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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
A dataset consisting of one year of CloudSat Cloud Profiling Radar (CPR) near-surface radar reflectivity Z associated with dry snowfall is examined in this study. The CPR observations are converted to snowfall rates S using derived Ze –S relationships, which were created from backscatter cross sections of various nonspherical and spherical ice particle models. The CPR reflectivity histograms show that the dominant mode of global near-surface dry snowfall has extremely light reflectivity values (∼3–4 dBZe ), and an estimated 94% of all CPR dry snowfall observations are less than 10 dBZe . The average conditional global snowfall rate is calculated to be about 0.28 mm h−1, but is regionally highly variable as well as strongly sensitive to the ice particle model chosen. Further, ground clutter contamination is found in regions of complex terrain even when a vertical reflectivity continuity threshold is utilized. The potential of future multifrequency spaceborne radars is evaluated using proxy 35–13.6-GHz reflectivities and sensor specifications of the proposed Global Precipitation Measurement dual-frequency precipitation radar (DPR). It is estimated that because of its higher detectability threshold, only about 7%–1% of the near-surface radar reflectivity values and about 17%–4% of the total accumulation associated with global dry snowfall would be detected by a DPR-like instrument, but these results are very sensitive to the chosen ice particle model. These potential detection shortcomings can be partially mitigated by using snowfall-rate distributions derived by the CPR or other similar high-frequency active sensors.
Abstract
A dataset consisting of one year of CloudSat Cloud Profiling Radar (CPR) near-surface radar reflectivity Z associated with dry snowfall is examined in this study. The CPR observations are converted to snowfall rates S using derived Ze –S relationships, which were created from backscatter cross sections of various nonspherical and spherical ice particle models. The CPR reflectivity histograms show that the dominant mode of global near-surface dry snowfall has extremely light reflectivity values (∼3–4 dBZe ), and an estimated 94% of all CPR dry snowfall observations are less than 10 dBZe . The average conditional global snowfall rate is calculated to be about 0.28 mm h−1, but is regionally highly variable as well as strongly sensitive to the ice particle model chosen. Further, ground clutter contamination is found in regions of complex terrain even when a vertical reflectivity continuity threshold is utilized. The potential of future multifrequency spaceborne radars is evaluated using proxy 35–13.6-GHz reflectivities and sensor specifications of the proposed Global Precipitation Measurement dual-frequency precipitation radar (DPR). It is estimated that because of its higher detectability threshold, only about 7%–1% of the near-surface radar reflectivity values and about 17%–4% of the total accumulation associated with global dry snowfall would be detected by a DPR-like instrument, but these results are very sensitive to the chosen ice particle model. These potential detection shortcomings can be partially mitigated by using snowfall-rate distributions derived by the CPR or other similar high-frequency active sensors.
Abstract
The assimilation of cloud- and precipitation-affected observations into weather forecasting systems requires very fast calculations of radiative transfer in the presence of multiple scattering. At the European Centre for Medium-Range Weather Forecasts (ECMWF), performance limitations mean that only a single cloudy calculation (including any precipitation) can be made, and the simulated radiance is a weighted combination of cloudy- and clear-sky radiances. Originally, the weight given to the cloudy part was the maximum cloud fraction in the atmospheric profile. However, this weighting was excessive, and because of nonlinear radiative transfer (the “beamfilling effect”) there were biases in areas of cloud and precipitation. A new approach instead uses the profile average cloud fraction, and decreases RMS errors by 40% in areas of rain or heavy clouds when “truth” comes from multiple independent column simulations. There is improvement all the way from low (e.g., 19 GHz) to high (e.g., 183 GHz) microwave frequencies. There is also improvement when truth comes from microwave imager observations. One minor problem is that biases increase slightly in mid- and upper-tropospheric sounding channels in light-cloud situations, which shows that future improvements will require the cloud fraction to vary according to the optical properties at different frequencies.
Abstract
The assimilation of cloud- and precipitation-affected observations into weather forecasting systems requires very fast calculations of radiative transfer in the presence of multiple scattering. At the European Centre for Medium-Range Weather Forecasts (ECMWF), performance limitations mean that only a single cloudy calculation (including any precipitation) can be made, and the simulated radiance is a weighted combination of cloudy- and clear-sky radiances. Originally, the weight given to the cloudy part was the maximum cloud fraction in the atmospheric profile. However, this weighting was excessive, and because of nonlinear radiative transfer (the “beamfilling effect”) there were biases in areas of cloud and precipitation. A new approach instead uses the profile average cloud fraction, and decreases RMS errors by 40% in areas of rain or heavy clouds when “truth” comes from multiple independent column simulations. There is improvement all the way from low (e.g., 19 GHz) to high (e.g., 183 GHz) microwave frequencies. There is also improvement when truth comes from microwave imager observations. One minor problem is that biases increase slightly in mid- and upper-tropospheric sounding channels in light-cloud situations, which shows that future improvements will require the cloud fraction to vary according to the optical properties at different frequencies.
