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Applications of Dynamic Land Surface Information for Passive Microwave Precipitation RetrievalAbstract
Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (<5 mm h−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.
Abstract
Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (<5 mm h−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.
Abstract
The upcoming Global Precipitation Measurement mission will provide considerably more overland observations over complex terrain, high-elevation river basins, and cold surfaces, necessitating an improved assessment of the microwave land surface emissivity. Current passive microwave overland rainfall algorithms developed for the Tropical Rainfall Measuring Mission (TRMM) rely upon hydrometeor scattering-induced signatures at high-frequency (85 GHz) brightness temperatures (TBs) and are empirical in nature. A multiyear global database of microwave surface emissivities encompassing a wide range of surface conditions was retrieved from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) radiometric clear scenes using companion A-Train [CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Atmospheric Infrared Sounder (AIRS)] data. To account for the correlated emissivity structure, the procedure first derives the TRMM Microwave Imager–like nine-channel emissivity principal component (PC) structure. Relations are derived to estimate the emissivity PCs directly from the instantaneous TBs, which allows subsequent TB observations to estimate the PC structure and reconstruct the emissivity vector without need for ancillary data regarding the surface or atmospheric conditions. Radiative transfer simulations matched the AMSR-E TBs within 5–7-K RMS difference in the absence of precipitation. Since the relations are derived specifically for clear-scene conditions, discriminant analysis was performed to find the PC discriminant that best separates clear and precipitation scenes. When this technique is applied independently to two years of TRMM data, the PC-based discriminant demonstrated superior relative operating characteristics relative to the established 85-GHz scattering index, most notably during cold seasons.
Abstract
The upcoming Global Precipitation Measurement mission will provide considerably more overland observations over complex terrain, high-elevation river basins, and cold surfaces, necessitating an improved assessment of the microwave land surface emissivity. Current passive microwave overland rainfall algorithms developed for the Tropical Rainfall Measuring Mission (TRMM) rely upon hydrometeor scattering-induced signatures at high-frequency (85 GHz) brightness temperatures (TBs) and are empirical in nature. A multiyear global database of microwave surface emissivities encompassing a wide range of surface conditions was retrieved from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) radiometric clear scenes using companion A-Train [CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Atmospheric Infrared Sounder (AIRS)] data. To account for the correlated emissivity structure, the procedure first derives the TRMM Microwave Imager–like nine-channel emissivity principal component (PC) structure. Relations are derived to estimate the emissivity PCs directly from the instantaneous TBs, which allows subsequent TB observations to estimate the PC structure and reconstruct the emissivity vector without need for ancillary data regarding the surface or atmospheric conditions. Radiative transfer simulations matched the AMSR-E TBs within 5–7-K RMS difference in the absence of precipitation. Since the relations are derived specifically for clear-scene conditions, discriminant analysis was performed to find the PC discriminant that best separates clear and precipitation scenes. When this technique is applied independently to two years of TRMM data, the PC-based discriminant demonstrated superior relative operating characteristics relative to the established 85-GHz scattering index, most notably during cold seasons.
Abstract
This study uses Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Ka-precipitation radar observations to quantify the snowfall detection performance for different channel (frequency) combinations. Results showed that the low-frequency-channel set contains limited snow detection information with a 0.34 probability of detection (POD). Much better performance is evident using the high-frequency channels (i.e., POD = 0.74). In addition, if only one high-frequency channel is allowed to be added to the low-frequency-channel set, adding the 183 ± 3 GHz channel presents the largest POD improvement (from 0.34 to 0.50). However, this does not imply that the water vapor is the key information for snowfall detection. Only using the high-frequency water vapor channels showed poor snowfall detection with POD at 0.13. Further analysis of all 8191 possible GMI channel combinations showed that the 166-GHz channels are indispensable for any channel combination with POD greater than 0.70. This suggests that the scattering signature, not the water vapor effect, is essential for snowfall detection. Data analysis and model simulation support this explanation. Finally, the GPM constellation radiometers are grouped into six categories based on the channel availability and their snowfall detection capability is estimated, using channels available on GMI. It is found that type-4 radiometer (all channels) has the best snowfall detection performance with a POD of 0.77. The POD values are only slightly smaller for the type-3 radiometer (high-frequency channels) and type-5 radiometer (all channels except 183 channels).
