sub>2</sub> emissions of a city with the Copernicus Anthropogenic CO<sub>2</sub> Monitoring satellite mission." Atmospheric Measurement Techniques 13, no. 12 (December 15, 2020): 6733–54. http://dx.doi.org/10.5194/amt-13-6733-2020.
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Abstract. We investigate the potential of the Copernicus Anthropogenic Carbon Dioxide (CO2) Monitoring (CO2M) mission, a proposed constellation of CO2 imaging satellites, to estimate the CO2 emissions of a city on the example of Berlin, the capital of Germany. On average, Berlin emits about 20 Mt CO2 yr−1 during satellite overpass (11:30 LT). The study uses synthetic satellite observations of a constellation of up to six satellites generated from 1 year of high-resolution atmospheric transport simulations. The emissions were estimated by (1) an analytical atmospheric inversion applied to the plume of Berlin simulated by the same model that was used to generate the synthetic observations and (2) a mass-balance approach that estimates the CO2 flux through multiple cross sections of the city plume detected by a plume detection algorithm. The plume was either detected from CO2 observations alone or from additional nitrogen dioxide (NO2) observations on the same platform. The two approaches were set up to span the range between (i) the optimistic assumption of a perfect transport model that provides an accurate prediction of plume location and CO2 background and (ii) the pessimistic assumption that plume location and background can only be determined reliably from the satellite observations. Often unfavorable meteorological conditions allowed us to successfully apply the analytical inversion to only 11 out of 61 overpasses per satellite per year on average. From a single overpass, the instantaneous emissions of Berlin could be estimated with an average precision of 3.0 to 4.2 Mt yr−1 (15 %–21 % of emissions during overpass) depending on the assumed instrument noise ranging from 0.5 to 1.0 ppm. Applying the mass-balance approach required the detection of a sufficiently large plume, which on average was only possible on three overpasses per satellite per year when using CO2 observations for plume detection. This number doubled to six estimates when the plumes were detected from NO2 observations due to the better signal-to-noise ratio and lower sensitivity to clouds of the measurements. Compared to the analytical inversion, the mass-balance approach had a lower precision ranging from 8.1 to 10.7 Mt yr−1 (40 % to 53 %), because it is affected by additional uncertainties introduced by the estimation of the location of the plume, the CO2 background field, and the wind speed within the plume. These uncertainties also resulted in systematic biases, especially without the NO2 observations. An additional source of bias was non-separable fluxes from outside of Berlin. Annual emissions were estimated by fitting a low-order periodic spline to the individual estimates to account for the seasonal variability of the emissions, but we did not account for the diurnal cycle of emissions, which is an additional source of uncertainty that is difficult to characterize. The analytical inversion was able to estimate annual emissions with an accuracy of < 1.1 Mt yr−1 (< 6 %) even with only one satellite, but this assumes perfect knowledge of plume location and CO2 background. The accuracy was much smaller when applying the mass-balance approach, which determines plume location and background directly from the satellite observations. At least two satellites were necessary for the mass-balance approach to have a sufficiently large number of estimates distributed over the year to robustly fit a spline, but even then the accuracy was low (> 8 Mt yr−1 (>40 %)) when using the CO2 observations alone. When using the NO2 observations to detect the plume, the accuracy could be greatly improved to 22 % and 13 % with two and three satellites, respectively. Using the complementary information provided by the CO2 and NO2 observations on the CO2M mission, it should be possible to quantify annual emissions of a city like Berlin with an accuracy of about 10 % to 20 %, even in the pessimistic case that plume location and CO2 background have to be determined from the observations alone. This requires, however, that the temporal coverage of the constellation is sufficiently high to resolve the temporal variability of emissions.
14
Robertson, Franklin R., Jason B. Roberts, Michael G. Bosilovich, Abderrahim Bentamy, Carol Anne Clayson, Karsten Fennig, Marc Schröder, et al. "Uncertainties in Ocean Latent Heat Flux Variations over Recent Decades in Satellite-Based Estimates and Reduced Observation Reanalyses." Journal of Climate 33, no. 19 (October 1, 2020): 8415–37. http://dx.doi.org/10.1175/jcli-d-19-0954.1.
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AbstractFour state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.
15
Yang, Y., Y. Zhao, and L. Zhang. "EVALUATING THE INFLUENCE OF SATELLITE OBSERVATION ON INVERSING NOX EMISSION AT REGIONAL SCALE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W9 (October 25, 2019): 211–17. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w9-211-2019.
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Abstract. In order to explore the influence of satellite observation data on the top-down NOx estimates at regional scale, the top-down NOx emissions for Yangtze River Delta (YRD) region at 9 km spatial resolution were developed with Peking University Ozone Monitoring Instrument NO2 product (POMINO) v1 and POMINO v2 satellite observation data in January and July of 2016. The differences of top-down NOx estimates derived from the two satellites were quantitative evaluated, and the reasons were comprehensively analyzed. The total NOx emissions based on POMINO v2 in January and July was 27% and 45% higher than those derived with POMINO v1, respectively. It indicated that the difference of top-down estimate derived from different satellite observation in summer was larger than that in winter. Considering that the difference between the two observations in January was similar to that in July, it was mainly because that the sensitivity of NO2 concentration to emissions was larger in summer than in winter. Top-down estimates derived from the two satellite observation were evaluated with air quality model (AQM) and ground observation. The model performances derived from top-down NOx emission based on POMINO v1 were better than those based on POMINO v2. The probable reason was that the NO2 vertical column densities (VCD) in POMINO v1 was closer to available ground-based MAX-DOAS observations during cloudless days and the satellite observation of cloudless was usually selected to inversing NOx emission.
16
Shen, Youjiang, Dedi Liu, Liguang Jiang, Christian Tøttrup, Daniel Druce, Jiabo Yin, Karina Nielsen, Peter Bauer-Gottwein, Jun Wang, and Xin Zhao. "Estimating Reservoir Release Using Multi-Source Satellite Datasets and Hydrological Modeling Techniques." Remote Sensing 14, no. 4 (February 9, 2022): 815. http://dx.doi.org/10.3390/rs14040815.
