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- Author or Editor: Christian D. Kummerow x
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Abstract
A spatiotemporal correlation technique has been developed to combine satellite rainfall measurements using the spatial and temporal correlation of the rainfall fields to overcome problems of limited and infrequent measurements while accounting for the measurement accuracies. The relationship between the temporal and spatial correlation of the rainfall field is exploited to provide information about rainfall beyond instantaneous measurements. The technique is developed using synthetic radar data. Nine months of Operational Program for the Exchange of Weather Radar (OPERA) data are used on grid sizes of 100, 248, and 500 km with pixel resolutions of 8, 12, and 24 km to simulate satellite fields of view and are then applied to the real satellite data over the Southwest to calculate 3-h rainfall accumulations. The results are compared with the simple averaging technique, which takes a simple mean of the measurements as a constant rainfall rate over the entire accumulation period. Using synthetic data, depending on the time separation of the measurements and their accuracy, a spatiotemporal correlation technique has shown the potential to yield improvements of up to 40% in absolute error and up to 25% in root-mean-square error when compared with the simple averaging technique. When applied to the real satellite data over the Southeast, the technique showed much less skill (general improvement of only 2%–6%).
Abstract
A spatiotemporal correlation technique has been developed to combine satellite rainfall measurements using the spatial and temporal correlation of the rainfall fields to overcome problems of limited and infrequent measurements while accounting for the measurement accuracies. The relationship between the temporal and spatial correlation of the rainfall field is exploited to provide information about rainfall beyond instantaneous measurements. The technique is developed using synthetic radar data. Nine months of Operational Program for the Exchange of Weather Radar (OPERA) data are used on grid sizes of 100, 248, and 500 km with pixel resolutions of 8, 12, and 24 km to simulate satellite fields of view and are then applied to the real satellite data over the Southwest to calculate 3-h rainfall accumulations. The results are compared with the simple averaging technique, which takes a simple mean of the measurements as a constant rainfall rate over the entire accumulation period. Using synthetic data, depending on the time separation of the measurements and their accuracy, a spatiotemporal correlation technique has shown the potential to yield improvements of up to 40% in absolute error and up to 25% in root-mean-square error when compared with the simple averaging technique. When applied to the real satellite data over the Southeast, the technique showed much less skill (general improvement of only 2%–6%).
Abstract
The Goddard profiling algorithm (GPROF) uses Bayesian probability theory to retrieve rainfall over the global oceans. A critical component of GPROF and most Bayes theorem–based retrieval frameworks is the specification of uncertainty in the observations being utilized to retrieve the parameter of interest. In the case of GPROF, for any sensor, uncertainties in microwave brightness temperatures (Tbs) arise from radiative transfer model errors, satellite sensor noise and/or degradation, and nonlinear, scene-dependent Tb offsets added during sensor intercalibration procedures. All mentioned sources impact sensors in a varying fashion, in part because of sensor-dependent fields of view. It is found that small errors in assumed Tb uncertainty (ranging from 0.57 K at 10 GHz to 2.29 K at 85 GHz) lead to a 3.6% change in the retrieved global-average oceanic rainfall rate, and 10%–20% (20%–40%) shifts in the pixel-level (monthly) frequency distributions for given rainfall bins. A mathematical expression describing the sensitivity of retrieved rainfall to uncertainty is developed here. The strong global sensitivity is linked to rainfall variance scaling systematically as Tb varies. For ocean scenes, the same emission-dominated rainfall–Tb physics used in passive microwave rainfall retrieval is also responsible for the substantial underestimation (overestimation) of global rainfall if uncertainty is overestimated (underestimated). Proper uncertainties are required to quantify variability in surface rainfall, assess long-term trends, and provide robust rainfall benchmarks for general circulation model evaluations. The implications for assessing global and regional biases in active versus passive microwave rainfall products, and for achieving rainfall product agreement among a constellation of orbiting microwave radiometers [employed in the Global Precipitation Measurement (GPM) mission], are also discussed.
