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Abstract
Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992–2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets.
Abstract
Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992–2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets.
Abstract
Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between the two extremes. The evaluated satellite rainfall products are the Tropical Rainfall Measuring Mission 3B42 and 3B42RT products, the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), the Naval Research Laboratory’s blended product (NRLB), and two versions of the Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMaP_MVK and GSMaP_MVK+). The validation and intercomparison of these products is done for the whole as well as different parts of the country. Validation results are reasonably good for daily rainfall over such complex terrain. The best results were obtained for the eastern plain, and the performance of the products was relatively poor over the Pacific coast. In comparing the different satellite products, it was seen that PERSIANN and GSMaP-MVK exhibited poor performance, with significant overestimation by PERSSIAN and serious underestimation by GSMaP-MVK. CMORPH and GSMaP-MVK+ exhibited the best performance among the products evaluated here.
Abstract
Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between the two extremes. The evaluated satellite rainfall products are the Tropical Rainfall Measuring Mission 3B42 and 3B42RT products, the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), the Naval Research Laboratory’s blended product (NRLB), and two versions of the Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMaP_MVK and GSMaP_MVK+). The validation and intercomparison of these products is done for the whole as well as different parts of the country. Validation results are reasonably good for daily rainfall over such complex terrain. The best results were obtained for the eastern plain, and the performance of the products was relatively poor over the Pacific coast. In comparing the different satellite products, it was seen that PERSIANN and GSMaP-MVK exhibited poor performance, with significant overestimation by PERSSIAN and serious underestimation by GSMaP-MVK. CMORPH and GSMaP-MVK+ exhibited the best performance among the products evaluated here.
Abstract
Monsoon rainfall is central to the climate of West Africa, and understanding its variability is a challenge for which satellite rainfall products could be well suited to contribute to. Their quality in this region has received less attention than elsewhere. The focus is set on the scales associated with atmospheric variability, and a meteorological benchmark is set up with ground-based observations from the African Monsoon Multidisciplinary Analysis (AMMA) program. The investigation is performed at various scales of accumulation using four gauge networks. The seasonal cycle is analyzed using 10-day-averaged products, the synoptic-scale variability is analyzed using daily means, and the diurnal cycle of rainfall is analyzed at the seasonal scale using a composite and at the diurnal scale using 3-hourly accumulations. A novel methodology is introduced that accounts for the errors associated with the areal–time rainfall averages. The errors from both satellite and ground rainfall data are computed using dedicated techniques that come down to an estimation of the sampling errors associated to these measurements. The results show that the new generation of combined infrared–microwave (IR–MW) satellite products is describing the rain variability similarly to ground measurements. At the 10-day scale, all products reveal high regional and seasonal skills. The day-to-day comparison indicates that some products perform better than others, whereas all of them exhibit high skills when the spectral band of African easterly waves is considered. The seasonal variability of the diurnal scale as well as its relative daily importance is only captured by some products. Plans for future extensive intercomparison exercises are briefly discussed.
Abstract
Monsoon rainfall is central to the climate of West Africa, and understanding its variability is a challenge for which satellite rainfall products could be well suited to contribute to. Their quality in this region has received less attention than elsewhere. The focus is set on the scales associated with atmospheric variability, and a meteorological benchmark is set up with ground-based observations from the African Monsoon Multidisciplinary Analysis (AMMA) program. The investigation is performed at various scales of accumulation using four gauge networks. The seasonal cycle is analyzed using 10-day-averaged products, the synoptic-scale variability is analyzed using daily means, and the diurnal cycle of rainfall is analyzed at the seasonal scale using a composite and at the diurnal scale using 3-hourly accumulations. A novel methodology is introduced that accounts for the errors associated with the areal–time rainfall averages. The errors from both satellite and ground rainfall data are computed using dedicated techniques that come down to an estimation of the sampling errors associated to these measurements. The results show that the new generation of combined infrared–microwave (IR–MW) satellite products is describing the rain variability similarly to ground measurements. At the 10-day scale, all products reveal high regional and seasonal skills. The day-to-day comparison indicates that some products perform better than others, whereas all of them exhibit high skills when the spectral band of African easterly waves is considered. The seasonal variability of the diurnal scale as well as its relative daily importance is only captured by some products. Plans for future extensive intercomparison exercises are briefly discussed.