Abstract:
Reservoir release is an essential variable as it affects hydrological processes and water availability downstream. This study aims to estimate reservoir release using a satellite-based approach, specially focusing on the impacts of inflow simulations and reservoir water storage change (RWSC) on release estimates. Ten inflow simulations based on hydrological models and blending schemes are used in combination with three RWSC estimates based on two satellite-based approaches. A case study is performed at the Ankang reservoir, China. The results demonstrate that release estimates show high skill, with normalized root-mean-square error (NRMSE) less than 0.12 and Kling-Gupta Efficiency (KGE) over 0.65. The performance of release estimates is varying with and influenced by inflow simulations and RWSC estimates, with NRMSE ranging from 0.09–0.12 and KGE from 0.65–0.74. Based on time-varying Bayesian Model Averaging (BMA) approaches and synthetic aperture radar (SAR) satellite datasets, more accurate inflow and RWSC estimates can be obtained, thus facilitating substantially release estimates. With multi-source satellite datasets, temporal scale of reservoir estimates is increased (monthly and bi-weekly), acting as a key supplement to in situ records. Overall, this study explores the possibility to reconstruct and facilitate reservoir release estimates in poorly gauged dammed basins using hydrological modeling techniques and multi-source satellite datasets.
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Tan, Jackson, Walter A. Petersen, and Ali Tokay. "A Novel Approach to Identify Sources of Errors in IMERG for GPM Ground Validation." Journal of Hydrometeorology 17, no. 9 (September 1, 2016): 2477–91. http://dx.doi.org/10.1175/jhm-d-16-0079.1.
Abstract:
Abstract The comparison of satellite and high-quality, ground-based estimates of precipitation is an important means to assess the confidence in satellite-based algorithms and to provide a benchmark for their continued development and future improvement. To these ends, it is beneficial to identify sources of estimation uncertainty, thereby facilitating a precise understanding of the origins of the problem. This is especially true for new datasets such as the Integrated Multisatellite Retrievals for GPM (IMERG) product, which provides global precipitation gridded at a high resolution using measurements from different sources and techniques. Here, IMERG is evaluated against a dense network of gauges in the mid-Atlantic region of the United States. A novel approach is presented, leveraging ancillary variables in IMERG to attribute the errors to the individual instruments or techniques within the algorithm. As a whole, IMERG exhibits some misses and false alarms for rain detection, while its rain-rate estimates tend to overestimate drizzle and underestimate heavy rain with considerable random error. Tracing the errors to their sources, the most reliable IMERG estimates come from passive microwave satellites, which in turn exhibit a hierarchy of performance. The morphing technique has comparable proficiency with the less skillful satellites, but infrared estimations perform poorly. The approach here demonstrated that, underlying the overall reasonable performance of IMERG, different sources have different reliability, thus enabling both IMERG users and developers to better recognize the uncertainty in the estimate. Future validation efforts are urged to adopt such a categorization to bridge between gridded rainfall and instantaneous satellite estimates.
18
Rodriguez-Puebla, C., R. T. Pinker, and S. Nigam. "Relationship between downwelling surface shortwave radiative fluxes and sea surface temperature over the tropical Pacific: AMIP II models versus satellite estimates." Annales Geophysicae 26, no. 4 (May 13, 2008): 785–94. http://dx.doi.org/10.5194/angeo-26-785-2008.
Abstract:
Abstract. Incident shortwave radiation at the Earth's surface is the driving force of the climate system. Understanding the relationship between this forcing and the sea surface temperature, in particular, over the tropical Pacific Ocean is a topic of great interest because of possible climatic implications. The objective of this study is to investigate the relationship between downwelling shortwave radiative fluxes and sea surface temperature by using available data on radiative fluxes. We assess first the shortwave radiation from three General Circulation Models that participated in the second phase of the Atmospheric Model Intercomparison Project (AMIP II) against estimates of such fluxes from satellites. The shortwave radiation estimated from the satellite is based on observations from the International Satellite Cloud Climatology Project D1 data and the University of Maryland Shortwave Radiation Budget model (UMD/SRB). Model and satellite estimates of surface radiative fluxes are found to be in best agreement in the central equatorial Pacific, according to mean climatology and spatial correlations. We apply a Canonical Correlation Analysis to determine the interrelated areas where shortwave fluxes and sea surface temperature are most sensitive to climate forcing. Model simulations and satellite estimates of shortwave fluxes both capture well the interannual signal of El Niño-like variability. The tendency for an increase in shortwave radiation from the UMD/SRB model is not captured by the AMIP II models.
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Dowd, Emily, Alistair J. Manning, Bryn Orth-Lashley, Marianne Girard, James France, Rebecca E. Fisher, Dave Lowry, et al. "First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK." Atmospheric Measurement Techniques 17, no. 5 (March 18, 2024): 1599–615. http://dx.doi.org/10.5194/amt-17-1599-2024.
Abstract:
Abstract. Atmospheric methane (CH4) is the second-most-important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ∼ 60 % of global CH4 emissions, of which 20 % come from oil and gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat's satellites, of the CH4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK, in the spring and summer of 2023 and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME), to estimate the flux from both satellite- and ground-based observation methods and assess the leak's contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat's emission estimates to be in broad agreement with those made from the in situ measurements. During the study period (March–June 2023) GHGSat's emission estimates are 236–1357 kg CH4 h−1, whereas the mobile surface measurements are 634–846 kg CH4 h−1. The large variability is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH4 h−1, which are lower than the satellite- and mobile-survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat's observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including unplanned fugitive releases that are inherently challenging to identify, track, and estimate their impact and duration. Rapid, widespread access to such data to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing anthropogenic contributions to climate change.
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Harsa, Hastuadi, Agus Buono, Rahmat Hidayat, Jaumil Achyar, Sri Noviati, Roni Kurniawan, and Alfan S. Praja. "Fine-tuning satellite-based rainfall estimates." IOP Conference Series: Earth and Environmental Science 149 (May 2018): 012047. http://dx.doi.org/10.1088/1755-1315/149/1/012047.
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McCord, Mark R., Yongliang Yang, Zhuojun Jiang, Benjamin Coifman, and Prem K. Goel. "Estimating Annual Average Daily Traffic from Satellite Imagery and Air Photos: Empirical Results." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (January 2003): 136–42. http://dx.doi.org/10.3141/1855-17.