Abstract
The Goddard profiling algorithm (GPROF) uses Bayesian probability theory to retrieve rainfall over the global oceans. A critical component of GPROF and most Bayes theorem–based retrieval frameworks is the specification of uncertainty in the observations being utilized to retrieve the parameter of interest. In the case of GPROF, for any sensor, uncertainties in microwave brightness temperatures (Tbs) arise from radiative transfer model errors, satellite sensor noise and/or degradation, and nonlinear, scene-dependent Tb offsets added during sensor intercalibration procedures. All mentioned sources impact sensors in a varying fashion, in part because of sensor-dependent fields of view. It is found that small errors in assumed Tb uncertainty (ranging from 0.57 K at 10 GHz to 2.29 K at 85 GHz) lead to a 3.6% change in the retrieved global-average oceanic rainfall rate, and 10%–20% (20%–40%) shifts in the pixel-level (monthly) frequency distributions for given rainfall bins. A mathematical expression describing the sensitivity of retrieved rainfall to uncertainty is developed here. The strong global sensitivity is linked to rainfall variance scaling systematically as Tb varies. For ocean scenes, the same emission-dominated rainfall–Tb physics used in passive microwave rainfall retrieval is also responsible for the substantial underestimation (overestimation) of global rainfall if uncertainty is overestimated (underestimated). Proper uncertainties are required to quantify variability in surface rainfall, assess long-term trends, and provide robust rainfall benchmarks for general circulation model evaluations. The implications for assessing global and regional biases in active versus passive microwave rainfall products, and for achieving rainfall product agreement among a constellation of orbiting microwave radiometers [employed in the Global Precipitation Measurement (GPM) mission], are also discussed.
Abstract
Although zonal mean rain rates from the Tropical Rainfall Measuring Mission (TRMM) are in good (<10%) agreement between the TRMM Microwave Imager (TMI) and precipitation radar (PR) rainfall algorithms, significant uncertainties remain in some regions where these estimates differ by as much as 30% over the period of record. Previous comparisons of these algorithms with ground validation (GV) rainfall have shown significant (>10%) biases of differing sign at various GV locations. Reducing these biases is important in the context of developing a database of cloud profiles for passive microwave retrievals that is based upon the PR-measured profiles. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution, and cloud water path (cLWP) are retrieved for each radar profile. The modular nature of the framework provides the opportunity to test the sensitivity of the retrieval to the inclusion of different measurements, retrieved parameters, and models for microwave scattering properties of hydrometeors. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, Florida, shows agreement within 2%, which exceeds previous algorithms’ ability to match rainfall at these two sites. Errors between observed and simulated brightness temperatures are reduced and climatological features of the DSD, as measured by disdrometers at these two locations, are also reproduced in the output of the combined algorithm.
Abstract
Although zonal mean rain rates from the Tropical Rainfall Measuring Mission (TRMM) are in good (<10%) agreement between the TRMM Microwave Imager (TMI) and precipitation radar (PR) rainfall algorithms, significant uncertainties remain in some regions where these estimates differ by as much as 30% over the period of record. Previous comparisons of these algorithms with ground validation (GV) rainfall have shown significant (>10%) biases of differing sign at various GV locations. Reducing these biases is important in the context of developing a database of cloud profiles for passive microwave retrievals that is based upon the PR-measured profiles. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution, and cloud water path (cLWP) are retrieved for each radar profile. The modular nature of the framework provides the opportunity to test the sensitivity of the retrieval to the inclusion of different measurements, retrieved parameters, and models for microwave scattering properties of hydrometeors. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, Florida, shows agreement within 2%, which exceeds previous algorithms’ ability to match rainfall at these two sites. Errors between observed and simulated brightness temperatures are reduced and climatological features of the DSD, as measured by disdrometers at these two locations, are also reproduced in the output of the combined algorithm.
