A Next-generation Microwave Rainfall Retrieval Algorithm for use by TRMM and GPM

Kummerow, Christian D.; Masunaga, Hirohiko; Bauer, Péter

doi:10.1007/978-1-4020-5835-6_19

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…This has been further supported by the Aerosol/ Clouds/Ecosystems requirements definition and subsequent refinements resulting in the Global Precipitation and Cloud Mission and improved cloud measurement system concepts (Sadowy et al 2003;Rahmat-Samii et al 2005). The CloudSat/CALIPSO genre of spaceborne active sensing, marking the advent of these next-generation satellite capabilities, will be followed in the near future by such systems as NASA's Global Precipitation Measurement mission (e.g., Kummerow et al 2007), the European Space Agency (ESA) Earth, Clouds, Aerosols, and Radiation Explorer (EarthCARE; ESA 2001), and possible future NASA Decadal Survey mission concepts thereafter. These future research-grade missions may include scanning active systems, providing the first true 3D cloud observations.…”

Section: Discussionmentioning

confidence: 99%

Model-Evaluation Tools for Three-Dimensional Cloud Verification via Spaceborne Active Sensors

Miller

Weeks

Bullock

et al. 2014

Journal of Applied Meteorology and Climatology

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Clouds pose many operational hazards to the aviation community in terms of ceilings and visibility, turbulence, and aircraft icing. Realistic descriptions of the three-dimensional (3D) distribution and temporal evolution of clouds in numerical weather prediction models used for flight planning and routing are therefore of central importance. The introduction of satellite-based cloud radar (CloudSat) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) sensors to the National Aeronautics and Space Administration A-Train is timely in light of these needs but requires a new paradigm of model-evaluation tools that are capable of exploiting the vertical-profile information. Early results from the National Center for Atmospheric Research Model Evaluation Toolkit (MET), augmented to work with the emergent satellite-based active sensor observations, are presented here. Existing horizontal-plane statistical evaluation techniques have been adapted to operate on observations in the vertical plane and have been extended to 3D object evaluations, leveraging blended datasets from the active and passive A-Train sensors. Case studies of organized synoptic-scale and mesoscale distributed cloud systems are presented to illustrate the multiscale utility of the MET tools. Definition of objects on the basis of radar-reflectivity thresholds was found to be strongly dependent on the model’s ability to resolve details of the cloud’s internal hydrometeor distribution. Contoured-frequency-by-altitude diagrams provide a useful mechanism for evaluating the simulated and observed 3D distributions for regional domains. The expanded MET provides a new dimension to model evaluation and positions the community to better exploit active-sensor satellite observing systems that are slated for launch in the near future.

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Section: Discussionmentioning

confidence: 99%

Model-Evaluation Tools for Three-Dimensional Cloud Verification via Spaceborne Active Sensors

Miller

Weeks

Bullock

et al. 2014

Journal of Applied Meteorology and Climatology

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“…As briefly noted, the version 7 of the standard TMI-2A12 product uses a scattering index together with regressionbased models for rainfall retrieval over land. However, over ocean, this product relies on the Goddard Profiling Algorithm [GPROF: see, [23][24][25], which is called a Bayesian algorithm as it makes use of a priori collected database of rainfall-radiance pairs.…”

Section: Tmi-2a12 and Differences With Sharpmentioning

confidence: 99%

“…Over ocean the TMI-2A12 relies on the GPROF algorithm. As of noted by Kummerow et al [24], in this algorithm the expected value of rainfall profile of interest is obtained as follows:…”

Section: Tmi-2a12: Ocean Retrievalmentioning

confidence: 99%

Evaluation of ShARP Passive Rainfall Retrievals over Snow-Covered Land Surfaces and Coastal Zones

Ebtehaj

Bras

Foufoula‐Georgiou

2016

Journal of Hydrometeorology

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For precipitation retrievals over land, using satellite measurements in microwave bands, it is important to properly discriminate the weak rainfall signals from strong and highly variable background surface emission. Traditionally, land rainfall retrieval methods often rely on a weak signal of rainfall scattering on high-frequency channels (85 GHz) and make use of empirical thresholding and regression-based techniques. Due to the increased ground surface signal interference, precipitation retrieval over radiometrically complex land surfaces-especially over snow-covered lands, deserts and coastal areas-is of particular challenge for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken locally linear embedding Algorithm for Retrieval of Precipitation (ShARP), over a radiometrically complex terrain and coastal areas using the data provided by the Tropical Rainfall Measuring Mission (TRMM) satellite. To this end, the ShARP retrieval experiments are performed over a region in South and Southeast Asia, partly covering the Tibetan Highlands, Himalayas, Ganges-Brahmaputra-Meghna river basins and its delta. This region is unique in terms of its diverse land surface radiation regime and precipitation type within the TRMM field of view. We elucidate promising results by ShARP over snow covered land surfaces and at the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM-2A12 product. Specifically, using the TRMM-2A25 radar product as a reference, we provide evidence that the ShARP algorithm can significantly reduce the rainfall over estimation due to the background snow contamination and markedly improve detection and retrieval of rainfall at the vicinity of coastlines. During the calendar year 2013, we demonstrate that over the study domain the root mean squared difference can be reduced up to 38% annually, while the reduction can reach up to 70% during the cold months.

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“…PMW sensors measure the natural electromagnetic Earth emissions at microwave wavelengths, which are affected by rain drops in several ways. This allows for a more direct estimate of precipitation from space using radiative transfer modelling [23] (see References [24,25] for an update). Nonetheless, PMW sensors have poor temporal and spatial resolution due to their low orbits and the antenna diffraction limit at microwave wavelengths [26].…”

Section: Introductionmentioning

confidence: 99%

Decorrelation of Satellite Precipitation Estimates in Space and Time

et al. 2018

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Precise estimates of precipitation are required for many environmental tasks, including water resources management, improvement of numerical model outputs, nowcasting and evaluation of anthropogenic impacts on global climate. Nonetheless, the availability of such estimates is hindered by technical limitations. Rain gauge and ground radar measurements are limited to land, and the retrieval of quantitative precipitation estimates from satellite has several problems including the indirectness of infrared-based geostationary estimates, and the low orbit of those microwave instruments capable of providing a more precise measurement but suffering from poor temporal sampling. To overcome such problems, data fusion methods have been devised to take advantage of synergisms between available data, but these methods also present issues and limitations. Future improvements in satellite technology are likely to follow two strategies. One is to develop geostationary millimeter-submillimeter wave soundings, and the other is to deploy a constellation of improved polar microwave sensors. Here, we compare both strategies using a simulated precipitation field. Our results show that spatial correlation and RMSE would be little affected at the monthly scale in the constellation, but that the precise location of the maximum of precipitation could be compromised; depending on the application, this may be an issue.

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A Next-generation Microwave Rainfall Retrieval Algorithm for use by TRMM and GPM

Cited by 8 publications

References 35 publications

Model-Evaluation Tools for Three-Dimensional Cloud Verification via Spaceborne Active Sensors

Model-Evaluation Tools for Three-Dimensional Cloud Verification via Spaceborne Active Sensors

Evaluation of ShARP Passive Rainfall Retrievals over Snow-Covered Land Surfaces and Coastal Zones

Decorrelation of Satellite Precipitation Estimates in Space and Time

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