Abstract:
Vehicles can be identified in high-resolution satellite imagery that recently has become available to the civilian community. The vehicle information contained in this imagery, and in air-based imagery, could be used in annual average daily traffic (AADT) estimation, a task conducted by many transportation agencies around the world. However, because the imagery provides information equivalent to traffic counts of very short duration, it is possible that the information is too noisy to be of use. Empirical differences between AADT estimated from 14 satellite images and air photos of Interstate segments in Ohio and the corresponding AADT estimated from traditional, ground-based estimates are presented. The distribution in differences appears relatively unbiased, implying that averaging the estimates of several images of the same segment can decrease estimate errors. The empirical errors are small enough to indicate that AADT estimation errors and ground-based sampling efforts could both be reduced by combining satellite-based data with traditional ground-based data. The differences between the image-based and the ground-based estimates are smaller in the few cases in which ground-based estimates inspired greater confidence, implying that the image-based estimates may be better than what is indicated in the distribution of differences. Evidence also suggests that the differences tend to decrease when the image leads to longer equivalent traffic count duration, indicating the potential to condition the use of the image-based data on this readily available parameter.
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Fisher, Brad, and David B. Wolff. "Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI, AMSR-E, SSM/I, AMSU-B, and the TRMM PR." Journal of Applied Meteorology and Climatology 50, no. 5 (May 2011): 994–1023. http://dx.doi.org/10.1175/2010jamc2487.1.
Abstract:
AbstractPassive and active microwave rain sensors on board Earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscures the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different spaceborne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25° resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands, and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean, and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite, and the regional climatology. The most significant result from this study found that each of the satellites incurred negative long-term oceanic retrieval biases of 10%–30%.
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Mehrjardi, Mohamad Fakhari, Hilmi Sanusi, Mohd Alauddin Mohd Ali, and Montadar Abas Taher. "Proportional Derivative Controller Using Discrete Kalman Filter Estimation Method for Spacecraft Attitude Control." Applied Mechanics and Materials 789-790 (September 2015): 923–26. http://dx.doi.org/10.4028/www.scientific.net/amm.789-790.923.
Abstract:
This paper deals with the objective of controlling a satellite by driving a six-state discrete Kalman Filter to estimate angular rates of satellite base on control sensor noisy data. A typical satellite is assumed in a special orbit and orbital angular velocity and orbital angular acceleration are established. For completion of simulation linear dynamics model of satellites and environment disturbances model such as solar pressure and gravity gradient torque is derived as well. The simulation is progressed at discrete ten second which assumed as data updating rate from sensor. The noisy measurements are produced by sensor and these data is sent to the discrete Kalman Filter part to estimate the attitude and attitude rate. A right balance for Plant noise covariance matrix is determined and also results show that the rate estimates are appropriate for space missions.
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Goldberg, Daniel L., Susan C. Anenberg, Zifeng Lu, David G. Streets, Lok N. Lamsal, Erin E McDuffie, and Steven J. Smith. "Urban NO x emissions around the world declined faster than anticipated between 2005 and 2019." Environmental Research Letters 16, no. 11 (October 20, 2021): 115004. http://dx.doi.org/10.1088/1748-9326/ac2c34.
Abstract:
Abstract Emission inventory development for air pollutants, by compiling records from individual emission sources, takes many years and involves extensive multi-national effort. A complementary method to estimate air pollution emissions is in the use of satellite remote sensing. In this study, NO2 observations from the Ozone Monitoring Instrument are combined with re-analysis meteorology to estimate urban nitrogen oxide (NO X ) emissions for 80 global cities between 2005 and 2019. The global average downward trend in satellite-derived urban NOX emissions was 3.1%–4.0% yr−1 between 2009 and 2018 while inventories show a 0%–2.2% yr−1 drop over the same timeframe. This difference is primarily driven by discrepancies between satellite-derived urban NO X emissions and inventories in Africa, China, India, Latin America, and the Middle East. In North America, Europe, Korea, Japan, and Australasia, NOX emissions dropped similarly as reported in the inventories. In Europe, Korea, and Japan only, the temporal trends match the inventories well, but the satellite estimate is consistently larger over time. While many of the discrepancies between satellite-based and inventory emissions estimates represent real differences, some of the discrepancies might be related to the assumptions made to compare the satellite-based estimates with inventory estimates, such as the spatial disaggregation of emissions inventories. Our work identifies that the three largest uncertainties in the satellite estimate are the tropospheric column measurements, wind speed and direction, and spatial definition of each city.
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Vogt, M., T. Hashioka, M. R. Payne, E. T. Buitenhuis, C. Le Quéré, S. Alvain, M. N. Aita, et al. "The distribution, dominance patterns and ecological niches of plankton functional types in Dynamic Green Ocean Models and satellite estimates." Biogeosciences Discussions 10, no. 11 (November 4, 2013): 17193–247. http://dx.doi.org/10.5194/bgd-10-17193-2013.
Abstract:
Abstract. We compare the spatial and temporal representation of phytoplankton functional types (pPFTs) in four different Dynamic Green Ocean Models (DGOMs; CCSM-BEC, NEMURO, PISCES and PlankTOM5) to derived phytoplankton distributions from two independent satellite estimates, with a particular focus on diatom distributions. Global annual mean surface biomass estimates for diatoms vary between 0.23 mmol C m−3 and 0.77 mmol C m−3 in the models, and are comparable to a satellite-derived estimate (0.41 mmol C m−3). All models consistently simulate a higher zonal mean diatom biomass contribution in the high latitudes than in the low latitudes, but the relative diatom contribution varies substantially between models with largest differences in the high latitudes (20% to 100% of total biomass). We investigate phytoplankton distribution in terms of annual and monthly mean dominance patterns, i.e. the distribution of locations where a given PFT contributes more than 50% to total biomass. In all models, diatoms tend to dominate large areas of the high latitudes of both hemispheres, and the area of the surface ocean dominated by diatoms is significantly higher in the models than in the satellite estimates. We estimate the realized ecological niches filled by the dominant pPFT at each location as a function of annual mean surface nitrate concentration (NO3), sea surface temperature (SST), and mixed layer depth. A general additive model (GAM) is used to map the probability of dominance of all pPFTs in niche and geographic space. Models tend to simulate diatom dominance over a wider temperature and nutrient range, whereas satellites confine diatom dominance to a narrower niche of low-intermediate annual mean temperatures (annual mean SST < 10 °C), but allow for niches in different ranges of surface NO3 concentrations. For annual mean diatom dominance, the statistically modelled probability of dominance explains the majority of the variance in the data (65.2–66.6%). For the satellite estimates, the explained deviance is much lower (44.6% and 32.7%). The differences in the representation of diatoms among models and compared to satellite estimates highlights the need to better resolve phytoplankton succession and phenology in the models. This work is part of the marine ecosystem inter-comparison project (MAREMIP).