Abstract
In light of the upcoming launch of the Global Precipitation Measurement (GPM) mission, a parametric retrieval algorithm of the nonraining parameters over the global oceans is developed with the ability to accommodate all currently existing and planned spaceborne microwave window channel sensors and imagers. The physical retrieval is developed using all available sensor channels in a full optimal estimation inversion. This framework requires that retrieved parameters be physically consistent with all observed satellite radiances regardless of the sensor being used. The retrieval algorithm has been successfully applied to the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the Special Sensor Microwave Imager (SSM/I), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) with geophysical parameter retrieval results comparable to independent studies using sensor-optimized algorithms. The optimal estimation diagnostics characterize the retrieval further, providing errors associated with each of the retrieved parameters, indicating whether the retrieved state is physically consistent with observed radiances, and yielding information on how well simulated radiances agree with observed radiances. This allows for the quantitative assessment of potential calibration issues in either the model or sensor. In addition, there is an expected, consistent response of these diagnostics based on the scene being observed, such as in the case of a raining scene, allowing for the emergence of a rainfall detection scheme providing a new capability in rainfall identification for use in passive microwave rainfall and cloud property retrievals.
Abstract
In light of the upcoming launch of the Global Precipitation Measurement (GPM) mission, a parametric retrieval algorithm of the nonraining parameters over the global oceans is developed with the ability to accommodate all currently existing and planned spaceborne microwave window channel sensors and imagers. The physical retrieval is developed using all available sensor channels in a full optimal estimation inversion. This framework requires that retrieved parameters be physically consistent with all observed satellite radiances regardless of the sensor being used. The retrieval algorithm has been successfully applied to the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the Special Sensor Microwave Imager (SSM/I), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) with geophysical parameter retrieval results comparable to independent studies using sensor-optimized algorithms. The optimal estimation diagnostics characterize the retrieval further, providing errors associated with each of the retrieved parameters, indicating whether the retrieved state is physically consistent with observed radiances, and yielding information on how well simulated radiances agree with observed radiances. This allows for the quantitative assessment of potential calibration issues in either the model or sensor. In addition, there is an expected, consistent response of these diagnostics based on the scene being observed, such as in the case of a raining scene, allowing for the emergence of a rainfall detection scheme providing a new capability in rainfall identification for use in passive microwave rainfall and cloud property retrievals.
Abstract
Analyses of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates reveal a substantial disagreement between its active [Precipitation Radar (PR)] and passive [TRMM Microwave Imager (TMI)] sensors over certain regions. This study focuses on understanding the role of the synoptic state of atmosphere in these discrepancies over land regions where passive microwave (PMW) retrievals are limited to scattering signals. As such the variability in the relationship between the ice-induced scattering signal and the surface rainfall is examined. Using the Amazon River and central Africa regions as a test bed, it is found that the systematic difference seen between PR and TMI rainfall estimates is well correlated with both the precipitating system structure and the level of its organization. Relying on a clustering technique to group raining scenes into three broad but distinct organizational categories, it is found that, relative to the PR, deep-organized systems are typically overestimated by TMI while the shallower ones are underestimated. Results suggest that the storm organization level can explain up to 50% of the regional systematic difference between the two sensors. Because of its potential for retrieval improvement, the ability to forecast the level of systems organization is tested. The state of the atmosphere is found to favor certain storm types when constrained by CAPE, wind shear, dewpoint depression, and vertical humidity distribution. Among other findings, the observations reveal that the ratio between boundary layer and midtropospheric moisture correlates well with the organization level of convection. If adjusted by the observed PR-to-TMI ratio under a given environment, the differences between PMW and PR rainfall estimates are diminished, at maximum, by 30% in RMSE and by 40% in the mean.
Abstract
Analyses of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates reveal a substantial disagreement between its active [Precipitation Radar (PR)] and passive [TRMM Microwave Imager (TMI)] sensors over certain regions. This study focuses on understanding the role of the synoptic state of atmosphere in these discrepancies over land regions where passive microwave (PMW) retrievals are limited to scattering signals. As such the variability in the relationship between the ice-induced scattering signal and the surface rainfall is examined. Using the Amazon River and central Africa regions as a test bed, it is found that the systematic difference seen between PR and TMI rainfall estimates is well correlated with both the precipitating system structure and the level of its organization. Relying on a clustering technique to group raining scenes into three broad but distinct organizational categories, it is found that, relative to the PR, deep-organized systems are typically overestimated by TMI while the shallower ones are underestimated. Results suggest that the storm organization level can explain up to 50% of the regional systematic difference between the two sensors. Because of its potential for retrieval improvement, the ability to forecast the level of systems organization is tested. The state of the atmosphere is found to favor certain storm types when constrained by CAPE, wind shear, dewpoint depression, and vertical humidity distribution. Among other findings, the observations reveal that the ratio between boundary layer and midtropospheric moisture correlates well with the organization level of convection. If adjusted by the observed PR-to-TMI ratio under a given environment, the differences between PMW and PR rainfall estimates are diminished, at maximum, by 30% in RMSE and by 40% in the mean.
