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Abstract
The second intercomparison project of the Global Precipitation Climatology Project examined the estimation of midlatitude, cool-season precipitation. As part of that effort, the authors report here on the results of two microwave techniques the Goddard scattering algorithm and the physical retrieval algorithm of Kummerow. Results from the estimation of instantaneous rain rate for five overpasses of the Special Sensor Microwave/Imager (SSM/I) are presented in a case study mode to illustrate both the strong and weak points of each technique. These five cases represent a sampling of the various types of precipitating systems observed. Results for the complete set of 20 swaths chosen by the United Kingdom Meteorological Office are then categorized by scatterplots and statistics of instantaneous radar versus microwave-estimated rain rate, rain/no-rain contingency tables, and scatterplots of arch coverage of rainfall.
Neither algorithm produced a good statistical correlation with the radar data, yet in general, both did well at determining rainy areas. Two reasons are suggested for the low correlation coefficients between both algorithms and the radar data. Time differences between the SSM/I overpass and the radar observations can occasionally account for some of the differences. The primary reason for the low correlations, however, appears to be the predominance of very light rain in the area of interest during the winter. Both algorithms are in good spatial agreement with the radar when the radar data are restricted to rates above 1 mm h−1. When all radar rain rates are included, the radar areal coverage increases by as much as a factor of 10 in some cases. Because the Kummerow algorithm does not handle such low rain rates over land very well, and because the Goddard scattering algorithm uses 1 mm h−1 as the minimum reliably detectable rain rate, regimes that contain large arm of very fight rain present inherent difficulties for these retrieval methods. Therefore, the proliferation of low rain rates observed during the experiment is the main contributor to low correlation coefficients and high root-mean-square differences. Misidentification of cold surface (e.g., snow cover) as precipitation was also a problem in several instances.
Abstract
The second intercomparison project of the Global Precipitation Climatology Project examined the estimation of midlatitude, cool-season precipitation. As part of that effort, the authors report here on the results of two microwave techniques the Goddard scattering algorithm and the physical retrieval algorithm of Kummerow. Results from the estimation of instantaneous rain rate for five overpasses of the Special Sensor Microwave/Imager (SSM/I) are presented in a case study mode to illustrate both the strong and weak points of each technique. These five cases represent a sampling of the various types of precipitating systems observed. Results for the complete set of 20 swaths chosen by the United Kingdom Meteorological Office are then categorized by scatterplots and statistics of instantaneous radar versus microwave-estimated rain rate, rain/no-rain contingency tables, and scatterplots of arch coverage of rainfall.
Neither algorithm produced a good statistical correlation with the radar data, yet in general, both did well at determining rainy areas. Two reasons are suggested for the low correlation coefficients between both algorithms and the radar data. Time differences between the SSM/I overpass and the radar observations can occasionally account for some of the differences. The primary reason for the low correlations, however, appears to be the predominance of very light rain in the area of interest during the winter. Both algorithms are in good spatial agreement with the radar when the radar data are restricted to rates above 1 mm h−1. When all radar rain rates are included, the radar areal coverage increases by as much as a factor of 10 in some cases. Because the Kummerow algorithm does not handle such low rain rates over land very well, and because the Goddard scattering algorithm uses 1 mm h−1 as the minimum reliably detectable rain rate, regimes that contain large arm of very fight rain present inherent difficulties for these retrieval methods. Therefore, the proliferation of low rain rates observed during the experiment is the main contributor to low correlation coefficients and high root-mean-square differences. Misidentification of cold surface (e.g., snow cover) as precipitation was also a problem in several instances.
Climatologies of convective precipitation were derived from passive microwave observations from the Special Sensor Microwave Imager using a scattering-based algorithm of Adler et al. Data were aggregated over periods of 3–5 months using data from 4 to 5 years. Data were also stratified by satellite overpass times (primarily 06 00 and 18 00 local time). Four regions [Mexico, Amazonia, western Africa, and the western equatorial Pacific Ocean (TOGA COARE area)] were chosen for their meteorological interest and relative paucity of conventional observations.