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Lin, Xiaojuan, Ronald van der A, Jos de Laat, Henk Eskes, Frédéric Chevallier, Philippe Ciais, Zhu Deng, et al. "Monitoring and quantifying CO2 emissions of isolated power plants from space." Atmospheric Chemistry and Physics 23, no. 11 (June 15, 2023): 6599–611. http://dx.doi.org/10.5194/acp-23-6599-2023.
Abstract:
Abstract. Top-down CO2 emission estimates based on satellite observations are of great importance for independently verifying the accuracy of reported emissions and emission inventories. Difficulties in verifying these satellite-derived emissions arise from the fact that emission inventories often provide annual mean emissions, while estimates from satellites are available only for a limited number of overpasses. Previous studies have derived CO2 emissions for power plants from the Orbiting Carbon Observatory-2 and 3 (OCO-2 and OCO-3) satellite observations of their exhaust plumes, but the accuracy and the factors affecting these emissions are uncertain. Here we advance monitoring and quantifying point source carbon emissions by focusing on how to improve the accuracy of carbon emission using different wind data estimates. We have selected only isolated power plants for this study, to avoid complications linked to multiple sources in close proximity. We first compared the Gaussian plume model and cross-sectional flux methods for estimating CO2 emission of power plants. Then we examined the sensitivity of the emission estimates to possible choices for the wind field. For verification we have used power plant emissions that are reported on an hourly basis by the Environmental Protection Agency (EPA) in the US. By using the OCO-2 and OCO-3 observations over the past 4 years we identified emission signals of isolated power plants and arrived at a total of 50 collocated cases involving 22 power plants. We correct for the time difference between the moment of the emission and the satellite observation. We found the wind field halfway the height of the planetary boundary layer (PBL) yielded the best results. We also found that the instantaneous satellite estimated emissions of these 50 cases, and reported emissions display a weak correlation (R2=0.12). The correlation improves with averaging over multiple observations of the 22 power plants (R2=0.40). The method was subsequently applied to 106 power plant cases worldwide and yielded a total emission of 1522 ± 501 Mt CO2 yr−1, estimated to be about 17 % of the power sector emissions of our selected countries. The improved correlation highlights the potential for future planned satellite missions with a greatly improved coverage to monitor a significant fraction of global power plant emissions.
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Wernecke, Andreas, Dirk Notz, Stefan Kern, and Thomas Lavergne. "Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals." Cryosphere 18, no. 5 (May 17, 2024): 2473–86. http://dx.doi.org/10.5194/tc-18-2473-2024.
Abstract:
Abstract. The net Arctic sea-ice area (SIA) can be estimated from the sea-ice concentration (SIC) by passive microwave measurements from satellites. To be a truly useful metric, for example of the sensitivity of the Arctic sea-ice cover to global warming, we need, however, reliable estimates of its uncertainty. Here we retrieve this uncertainty by taking into account the spatial and temporal error correlations of the underlying local sea-ice concentration products. As 1 example year, we find that in 2015 the average observational uncertainties of the SIA are 306 000 km2 for daily estimates, 275 000 km2 for weekly estimates, and 164 000 km2 for monthly estimates. The sea-ice extent (SIE) uncertainty for that year is slightly smaller, with 296 000 km2 for daily estimates, 261 000 km2 for weekly estimates, and 156 000 km2 for monthly estimates. These daily uncertainties correspond to about 7 % of the 2015 sea-ice minimum and are about half of the spread in estimated SIA and SIE from different passive microwave SIC products. This shows that random SIC errors play a role in SIA uncertainties comparable to inter-SIC-product biases. We further show that the September SIA, which is traditionally the month with the least amount of Arctic sea ice, declined by 105 000±9000 km2 a−1 for the period from 2002 to 2017. This is the first estimate of a SIA trend with an explicit representation of temporal error correlations.
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Kidd, C., and V. Levizzani. "Status of satellite precipitation retrievals." Hydrology and Earth System Sciences 15, no. 4 (April 5, 2011): 1109–16. http://dx.doi.org/10.5194/hess-15-1109-2011.
Abstract:
Abstract. Satellites offer an unrivalled vantage point to observe and measure Earth system processes and parameters. Precipitation (rain and snow) in particular, benefit from such observations since precipitation is spatially and temporally highly variable and with satellites overcoming some of the deficiencies of conventional gauge and radar measurements. This paper provides an overall review of quantitative precipitation estimation, covering the basis of the satellite systems used in the observation of precipitation, the dissemination and processing of this data, and the generation, availability and validation of these precipitation estimates. A selection of applications utilising these precipitation estimates are then outlined to exemplify the utility of such products.
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Yuan, Zhimin, Changsheng Cai, Lin Pan, and Cuilin Kuang. "An Improved Multi-Satellite Method for Evaluating Real-Time BDS Satellite Clock Offset Products." Remote Sensing 12, no. 21 (November 5, 2020): 3638. http://dx.doi.org/10.3390/rs12213638.
