Search Results
You are looking at 1 - 10 of 17 items for
- Author or Editor: Ralph R. Ferraro x
- Refine by Access: All Content x
Abstract
The microwave coastal rain identification procedure that has been used by NASA for over 10 yr, and also more recently by NOAA, for different instruments beginning with the Special Sensor Microwave Imager (SSM/I), is updated for use with Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E microwave data. Since the development of the SSM/I algorithm, a wealth of both space-based and ground-based radar-rainfall estimates have become available, and here some of these data are used with collocated TMI and AMSR-E data to improve the estimation of coastal rain areas from microwave data. Two major improvements are made. The first involves finding the conditions where positive rain rates should be estimated rather than leaving the areas without estimates as in the previous algorithm. The second is a modification to the final step of the rain identification method; previously, a straight brightness temperature cutoff was used, but this is modified to a polarization-corrected temperature criterion. These modifications are made for the TRMM version 6 product release and the third (1 September) release of AMSR-E products to the public, both in 2004. The modifications are slightly different for each of these two sensors.
Abstract
The microwave coastal rain identification procedure that has been used by NASA for over 10 yr, and also more recently by NOAA, for different instruments beginning with the Special Sensor Microwave Imager (SSM/I), is updated for use with Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E microwave data. Since the development of the SSM/I algorithm, a wealth of both space-based and ground-based radar-rainfall estimates have become available, and here some of these data are used with collocated TMI and AMSR-E data to improve the estimation of coastal rain areas from microwave data. Two major improvements are made. The first involves finding the conditions where positive rain rates should be estimated rather than leaving the areas without estimates as in the previous algorithm. The second is a modification to the final step of the rain identification method; previously, a straight brightness temperature cutoff was used, but this is modified to a polarization-corrected temperature criterion. These modifications are made for the TRMM version 6 product release and the third (1 September) release of AMSR-E products to the public, both in 2004. The modifications are slightly different for each of these two sensors.
Abstract
Rainfall algorithms developed for the DMSP Special Sensor Microwave/Imager are presented and then “calibrated” against ground-based radar measurements to develop instantaneous rain-rate retrieval algorithms. These include both scattering- and emission-based algorithms. Radar data from Japan, the United States, and the United Kingdom have been used in the investigation. Because of the difficulties in accurately matching the satellite and radar measurements in both time and space, an approach where both measurements are grouped in 1 mm h−1 rain-rate bins provides a much more accurate set of measurements to be used in the derivation of coefficients for instantaneous rain-rate retrieval. Both linear and nonlinear relationships are developed, with the nonlinear fits being more accurate and supported by model simulations. An application of the derived instantaneous rain-rate relationships to an independent case is presented, with approximately a 10% error for the scattering algorithm when compared with radar-derived rain rates.
Abstract
Rainfall algorithms developed for the DMSP Special Sensor Microwave/Imager are presented and then “calibrated” against ground-based radar measurements to develop instantaneous rain-rate retrieval algorithms. These include both scattering- and emission-based algorithms. Radar data from Japan, the United States, and the United Kingdom have been used in the investigation. Because of the difficulties in accurately matching the satellite and radar measurements in both time and space, an approach where both measurements are grouped in 1 mm h−1 rain-rate bins provides a much more accurate set of measurements to be used in the derivation of coefficients for instantaneous rain-rate retrieval. Both linear and nonlinear relationships are developed, with the nonlinear fits being more accurate and supported by model simulations. An application of the derived instantaneous rain-rate relationships to an independent case is presented, with approximately a 10% error for the scattering algorithm when compared with radar-derived rain rates.
Abstract
This study compares monthly total precipitable water (TPW) from the National Aeronautics and Space Administration (NASA) Water Vapor Project (NVAP) and reanalyses of the National Centers for Environmental Prediction (NCEP) (R-1), NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) (R-2), and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) from January 1988 through December 1999. Based on the means, NVAP exhibits systematic wetter land regions relative to the other datasets reflecting differences in their analyses due to paucity in radiosonde observations. ERA-40 is wetter in the atmospheric convergence zones than the U.S. reanalyses and NVAP ranges in between. Differences in the annual cycle between the reanalyses (especially R-2) and NVAP are also noticeable over the tropical oceans. Analyses on the interannual variabilities show that the ENSO-related spatial pattern in ERA-40 follows more coherently that of NVAP than those of the U.S. reanalyses. The 1997/98 El Niño’s effect on TPW is shown to be strongest only in NVAP, R-1, and ERA-40 during the period of study. All the datasets show TPW decreases in the Tropics following the 1991 Mt. Pinatubo eruption. By subtracting SST-estimated TPW from the datasets, only NVAP and ERA-40 can well represent the spatial pattern of convergence and/or moist-air advection zones in the Tropics. Even though all the datasets are viable for water cycle and climate analyses with discrepancies (wetness and dryness) to be aware of, this study has found that NVAP and ERA-40 perform better than the U.S. reanalyses during the 12-yr period.