The strong diurnal variation over Mexico and the southern United States was the most striking aspect of the climatologies. Pronounced morning maxima occurred offshore, often in concavities in the coastline, the result of the increased convergence caused by the coastline shape. The major feature of the evening rain field was a linear-shaped maximum along the western slope of the Sierra Madre Occidental. Topography exerted a strong control on the rainfall in other areas, particularly near the Nicaragua/Honduras border and in Guatemala, where maxima in excess of 700 mm month−1 were located adjacent to local maxima in terrain. The correlation between the estimates and monthly gage data over the southern United States was low (0.45), due mainly to poortemporal sampling in any month and an inadequate sampling of the diurnal cycle.
Over the Amazon Basin the differences in morning versus evening rainfall were complex, with an alternating series of morning/evening maxima aligned southwest to northeast from the Andes to the northeast Brazilian coast. Areal extent of rainfall in Amazonia was slightly higher in the evening, but a maximum in morning precipitation was found on the Amazon River just east of Manaus. Precipitation over the water in the ITCZ north of Brazil was more pronounced in the morning, and a pronounced land-/sea-breeze circulation was found along the northeast coast of Brazil. Inter-comparison of four years revealed 1992 to be the driest over Amazonia, with about a 23% decrease in mean rain rate compared to the 4-year mean estimated rain rate.
The major rain feature of tropical western Africa was found on the west coast—a pronounced overland evening maximum directly between the coast and a high mountain peak, and a morning maximum directly offshore. An intense, localized morning maximum of over 1000 mm month−1 was found at a concavity in the coast at the Bight of Bonny. In the region of the TOGA COARE experiment, precipitation in the ITCZ was greater in November 1989–February 1990, compared to the same period in 1988–1989, notably in the region five degrees either side of the equator from 160°E to the dateline. There was a clear preference in both seasons for morning precipitation over the water. Interesting diurnal effects were found over and offshore of New Guinea and the Solomon Islands. For the eight months studied, averaging both the gauges and Goddard Scattering Algorithm estimates to 2.5° grid boxes yielded a correlation of 0.73, bias of −59.5 mm, and a root-mean-square difference of 131.8 mm—29% and 64%, respectively, of the mean monthly observed rainfall.
Climatologies of convective precipitation were derived from passive microwave observations from the Special Sensor Microwave Imager using a scattering-based algorithm of Adler et al. Data were aggregated over periods of 3–5 months using data from 4 to 5 years. Data were also stratified by satellite overpass times (primarily 06 00 and 18 00 local time). Four regions [Mexico, Amazonia, western Africa, and the western equatorial Pacific Ocean (TOGA COARE area)] were chosen for their meteorological interest and relative paucity of conventional observations.
The strong diurnal variation over Mexico and the southern United States was the most striking aspect of the climatologies. Pronounced morning maxima occurred offshore, often in concavities in the coastline, the result of the increased convergence caused by the coastline shape. The major feature of the evening rain field was a linear-shaped maximum along the western slope of the Sierra Madre Occidental. Topography exerted a strong control on the rainfall in other areas, particularly near the Nicaragua/Honduras border and in Guatemala, where maxima in excess of 700 mm month−1 were located adjacent to local maxima in terrain. The correlation between the estimates and monthly gage data over the southern United States was low (0.45), due mainly to poortemporal sampling in any month and an inadequate sampling of the diurnal cycle.
Over the Amazon Basin the differences in morning versus evening rainfall were complex, with an alternating series of morning/evening maxima aligned southwest to northeast from the Andes to the northeast Brazilian coast. Areal extent of rainfall in Amazonia was slightly higher in the evening, but a maximum in morning precipitation was found on the Amazon River just east of Manaus. Precipitation over the water in the ITCZ north of Brazil was more pronounced in the morning, and a pronounced land-/sea-breeze circulation was found along the northeast coast of Brazil. Inter-comparison of four years revealed 1992 to be the driest over Amazonia, with about a 23% decrease in mean rain rate compared to the 4-year mean estimated rain rate.
