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Christian Kummerow

Abstract

There are currently large numbers of rainfall retrieval algorithms based upon passive microwave radiances. Most of these algorithms are physically based in that they use explicit physical assumptions to derive relationships between brightness temperatures (Tb’s) and rainfall. If these assumptions involve observable quantities, then the physical basis of the algorithms can be extended to determine fundamental uncertainties in the retrieved precipitation fields. In this paper this process begins by examining the largest uncertainty in many of the physical models—the homogeneous rainfall assumption. Four months of Tropical Oceans Global Atmosphere Coupled Ocean–Atmosphere Response Experiment shipborne radar data is used to describe the horizontal characteristics of rain. The vertical hydrometeor structures needed to simulate the upwelling Tb are taken from a dynamical cloud model. Radiative transfer computations were performed using a fully three-dimensional Monte Carlo solution in order to test all aspects of the beamfilling problem. Results show that biases as well as random errors depend upon the assumed vertical structure of hydrometeors, the manner in which inhomogeneity is modeled in the retrieval, and the manner in which the radiative transfer problem is handled. Unlike previous works, the goal of this paper is not to determine a mean beamfilling correction or a vertical hydrometeor profile that should be applied to specific retrieval algorithms. Rather, it is to explore the impact of inhomogeneous rainfall upon the predicted brightness temperatures so that these relations may eventually be used to develop a physically based error model for microwave precipitation retrievals. Because the predicted Tb’s depend upon assumed cloud vertical structures, the paper offers a procedure to account for the uncertainty introduced by rainfall inhomogeneity rather than a general result. The impact of inhomogeneous rainfall upon specific algorithms must still be investigated within the context of that specific algorithm.

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Christian Kummerow and Louis Giglio

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A multichannel physical approach for retrieving rainfall and its vertical structure from SSM/I observations is examined. While a companion paper was devoted exclusively to the description of the algorithm, its strengths, and its limitations, the main focus of this paper is to report on the results, applicability, and expected accuracies from this algorithm. Some examples are given that compare retrieved results with ground-based radar data from different geographical regions to illustrate the performance and utility of the algorithm under distinct rainfall conditions. Move quantitative validation is accomplished using two months of radar data from Darwin, Australia, and the radar network over Japan. Instantaneous comparisons at Darwin indicate that root-mean-square errors for 1.25° areas over water are 0.09 mm h−1 compared to the mean rainfall value of 0.224 mm h−1 while the correlation exceeds 0.9. Similar results are obtained over the Japanese validation site with rms errors of 0.6 1 5 mm h−1 compared to the mean of 0.880 mm h−1 and a correlation of 0.9. Results are less encouraging over land with root-mean-square errors somewhat larger than the mean rain rates and correlations of only 0.71 and 0.62 for Darwin and Japan, respectively. These validation studies are further used in combination with the theoretical treatment of expected accuracies developed in the companion paper to define error estimates on a broader scale than individual radar sites from which the errors may be analyzed. Comparisons with simpler techniques that are based on either emission or scattering measurements are used to illustrate the fact that the current algorithm, while better correlated with the emission methods over water, cannot be reduced to either of these simpler methods.

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Christian Kummerow and Louis Giglio

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This paper describes a multichannel physical approach for retrieving rainfall and vertical structure information from satellite-based passive microwave observations. The algorithm makes use of statistical inversion techniques based upon theoretically calculated relations between rainfall rates and brightness temperatures. Potential errors introduced into the theoretical calculations by the unknown vertical distribution of hydrometeors are overcome by explicitly accounting for diverse hydrometcor profiles. This is accomplished by allowing for a number of different vertical distributions in the theoretical brightness temperature calculations and requiring consistency between the observed and calculated brightness temperatures. This paper will focus primarily on the theoretical aspects of the retrieval algorithm, which includes a procedure used to account for inhomogeneities of the rainfall within the satellite field of view as well as a detailed description of the algorithm as it is applied over both ocean and land surfaces. The residual error between observed and calculated brightness temperatures is found to be an important quantity in assessing the uniqueness of the solution. At is further found that the residual error is a meaningful quantity that can be used to derive expected accuracies from this retrieval technique. Examples comparing the retrieved results as well as the detailed analysis of the algorithm performance under various circumstances are the subject of a companion paper.