Abstract
This study compares monthly total precipitable water (TPW) from the National Aeronautics and Space Administration (NASA) Water Vapor Project (NVAP) and reanalyses of the National Centers for Environmental Prediction (NCEP) (R-1), NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) (R-2), and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) from January 1988 through December 1999. Based on the means, NVAP exhibits systematic wetter land regions relative to the other datasets reflecting differences in their analyses due to paucity in radiosonde observations. ERA-40 is wetter in the atmospheric convergence zones than the U.S. reanalyses and NVAP ranges in between. Differences in the annual cycle between the reanalyses (especially R-2) and NVAP are also noticeable over the tropical oceans. Analyses on the interannual variabilities show that the ENSO-related spatial pattern in ERA-40 follows more coherently that of NVAP than those of the U.S. reanalyses. The 1997/98 El Niño’s effect on TPW is shown to be strongest only in NVAP, R-1, and ERA-40 during the period of study. All the datasets show TPW decreases in the Tropics following the 1991 Mt. Pinatubo eruption. By subtracting SST-estimated TPW from the datasets, only NVAP and ERA-40 can well represent the spatial pattern of convergence and/or moist-air advection zones in the Tropics. Even though all the datasets are viable for water cycle and climate analyses with discrepancies (wetness and dryness) to be aware of, this study has found that NVAP and ERA-40 perform better than the U.S. reanalyses during the 12-yr period.
Abstract
Extended precipitation forecasts, with leads of weeks to seasons, are valuable for planning water use and are produced by the U.S. National Weather Service. Forecast skill tends to be low and any skill improvement could be valuable. Here, methods are discussed for improving statistical precipitation forecasting over the contiguous United States. Monthly precipitation is forecast using predictors from the previous month. Testing shows that improvements are obtained from both improved statistical methods and from the use of satellite-based ocean-area precipitation predictors. The statistical superensemble method gives higher skill compared to traditional statistical forecasting. Ensemble statistical forecasting combines individual forecasts. The proposed superensemble is a weighted mean of many forecasts or of forecasts from different prediction systems and uses the forecast reliability estimate to define weights. The method is tested with different predictors to show its skill and how skill can be improved using additional predictors. Cross validation is used to evaluate the skill. Although predictions are strongly influenced by ENSO, in the superensemble other regions contribute more to the forecast skill. The superensemble optimally combines forecasts based on different predictor regions and predictor types. The contribution from multiple predictor regions improves skill and reduces the ENSO spring barrier. Adding satellite-based ocean-area precipitation predictors noticeably increases forecast skill. The resulting skill is comparable to that from dynamic-model forecasts, but the regions with best forecast skill may be different. This paper shows that the statistical superensemble forecasts may be complementary to dynamic forecasts and that combining them may further increase forecast skill.
Abstract
Extended precipitation forecasts, with leads of weeks to seasons, are valuable for planning water use and are produced by the U.S. National Weather Service. Forecast skill tends to be low and any skill improvement could be valuable. Here, methods are discussed for improving statistical precipitation forecasting over the contiguous United States. Monthly precipitation is forecast using predictors from the previous month. Testing shows that improvements are obtained from both improved statistical methods and from the use of satellite-based ocean-area precipitation predictors. The statistical superensemble method gives higher skill compared to traditional statistical forecasting. Ensemble statistical forecasting combines individual forecasts. The proposed superensemble is a weighted mean of many forecasts or of forecasts from different prediction systems and uses the forecast reliability estimate to define weights. The method is tested with different predictors to show its skill and how skill can be improved using additional predictors. Cross validation is used to evaluate the skill. Although predictions are strongly influenced by ENSO, in the superensemble other regions contribute more to the forecast skill. The superensemble optimally combines forecasts based on different predictor regions and predictor types. The contribution from multiple predictor regions improves skill and reduces the ENSO spring barrier. Adding satellite-based ocean-area precipitation predictors noticeably increases forecast skill. The resulting skill is comparable to that from dynamic-model forecasts, but the regions with best forecast skill may be different. This paper shows that the statistical superensemble forecasts may be complementary to dynamic forecasts and that combining them may further increase forecast skill.
Abstract
This paper presents empirically derived equations for Predicting radar reflectivity and the fraction of the field-of-view of a microwave radiometer that is filled with rain. We compare the horizontally and vertically polarized 37 GHz brightness temperatures from the Nimbus 7 SMMR and with high resolution, high quality coincident radar data. Algorithms for horizontal, vertical, and circular polarization are presented. Our results are compared with other investigators.