The major rain feature of tropical western Africa was found on the west coast—a pronounced overland evening maximum directly between the coast and a high mountain peak, and a morning maximum directly offshore. An intense, localized morning maximum of over 1000 mm month−1 was found at a concavity in the coast at the Bight of Bonny. In the region of the TOGA COARE experiment, precipitation in the ITCZ was greater in November 1989–February 1990, compared to the same period in 1988–1989, notably in the region five degrees either side of the equator from 160°E to the dateline. There was a clear preference in both seasons for morning precipitation over the water. Interesting diurnal effects were found over and offshore of New Guinea and the Solomon Islands. For the eight months studied, averaging both the gauges and Goddard Scattering Algorithm estimates to 2.5° grid boxes yielded a correlation of 0.73, bias of −59.5 mm, and a root-mean-square difference of 131.8 mm—29% and 64%, respectively, of the mean monthly observed rainfall.
Abstract
A technique is described to use Tropical Rainfall Measuring Mission (TRMM) combined radar–radiometer information to adjust geosynchronous infrared satellite data [the TRMM Adjusted Geostationary Operational Environmental Satellite Precipitation Index (AGPI)]. The AGPI is then merged with rain gauge information (mostly over land) to provide finescale (1° latitude × 1° longitude) pentad and monthly analyses, respectively. The TRMM merged estimates are 10% higher than those from the Global Precipitation Climatology Project (GPCP) when integrated over the tropical oceans (37°N–37°S) for 1998, with 20% differences noted in the most heavily raining areas. In the dry subtropics the TRMM values are smaller than the GPCP estimates. The TRMM merged product tropical-mean estimates for 1998 are 3.3 mm day−1 over ocean and 3.1 mm day−1 over land and ocean combined. Regional differences are noted between the western and eastern Pacific Ocean maxima when TRMM and GPCP are compared. In the eastern Pacific rain maximum the TRMM and GPCP mean values are nearly equal, which is very different from the other tropical rainy areas where TRMM merged product estimates are higher. This regional difference may indicate that TRMM is better at taking into account the vertical structure of the rain systems and the difference in structure between the western and eastern (shallower) Pacific convection.
Comparisons of these TRMM merged analysis estimates with surface datasets shows varied results; the bias is near zero when compared with western Pacific Ocean atoll rain gauge data, but is significantly positive as compared with Kwajalein radar estimates (adjusted by rain gauges). Over land the TRMM estimates also show a significant positive bias. The inclusion of gauge information in the final merged product significantly reduces the bias over land, as expected.
The monthly precipitation patterns produced by the TRMM merged data process clearly show the evolution of the El Niño–Southern Oscillation (ENSO) tropical precipitation pattern from early 1998 (El Niño) to early 1999 (La Niña) and beyond. The El Niño-minus-La Niña difference map shows the expected eastern Pacific maximum, the “Maritime Continent” minima, and other tropical and midlatitude features, very similar to those detected by the GPCP analyses. However, summing the El Niño-minus-La Niña differences over the global tropical oceans yields divergent answers for interannual changes from TRMM, GPCP, and other estimates. This emphasizes the need for additional validation and analysis before it is feasible to understand the relations between global precipitation anomalies and Pacific Ocean ENSO temperature changes.
Abstract
A technique is described to use Tropical Rainfall Measuring Mission (TRMM) combined radar–radiometer information to adjust geosynchronous infrared satellite data [the TRMM Adjusted Geostationary Operational Environmental Satellite Precipitation Index (AGPI)]. The AGPI is then merged with rain gauge information (mostly over land) to provide finescale (1° latitude × 1° longitude) pentad and monthly analyses, respectively. The TRMM merged estimates are 10% higher than those from the Global Precipitation Climatology Project (GPCP) when integrated over the tropical oceans (37°N–37°S) for 1998, with 20% differences noted in the most heavily raining areas. In the dry subtropics the TRMM values are smaller than the GPCP estimates. The TRMM merged product tropical-mean estimates for 1998 are 3.3 mm day−1 over ocean and 3.1 mm day−1 over land and ocean combined. Regional differences are noted between the western and eastern Pacific Ocean maxima when TRMM and GPCP are compared. In the eastern Pacific rain maximum the TRMM and GPCP mean values are nearly equal, which is very different from the other tropical rainy areas where TRMM merged product estimates are higher. This regional difference may indicate that TRMM is better at taking into account the vertical structure of the rain systems and the difference in structure between the western and eastern (shallower) Pacific convection.