Abstract
Four independently developed high-resolution precipitation products [HRPPs; the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center Morphing Method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the National Research Laboratory (NRL) blended precipitation dataset (NRL-blended)], with a spatial resolution of 0.25° and a temporal resolution of 3 h, were compared with surface rain measurements for the four summer seasons (June, July, and August) from 2003 to 2006. Surface measurements are 1-min rain gauge data from the Automated Weather Station (AWS) network operated by the Korean Meteorological Administration (KMA) over South Korea, which consists of about 520 sites. The summer mean rainfall and diurnal cycles of TMPA are comparable to those of the AWS, but with larger magnitudes. The closer agreement of TMPA with surface observations is due to the adjustment of the real-time version of TMPA products to monthly gauge measurements. However, the adjustment seems to result in significant overestimates for light or moderate rain events and thus increased RMS error. In the other three products (CMORPH, PERSIANN, and NRL-blended), significant underestimates are evident in the summer mean distribution and in scatterplots for the grid-by-grid comparison. The magnitudes of the diurnal cycles of the three products appear to be much smaller than those suggested by AWS, although CMORPH shows nearly the same diurnal phase as in AWS. Such underestimates by three methods are likely due to the deficiency of the passive microwave (PMW)-based rainfall retrievals over the South Korean region. More accurate PMW measurements (in particular by the improved land algorithm) seem to be a prerequisite for better estimates of the rain rate by HRPP algorithms. This paper further demonstrates the capability of the Korean AWS network data for validating satellite-based rain products.
Abstract
Four independently developed high-resolution precipitation products [HRPPs; the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center Morphing Method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the National Research Laboratory (NRL) blended precipitation dataset (NRL-blended)], with a spatial resolution of 0.25° and a temporal resolution of 3 h, were compared with surface rain measurements for the four summer seasons (June, July, and August) from 2003 to 2006. Surface measurements are 1-min rain gauge data from the Automated Weather Station (AWS) network operated by the Korean Meteorological Administration (KMA) over South Korea, which consists of about 520 sites. The summer mean rainfall and diurnal cycles of TMPA are comparable to those of the AWS, but with larger magnitudes. The closer agreement of TMPA with surface observations is due to the adjustment of the real-time version of TMPA products to monthly gauge measurements. However, the adjustment seems to result in significant overestimates for light or moderate rain events and thus increased RMS error. In the other three products (CMORPH, PERSIANN, and NRL-blended), significant underestimates are evident in the summer mean distribution and in scatterplots for the grid-by-grid comparison. The magnitudes of the diurnal cycles of the three products appear to be much smaller than those suggested by AWS, although CMORPH shows nearly the same diurnal phase as in AWS. Such underestimates by three methods are likely due to the deficiency of the passive microwave (PMW)-based rainfall retrievals over the South Korean region. More accurate PMW measurements (in particular by the improved land algorithm) seem to be a prerequisite for better estimates of the rain rate by HRPP algorithms. This paper further demonstrates the capability of the Korean AWS network data for validating satellite-based rain products.
