The TRMM &amp;lsquo;Day-1&amp;rsquo; Radar/Radiometer Combined Rain-Profiling Algorithm

Haddad, Ziad S.; Smith, Eric A.; Kummerow, Christian D.; Iguchi, Toshio; Farrar, Michael R.; Durden, Stephen L.; Alves, Marcos Nopper; Olson, William S.

doi:10.2151/jmsj1965.75.4_799

Cited by 186 publications

(91 citation statements)

References 10 publications

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“…TMI is a passive detector (radiometer) with nine channels: horizontal and vertical polarizations at 10.7, 19.4, 37.0, and 85.5 GHz and only vertical polarization at 21.3 GHz. So far, several precipitation retrieval algorithms have been developed [36,37]. At present, the best estimate of precipitation at fine spatial resolution of 0.25˝for the areas between 50˝S and 50˝N is produced by the TMPA; this algorithm is designed to combine precipitation estimates from various satellite systems, as well as land surface precipitation gauge analyses when possible [7,38].…”

Section: Satellite Precipitation Productsmentioning

confidence: 99%

Evaluation of IMERG and TRMM 3B43 Monthly Precipitation Products over Mainland China

2016

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Abstract:As the successor of the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission significantly improves the spatial resolution of precipitation estimates from 0.25˝to 0.1˝. The present study analyzed the error structures of Integrated Multisatellite Retrievals for GPM (IMERG) monthly precipitation products over Mainland China from March 2014 to February 2015 using gauge measurements at multiple spatiotemporal scales. Moreover, IMERG products were also compared with TRMM 3B43 products. The results show that: (1) overall, IMERG can capture the spatial patterns of precipitation over China well. It performs a little better than TRMM 3B43 at seasonal and monthly scales; (2) the performance of IMERG varies greatly spatially and temporally. IMERG performs better at low latitudes than at middle latitudes, and shows worse performance in winter than at other times; (3) compared with TRMM 3B43, IMERG significantly improves the estimation accuracy of precipitation over the Xinjiang region and the Qinghai-Tibetan Plateau, especially over the former where IMERG increases Pearson correlation coefficient by 0.18 and decreases root-mean-square error by 54.47 mm for annual precipitation estimates. However, most IMERG products over these areas are unreliable; and (4) IMERG shows poor performance in winter as TRMM 3B43 even if GPM improved its ability to sense frozen precipitation. Most of them over North China are unreliable during this period.

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Section: Satellite Precipitation Productsmentioning

confidence: 99%

Evaluation of IMERG and TRMM 3B43 Monthly Precipitation Products over Mainland China

2016

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“…The purpose of TRMM 3B42 rainfall product [Huffman et al, 2007;Haddad et al, 1997;Adler et al, 2000] is to produce the merged infrared (IR) and microwave (MW) precipitation estimates. The TRMM 3B42 rainfall is sampled at 0.25°× 0.25°spatial resolution with longitudinal global coverage extending from 50°S to 50°N latitude.…”

Section: Trmm 3b42 Rainfallmentioning

confidence: 99%

Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region

2014

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Rainfall is probably the most important parameter that is predicted by numerical weather prediction models, though the skill of rainfall prediction is the poorest compared to other parameters, e.g., temperature and humidity. In this study, the impact of rainfall assimilation on mesoscale model forecasts is evaluated during Indian summer monsoon 2011. The Weather Research and Forecasting (WRF) model and its four-dimensional variational data assimilation system are used to assimilate the Tropical Rainfall Measuring Mission 3B42 and Japan Aerospace Exploration Agency Global Satellite Mapping of Precipitation retrieved rainfall. A total of five experiments are performed daily with and without assimilation of rainfall data during the entire month of July 2011. Separate assimilation experiments are performed to assess the sensitivity of WRF model forecast with strict and less strict quality control. Assimilation of rainfall improves the forecast of temperature, specific humidity, and wind speed. Domain average improvement parameter of rainfall forecast is also improved over the Indian landmass when compared with NOAA Climate Prediction Center Morphing technique and Indian Meteorological Department gridded rainfall.

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“…TRMM 2A12 and 2A25, along with the combined 2B31 (Haddad et al 1997a(Haddad et al , 1997b instantaneous rain rates, were matched at the scale of the TMI footprint and were statistically compared to the 2A53 GV ra- dar rain-rate spectra at MELB. Six years of regional TRMM overpass data were used (1999 to 2004).…”

Section: Rain-rate Distributionsmentioning

confidence: 99%

TRMM 2A12 Land Precipitation Product - Status and Future Plans

Wang

Liu

Ferraro

et al. 2009

Journal of the Meteorological Society of Japan

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The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 2A12 product consists of unique components configured for land and oceanic precipitation retrievals. This design was based on the vastly different physical characteristics of the retrieval, involving primarily emission over ocean and entirely scattering over land. This paper describes the current status of the TRMM Version 6 (V6) 2A12 product over land and envisioned improvements for TRMM TMI V7 and GPM GMI V1.On a global scale, the 2A12 land algorithm exhibits biases when compared with the TRMM 2A25 (Precipitation Radar (PR) based) and rain gauges. These range from 6 percent for GPCC to 20 percent for 2A25. Closer comparison also reveals regional and seasonal biases, with the largest positive biases found in warm-season convective zones and over semi-arid regions. Some negative biases are found in warm-rain precipitation regimes where scattering at 85 GHz is unable to detect a precipitation signal. On an instantaneous time scale, 2A12 land also produces a positive bias when compared with high-quality radar data from Melbourne, Florida, a TRMM ground validation site. The largest discrepancies occur for rain rates of less than 2 mm h -1 . A number of known "anomalies" are highlighted, including overestimation of rainfall in deep convective systems, underestimation in warm-rain regimes, and a number of features associated with the

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The TRMM ‘Day-1’ Radar/Radiometer Combined Rain-Profiling Algorithm

Cited by 186 publications

References 10 publications

Evaluation of IMERG and TRMM 3B43 Monthly Precipitation Products over Mainland China

Evaluation of IMERG and TRMM 3B43 Monthly Precipitation Products over Mainland China

Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region

TRMM 2A12 Land Precipitation Product - Status and Future Plans

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