Abstract
This study focuses on improving the retrieval of rain from measured microwave brightness temperatures and the capability of the retrieved field to represent the mesoscale structure of a small intense hurricane. For this study, a database is constructed from collocated Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the TRMM Microwave Imager (TMI) data resulting in about 50 000 brightness temperature vectors associated with their corresponding rain-rate profiles. The database is then divided in two: a retrieval database of about 35 000 rain profiles and a test database of about 25 000 rain profiles. Although in principle this approach is used to build a database over both land and ocean, the results presented here are only given for ocean surfaces, for which the conditions for the retrieval are optimal. An algorithm is built using the retrieval database. This algorithm is then used on the test database, and results show that the error can be constrained to reasonable levels for most of the observed rain ranges. The relative error is nonetheless sensitive to the rain rate, with maximum errors at the low and high ends of the rain intensities (+60% and −30%, respectively) and a minimum error between 1 and 7 mm h−1. The retrieval method is optimized to exhibit a low total bias for climatological purposes and thus shows a high standard deviation on point-to-point comparisons. The algorithm is applied to the case of Hurricane Bret (1999). The retrieved rain field is analyzed in terms of structure and intensity and is then compared with the TRMM PR original rain field. The results show that the mesoscale structures are indeed well reproduced even if the retrieved rain misses the highest peaks of precipitation. Nevertheless, the mesoscale asymmetries are well reproduced and the maximum rain is found in the correct quadrant. Once again, the total bias is low, which allows for future calculation of the heat sources/sinks associated with precipitation production and evaporation.
Abstract
This study focuses on improving the retrieval of rain from measured microwave brightness temperatures and the capability of the retrieved field to represent the mesoscale structure of a small intense hurricane. For this study, a database is constructed from collocated Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the TRMM Microwave Imager (TMI) data resulting in about 50 000 brightness temperature vectors associated with their corresponding rain-rate profiles. The database is then divided in two: a retrieval database of about 35 000 rain profiles and a test database of about 25 000 rain profiles. Although in principle this approach is used to build a database over both land and ocean, the results presented here are only given for ocean surfaces, for which the conditions for the retrieval are optimal. An algorithm is built using the retrieval database. This algorithm is then used on the test database, and results show that the error can be constrained to reasonable levels for most of the observed rain ranges. The relative error is nonetheless sensitive to the rain rate, with maximum errors at the low and high ends of the rain intensities (+60% and −30%, respectively) and a minimum error between 1 and 7 mm h−1. The retrieval method is optimized to exhibit a low total bias for climatological purposes and thus shows a high standard deviation on point-to-point comparisons. The algorithm is applied to the case of Hurricane Bret (1999). The retrieved rain field is analyzed in terms of structure and intensity and is then compared with the TRMM PR original rain field. The results show that the mesoscale structures are indeed well reproduced even if the retrieved rain misses the highest peaks of precipitation. Nevertheless, the mesoscale asymmetries are well reproduced and the maximum rain is found in the correct quadrant. Once again, the total bias is low, which allows for future calculation of the heat sources/sinks associated with precipitation production and evaporation.