Abstract
The National Oceanic and Atmospheric Administration National Climatic Data Center has served as the archive of the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) data from the F-8, F-10, F-11, F-13, F-14, and F-15 platforms covering the period from July 1987 to the present. Passive microwave satellite measurements from SSM/I have been used to generate climate products in support of national and international programs. The SSM/I temperature data record (TDR) and sensor data record (SDR) datasets have been reprocessed and stored as network Common Data Form (netCDF) 3-hourly files. In addition to reformatting the data, a normalized anomaly (z score) for each footprint temperature value was calculated by subtracting each radiance value with the corresponding monthly 1° grid climatological mean and dividing it by the associated climatological standard deviation. Threshold checks were also used to detect radiance, temporal, and geolocation values that were outside the expected ranges. The application of z scores and threshold parameters in the form of embedded quality flags has improved the fidelity of the SSM/I TDR/SDR period of record for climatological applications. This effort has helped to preserve and increase the data maturity level of the longest satellite passive microwave period of record while completing a key first step before developing a homogenized and intercalibrated SSM/I climate data record in the near future.
Abstract
The National Oceanic and Atmospheric Administration National Climatic Data Center has served as the archive of the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) data from the F-8, F-10, F-11, F-13, F-14, and F-15 platforms covering the period from July 1987 to the present. Passive microwave satellite measurements from SSM/I have been used to generate climate products in support of national and international programs. The SSM/I temperature data record (TDR) and sensor data record (SDR) datasets have been reprocessed and stored as network Common Data Form (netCDF) 3-hourly files. In addition to reformatting the data, a normalized anomaly (z score) for each footprint temperature value was calculated by subtracting each radiance value with the corresponding monthly 1° grid climatological mean and dividing it by the associated climatological standard deviation. Threshold checks were also used to detect radiance, temporal, and geolocation values that were outside the expected ranges. The application of z scores and threshold parameters in the form of embedded quality flags has improved the fidelity of the SSM/I TDR/SDR period of record for climatological applications. This effort has helped to preserve and increase the data maturity level of the longest satellite passive microwave period of record while completing a key first step before developing a homogenized and intercalibrated SSM/I climate data record in the near future.
Abstract
Rainfall products from radar, satellite, rain gauges, and combinations have been evaluated for a season of record rainfall in a heavily instrumented study domain in Oklahoma. Algorithm performance is evaluated in terms of spatial scale, temporal scale, and rainfall intensity. Results from this study will help users of rainfall products to understand their errors. Moreover, it is intended that developers of rainfall algorithms will use the results presented herein to optimize the contribution from available sensors to yield the most skillful multisensor rainfall products.
Abstract
Rainfall products from radar, satellite, rain gauges, and combinations have been evaluated for a season of record rainfall in a heavily instrumented study domain in Oklahoma. Algorithm performance is evaluated in terms of spatial scale, temporal scale, and rainfall intensity. Results from this study will help users of rainfall products to understand their errors. Moreover, it is intended that developers of rainfall algorithms will use the results presented herein to optimize the contribution from available sensors to yield the most skillful multisensor rainfall products.
Abstract
This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minimizes the simulated land surface parameter (soil moisture, temperature, etc.) errors using the Noah land surface model with the Nelder–Mead (downhill simplex) method. In addition to improving the simulation skills, this method also impedes the adverse impacts of single-source precipitation data errors. Analysis has indicated that the results from the optimally merged precipitation product have fewer errors in other land surface states and fluxes such as evapotranspiration (ET), discharge R, and skin temperature T than do simulation results obtained by forcing the model using the precipitation products individually. It is also found that, using this method, the true knowledge of soil moisture information minimized land surface modeling errors better than the knowledge of other land surface parameters (ET, R, and T). Results have also shown that, although it does not have the true precipitation information, the method has associated heavier weights with the precipitation product that has intensity, amount, and frequency that are similar to those of the true precipitation.
Abstract
This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minimizes the simulated land surface parameter (soil moisture, temperature, etc.) errors using the Noah land surface model with the Nelder–Mead (downhill simplex) method. In addition to improving the simulation skills, this method also impedes the adverse impacts of single-source precipitation data errors. Analysis has indicated that the results from the optimally merged precipitation product have fewer errors in other land surface states and fluxes such as evapotranspiration (ET), discharge R, and skin temperature T than do simulation results obtained by forcing the model using the precipitation products individually. It is also found that, using this method, the true knowledge of soil moisture information minimized land surface modeling errors better than the knowledge of other land surface parameters (ET, R, and T). Results have also shown that, although it does not have the true precipitation information, the method has associated heavier weights with the precipitation product that has intensity, amount, and frequency that are similar to those of the true precipitation.