Comparisons of these TRMM merged analysis estimates with surface datasets shows varied results; the bias is near zero when compared with western Pacific Ocean atoll rain gauge data, but is significantly positive as compared with Kwajalein radar estimates (adjusted by rain gauges). Over land the TRMM estimates also show a significant positive bias. The inclusion of gauge information in the final merged product significantly reduces the bias over land, as expected.
The monthly precipitation patterns produced by the TRMM merged data process clearly show the evolution of the El Niño–Southern Oscillation (ENSO) tropical precipitation pattern from early 1998 (El Niño) to early 1999 (La Niña) and beyond. The El Niño-minus-La Niña difference map shows the expected eastern Pacific maximum, the “Maritime Continent” minima, and other tropical and midlatitude features, very similar to those detected by the GPCP analyses. However, summing the El Niño-minus-La Niña differences over the global tropical oceans yields divergent answers for interannual changes from TRMM, GPCP, and other estimates. This emphasizes the need for additional validation and analysis before it is feasible to understand the relations between global precipitation anomalies and Pacific Ocean ENSO temperature changes.
IMERG V06: Changes to the Morphing AlgorithmAbstract
As the U.S. Science Team’s globally gridded precipitation product from the NASA–JAXA Global Precipitation Measurement (GPM) mission, the Integrated Multi-Satellite Retrievals for GPM (IMERG) estimates the surface precipitation rates at 0.1° every half hour using spaceborne sensors for various scientific and societal applications. One key component of IMERG is the morphing algorithm, which uses motion vectors to perform quasi-Lagrangian interpolation to fill in gaps in the passive microwave precipitation field using motion vectors. Up to IMERG V05, the motion vectors were derived from the large-scale motions of infrared observations of cloud tops. This study details the changes introduced in IMERG V06 to derive motion vectors from large-scale motions of selected atmospheric variables in numerical models, which allow IMERG estimates to be extended from the 60°N–60°S latitude band to the entire globe. Evaluation against both instantaneous passive microwave retrievals and ground measurements demonstrates the general improvement in the precipitation field of the new approach. Most of the model variables tested exhibited similar performance, but total precipitable water vapor was chosen as the source of the motion vectors for IMERG V06 due to its competitive performance and global completeness. Continuing assessments will provide further insights into possible refinements of this revised morphing scheme in future versions of IMERG.
Abstract
As the U.S. Science Team’s globally gridded precipitation product from the NASA–JAXA Global Precipitation Measurement (GPM) mission, the Integrated Multi-Satellite Retrievals for GPM (IMERG) estimates the surface precipitation rates at 0.1° every half hour using spaceborne sensors for various scientific and societal applications. One key component of IMERG is the morphing algorithm, which uses motion vectors to perform quasi-Lagrangian interpolation to fill in gaps in the passive microwave precipitation field using motion vectors. Up to IMERG V05, the motion vectors were derived from the large-scale motions of infrared observations of cloud tops. This study details the changes introduced in IMERG V06 to derive motion vectors from large-scale motions of selected atmospheric variables in numerical models, which allow IMERG estimates to be extended from the 60°N–60°S latitude band to the entire globe. Evaluation against both instantaneous passive microwave retrievals and ground measurements demonstrates the general improvement in the precipitation field of the new approach. Most of the model variables tested exhibited similar performance, but total precipitable water vapor was chosen as the source of the motion vectors for IMERG V06 due to its competitive performance and global completeness. Continuing assessments will provide further insights into possible refinements of this revised morphing scheme in future versions of IMERG.
