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Tufa Dinku and Emmanouil N. Anagnostou

Abstract

This paper extends the work of Dinku and Anagnostou overland rain retrieval algorithm for use with Special Sensor Microwave Imager (SSM/I) observations. In Dinku and Anagnostou, Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) rainfall estimates were used to calibrate TRMM Microwave Imager (TMI) retrieval. Regional differences in PR-based TMI calibration were investigated by testing the algorithm over four geographic regions, consisting of Africa, northern South America (containing the Amazon basin), the continental United States, and south Asia. In this paper the performance of Dinku and Anagnostou's technique applied on SSM/I data over three of these regions (Africa, Amazon, and South Asia) is demonstrated. Two approaches are investigated for using PR rainfall products to calibrate the algorithm parameters. In the first approach, TMI channels are remapped to the spatial resolutions of the corresponding SSM/I channels; then, PR is used to calibrate the rain retrieval on the remapped TMI data. In the second approach, the PR-based TMI algorithm calibration is performed at a coarser (0.25°) resolution. To assess the quality of algorithm estimates with respect to PR, rainfall fields derived from Dinku and Anagnostou, applied to SSM/I observations (using parameters determined from both approaches), are compared with matched (within ±15 min of the satellites' overpass time difference) PR surface rain rates. Calibration data come from the wet seasons (January–March) of 2000 and 2001. To assess the quality of the estimates with respect to PR, data from a 5-month period (December–April) of 2002, 2003, and 2004 are used. In comparison with the latest version of the Goddard profiling (GPROF) algorithm rain estimates, the current algorithm shows significant improvements in terms of both bias and random error reduction. The paper also shows that rain estimation based on TMI observations is associated with lower error statistics in comparison with the corresponding SSM/I retrievals.

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Alemu Tadesse and Emmanouil N. Anagnostou

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This paper presents development of a statistical procedure for estimation of ensemble rainfall fields from a combination of ground radar observations and in situ rain gauge measurements. The uncertainty framework characterizes radar-rainfall estimation algorithm limitation accounting for rain gauge sampling uncertainty. The procedure is applied on a multicomponent rainfall estimation algorithm, which utilizes a rain-path attenuation correction technique, a power-law reflectivity-to-rainfall (ZR) relationship, and a parameter to differentiate between convective (C) and stratiform (S) regimes in the ZR conversion. Uncertainty is explicitly accounted for by evaluating the algorithm’s parameter set posterior probability density function (known as parameters’ equifinality) on the basis of the Generalized Likelihood Uncertainty Estimation (GLUE) framework. The study is facilitated by NASA’s C-band Doppler radar [named the Tropical Ocean Global Atmosphere (TOGA)] observations and four dense rain gauge clusters available from the Tropical Rainfall Measuring Mission (TRMM)-Large-Scale Biosphere–Atmosphere (LBA) experiment, conducted between January and February of 1999 in Southwest Amazon. Statistics are proposed for jointly evaluating the wideness of radar retrieval uncertainty limits [uncertainty ratio (UR)] and the percentage of observations that fall within those error bounds [exceedance ratio (ER)]. Results show that the parameter range selected in GLUE could characterize the radar-rainfall estimation uncertainty. Combined assessment of UR and ER for a varying range of parameters’ equifinality provides an objective basis for comparing rain retrieval algorithms and determining uncertainty bounds. Ensemble radar-rainfall fields derived on the basis of this procedure can be used to statistically assess satellite rain retrieval algorithms and derive ensemble hydrologic predictions driven by radar-rainfall input (e.g., runoff and soil moisture).

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Dimitrios Stampoulis and Emmanouil N. Anagnostou

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An extensive evaluation of two global-scale high-resolution satellite rainfall products is performed using 8 yr (2003–10) of reference rainfall data derived from a network of rain gauges over Europe. The comparisons are performed at a daily temporal scale and 0.25° spatial grid resolution. The satellite rainfall techniques investigated in this study are the Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 (gauge-calibrated version) and the Climate Prediction Center morphing technique (CMORPH). The intercomparison and validation of these satellite products is performed both qualitatively and quantitatively. In the qualitative part of the analysis, error maps of various validation statistics are shown, whereas the quantitative analysis provides information about the performance of the satellite products relative to the rainfall magnitude or ground elevation. Moreover, a time series analysis of certain error statistics is used to depict the temporal variations of the accuracy of the two satellite techniques. The topographical and seasonal influences on the performance of the two satellite products over the European domain are also investigated. The error statistics presented herein indicate that both orography and seasonal variability affect the efficiency of the satellite rainfall retrieval techniques. Specifically, both satellite techniques underestimate rainfall over higher elevations, especially during the cold season, and their performance is subject to seasonal changes. A significant difference between the two satellite products is that TRMM 3B42 V6 generally overestimates rainfall, while CMORPH underestimates it. CMORPH’s mean error is shown to be of higher magnitude than that of 3B42 V6, while in terms of random error variance, CMORPH exhibits lower (higher) values than those of 3B42 V6 in the winter (summer) months.