Abstract
A surface-precipitation-rate retrieval algorithm for 13-channel Advanced Microwave Sounding Unit (AMSU) millimeter-wave spectral observations from 23 to 191 GHz is described. It was trained using cloud-resolving fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) simulations over 106 global storms. The resulting retrievals from the U.S. NOAA-15 and NOAA-16 operational weather satellites are compared with average annual accumulations (mm yr−1) for 2006–07 observed by 787 rain gauges globally distributed across 11 surface classifications defined using Advanced Very High Resolution Radiometer infrared spectral images and two classifications defined geographically. Most surface classifications had bias ratios for AMSU/gauges that ranged from 0.88 to 1.59, although higher systematic AMSU overestimates by factors of 2.4, 3.1, and 9 were found for grassland, shrubs over bare ground, and pure bare ground, respectively. The retrievals were then empirically corrected using these observed biases for each surface type. Global images of corrected average annual accumulations of rain, snow, and convective and stratiform precipitation are presented for the period 2002–07. Most results are consistent with Global Precipitation Climatology Project estimates. Evidence based on MM5 simulations suggests that near-surface evaporation of precipitation may have necessitated most of the corrections for undervegetated surfaces. A new correction for radio-frequency interference affecting AMSU is also presented for the same two NOAA satellites and improves retrieval accuracies.
Abstract
A surface-precipitation-rate retrieval algorithm for 13-channel Advanced Microwave Sounding Unit (AMSU) millimeter-wave spectral observations from 23 to 191 GHz is described. It was trained using cloud-resolving fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) simulations over 106 global storms. The resulting retrievals from the U.S. NOAA-15 and NOAA-16 operational weather satellites are compared with average annual accumulations (mm yr−1) for 2006–07 observed by 787 rain gauges globally distributed across 11 surface classifications defined using Advanced Very High Resolution Radiometer infrared spectral images and two classifications defined geographically. Most surface classifications had bias ratios for AMSU/gauges that ranged from 0.88 to 1.59, although higher systematic AMSU overestimates by factors of 2.4, 3.1, and 9 were found for grassland, shrubs over bare ground, and pure bare ground, respectively. The retrievals were then empirically corrected using these observed biases for each surface type. Global images of corrected average annual accumulations of rain, snow, and convective and stratiform precipitation are presented for the period 2002–07. Most results are consistent with Global Precipitation Climatology Project estimates. Evidence based on MM5 simulations suggests that near-surface evaporation of precipitation may have necessitated most of the corrections for undervegetated surfaces. A new correction for radio-frequency interference affecting AMSU is also presented for the same two NOAA satellites and improves retrieval accuracies.
Abstract
A satellite microwave emission brightness temperature histograms (METH) technique has been applied to Special Sensor Microwave Imager (SSM/I) data taken on board the Defense Meteorological Satellite Program (DMSP) satellites and preprocessed by Remote Sensing Systems (RSS) Co. to produce 21 yr (July 1987–present) of oceanic rainfall products. These rain products are used as input to the Global Precipitation Climatology Project (GPCP) rain maps. Analysis of the METH product using SSM/I version-4 (V4) data shows jumps in vertically polarized 19-GHz brightness temperatures that are attributed to changes in DMSP satellites. A version-6 (V6) SSM/I that corrects for intersatellite differences was released by RSS in 2006. The jumps in the time series are reduced, with most of the changes occurring in the early part of the DMSP F13 data. The bias between RSS V6 and V4 of brightness temperature at 19 and 22 GHz is less than 0.5 K. METH rain rates were reprocessed using V6 data and were analyzed. The 20-yr global mean difference between the METH V4 and V6 is less than 0.3%, with differences as large as 3% in individual years. Trend analyses show increases in the oceanic rain belts, such as the intertropical convergence zone and the South Pacific convergence zone, and in the Bay of Bengal. These rain-rate trends, from both linear trend analysis and empirical mode decomposition analysis, are comparable to the version-2 GPCP analyses but are smaller than those found in the unified microwave ocean retrieval algorithm.