Abstract
This study uses Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Ka-precipitation radar observations to quantify the snowfall detection performance for different channel (frequency) combinations. Results showed that the low-frequency-channel set contains limited snow detection information with a 0.34 probability of detection (POD). Much better performance is evident using the high-frequency channels (i.e., POD = 0.74). In addition, if only one high-frequency channel is allowed to be added to the low-frequency-channel set, adding the 183 ± 3 GHz channel presents the largest POD improvement (from 0.34 to 0.50). However, this does not imply that the water vapor is the key information for snowfall detection. Only using the high-frequency water vapor channels showed poor snowfall detection with POD at 0.13. Further analysis of all 8191 possible GMI channel combinations showed that the 166-GHz channels are indispensable for any channel combination with POD greater than 0.70. This suggests that the scattering signature, not the water vapor effect, is essential for snowfall detection. Data analysis and model simulation support this explanation. Finally, the GPM constellation radiometers are grouped into six categories based on the channel availability and their snowfall detection capability is estimated, using channels available on GMI. It is found that type-4 radiometer (all channels) has the best snowfall detection performance with a POD of 0.77. The POD values are only slightly smaller for the type-3 radiometer (high-frequency channels) and type-5 radiometer (all channels except 183 channels).
Abstract
Current microwave precipitation retrieval algorithms utilize the instantaneous brightness temperature (TB) to estimate precipitation rate. This study presents a new idea that can be used to improve existing algorithms: using TB temporal variation 
Abstract
Current microwave precipitation retrieval algorithms utilize the instantaneous brightness temperature (TB) to estimate precipitation rate. This study presents a new idea that can be used to improve existing algorithms: using TB temporal variation
Abstract
Rainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounders (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Units-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all 10 satellites, 5 imagers, 6 satellites with very different equator crossing times, and GMI only. Results show that Δe from all 10 satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, compared with the Integrated Multisatellite Retrievals for GPM (IMERG) Final run product. The 6-satellites scheme has comparable performance with the all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.
Abstract
Rainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounders (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Units-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all 10 satellites, 5 imagers, 6 satellites with very different equator crossing times, and GMI only. Results show that Δe from all 10 satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, compared with the Integrated Multisatellite Retrievals for GPM (IMERG) Final run product. The 6-satellites scheme has comparable performance with the all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.
Abstract
A prototype precipitation algorithm for the Advanced Technology Microwave Sounder (ATMS) was developed by using 3-yr coincident ground radar and ATMS observations over the continental United States (CONUS). Several major improvements to a previously published algorithm for the Special Sensor Microwave Imager/Sounder (SSMIS) include 1) considering the different footprint size of ATMS pixels, 2) calculating the uncertainty associated with the precipitation estimation, and 3) extending the algorithm to the 60°S–60°N region using only CONUS observations to construct the database. It is found that the retrieved and radar-observed rain rates agree well (e.g., correlation 0.66) and the one-standard-deviation error bar provides valuable retrieval uncertainty information. The geospatial pattern from the retrieved rain rate is largely consistent with that from radar observations. For the snowfall performance, the ATMS-retrieved results clearly capture the snowfall events over the Rocky Mountain region, while radar observations almost entirely miss the snowfall events over this region. Further, this algorithm is applied to the 60°S–60°N land region. The representative nature of rainfall over CONUS permitted the application of this algorithm to 60°S–60°N for rainfall retrieval, evidenced by the progress and retreat of the major rainbands. However, an artificially large snowfall rate is observed in several regions (e.g., Tibetan Plateau and Siberia) because of frequent false detection and overestimation caused by much colder brightness temperatures.
Abstract
A prototype precipitation algorithm for the Advanced Technology Microwave Sounder (ATMS) was developed by using 3-yr coincident ground radar and ATMS observations over the continental United States (CONUS). Several major improvements to a previously published algorithm for the Special Sensor Microwave Imager/Sounder (SSMIS) include 1) considering the different footprint size of ATMS pixels, 2) calculating the uncertainty associated with the precipitation estimation, and 3) extending the algorithm to the 60°S–60°N region using only CONUS observations to construct the database. It is found that the retrieved and radar-observed rain rates agree well (e.g., correlation 0.66) and the one-standard-deviation error bar provides valuable retrieval uncertainty information. The geospatial pattern from the retrieved rain rate is largely consistent with that from radar observations. For the snowfall performance, the ATMS-retrieved results clearly capture the snowfall events over the Rocky Mountain region, while radar observations almost entirely miss the snowfall events over this region. Further, this algorithm is applied to the 60°S–60°N land region. The representative nature of rainfall over CONUS permitted the application of this algorithm to 60°S–60°N for rainfall retrieval, evidenced by the progress and retreat of the major rainbands. However, an artificially large snowfall rate is observed in several regions (e.g., Tibetan Plateau and Siberia) because of frequent false detection and overestimation caused by much colder brightness temperatures.