Abstract
An evaluation of the version-5 precipitation radar (PR; algorithm 2A25) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; algorithm 2A12) rainfall products is performed across the Tropics in two ways: 1) by comparing long-term TRMM rainfall products with Global Precipitation Climatology Centre (GPCC) global rain gauge analyses and 2) by comparing the rainfall estimates from the PR and TMI on a rainfall feature-by-feature basis within the narrow swath of the PR using a 1-yr database of classified precipitation features (PFs). The former is done to evaluate the overall biases of the TMI and PR relative to “ground truth” to examine regional differences in the estimates; the latter allows a direct comparison of the estimates with the same sampling area, also identifying relative biases as a function of storm type. This study finds that the TMI overestimates rainfall in most of the deep Tropics and midlatitude warm seasons over land with respect to both the GPCC gauge analysis and the PR (which agrees well with the GPCC gauges in the deep Tropics globally), in agreement with past results. The PR is generally higher than the TMI in midlatitude cold seasons over land areas with gauges. The analysis by feature type reveals that the TMI overestimates relative to the PR are due to overestimates in mesoscale convective systems and in most features with 85-GHz polarization-corrected temperature of less than 250 K (i.e., with a significant optical depth of precipitation ice). The PR tended to be higher in PFs without an ice-scattering signature of less than 250 K. Normalized for a subset of features with a large rain volume (exceeding 104 mm h−1 km2) independent of the PF classification, features with TMI > PR in the Tropics tended to have a higher fraction of stratiform rainfall, higher IR cloud tops, more intense radar profiles and 85-GHz ice-scattering signatures, and larger rain areas, whereas the converse is generally true for features with PR > TMI. Subtropical-area PF bias characteristics tended not to have such a clear relationship (especially over the ocean), a result that is hypothesized to be due to the influence of more variable storm environments and the presence of frontal rain. Melting-layer effects in stratiform rain and a bias in the ice-scattering–rain relationship were linked to the TMI producing more rainfall than the PR. However, noting the distinct characteristic biases Tropics-wide by feature type, this study reveals that accounting for regime-dependent biases caused by the differing horizontal and vertical morphologies of precipitating systems may lead to a reduction in systematic relative biases in a microwave precipitation algorithm.
Abstract
An evaluation of the version-5 precipitation radar (PR; algorithm 2A25) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; algorithm 2A12) rainfall products is performed across the Tropics in two ways: 1) by comparing long-term TRMM rainfall products with Global Precipitation Climatology Centre (GPCC) global rain gauge analyses and 2) by comparing the rainfall estimates from the PR and TMI on a rainfall feature-by-feature basis within the narrow swath of the PR using a 1-yr database of classified precipitation features (PFs). The former is done to evaluate the overall biases of the TMI and PR relative to “ground truth” to examine regional differences in the estimates; the latter allows a direct comparison of the estimates with the same sampling area, also identifying relative biases as a function of storm type. This study finds that the TMI overestimates rainfall in most of the deep Tropics and midlatitude warm seasons over land with respect to both the GPCC gauge analysis and the PR (which agrees well with the GPCC gauges in the deep Tropics globally), in agreement with past results. The PR is generally higher than the TMI in midlatitude cold seasons over land areas with gauges. The analysis by feature type reveals that the TMI overestimates relative to the PR are due to overestimates in mesoscale convective systems and in most features with 85-GHz polarization-corrected temperature of less than 250 K (i.e., with a significant optical depth of precipitation ice). The PR tended to be higher in PFs without an ice-scattering signature of less than 250 K. Normalized for a subset of features with a large rain volume (exceeding 104 mm h−1 km2) independent of the PF classification, features with TMI > PR in the Tropics tended to have a higher fraction of stratiform rainfall, higher IR cloud tops, more intense radar profiles and 85-GHz ice-scattering signatures, and larger rain areas, whereas the converse is generally true for features with PR > TMI. Subtropical-area PF bias characteristics tended not to have such a clear relationship (especially over the ocean), a result that is hypothesized to be due to the influence of more variable storm environments and the presence of frontal rain. Melting-layer effects in stratiform rain and a bias in the ice-scattering–rain relationship were linked to the TMI producing more rainfall than the PR. However, noting the distinct characteristic biases Tropics-wide by feature type, this study reveals that accounting for regime-dependent biases caused by the differing horizontal and vertical morphologies of precipitating systems may lead to a reduction in systematic relative biases in a microwave precipitation algorithm.