Abstract
This paper presents empirically derived equations for Predicting radar reflectivity and the fraction of the field-of-view of a microwave radiometer that is filled with rain. We compare the horizontally and vertically polarized 37 GHz brightness temperatures from the Nimbus 7 SMMR and with high resolution, high quality coincident radar data. Algorithms for horizontal, vertical, and circular polarization are presented. Our results are compared with other investigators.
Abstract
A prototype generic, unified land surface classification and screening methodology for Global Precipitation Measurement (GPM)-era microwave land precipitation retrieval algorithms by using ancillary datasets is developed. As an alternative to the current radiometer-determined approach, the new methodology is shown to be promising in improving rain detection by providing better surface-cover-type information. The early prototype new surface screening scheme was applied to the current version of the Goddard profiling algorithm that is used for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (GPROFV6). It has shown improvements in surface-cover-type classification and hence better precipitation retrieval comparisons with TRMM precipitation radar level-2 (L2) (2A25) data and the Global Precipitation Climatology Project (GPCP) version-2.1 (GPCPV2.1) datasets. The new ancillary data approach removes the current dependency of the screening step on relatively different satellite-specific channels and ensures the comparability and continuity of satellite-based precipitation products from different platforms. This is particularly important for advancing the current state of precipitation retrieval over land and for use in merged rainfall products.
Abstract
A prototype generic, unified land surface classification and screening methodology for Global Precipitation Measurement (GPM)-era microwave land precipitation retrieval algorithms by using ancillary datasets is developed. As an alternative to the current radiometer-determined approach, the new methodology is shown to be promising in improving rain detection by providing better surface-cover-type information. The early prototype new surface screening scheme was applied to the current version of the Goddard profiling algorithm that is used for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (GPROFV6). It has shown improvements in surface-cover-type classification and hence better precipitation retrieval comparisons with TRMM precipitation radar level-2 (L2) (2A25) data and the Global Precipitation Climatology Project (GPCP) version-2.1 (GPCPV2.1) datasets. The new ancillary data approach removes the current dependency of the screening step on relatively different satellite-specific channels and ensures the comparability and continuity of satellite-based precipitation products from different platforms. This is particularly important for advancing the current state of precipitation retrieval over land and for use in merged rainfall products.
Abstract
The Goddard profiling algorithm 2010 (GPROF2010) was revised for the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instrument. The GPROF2010 land algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), which observes slightly different central frequencies than AMSR-E. A linear transfer function was developed to convert AMSR-E brightness temperatures to their corresponding TMI frequency for raining and nonraining instantaneous fields of view (IFOVs) using collocated brightness temperature and TRMM precipitation radar (PR) measurements. Previous versions of the algorithm separated rain from surface ice, snow, and desert using a series of empirical procedures. These occasionally failed to separate raining and nonraining scenes, leading to failed detection and false alarms of rain. The new GPROF2010, version 2 (GPROF2010V2), presented here, prefaced the heritage screening procedures by referencing annual desert and monthly snow climatologies to identify IFOVs where rain retrievals were unreliable. Over a decade of satellite- and ground-based observations from the Interactive Multisensor Snow and Ice Mapping System (IMS) and AMSR-E allowed for the creation of a medium-resolution (0.25° × 0.25°) climatology of monthly snow and ice cover. The scattering signature of rain over ice and snow is not well defined because of complex emissivity signals dependent on snow depth, age, and melting, such that using a static climatology was a more stable approach to defining surface types. GPROF2010V2 was subsequently used for the precipitation environmental data record (EDR) for the AMSR2 sensor aboard the Global Change Observation Mission–Water 1 (GCOM-W1).
Abstract
The Goddard profiling algorithm 2010 (GPROF2010) was revised for the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instrument. The GPROF2010 land algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), which observes slightly different central frequencies than AMSR-E. A linear transfer function was developed to convert AMSR-E brightness temperatures to their corresponding TMI frequency for raining and nonraining instantaneous fields of view (IFOVs) using collocated brightness temperature and TRMM precipitation radar (PR) measurements. Previous versions of the algorithm separated rain from surface ice, snow, and desert using a series of empirical procedures. These occasionally failed to separate raining and nonraining scenes, leading to failed detection and false alarms of rain. The new GPROF2010, version 2 (GPROF2010V2), presented here, prefaced the heritage screening procedures by referencing annual desert and monthly snow climatologies to identify IFOVs where rain retrievals were unreliable. Over a decade of satellite- and ground-based observations from the Interactive Multisensor Snow and Ice Mapping System (IMS) and AMSR-E allowed for the creation of a medium-resolution (0.25° × 0.25°) climatology of monthly snow and ice cover. The scattering signature of rain over ice and snow is not well defined because of complex emissivity signals dependent on snow depth, age, and melting, such that using a static climatology was a more stable approach to defining surface types. GPROF2010V2 was subsequently used for the precipitation environmental data record (EDR) for the AMSR2 sensor aboard the Global Change Observation Mission–Water 1 (GCOM-W1).