Abstract:
Two methods are widely used for evaluating the precision of satellite clock products, namely the single-satellite method (SSM) and the multi-satellite method (MSM). In the satellite clock product evaluation, an important issue is how to eliminate the timescale difference. The SSM selects a reference satellite to eliminate the timescale difference by between-satellite differencing, but its evaluation results are susceptible to the gross errors in the referenced satellite clock offsets. In the MSM, the timescale difference is first estimated and then removed. Unlike the GPS, the BeiDou Navigation Satellite System (BDS) consists of three types of satellites, namely geosynchronous earth orbit (GEO), inclined geosynchronous orbit (IGSO), and medium earth orbit (MEO) satellites. The three types of satellites have uneven orbital accuracy. In the generation of satellite clock products, the orbital errors are partly assimilated into the clock offsets. If neglecting the orbital accuracy difference of the three types of BeiDou satellites, the MSM will obtain biased estimates of the timescale difference and finally affect the clock product evaluation. In this study, an improved multi-satellite method (IMSM) is proposed for evaluating the real-time BDS clock products by removing the assimilated orbital errors of the three types of BDS satellites when estimating the timescale difference. Three real-time BDS clock products disseminated by three different International GNSS Service (IGS) analysis centers, namely CLK16, CLK20, and CLK93, over a period of two months are used to validate this method. The results indicate that the assimilated orbital errors have a significant impact on the estimation of the timescale difference. Subsequently, the IMSM is compared with the SSM in which the referenced satellite is rigorously chosen, and their RMS difference is only 0.08 ns, which suggests that the evaluation results obtained by the IMSM are accurate. Compared with the traditional MSM, the IMSM improves the RMS by 0.16, 0.11, and 0.07 ns for CLK16, CLK20, and CLK93, respectively. Finally, three real-time BDS clock products are evaluated using the proposed method, and results reveal a significant precision difference among them.
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Cheng, Weiwei, Guigen Nie, and Jian Zhu. "Characterizing Periodic Variations of Atomic Frequency Standards via Their Frequency Stability Estimates." Sensors 23, no. 11 (June 5, 2023): 5356. http://dx.doi.org/10.3390/s23115356.
Abstract:
The onboard atomic frequency standard (AFS) is a crucial element of Global Navigation Satellite System (GNSS) satellites. However, it is widely accepted that periodic variations can influence the onboard AFS. The presence of non-stationary random processes in AFS signals can lead to inaccurate separation of the periodic and stochastic components of satellite AFS clock data when using least squares and Fourier transform methods. In this paper, we characterize the periodic variations of AFS using Allan and Hadamard variances and demonstrate that the Allan and Hadamard variances of the periodics are independent of the variances of the stochastic component. The proposed model is tested against simulated and real clock data, revealing that our approach provides more precise characterization of periodic variations compared to the least squares method. Additionally, we observe that overfitting periodic variations can improve the precision of GPS clock bias prediction, as indicated by a comparison of fitting and prediction errors of satellite clock bias.
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Maggioni, Viviana, Mathew R. P. Sapiano, Robert F. Adler, Yudong Tian, and George J. Huffman. "An Error Model for Uncertainty Quantification in High-Time-Resolution Precipitation Products." Journal of Hydrometeorology 15, no. 3 (June 1, 2014): 1274–92. http://dx.doi.org/10.1175/jhm-d-13-0112.1.
Abstract:
Abstract This study proposes a new framework, Precipitation Uncertainties for Satellite Hydrology (PUSH), to provide time-varying, global estimates of errors for high-time-resolution, multisatellite precipitation products using a technique calibrated with high-quality validation data. Errors are estimated for the widely used Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product at daily/0.25° resolution, using the NOAA Climate Prediction Center (CPC) Unified gauge dataset as the benchmark. PUSH estimates the probability distribution of reference precipitation given the satellite observation, from which the error can be computed as the difference (or ratio) between the satellite product and the estimated reference. The framework proposes different modeling approaches for each combination of rain and no-rain cases: correct no-precipitation detection (both satellite and gauges measure no precipitation), missed precipitation (satellite records a zero, but the gauges detect precipitation), false alarm (satellite detects precipitation, but the reference is zero), and hit (both satellite and gauges detect precipitation). Each case is explored and explicitly modeled to create a unified approach that combines all four scenarios. Results show that the estimated probability distributions are able to reproduce the probability density functions of the benchmark precipitation, in terms of both expected values and quantiles of the distribution. The spatial pattern of the error is also adequately reproduced by PUSH, and good agreement between observed and estimated errors is observed. The model is also able to capture missed precipitation and false detection uncertainties, whose contribution to the total error can be significant. The resulting error estimates could be attached to the corresponding high-resolution satellite precipitation products.
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Bastos, Eduardo J. de Brito, Jaidete M. de Souza, and Tantravahi V. Ramana Rao. "Potential evapotranspiration estimates for northeast Brazil using GOES-8 data." Revista Brasileira de Engenharia Agrícola e Ambiental 4, no. 3 (December 2000): 348–54. http://dx.doi.org/10.1590/s1415-43662000000300008.
Abstract:
In this study, an empirical method proposed by Caselles et al. (1992a) is utilized to determine the potential evapotranspiration (ETP) on a regional scale. This method uses the global solar radiation data retrieved by the global radiation model GL1.0, which in turn utilizes data from the visible channel of the GOES-8 satellite. This method is applied to the northeast region of Brazil, using daily and monthly climatological data as the ground truth information to estimate the ETP and the estimated daily ETP data for September, 1997. The methodology involved three steps: 1) to perform a spatial regionalization of the ETP using the method of Ward, which is available in the Statistical Package for the Social Sciences (SPSS); 2) to obtain the correlation between the ETP as estimated by the methods of Jensen & Haise (1963) - MJH, Caselles (1992a) - MCA, and the Penman's combined method (1948) - MCP; 3) to test the sensibility of the empirical formulations proposed and to assess the estimates using the satellite-based global solar radiation provided by the GL1.0 model. The spatial regionalization shows two distinct regions in the Northeastern Brazil. The MCA yielded better results than the MJH. The ETP estimates using satellite data were satisfactory, showing a maximum error of 20% when compared with the ground truth data.
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Liu, Lei, Xiuying Zhang, Wen Xu, Xuejun Liu, Xuehe Lu, Jing Wei, Yi Li, Yuyu Yang, Zhen Wang, and Anthony Y. H. Wong. "Reviewing global estimates of surface reactive nitrogen concentration and deposition using satellite retrievals." Atmospheric Chemistry and Physics 20, no. 14 (July 22, 2020): 8641–58. http://dx.doi.org/10.5194/acp-20-8641-2020.