Abstract
Observational and modeling studies have revealed the relationships between convective–stratiform rain proportion and the vertical distributions of vertical motion, latent heating, and moistening in mesoscale convective systems. Therefore, remote sensing techniques that can be used to quantify the area coverage of convective or stratiform rainfall could provide useful information regarding the dynamic and thermodynamic processes in these systems. In the current study, two methods for deducing the area coverage of convective precipitation from satellite passive microwave radiometer measurements are combined to yield an improved method. If sufficient microwave scattering by ice-phase precipitation is detected, the method relies mainly on the degree of polarization in oblique-view, 85.5-GHz radiances to estimate the fraction of the radiometer footprint covered by convection. In situations where ice scattering is minimal, the method draws mostly on texture information in radiometer imagery at lower microwave frequencies to estimate the convective area fraction.
Based upon observations of 10 organized convective systems over ocean and nine systems over land, instantaneous, 0.5°-resolution estimates of convective area fraction from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are compared with nearly coincident estimates from the TRMM precipitation radar (PR). TMI convective area fraction estimates are low-biased relative to PR estimates, with TMI–PR correlation coefficients of 0.78 and 0.84 over ocean and land surfaces, respectively. TMI monthly average convective area percentages in the Tropics and subtropics from February 1998 are greatest along the intertropical convergence zone and in the continental regions of the Southern (summer) Hemisphere. Although convective area percentages from the TMI are systematically lower than those derived from the PR, the monthly patterns of convective coverage are similar. Systematic differences in TMI and PR convective area percentages do not show any clear correlation or anticorrelation with differences in retrieved rain depths, and so discrepancies between TRMM version-5 TMI- and PR-retrieved rain depths are likely due to other sensor or algorithmic differences.
Abstract
Observational and modeling studies have revealed the relationships between convective–stratiform rain proportion and the vertical distributions of vertical motion, latent heating, and moistening in mesoscale convective systems. Therefore, remote sensing techniques that can be used to quantify the area coverage of convective or stratiform rainfall could provide useful information regarding the dynamic and thermodynamic processes in these systems. In the current study, two methods for deducing the area coverage of convective precipitation from satellite passive microwave radiometer measurements are combined to yield an improved method. If sufficient microwave scattering by ice-phase precipitation is detected, the method relies mainly on the degree of polarization in oblique-view, 85.5-GHz radiances to estimate the fraction of the radiometer footprint covered by convection. In situations where ice scattering is minimal, the method draws mostly on texture information in radiometer imagery at lower microwave frequencies to estimate the convective area fraction.
Based upon observations of 10 organized convective systems over ocean and nine systems over land, instantaneous, 0.5°-resolution estimates of convective area fraction from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are compared with nearly coincident estimates from the TRMM precipitation radar (PR). TMI convective area fraction estimates are low-biased relative to PR estimates, with TMI–PR correlation coefficients of 0.78 and 0.84 over ocean and land surfaces, respectively. TMI monthly average convective area percentages in the Tropics and subtropics from February 1998 are greatest along the intertropical convergence zone and in the continental regions of the Southern (summer) Hemisphere. Although convective area percentages from the TMI are systematically lower than those derived from the PR, the monthly patterns of convective coverage are similar. Systematic differences in TMI and PR convective area percentages do not show any clear correlation or anticorrelation with differences in retrieved rain depths, and so discrepancies between TRMM version-5 TMI- and PR-retrieved rain depths are likely due to other sensor or algorithmic differences.
Abstract
Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote sensing error and, especially in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that rms random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain gauge and radar data. This relationship is examined using Special Sensor Microwave Imager (SSM/I) satellite data obtained over the western equatorial Pacific during the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. Rms error inferred directly from SSM/I rainfall estimates is found to be larger than was predicted from surface data and to depend less on local rain rate than was predicted. Preliminary examination of Tropical Rainfall Measuring Mission (TRMM) microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be computed directly from the satellite data.
Abstract
Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote sensing error and, especially in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that rms random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain gauge and radar data. This relationship is examined using Special Sensor Microwave Imager (SSM/I) satellite data obtained over the western equatorial Pacific during the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. Rms error inferred directly from SSM/I rainfall estimates is found to be larger than was predicted from surface data and to depend less on local rain rate than was predicted. Preliminary examination of Tropical Rainfall Measuring Mission (TRMM) microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be computed directly from the satellite data.