Abstract
The SSM/I has been used successfully to estimate precipitation and to determine the fields of view (FOV) that contain precipitating clouds. The use of multivariate logistic regression with the SSM/I brightness temperatures to estimate the probability that it is raining in an FOV is examined. The predictors used in this study are those that have been evaluated by other investigators to estimate rain events using other procedures. The logistic regression technique is applied to a matched set of SSM/I and radar data for a limited area from June to August 1989. For this limited dataset the results are quite good. In one example, if the predicted probability is less than 0.1, the radar data shows only 2 of 340 FOVs have precipitation. If the predicted probability is greater than 0.9, the radar data shows precipitation in 748 of 774 FOVS. These probabilities can be used for both instantaneous and climate timescale retrievals.
Abstract
The SSM/I has been used successfully to estimate precipitation and to determine the fields of view (FOV) that contain precipitating clouds. The use of multivariate logistic regression with the SSM/I brightness temperatures to estimate the probability that it is raining in an FOV is examined. The predictors used in this study are those that have been evaluated by other investigators to estimate rain events using other procedures. The logistic regression technique is applied to a matched set of SSM/I and radar data for a limited area from June to August 1989. For this limited dataset the results are quite good. In one example, if the predicted probability is less than 0.1, the radar data shows only 2 of 340 FOVs have precipitation. If the predicted probability is greater than 0.9, the radar data shows precipitation in 748 of 774 FOVS. These probabilities can be used for both instantaneous and climate timescale retrievals.
The Special Sensor Microwave/Imager (SSM/I), first placed into operation in July 1987, has been making measurements of earth-emitted radiation for over eight years. These data are used to estimate both atmospheric and surface hydrological parameters and to generate a time series of global monthly mean products averaged to a 1° lat × 1° long grid. Specifically, this includes monthly estimates of rainfall and its frequency, cloud liquid water and cloud frequency, water vapor, snow cover frequency, and sea ice frequency. This study uses seasonal mean values to demonstrate the spatial and temporal distributions of these hydrological variables. Examples of interannual variability such as the 1993 flooding in the Mississippi Valley and the 1992–93 snow cover changes over the United States are used to demonstrate the utility of these data for regional applications.
The Special Sensor Microwave/Imager (SSM/I), first placed into operation in July 1987, has been making measurements of earth-emitted radiation for over eight years. These data are used to estimate both atmospheric and surface hydrological parameters and to generate a time series of global monthly mean products averaged to a 1° lat × 1° long grid. Specifically, this includes monthly estimates of rainfall and its frequency, cloud liquid water and cloud frequency, water vapor, snow cover frequency, and sea ice frequency. This study uses seasonal mean values to demonstrate the spatial and temporal distributions of these hydrological variables. Examples of interannual variability such as the 1993 flooding in the Mississippi Valley and the 1992–93 snow cover changes over the United States are used to demonstrate the utility of these data for regional applications.
Abstract
The three-dimensional (3D) structure of precipitation systems is highly dependent on hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural-network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes intercluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo-top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes.
Significance Statement
This study proposes a systematic model to identify precipitation profiles of distinct vertical structures and evaluate their dependence on environmental conditions. The model was developed using ground-based radar observations; however, there is potential to extend this model to reflectivity profiles from both ground- and satellite-based sensors. In addition, the identified precipitation regime clusters could be a proxy for the vertical structure of precipitation systems and assist in determining the structural variability within traditional precipitation type classification (e.g., convective versus stratiform). Moreover, identifying the precipitation regimes could also be used to improve satellite-based precipitation retrievals. Finally, a better understanding of precipitation structure would also help improve the initialization of climate models.
Abstract
The three-dimensional (3D) structure of precipitation systems is highly dependent on hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural-network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes intercluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo-top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes.
Significance Statement
This study proposes a systematic model to identify precipitation profiles of distinct vertical structures and evaluate their dependence on environmental conditions. The model was developed using ground-based radar observations; however, there is potential to extend this model to reflectivity profiles from both ground- and satellite-based sensors. In addition, the identified precipitation regime clusters could be a proxy for the vertical structure of precipitation systems and assist in determining the structural variability within traditional precipitation type classification (e.g., convective versus stratiform). Moreover, identifying the precipitation regimes could also be used to improve satellite-based precipitation retrievals. Finally, a better understanding of precipitation structure would also help improve the initialization of climate models.