Abstract:
Abstract. Since the industrial revolution, human activities have dramatically changed the nitrogen (N) cycle in natural systems. Anthropogenic emissions of reactive nitrogen (Nr) can return to the earth's surface through atmospheric Nr deposition. Increased Nr deposition may improve ecosystem productivity. However, excessive Nr deposition can cause a series of negative effects on ecosystem health, biodiversity, soil, and water. Thus, accurate estimations of Nr deposition are necessary for evaluating its environmental impacts. The United States, Canada and Europe have successively launched a number of satellites with sensors that allow retrieval of atmospheric NO2 and NH3 column density and therefore estimation of surface Nr concentration and deposition at an unprecedented spatiotemporal scale. Atmosphere NH3 column can be retrieved from atmospheric infra-red emission, while atmospheric NO2 column can be retrieved from reflected solar radiation. In recent years, scientists attempted to estimate surface Nr concentration and deposition using satellite retrieval of atmospheric NO2 and NH3 columns. In this study, we give a thorough review of recent advances of estimating surface Nr concentration and deposition using the satellite retrievals of NO2 and NH3, present a framework of using satellite data to estimate surface Nr concentration and deposition based on recent works, and summarize the existing challenges for estimating surface Nr concentration and deposition using the satellite-based methods. We believe that exploiting satellite data to estimate Nr deposition has a broad and promising prospect.
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Zhao, Lin, Zhong Hua Su, and Yong Hao. "Research on Satellite Multi-Sensor Attitude Determination System." Applied Mechanics and Materials 220-223 (November 2012): 1917–21. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1917.
Abstract:
An attitude determination system has been designed for the geocentric pointing triaxial stabilized satellites which employ a continuously running inertial rate sensor in conjunction with sun sensor and earth sensor. Earth/sun sensor data are processed to generate corrections to satellite attitude, gyro constant drift and earth sensor drift bias estimates. An extended Kalman filter based on the attitude determination system is derived in this paper for the satellite using two earth sensors, a two-axis digit sun sensor as attitude sensors and a three-axis gyro for the angular velocity. A simulation model is developed to estimate the attitude determination performance. Simulation results show that precision attitude determination is achieved using the selected attitude hardware and algorithms.
35
Callingham, Thomas M., Marius Cautun, Alis J. Deason, Carlos S. Frenk, Wenting Wang, Facundo A. Gómez, Robert J. J. Grand, Federico Marinacci, and Ruediger Pakmor. "The mass of the Milky Way from satellite dynamics." Monthly Notices of the Royal Astronomical Society 484, no. 4 (February 5, 2019): 5453–67. http://dx.doi.org/10.1093/mnras/stz365.
Abstract:
Abstract We present and apply a method to infer the mass of the Milky Way (MW) by comparing the dynamics of MW satellites to those of model satellites in the eagle cosmological hydrodynamics simulations. A distribution function (DF) for galactic satellites is constructed from eagle using specific angular momentum and specific energy, which are scaled so as to be independent of host halo mass. In this two-dimensional space, the orbital properties of satellite galaxies vary according to the host halo mass. The halo mass can be inferred by calculating the likelihood that the observed satellite population is drawn from this DF. Our method is robustly calibrated on mock eagle systems. We validate it by applying it to the completely independent suite of 30 auriga high-resolution simulations of MW-like galaxies: the method accurately recovers their true mass and associated uncertainties. We then apply it to 10 classical satellites of the MW with six-dimensional phase-space measurements, including updated proper motions from the Gaia satellite. The mass of the MW is estimated to be $M_{200}^{\rm {MW}}=1.17_{-0.15}^{+0.21}\times 10^{12}\, \mathrm{M}_{\odot }$ (68 per cent confidence limits). We combine our total mass estimate with recent mass estimates in the inner regions of the Galaxy to infer an inner dark matter (DM) mass fraction $M^\rm {DM}(\lt 20~\rm {kpc})/M^\rm {DM}_{200}=0.12$, which is typical of ${\sim }10^{12}\, \mathrm{M}_{\odot }$ lambda cold dark matter haloes in hydrodynamical galaxy formation simulations. Assuming a Navarro, Frenk and White (NFW) profile, this is equivalent to a halo concentration of $c_{200}^{\rm {MW}}=10.9^{+2.6}_{-2.0}$.
36
Sapiano, M. R. P., J. E. Janowiak, P. A. Arkin, H. Lee, T. M. Smith, and P. Xie. "Corrections for Temporal Discontinuities in the OPI." Journal of Atmospheric and Oceanic Technology 27, no. 3 (March 1, 2010): 457–69. http://dx.doi.org/10.1175/2009jtecha1366.1.
Abstract:
Abstract The longest record of precipitation estimated from satellites is the outgoing longwave radiation (OLR) precipitation index (OPI), which is based on polar-orbiting infrared observations from the Advanced Very High Resolution Radiometer (AVHRR) instrument that has flown onboard successive NOAA satellites. A significant barrier to the use of these data in studies of the climate of tropical precipitation (among other things) is the large bias caused by orbital drift that is present in the OLR data. Because the AVHRR instruments are deployed on the polar-orbiting spacecraft, OLR observations are recorded at specific times for each earth location for each day. Discontinuities are caused by the use of multiple satellites with different observing times as well as the orbital drift that occurs throughout the lifetime of each satellite. A regression-based correction is proposed based solely on the equator crossing time (ECT). The correction allows for separate means for each satellite as well as separate coefficients for each satellite ECT. The correction is calculated separately for each grid box but is applied only at locations where the correction is correlated with the OLR estimate. Thus, the correction is applied only where deemed necessary. The OPI is used to estimate precipitation from the OLR estimates based on the new corrected version of the OLR, the uncorrected OLR, and two earlier published corrected versions. One of the earlier corrections is derived by removing variations from AVHRR based on EOFs that are identified as containing spurious variations related to the ECT bias, whereas the other is based on OLR estimates from the High Resolution Infrared Radiation Sounder (HIRS) that have been corrected using diurnal models for each grid box. The new corrected version is shown to be free of nearly all of the ECT bias and has the lowest root mean square difference when compared to gauges and passive microwave estimates of precipitation. The EOF-based correction fails to remove all of the variations related to the ECT bias, whereas the correction based on HIRS removes much of the bias but appears to introduce erroneous trends caused by the water vapor signal to which these data are sensitive. The new correction for AVHRR OLR works well in the tropics where the OPI has the most skill, but users should be careful when interpreting trends outside this region.