Abstract
The microwave land surface emissivity (MLSE) over the continental United States was examined during 2011 as a function of prior rainfall conditions using two independent emissivity estimation techniques, one providing instantaneous estimates based on a clear-scene emissivity principal component (PC) analysis and the other based on physical radiative transfer modeling. Results show that over grass, closed shrub, and cropland, prior rainfall can cause the horizontally polarized 10-GHz brightness temperature (TB) to drop by as much as 20 K, with a corresponding emissivity drop of approximately 0.06, whereby prior rain exhibited little influence on the emissivity over forest because of the dense vegetation. The correlation between emissivity and its leading principal components and the prior rainfall over grass, closed shrub, and cropland is −0.6, while it is only −0.1 over forested areas. Forward-simulated TB using the PC-based emissivity derived from instantaneous Tropical Rainfall Measuring Mission (TRMM) satellite overpasses agrees much better with TRMM Microwave Imager (TMI) observations relative to a climatologically based emissivity, especially after a period of heavy rain. Two potential applications of the PC-based emissivity are demonstrated. The first exploits the time history change of the MLSE to estimate the amount of prior rainfall. The second application is a method to estimate the emissivity underneath precipitating radiometric scenes by first adjusting the surface-sensitive principal components that were derived under clear-sky scenes and then by reconstructing the joint emissivity (all channels simultaneously) from the modified PC structure. The results are applicable to future overland passive microwave rainfall retrieval algorithms to simultaneously detect and estimate precipitation amounts under dynamically changing surface conditions.
Abstract
The microwave land surface emissivity (MLSE) over the continental United States was examined during 2011 as a function of prior rainfall conditions using two independent emissivity estimation techniques, one providing instantaneous estimates based on a clear-scene emissivity principal component (PC) analysis and the other based on physical radiative transfer modeling. Results show that over grass, closed shrub, and cropland, prior rainfall can cause the horizontally polarized 10-GHz brightness temperature (TB) to drop by as much as 20 K, with a corresponding emissivity drop of approximately 0.06, whereby prior rain exhibited little influence on the emissivity over forest because of the dense vegetation. The correlation between emissivity and its leading principal components and the prior rainfall over grass, closed shrub, and cropland is −0.6, while it is only −0.1 over forested areas. Forward-simulated TB using the PC-based emissivity derived from instantaneous Tropical Rainfall Measuring Mission (TRMM) satellite overpasses agrees much better with TRMM Microwave Imager (TMI) observations relative to a climatologically based emissivity, especially after a period of heavy rain. Two potential applications of the PC-based emissivity are demonstrated. The first exploits the time history change of the MLSE to estimate the amount of prior rainfall. The second application is a method to estimate the emissivity underneath precipitating radiometric scenes by first adjusting the surface-sensitive principal components that were derived under clear-sky scenes and then by reconstructing the joint emissivity (all channels simultaneously) from the modified PC structure. The results are applicable to future overland passive microwave rainfall retrieval algorithms to simultaneously detect and estimate precipitation amounts under dynamically changing surface conditions.
Abstract
This study assesses the level-2 precipitation estimates from 10 radiometers relative to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) in two parts. First, nine sensors—four imagers [Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs)] and five sounders [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHSs)]—are evaluated over the 65°S–65°N region. Over ocean, imagers outperform sounders, primarily due to the usage of low-frequency channels. Furthermore, AMSR2 is clearly superior to SSMISs, likely due to the finer footprint size. Over land all sensors perform similarly except the noticeably worse performance from ATMS and SSMIS-F17. Second, we include the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) into the evaluation process, contrasting it against other sensors in the SAPHIR latitudes (30°S–30°N). SAPHIR has a slightly worse detection capability than other sounders over ocean but comparable detection performance to MHSs over land. The intensity estimates from SAPHIR show a larger normalized root-mean-square-error over both land and ocean, likely because only 183.3-GHz channels are available. Currently, imagers are preferred to sounders when level-2 estimates are incorporated into level-3 products. Our results suggest a sensor-specific priority order. Over ocean, this study indicates a priority order of AMSR2, SSMISs, MHSs and ATMS, and SAPHIR. Over land, SSMIS-F17, ATMS and SAPHIR should be given a lower priority than the other sensors.