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Tufa Dinku and Emmanouil N. Anagnostou

Abstract

Seasonal differences in the calibration of overland passive microwave rain retrieval are investigated using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR). Four geographic regions from southern Africa, South Asia, the Amazon basin, and the southeastern United States are selected. Three seasons are compared for each region. Two scenarios of algorithm calibration are considered. In the first, the parameter sets are derived by calibrating the TMI algorithm with PR in each season. In the second scenario, common parameter sets are derived from the combined dataset of all three seasons. The parameter sets from both scenarios are then applied to the validation dataset of each season to determine the effect of seasonal calibration. Furthermore, calibration parameters from one season are also applied to another season, and results are compared against those derived using the season’s own parameters. Appreciable seasonal differences are observed for the U.S. region, while there are no significant differences between using individual seasonal calibration and the all-season calibration for the other regions. However, using one season’s parameter set to retrieve rainfall for another season is associated with increased uncertainty. It is also shown that the performance of the retrieval varies by season.

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Mircea Grecu and Emmanouil N. Anagnostou

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A physically based methodology to incorporate passive microwave observations in a “rain-profiling algorithm” is developed for space- or airborne radars at frequencies exhibiting attenuation. The rain-profiling algorithm deploys a formulation for reflectivity attenuation correction that is mathematically equivalent to that of Hitschfeld and Bordan. In this formulation, the reflectivity–hydrometeor content (or rainfall rate) and reflectivity–attenuation relationships are expressed as a function of one variable in the drop size distribution parameterization, namely, the multiplicative factor in a normalized gamma distribution. The multiplicative factor parameter, mean cloud water content, and one parameter describing the precipitation phase are estimated in a Bayesian framework. This involves the minimization of differences between the 10-, 19-, 37-, and 85-GHz brightness temperature values predicted by a plane-parallel multilayer radiative transfer model and those observed by space- or airborne radiometers. A variational approach is devised to perform the minimization. The methodology is first tested using data simulated using a cloud model and is subsequently applied to coincident airborne brightness temperature and radar profile observations originating in the Kwajalein Experiment of the Tropical Rainfall Measuring Mission (TRMM). Results suggest improvements in rain estimation induced by the inclusion of the brightness temperature information in the retrieval framework if consistent modeling and quantification of errors are performed. Recommendations regarding the application of the method to TRMM satellite observations are formulated based on the findings of the study.

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Mircea Grecu and Emmanouil N. Anagnostou

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Procedures for passive microwave precipitation estimation over land are investigated based on a large database of Tropical Rainfall Measuring Mission (TRMM) observations. The procedures include components for rain area delineation, convective/stratiform (C/S) rain classification, and estimation of vertically integrated water content or surface rainfall rate. The investigated algorithms include neural network schemes for both the rain area and C/S classification and statistical algorithms for precipitation estimation. The coincident active and passive microwave observations from TRMM, with the active (TRMM precipitation radar) observations providing the reference values for the various precipitation parameters, are used for algorithm calibration and validation. The calibration and validation are based on 1 yr of data over the continental United States and a repetitive sampling strategy that make the results statistically significant. Good agreement is demonstrated with TRMM precipitation radar observations in rain delineation, and it is shown that C/S classification can considerably improve precipitation estimation. It is also shown that better performance may be achieved in estimating vertically integrated hydrometeor contents as compared with rainfall rates.

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Emmanouil N. Anagnostou and Christian Kummerow

Abstract

A better understanding of global climate calls for more accurate estimates of liquid and ice water content profiles of precipitating clouds and their associated latent heating profiles. Convective and stratiform precipitation regimes have different latent heating and therefore impact the earth’s climate differently. Classification of clouds over oceans has traditionally been part of more general rainfall retrieval schemes. These schemes are based on individual or combined visible and infrared, and microwave satellite observations. However, none of these schemes report validations of their cloud classification with independent ground observations. The objective of this study is to develop a scheme to classify convective and stratiform precipitating clouds using satellite brightness temperature observations. The proposed scheme probabilistically relates a quantity called variability index (VI) to the stratiform fractional precipitation coverage over the satellite field of view (FOV). The VI for a satellite pixel is the mean absolute 85-GHz brightness temperature difference between the pixel and the eight surrounding neighbor pixels. The classification scheme has been applied to four different rainfall regimes. All four regimes show that the frequency of stratiform rainfall in the satellite FOV increases as the satellite-based VI decreases. The results of this study demonstrate that the satellite-based VI is consistently related to the probability of occurrence of three classes (0%–40%, 40%–70%, and 70%–100%) of FOV stratiform coverage.