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Santos, Luiz Octavio Fabricio dos, Carlos Alexandre Santos Querino, Juliane Kayse Albuquerque da Silva Querino, Altemar Lopes Pedreira Junior, Aryanne Resende de Melo Moura, Nadja Gomes Machado, and Marcelo Sacardi Biudes. "Validation of rainfall data estimated by GPM satellite on Southern Amazon region." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 14, no. 1 (January 2, 2019): 1. http://dx.doi.org/10.4136/ambi-agua.2249.
Abstract:
Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency – ANA and National Institute of Meteorology – INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended.
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Kulawik, Susan, Debra Wunch, Christopher O'Dell, Christian Frankenberg, Maximilian Reuter, Tomohiro Oda, Frederic Chevallier, et al. "Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON." Atmospheric Measurement Techniques 9, no. 2 (February 29, 2016): 683–709. http://dx.doi.org/10.5194/amt-9-683-2016.
Abstract:
Abstract. Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (XCO2) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 ppm vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to error2 = a2 + b2/n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of ∼ 0.3 ppm, with biases larger than the TCCON-predicted bias uncertainty of 0.4 ppm at many stations. We find that GOSAT and CT2013b underpredict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53° N, MACC overpredicts between 26 and 37° N, and CT2013b underpredicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in JJA; there is correlation between 0.2 and 0.8 in the NH, with models showing 10–50 % the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45–67° N for GOSAT and CT2013b.
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Chua, Zhi-Weng, Yuriy Kuleshov, and Andrew Watkins. "Evaluation of Satellite Precipitation Estimates over Australia." Remote Sensing 12, no. 4 (February 19, 2020): 678. http://dx.doi.org/10.3390/rs12040678.
Abstract:
This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.
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Lewis, William E., Timothy L. Olander, Christopher S. Velden, Christopher Rozoff, and Stefano Alessandrini. "Analog Ensemble Methods for Improving Satellite-Based Intensity Estimates of Tropical Cyclones." Atmosphere 12, no. 7 (June 28, 2021): 830. http://dx.doi.org/10.3390/atmos12070830.
Abstract:
Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such method, the Advanced Dvorak Technique (ADT), was used to develop analog ensemble (AnEn) techniques that provide more precise estimates of TC intensity with instant access to information on the reliability of the estimate. The resulting methods, ADT-AnEn and ADT-based Error Analog Ensemble (ADTE-AnEn), were trained and tested using seventeen years of historical ADT intensity estimates using k-fold cross-validation with 10 folds. Using only two predictors, ADT-estimated current intensity (maximum wind speed) and TC center latitude, both AnEn techniques produced significant reductions in mean absolute error and bias for all TC intensity classes in the North Atlantic and for most intensity classes in the Eastern Pacific. The ADTE-AnEn performed better for extreme intensities in both basins (significantly so in the Eastern Pacific) and will be incorporated in the University of Wisconsin’s Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) workflow for further testing during operations in 2021.
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Pathak, Harshavardhana Sunil, Sreedharan Krishnakumari Satheesh, Krishnaswamy Krishna Moorthy, and Ravi Shankar Nanjundiah. "Assessment of regional aerosol radiative effects under the SWAAMI campaign – Part 2: Clear-sky direct shortwave radiative forcing using multi-year assimilated data over the Indian subcontinent." Atmospheric Chemistry and Physics 20, no. 22 (November 23, 2020): 14237–52. http://dx.doi.org/10.5194/acp-20-14237-2020.
Abstract:
Abstract. Clear-sky, direct shortwave aerosol radiative forcing (ARF) has been estimated over the Indian region, for the first time employing multi-year (2009–2013) gridded, assimilated aerosol products, as an important part of the South West Asian Aerosol Monsoon Interactions (SWAAMI) which is a joint Indo-UK research field campaign focused at understanding the variabilities in atmospheric aerosols and their interactions with the Indian summer monsoon. The aerosol datasets have been constructed following statistical assimilation of concurrent data from a dense network of ground-based observatories and multi-satellite products, as described in Part 1 of this two-part paper. The ARF, thus estimated, is assessed for its superiority or otherwise over other ARF estimates based on satellite-retrieved aerosol products, over the Indian region, by comparing the radiative fluxes (upward) at the top of the atmosphere (TOA) estimated using assimilated and satellite products with spatiotemporally matched radiative flux values provided by CERES (Clouds and Earth's Radiant Energy System) single-scan footprint (SSF) product. This clearly demonstrated improved accuracy of the forcing estimates using the assimilated vis-à-vis satellite-based aerosol datasets at regional, subregional and seasonal scales. The regional distribution of diurnally averaged ARF estimates has revealed (a) significant differences from similar estimates made using currently available satellite data, not only in terms of magnitude but also the sign of TOA forcing; (b) the largest magnitudes of surface cooling and atmospheric warming over the Indo-Gangetic Plain (IGP) and arid regions from north-western India; and (c) negative TOA forcing over most parts of the Indian region, except for three subregions – the IGP, north-western India and eastern parts of peninsular India where the TOA forcing changes to positive during pre-monsoon season. Aerosol-induced atmospheric warming rates, estimated using the assimilated data, demonstrate substantial spatial heterogeneities (∼0.2 to 2.0 K d−1) over the study domain with the IGP demonstrating relatively stronger atmospheric heating rates (∼0.6 to 2.0 K d−1). There exists a strong seasonality as well, with atmospheric warming being highest during pre-monsoon and lowest during winter seasons. It is to be noted that the present ARF estimates demonstrate substantially smaller uncertainties than their satellite counterparts, which is a natural consequence of reduced uncertainties in assimilated vis-à-vis satellite aerosol properties. The results demonstrate the potential application of the assimilated datasets and ARF estimates for improving accuracies of climate impact assessments at regional and subregional scales.
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Kidd, C., and V. Levizzani. "Status of satellite precipitation retrievals." Hydrology and Earth System Sciences Discussions 7, no. 5 (October 18, 2010): 8157–77. http://dx.doi.org/10.5194/hessd-7-8157-2010.