Abstract
This study assesses the level-2 precipitation estimates from 10 radiometers relative to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) in two parts. First, nine sensors—four imagers [Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs)] and five sounders [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHSs)]—are evaluated over the 65°S–65°N region. Over ocean, imagers outperform sounders, primarily due to the usage of low-frequency channels. Furthermore, AMSR2 is clearly superior to SSMISs, likely due to the finer footprint size. Over land all sensors perform similarly except the noticeably worse performance from ATMS and SSMIS-F17. Second, we include the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) into the evaluation process, contrasting it against other sensors in the SAPHIR latitudes (30°S–30°N). SAPHIR has a slightly worse detection capability than other sounders over ocean but comparable detection performance to MHSs over land. The intensity estimates from SAPHIR show a larger normalized root-mean-square-error over both land and ocean, likely because only 183.3-GHz channels are available. Currently, imagers are preferred to sounders when level-2 estimates are incorporated into level-3 products. Our results suggest a sensor-specific priority order. Over ocean, this study indicates a priority order of AMSR2, SSMISs, MHSs and ATMS, and SAPHIR. Over land, SSMIS-F17, ATMS and SAPHIR should be given a lower priority than the other sensors.
Abstract
The Diaoyu Islands are a group of uninhabited islets located in the East China Sea between Japan, China, and Taiwan. Here, four mainstream gauge-adjusted multisatellite precipitation estimates [TRMM Multisatellite Precipitation Analysis, version 7 (TMPA-V7); CPC morphing technique–bias-corrected product (CMORPH-CRT); Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR); and Global Satellite Mapping of Precipitation–gauge adjusted (GSMaP_Gauge)] are adopted to detect the rainfall characteristics of the Diaoyu Islands area with a particular focus on typhoon contribution. Out of the four products, CMORPH-CRT and GSMaP_Gauge show much more similarity both in terms of the spatial patterns and error structures because of their use of the same morphing technique. Overall, GSMaP_Gauge performs better than the other three products, likely because of denser in situ observations integrated in its retrieval algorithms over East Asia. All rainfall products indicate that an apparent rain belt exists along the northeastern 45° direction of Taiwan extending to Kyushu of Japan, which is physically associated with the Kuroshio. The Diaoyu Islands are located on the central axis of this rain belt. During the period 2001–09, typhoon-induced rainfall accounted for 530 mm yr−1, and typhoons contributed on average approximately 30% of the annual precipitation budget over the Diaoyu Islands. Higher typhoon contribution was found over the southern warmer water of the Diaoyu Islands, while the northern cooler water presented less contribution ratio. Supertyphoon Chaba, the largest typhoon of 2004, recorded 53 h of rainfall accumulation totaling 235 mm on the Diaoyu Islands, and this event caused severe property damage and human casualties for Japan. Hence, the Diaoyu Islands play an important role in weather monitoring and forecasting for the neighboring countries and regions.
Abstract
The Diaoyu Islands are a group of uninhabited islets located in the East China Sea between Japan, China, and Taiwan. Here, four mainstream gauge-adjusted multisatellite precipitation estimates [TRMM Multisatellite Precipitation Analysis, version 7 (TMPA-V7); CPC morphing technique–bias-corrected product (CMORPH-CRT); Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR); and Global Satellite Mapping of Precipitation–gauge adjusted (GSMaP_Gauge)] are adopted to detect the rainfall characteristics of the Diaoyu Islands area with a particular focus on typhoon contribution. Out of the four products, CMORPH-CRT and GSMaP_Gauge show much more similarity both in terms of the spatial patterns and error structures because of their use of the same morphing technique. Overall, GSMaP_Gauge performs better than the other three products, likely because of denser in situ observations integrated in its retrieval algorithms over East Asia. All rainfall products indicate that an apparent rain belt exists along the northeastern 45° direction of Taiwan extending to Kyushu of Japan, which is physically associated with the Kuroshio. The Diaoyu Islands are located on the central axis of this rain belt. During the period 2001–09, typhoon-induced rainfall accounted for 530 mm yr−1, and typhoons contributed on average approximately 30% of the annual precipitation budget over the Diaoyu Islands. Higher typhoon contribution was found over the southern warmer water of the Diaoyu Islands, while the northern cooler water presented less contribution ratio. Supertyphoon Chaba, the largest typhoon of 2004, recorded 53 h of rainfall accumulation totaling 235 mm on the Diaoyu Islands, and this event caused severe property damage and human casualties for Japan. Hence, the Diaoyu Islands play an important role in weather monitoring and forecasting for the neighboring countries and regions.
Abstract
Previous studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) on board the SNPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective versus stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multisatellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers on board.
Abstract
Previous studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) on board the SNPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective versus stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multisatellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers on board.