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Tufa Dinku and Emmanouil N. Anagnostou

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The Tropical Rainfall Measuring Mission (TRMM) satellite carries a combination of active [precipitation radar (PR)] and multichannel passive microwave [the TRMM Microwave Imager (TMI)] sensors, which advance our ability to estimate rainfall over land. Rain retrieval from the TRMM PR is associated with an unprecedented accuracy and resolution but is limited in terms of sampling because of the narrow PR swath width (215 km). TMI provides wider coverage (760 km), but its observations are associated with a more complex relationship to precipitation in comparison with PR (especially over land). The PR rain estimates are used here for calibrating an overland TMI rain algorithm. The algorithm consists of 1) multichannel-based rain screening and convective/stratiform (C/S) classification schemes, and 2) nonlinear (linear) regressions for the rain-rate retrieval of stratiform (convective) rain regimes. This study examines regional differences in the algorithm performance. Four geographic regions consisting of central Africa (AFC), the Amazon (AMZ), the U.S. southern Plains (USA), and the Ganges–Brahmaputra–Meghna River basin (GBM) in south Asia are selected. Data from three summer months of 2000 and 2001 are used for calibration; validation is done using summer 2002 data. The current algorithm is also compared with the latest [version 6 (V6)] TRMM 2A12 product in terms of rain detection, and rain-rate retrieval error statistics on the basis of PR reference rainfall. The performance of the algorithm is different for the different regions. For instance, the reduction in random error (relative to 2A12 V6) is about 24%, 36%, 57%, and 165% for USA, AFC, AMZ, and GBM, respectively. However, significant difference between global (the four regions combined) and regional calibration is observed only for the GBM region.

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Alemu Tadesse and Emmanouil N. Anagnostou

Abstract

The study uses storm tracking information to evaluate error statistics of satellite rain estimation at different maturity stages of storm life cycles. Two satellite rain retrieval products are used for this purpose: (i) NASA’s Multisatellite Precipitation Analysis–Real Time product available at 25-km/hourly resolution (3B41-RT) and (ii) the University of California (Irvine) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product available at 4-km–hourly resolution. Both algorithms use geostationary satellite infrared (IR) observations calibrated to an array of passive microwave (PM) earth-orbiting satellite sensor rain retrievals. The techniques differ in terms of algorithmic structure and in the way they use the PM rainfall to calibrate the IR rain algorithms. The satellite retrievals are evaluated against rain gauge–calibrated radar rainfall estimates over the continental United States. Error statistics of hourly rain volumes are determined separately for thunderstorm and shower-type convective systems and for different storm life durations and stages of maturity. The authors show distinct differences between the two satellite retrieval error characteristics. The most notable difference is the strong storm life cycle dependence of 3B41-RT relative to the nearly independent PERSIANN behavior. Another is in the algorithm performance between thunderstorms and showers; 3B41-RT exhibits significant bias increase at longer storm life durations. PERSIANN exhibits consistently improved correlations relative to the 3B41-RT for all storm life durations and maturity stages. The findings of this study support the hypothesis that incorporating cloud type information into the retrieval (done by the PERSIANN algorithm) can help improve the satellite retrieval accuracy.

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Xinxuan Zhang and Emmanouil N. Anagnostou

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The study evaluated a numerical weather model (WRF)-based satellite precipitation adjustment technique with 81 heavy precipitation events that occurred in three tropical mountainous regions (Colombia, Peru, and Taiwan). The technique was applied on two widely used near-real-time global satellite precipitation products—the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Global Satellite Mapping of Precipitation project (GSMaP)—for each precipitation event. The WRF-adjusted satellite products along with the near-real-time and gauge-adjusted satellite products as well as the WRF simulation were evaluated by independent gauge networks at daily scale and event total scale. Results show that the near-real-time precipitation products exhibited severe underestimation relative to the gauge observations over the three tropical mountainous regions. The underestimation tended to be larger for higher rainfall accumulations. The WRF-based satellite adjustment provided considerable improvements to the near-real-time CMORPH and GSMaP products. Moreover, error metrics show that WRF-adjusted satellite products outperformed the gauge-adjusted counterparts for most of the events. The effectiveness of WRF-based satellite adjustment varied with events of different physical processes. Thus, the technique applied on satellite precipitation estimates of these events may exhibit inconsistencies in the bias correction.

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