Abstract:
Abstract. Satellites offer an unrivalled vantage point to observe and measure Earth system processes and parameters. Precipitation (rain and snow) in particular, benefit from such observations since precipitation is spatially and temporally highly variable and overcomes some of the deficiencies of conventional gauge and radar measurements. This paper provides an overall review of quantitative precipitation estimation, covering the basis of the satellite systems used in the observation of precipitation and the dissemination of this data, the processing of these measurements to generate the rainfall estimates and the availability, verification and validation of these precipitation estimates.
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Sadeghi, Morteza, Ardeshir Ebtehaj, Wade T. Crow, Lun Gao, Adam J. Purdy, Joshua B. Fisher, Scott B. Jones, Ebrahim Babaeian, and Markus Tuller. "Global Estimates of Land Surface Water Fluxes from SMOS and SMAP Satellite Soil Moisture Data." Journal of Hydrometeorology 21, no. 2 (February 2020): 241–53. http://dx.doi.org/10.1175/jhm-d-19-0150.1.
Abstract:
AbstractIn-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water flux cannot be directly measured, and its estimates as a residual of individual surface flux components often suffer from mass conservation errors due to accumulated systematic biases of individual fluxes. Here, for the first time, we provide direct estimates of global NWF based on near-surface satellite soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. We apply a recently developed analytical model derived via inversion of the linearized Richards’ equation. The model is parsimonious, yet yields unbiased estimates of long-term cumulative NWF that is generally well correlated with the terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) satellite. In addition, in conjunction with precipitation and evapotranspiration retrievals, the resultant NWF estimates provide a new means for retrieving global infiltration and runoff from satellite observations. However, the efficacy of the proposed approach over densely vegetated regions is questionable, due to the uncertainty of the satellite soil moisture retrievals and the lack of explicit parameterization of transpiration by deeply rooted plants in the proposed model. Future research is needed to advance this modeling paradigm to explicitly account for plant transpiration.
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Rayner, Nick A., Renate Auchmann, Janette Bessembinder, Stefan Brönnimann, Yuri Brugnara, Francesco Capponi, Laura Carrea, et al. "The EUSTACE Project: Delivering Global, Daily Information on Surface Air Temperature." Bulletin of the American Meteorological Society 101, no. 11 (November 1, 2020): E1924—E1947. http://dx.doi.org/10.1175/bams-d-19-0095.1.
Abstract:
AbstractDay-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.
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Reitman, Nadine G., Richard W. Briggs, William D. Barnhart, Alexandra E. Hatem, Jessica A. Thompson Jobe, Christopher B. DuRoss, Ryan D. Gold, et al. "Rapid Surface Rupture Mapping from Satellite Data: The 2023 Kahramanmaraş, Turkey (Türkiye), Earthquake Sequence." Seismic Record 3, no. 4 (October 1, 2023): 289–98. http://dx.doi.org/10.1785/0320230029.
Abstract:
Abstract The 6 February 2023 Kahramanmaraş, Turkey (Türkiye), earthquake sequence produced > 500 km of surface rupture primarily on the left-lateral East Anatolian (~345 km) and Çardak (~175 km) faults. Constraining the length and magnitude of surface displacement on the causative faults is critical for loss estimates, recovery efforts, rapid identification of impacted infrastructure, and fault displacement hazard analysis. To support these efforts, we rapidly mapped the surface rupture from satellite data with support from remote sensing and field teams, and released the results to the public in near-real time. Detailed surface rupture mapping commenced on 7 February and continued as high-resolution (< 1.0 m/pixel) optical images from WorldView satellites (2023 Maxar) became available. We interpreted the initial simplified rupture trace from subpixel offset fields derived from Advanced Land Observation Satellite2 and Sentinel-1A synthetic aperture radar image pairs available on 8 and 10 February, respectively. The mapping was released publicly on 10 February, with frequent updates, and published in final form four months postearthquake (Reitman, Briggs, et al., 2023). This publicly available, rapid mapping helped guide fieldwork and constrained U.S. Geological Survey finite-fault and loss estimate models, as well as stress change estimates and dynamic rupture models.
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Sahlu, Dejene, Semu A. Moges, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Dereje Hailu. "Evaluation of High-Resolution Multisatellite and Reanalysis Rainfall Products over East Africa." Advances in Meteorology 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/4957960.
Abstract:
The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Among the six satellite-based rainfall products, adjusted CMORPH exhibits the best accuracy of the wet season rainfall estimate. In the secondary rainy season, unadjusted CMORPH and 3B42V7 are nearly equivalent in terms of bias, POD, and CSI error metrics. All error metric statistics show that MSWEP outperform both unadjusted and gauge adjusted ERA-Interim estimates. The magnitude of error metrics is linearly increasing with increasing percentile threshold values of gauge rainfall categories. Overall, all precipitation datasets need further improvement in terms of detection during the occurrence of high rainfall intensity. MSWEP detects higher percentiles values better than satellite estimate in the wet and poor in the secondary rainy seasons.
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Barré, Jérôme, Hervé Petetin, Augustin Colette, Marc Guevara, Vincent-Henri Peuch, Laurence Rouil, Richard Engelen, et al. "Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models." Atmospheric Chemistry and Physics 21, no. 9 (May 17, 2021): 7373–94. http://dx.doi.org/10.5194/acp-21-7373-2021.
Abstract:
Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.
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Pandey, Ashish, S. K. Mishra, and Amar Kant Gautam. "Soil Erosion Modeling Using Satellite Rainfall Estimates." Journal of Water Resource and Hydraulic Engineering 4, no. 4 (October 1, 2015): 318–25. http://dx.doi.org/10.5963/jwrhe0404002.
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STUHLMANN, ROLF, EHRHARD RASCHKE, and MARTIN RIELAND. "SATELLITE ESTIMATES OF GLOBAL AND DIFFUSE RADIATION." International Journal of Solar Energy 8, no. 3 (January 1990): 137–54. http://dx.doi.org/10.1080/01425919008909716.
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Mganu Manyatsi, Absalom. "Evaluation of Satellite Rainfall Estimates for Swaziland." American Journal of Agriculture and Forestry 3, no. 3 (2015): 93. http://dx.doi.org/10.11648/j.ajaf.20150303.15.