Abstract
A satellite microwave emission brightness temperature histograms (METH) technique has been applied to Special Sensor Microwave Imager (SSM/I) data taken on board the Defense Meteorological Satellite Program (DMSP) satellites and preprocessed by Remote Sensing Systems (RSS) Co. to produce 21 yr (July 1987–present) of oceanic rainfall products. These rain products are used as input to the Global Precipitation Climatology Project (GPCP) rain maps. Analysis of the METH product using SSM/I version-4 (V4) data shows jumps in vertically polarized 19-GHz brightness temperatures that are attributed to changes in DMSP satellites. A version-6 (V6) SSM/I that corrects for intersatellite differences was released by RSS in 2006. The jumps in the time series are reduced, with most of the changes occurring in the early part of the DMSP F13 data. The bias between RSS V6 and V4 of brightness temperature at 19 and 22 GHz is less than 0.5 K. METH rain rates were reprocessed using V6 data and were analyzed. The 20-yr global mean difference between the METH V4 and V6 is less than 0.3%, with differences as large as 3% in individual years. Trend analyses show increases in the oceanic rain belts, such as the intertropical convergence zone and the South Pacific convergence zone, and in the Bay of Bengal. These rain-rate trends, from both linear trend analysis and empirical mode decomposition analysis, are comparable to the version-2 GPCP analyses but are smaller than those found in the unified microwave ocean retrieval algorithm.
Abstract
Over the decades, significant progress has been made in satellite precipitation product development. In particular, temporal resolution and timely availability have been improved by blended techniques. The resulting products, including near-real-time precipitation products, are widely used in various research and applications. However, the lack of support for user-defined areas or points of interest poses a major obstacle to quickly gaining knowledge of product quality and behavior on a local or regional scale. Current online operational intercomparison and validation services have not addressed this issue adequately. This paper describes an ongoing work to develop an online information system prototype for global satellite precipitation algorithm validation and intercomparison, to overcome current shortcomings by providing dynamic and customized information to users on the expected bias and accuracy of the products, and to give algorithm developers a better understanding of the strengths and weaknesses of different algorithmic approaches and data sources. An example is provided to illustrate the functionality and capabilities of the system, and future plans are discussed. The system being developed complements and accelerates the existing and ongoing validation activities in the community and contributes to the current NASA Tropical Rainfall Measuring Mission and the future NASA Global Precipitation Mission.
Abstract
Over the decades, significant progress has been made in satellite precipitation product development. In particular, temporal resolution and timely availability have been improved by blended techniques. The resulting products, including near-real-time precipitation products, are widely used in various research and applications. However, the lack of support for user-defined areas or points of interest poses a major obstacle to quickly gaining knowledge of product quality and behavior on a local or regional scale. Current online operational intercomparison and validation services have not addressed this issue adequately. This paper describes an ongoing work to develop an online information system prototype for global satellite precipitation algorithm validation and intercomparison, to overcome current shortcomings by providing dynamic and customized information to users on the expected bias and accuracy of the products, and to give algorithm developers a better understanding of the strengths and weaknesses of different algorithmic approaches and data sources. An example is provided to illustrate the functionality and capabilities of the system, and future plans are discussed. The system being developed complements and accelerates the existing and ongoing validation activities in the community and contributes to the current NASA Tropical Rainfall Measuring Mission and the future NASA Global Precipitation Mission.