Abstract
Satellite-based precipitation estimates provide valuable information where surface observations are not readily available, especially over the large expanses of the ocean where in situ precipitation observations are very sparse. This study compares monthly precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG) with gauge observations from 37 low-lying atolls from the Pacific Rainfall Database for the period June 2000–August 2020. Over the analysis period, IMERG estimates are slightly higher than the atoll observations by 0.67% with a monthly correlation of 0.68. Seasonally, DJF shows excellent agreement with a near-zero bias, while MAM shows IMERG is low by 4.6%, and JJA is high by 1.2%. SON exhibits the worst performance, with IMERG overestimating by 6.5% compared to the atolls. The seasonal correlations are well contained in the range 0.67–0.72, with the exception of SON at 0.62. Furthermore, SON has the highest RMSE at 4.70 mm day−1, making it the worst season for all metrics. Scatterplots of IMERG versus atolls show IMERG, on average, is generally low for light precipitation accumulations and high for intense precipitation accumulations, with best agreement at intermediate rates. Seasonal variations exist at light and intermediate rate accumulations, but IMERG consistently overestimates at intense precipitation rates. The differences between IMERG and atolls vary over time but do not exhibit any discernable trend or dependence on atoll population. The PACRAIN atoll gauges are not wind-loss corrected, so application of an appropriate adjustment would increase the precipitation amounts compared to IMERG. These results provide useful insight to users as well as valuable information for future improvements to IMERG.
Abstract
Satellite-based precipitation estimates provide valuable information where surface observations are not readily available, especially over the large expanses of the ocean where in situ precipitation observations are very sparse. This study compares monthly precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG) with gauge observations from 37 low-lying atolls from the Pacific Rainfall Database for the period June 2000–August 2020. Over the analysis period, IMERG estimates are slightly higher than the atoll observations by 0.67% with a monthly correlation of 0.68. Seasonally, DJF shows excellent agreement with a near-zero bias, while MAM shows IMERG is low by 4.6%, and JJA is high by 1.2%. SON exhibits the worst performance, with IMERG overestimating by 6.5% compared to the atolls. The seasonal correlations are well contained in the range 0.67–0.72, with the exception of SON at 0.62. Furthermore, SON has the highest RMSE at 4.70 mm day−1, making it the worst season for all metrics. Scatterplots of IMERG versus atolls show IMERG, on average, is generally low for light precipitation accumulations and high for intense precipitation accumulations, with best agreement at intermediate rates. Seasonal variations exist at light and intermediate rate accumulations, but IMERG consistently overestimates at intense precipitation rates. The differences between IMERG and atolls vary over time but do not exhibit any discernable trend or dependence on atoll population. The PACRAIN atoll gauges are not wind-loss corrected, so application of an appropriate adjustment would increase the precipitation amounts compared to IMERG. These results provide useful insight to users as well as valuable information for future improvements to IMERG.
Abstract
A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and a decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood), which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.
Abstract
A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and a decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood), which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.
Abstract
Monthly and daily products of the Global Precipitation Climatology Project (GPCP) are evaluated through a comparison with Finnish Meteorological Institute (FMI) gauge observations for the period January 1995–December 2007 to assess the quality of the GPCP estimates at high latitudes. At the monthly scale both the final GPCP combination satellite–gauge (SG) product is evaluated, along with the satellite-only multisatellite (MS) product. The GPCP daily product is scaled to sum to the monthly product, so it implicitly contains monthly-scale gauge influence, although it contains no daily gauge information. As expected, the monthly SG product agrees well with the FMI observations because of the inclusion of limited gauge information. Over the entire analysis period the SG estimates are biased low by 6% when the same wind-loss adjustment is applied to the FMI gauges as is used in the SG analysis. The interannual anomaly correlation is about 0.9. The satellite-only MS product has a lesser, but still reasonably good, interannual correlation (∼0.6) while retaining a similar bias due to the use of a climatological bias adjustment. These results indicate the value of using even a few gauges in the analysis and provide an estimate of the correlation error to be expected in the SG analysis over ocean and remote land areas where gauges are absent. The daily GPCP precipitation estimates compare reasonably well at the 1° latitude × 2° longitude scale with the FMI gauge observations in the summer with a correlation of 0.55, but less so in the winter with a correlation of 0.45. Correlations increase somewhat when larger areas and multiday periods are analyzed. The day-to-day occurrence of precipitation is captured fairly well by the GPCP estimates, but the corresponding precipitation event amounts tend to show wide variability. The results of this study indicate that the GPCP monthly and daily fields are useful for meteorological and hydrological studies but that there is significant room for improvement of satellite retrievals and analysis techniques in this region. It is hoped that the research here provides a framework for future high-latitude assessment efforts such as those that will be necessary for the upcoming satellite-based Global Precipitation Measurement (GPM) mission.
