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Abstract
Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.
Abstract
Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.
Abstract
Precipitation is the fundamental source of freshwater in the water cycle. It is critical for everyone, from subsistence farmers in Africa to weather forecasters around the world, to know when, where, and how much rain and snow is falling. The Global Precipitation Measurement (GPM) Core Observatory spacecraft, launched in February 2014, has the most advanced instruments to measure precipitation from space and, together with other satellite information, provides high-quality merged data on rain and snow worldwide every 30 min. Data from GPM and the predecessor Tropical Rainfall Measuring Mission (TRMM) have been fundamental to a broad range of applications and end-user groups and are among the most widely downloaded Earth science data products across NASA. End-user applications have rapidly become an integral component in translating satellite data into actionable information and knowledge used to inform policy and enhance decision-making at local to global scales. In this article, we present NASA precipitation data, capabilities, and opportunities from the perspective of end users. We outline some key examples of how TRMM and GPM data are being applied across a broad range of sectors, including numerical weather prediction, disaster modeling, agricultural monitoring, and public health research. This work provides a discussion of some of the current needs of the community as well as future plans to better support end-user communities across the globe to utilize this data for their own applications.
Abstract
Precipitation is the fundamental source of freshwater in the water cycle. It is critical for everyone, from subsistence farmers in Africa to weather forecasters around the world, to know when, where, and how much rain and snow is falling. The Global Precipitation Measurement (GPM) Core Observatory spacecraft, launched in February 2014, has the most advanced instruments to measure precipitation from space and, together with other satellite information, provides high-quality merged data on rain and snow worldwide every 30 min. Data from GPM and the predecessor Tropical Rainfall Measuring Mission (TRMM) have been fundamental to a broad range of applications and end-user groups and are among the most widely downloaded Earth science data products across NASA. End-user applications have rapidly become an integral component in translating satellite data into actionable information and knowledge used to inform policy and enhance decision-making at local to global scales. In this article, we present NASA precipitation data, capabilities, and opportunities from the perspective of end users. We outline some key examples of how TRMM and GPM data are being applied across a broad range of sectors, including numerical weather prediction, disaster modeling, agricultural monitoring, and public health research. This work provides a discussion of some of the current needs of the community as well as future plans to better support end-user communities across the globe to utilize this data for their own applications.
Abstract
Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.
Abstract
Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.
Abstract
The Integrated Multisatellite Retrievals for GPM (IMERG), a global high-resolution gridded precipitation dataset, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scales and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final Run of IMERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5°) and accumulation periods (from 0.5 to 24 h) over a region in the southeastern United States. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multisatellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.
Abstract
The Integrated Multisatellite Retrievals for GPM (IMERG), a global high-resolution gridded precipitation dataset, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scales and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final Run of IMERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5°) and accumulation periods (from 0.5 to 24 h) over a region in the southeastern United States. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multisatellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.
Intercalibration of the GPM Microwave Radiometer ConstellationAbstract
The Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to unify and advance precipitation measurements using both research and operational microwave sensors. This requires consistency in the input brightness temperatures (Tb), which is accomplished by intercalibrating the constellation radiometers using the GPM Microwave Imager (GMI) as the calibration reference. The first step in intercalibrating the sensors involves prescreening the sensor Tb to identify and correct for calibration biases across the scan or along the orbit path. Next, multiple techniques developed by teams within the GPM Intersatellite Calibration Working Group (XCAL) are used to adjust the calibrations of the constellation radiometers to be consistent with GMI. Comparing results from multiple approaches helps identify flaws or limitations of a given technique, increase confidence in the results, and provide a measure of the residual uncertainty. The original calibration differences relative to GMI are generally within 2–3 K for channels below 92 GHz, although AMSR2 exhibits larger differences that vary with scene temperature. SSMIS calibration differences also vary with scene temperature but to a lesser degree. For SSMIS channels above 150 GHz, the differences are generally within ~2 K with the exception of SSMIS on board DMSP F19, which ranges from 7 to 11 K colder than GMI depending on frequency. The calibrations of the cross-track radiometers agree very well with GMI with values mostly within 0.5 K for the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) and the Microwave Humidity Sounder (MHS) sensors, and within 1 K for the Advanced Technology Microwave Sounder (ATMS).
Abstract
The Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to unify and advance precipitation measurements using both research and operational microwave sensors. This requires consistency in the input brightness temperatures (Tb), which is accomplished by intercalibrating the constellation radiometers using the GPM Microwave Imager (GMI) as the calibration reference. The first step in intercalibrating the sensors involves prescreening the sensor Tb to identify and correct for calibration biases across the scan or along the orbit path. Next, multiple techniques developed by teams within the GPM Intersatellite Calibration Working Group (XCAL) are used to adjust the calibrations of the constellation radiometers to be consistent with GMI. Comparing results from multiple approaches helps identify flaws or limitations of a given technique, increase confidence in the results, and provide a measure of the residual uncertainty. The original calibration differences relative to GMI are generally within 2–3 K for channels below 92 GHz, although AMSR2 exhibits larger differences that vary with scene temperature. SSMIS calibration differences also vary with scene temperature but to a lesser degree. For SSMIS channels above 150 GHz, the differences are generally within ~2 K with the exception of SSMIS on board DMSP F19, which ranges from 7 to 11 K colder than GMI depending on frequency. The calibrations of the cross-track radiometers agree very well with GMI with values mostly within 0.5 K for the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) and the Microwave Humidity Sounder (MHS) sensors, and within 1 K for the Advanced Technology Microwave Sounder (ATMS).
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.
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.