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Christian Kummerow and Louis Giglio

Abstract

Passive microwave observations of rainfall offer the ability to obtain very accurate instantaneous estimates of rainfall. Because passive microwave instruments are confined to polar-orbiting satellites, however, such estimates must interpolate across long time periods, during which no measurements are available. In this paper the authors discuss a technique that allows one to partially overcome the sampling limitations by using frequent infrared observations from geosynchronous platforms. To accomplish this, the technique compares all coincident microwave and infrared observations. From each coincident pair, the infrared temperature threshold is selected that corresponds to an area equal to the raining area observed in the microwave image. The mean conditional rainfall rate as determined from the microwave image is then assigned to pixels in the infrared image that are colder than the selected threshold. The calibration is also applied to a fixed threshold of 235 K for comparison with established infrared techniques. Once a calibration is determined, it is applied to all infrared images. Monthly accumulations for both methods are then obtained by summing rainfall from all available infrared images. Two examples are used to evaluate the performance of the technique. The first consists of a one-month period (February 1988) over Darwin, Australia, where good validation data are available from radar and rain gauges. For this case it was found that the technique approximately doubled the rain inferred by the microwave method alone and produced exceptional agreement with the validation data. The second example involved comparisons with atoll rain gauges in the western Pacific for June 1989. Results here are overshadowed by the fact that the hourly infrared estimates from established techniques, by themselves, produced very good correlations with the rain gauges. The calibration technique was not able to improve upon these results.

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Fang Wang and Christian Kummerow

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Cloud-resolving models (CRMs) offer an important pathway to interpret satellite observations of microphysical properties of storms. High-frequency microwave brightness temperatures (Tbs) respond to precipitating-sized ice particles and can therefore be compared with simulated Tbs at the same frequencies. By clustering the Tb vectors at these frequencies, the scene can be classified into distinct microphysical regimes (in other words, cloud types). A convective storm over the Amazon observed by the Tropical Rainfall Measuring Mission (TRMM) is simulated using the Regional Atmospheric Modeling System (RAMS) in a semi-ideal setting, and four regimes are defined within the scene using cluster analysis: the “clear sky/thin cirrus” cluster, the “cloudy” cluster, the “stratiform anvil” cluster, and the “convective” cluster. Cluster-by-cluster comparisons between the observations and the simulations disclose biases in the model that are consistent with an overproduction of supercooled water and an excess of large hail particles. While other problems cannot be completely ruled out, the method does provide some guidance to assess microphysical fidelity within each cluster or cloud type. Guided by the apparent model/observational discrepancies in the convective cloud cluster, the hail size parameter was adjusted in order to produce a greater number of smaller hail particles consistent with the observations. While the work cannot define microphysical errors in an unambiguously fashion, the cluster analysis is seen as useful to isolate individual microphysical inconsistencies that can then be addressed within each cluster of cloud type.

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Michael Garstang and Christian D. Kummerow
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Veljko Petković and Christian D. Kummerow

Abstract

An updated version of the Goddard Profiling Algorithm (GPROF 2014) with a new overland scheme was released with the launch of the Global Precipitation Mission (GPM) core satellite in February 2014. The algorithm is designed to provide consistent precipitation estimates over both ocean and land across diverse satellite platforms. This study tests the performance of the new retrieval, focusing specifically on an extreme rainfall event. Two contrasting 72-h precipitation events over the same area are used to compare the retrieved products against ground measurements. The first event is characterized by persistent and intense precipitation of an unusually strong and widespread system, which caused historical flooding of the central Balkan region of southeastern Europe in May 2014. The second event serves as a baseline case for a more typical midlatitude regime. Rainfall rates and 3-day accumulations given by five conically scanning radiometers (GMI; AMSR2; and SSMIS F16, F17, and F18) in the GPM constellation are compared against ground radar data from the Operational Program for Exchange of Weather Radar Information (OPERA) network and in situ measurements. Satellite products show good agreement with ground radars; the retrieval closely reproduces spatial and temporal characteristics of both events. Strong biases related to precipitation regimes are found when satellite and radar measurements are compared to ground gauges. While the GPM constellation performs well during the nonextreme event, showing ~10% negative bias, it underestimates gauge accumulations of the Balkan flood event by 60%. Analyses show that the biases are caused by the differences between the expected and observed ice-scattering signals, suggesting that better understanding of the environment and its impact on rain profiles is the key for successful retrievals in extreme events.

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David Randall and Christian D. Kummerow
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Veljko Petković and Christian D. Kummerow

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.

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Veljko Petković and Christian D. Kummerow

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%).

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