Abstract
Monthly and daily products of the Global Precipitation Climatology Project (GPCP) are evaluated through a comparison with Finnish Meteorological Institute (FMI) gauge observations for the period January 1995–December 2007 to assess the quality of the GPCP estimates at high latitudes. At the monthly scale both the final GPCP combination satellite–gauge (SG) product is evaluated, along with the satellite-only multisatellite (MS) product. The GPCP daily product is scaled to sum to the monthly product, so it implicitly contains monthly-scale gauge influence, although it contains no daily gauge information. As expected, the monthly SG product agrees well with the FMI observations because of the inclusion of limited gauge information. Over the entire analysis period the SG estimates are biased low by 6% when the same wind-loss adjustment is applied to the FMI gauges as is used in the SG analysis. The interannual anomaly correlation is about 0.9. The satellite-only MS product has a lesser, but still reasonably good, interannual correlation (∼0.6) while retaining a similar bias due to the use of a climatological bias adjustment. These results indicate the value of using even a few gauges in the analysis and provide an estimate of the correlation error to be expected in the SG analysis over ocean and remote land areas where gauges are absent. The daily GPCP precipitation estimates compare reasonably well at the 1° latitude × 2° longitude scale with the FMI gauge observations in the summer with a correlation of 0.55, but less so in the winter with a correlation of 0.45. Correlations increase somewhat when larger areas and multiday periods are analyzed. The day-to-day occurrence of precipitation is captured fairly well by the GPCP estimates, but the corresponding precipitation event amounts tend to show wide variability. The results of this study indicate that the GPCP monthly and daily fields are useful for meteorological and hydrological studies but that there is significant room for improvement of satellite retrievals and analysis techniques in this region. It is hoped that the research here provides a framework for future high-latitude assessment efforts such as those that will be necessary for the upcoming satellite-based Global Precipitation Measurement (GPM) mission.
Abstract
The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere.
Significance Statement
Studies of Earth’s climate require long-term global datasets based on observations to show how the climate functions and to validate numerical climate models. This study describes an important upgrade to the monthly and daily precipitation (rain and snow) products computed by the Global Precipitation Climatology Project. We use modern analysis schemes, add new sources of data, and deliver results on a finer-scale 0.5° × 0.5° latitude–longitude grid [roughly 55 km (34 mi) on a side at the equator]. The new data show improved agreement with other studies and depict more reasonable behavior in the Southern Ocean. The daily product shows improved estimates of how often different intensities of precipitation occur around the world, particularly the high amounts that drive floods and landslides.
Abstract
The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere.
Significance Statement
Studies of Earth’s climate require long-term global datasets based on observations to show how the climate functions and to validate numerical climate models. This study describes an important upgrade to the monthly and daily precipitation (rain and snow) products computed by the Global Precipitation Climatology Project. We use modern analysis schemes, add new sources of data, and deliver results on a finer-scale 0.5° × 0.5° latitude–longitude grid [roughly 55 km (34 mi) on a side at the equator]. The new data show improved agreement with other studies and depict more reasonable behavior in the Southern Ocean. The daily product shows improved estimates of how often different intensities of precipitation occur around the world, particularly the high amounts that drive floods and landslides.
Abstract
Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because 1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; 2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and 3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.
Abstract
Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because 1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; 2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and 3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.
Abstract
The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the surface observation–based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other finescale estimators. Examples are provided of a flood event and diurnal cycle determination.
Abstract
The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the surface observation–based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other finescale estimators. Examples are provided of a flood event and diurnal cycle determination.
