The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on 27 November 1997, and data from all the instruments first became available approximately 30 days after the launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, and applications of these results to areas such as data assimilation and model initialization. The TRMM Microwave Imager (TMI) calibration has been corrected and verified to account for a small source of radiation leaking into the TMI receiver. The precipitation radar calibration has been adjusted upward slightly (by 0.6 dB Z) to match better the ground reference targets; the visible and infrared sensor calibration remains largely unchanged. Two versions of the TRMM rainfall algorithms are discussed. The at-launch (version 4) algorithms showed differences of 40% when averaged over the global Tropics over 30-day periods. The improvements to the rainfall algorithms that were undertaken after launch are presented, and intercomparisons of these products (version 5) show agreement improving to 24% for global tropical monthly averages. The ground-based radar rainfall product generation is discussed. Quality-control issues have delayed the routine production of these products until the summer of 2000, but comparisons of TRMM products with early versions of the ground validation products as well as with rain gauge network data suggest that uncertainties among the TRMM algorithms are of approximately the same magnitude as differences between TRMM products and ground-based rainfall estimates. The TRMM field experiment program is discussed to describe active areas of measurements and plans to use these data for further algorithm improvements. In addition to the many papers in this special issue, results coming from the analysis of TRMM products to study the diurnal cycle, the climatological description of the vertical profile of precipitation, storm types, and the distribution of shallow convection, as well as advances in data assimilation of moisture and model forecast improvements using TRMM data, are discussed in a companion TRMM special issue in the Journal of Climate (1 December 2000, Vol. 13, No. 23).

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The status of the tropical rainfall measuring mission (TRMM) after two years in orbit

An increasing number of satellite-based rainfall products are now available in near–real time over the Internet to help meet the needs of weather forecasters and climate scientists, as well as a wide range of decision makers, including hydrologists, agriculturalists, emergency managers, and industrialists. Many of these satellite products are so newly developed that a comprehensive evaluation has not yet been undertaken. This article provides potential users of short-interval satellite rainfall estimates with information on the accuracy of such estimates. Since late 2002 the authors have been performing daily validation and intercomparisons of several operational satellite rainfall retrieval algorithms over Australia, the United States, and northwestern Europe. Short-range quantitative precipitation forecasts from four numerical weather prediction (NWP) models are also included for comparison. Synthesis of four years of daily rainfall validation results shows that the satellite-derived estimates of precip...

Comparison of near-real-time precipitation estimates from satellite observations and numerical models

A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset ...

PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the sc...

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The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors

[1] The accuracy of forcing data greatly impacts the ability of land surface models (LSMs) to produce realistic simulations of land surface processes. With this in mind, the multi-institutional North American Land Data Assimilation System (NLDAS) project has produced retrospective (1996–2002) and real-time (1999–present) data sets to support its LSM modeling activities. Featuring 0.125° spatial resolution, hourly temporal resolution, nine primary forcing fields, and six secondary validation/model development fields, each data set is based on a backbone of Eta Data Assimilation System/Eta data and is supplemented with observation-based precipitation and radiation data. Hourly observation-based precipitation data are derived from a combination of daily National Center for Environmental Prediction Climate Prediction Center (CPC) gauge-based precipitation analyses and hourly National Weather Service Doppler radar-based (WSR-88D) precipitation analyses, wherein the hourly radar-based analyses are used to temporally disaggregate the daily CPC analyses. NLDAS observation-based shortwave values are derived from Geostationary Operational Environmental Satellite radiation data processed at the University of Maryland and at the National Environmental Satellite Data and Information Service. Extensive quality control and validation efforts have been conducted on the NLDAS forcing data sets, and favorable comparisons have taken place with Oklahoma Mesonet, Atmospheric Radiation Measurement Program/cloud and radiation test bed, and Surface Radiation observation data. The real-time forcing data set is constantly evolving to make use of the latest advances in forcing-related data sets, and all of the real-time and retrospective data are available online at http://ldas.gsfc.nasa.gov for visualization and downloading in both full and subset forms.

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Real‐time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project

A detailed description of the operational WSR-88D rainfall estimation algorithm is presented. This algorithm, called the Precipitation Processing System, produces radar-derived rainfall products in real time for forecasters in support of the National Weather Service’s warning and forecast missions. It transforms reflectivity factor measurements into rainfall accumulations and incorporates rain gauge data to improve the radar estimates. The products are used as guidance to issue flood watches and warnings to the public and as input into numerical hydrologic and atmospheric models. The processing steps to quality control and compute the rainfall estimates are described, and the current deficiencies and future plans for improvement are discussed.

The WSR-88D Rainfall Algorithm

A procedure for real-time adjustment of range-dependent biases in Weather Surveillance Radar-1988 Doppler version (WSR-88D) rainfall estimates due to nonuniform vertical profile of reflectivity is proposed. Using volume-scan measurements of effective reflectivity, the procedure estimates conditional mean and variance profiles of point reflectivity in the vertical and calculates, as a function of elevation angle and slant range, a multiplicative adjustment factor to be applied to the apparent radar rain rate. As a by-product, the maximum effective coverage of radar is also delineated, outside of which radar rainfall estimates are subject to beam overshooting. To evaluate the procedure, unadjusted and adjusted radar rainfall estimates from a Pacific Northwest winter storm are examined and compared with rain gauge data.

Real-time adjustment of range-dependent biases in WSR-88D rainfall estimates due to nonuniform vertical profile of reflectivity

This paper describes techniques used operationally by the National Weather Service (NWS) to prepare gridded multisensor (gauge, radar, and satellite) quantitative precipitation estimates (QPEs) for input into hydrologic forecast models and decision- making systems for river forecasting, flood and flash flood warning, and other hydrologic monitoring purposes. Advanced hydrologic prediction techniques require a spatially continuous representation of the precipitation field, and remote sensor input is critical to achiev- ing this continuity. Although detailed descriptions of individual remote sensor estimation algorithms have been published, this review provides a summary of how the estimates from these various sources are merged into finished products. Emphasis is placed on the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) and the Advanced Weather Interactive Processing System (AWIPS) Multisensor Precipitation Estimator (MPE) algorithms that utilize a combination of in situ rain gauges and remotely sensed measurements to provide a real-time suite of gridded radar and multisensor precipitation products. These two algorithm suites work in series to combine both computer-automated and human-interactive techniques, and they are used routinely at NWS field offices (river forecast centers (RFCs) and weather forecast offices (WFOs)) to support NWS's broader hydrologic missions. The resulting precipitation products are also available to scientists and engineers outside the NWS; a summary of charac- teristics and sources of these products is presented. DOI: 10.1061/(ASCE)HE.1943-5584.0000523. © 2013 American Society of Civil Engineers.

Radar and Multisensor Precipitation Estimation Techniques in National Weather Service Hydrologic Operations

The Range Correction Algorithm (RCA, Seo et al 2000), which has been developed by the Office of Hydrologic Development (OHD), National Weather Service (NWS), National Oceanic and Atmospheric Administration (NOAA), functions to reduce rangedependent biases in radar precipitation estimates due to nonuniform vertical profiles of reflectivity (VPR) It improves estimates of stratiform precipitation by approximating the mean VPR over the entire radar umbrella, then using the mean VPR to calculate and apply range-dependent multiplicative correction factors to radar reflectivity and/or Z-R precipitation estimates RCA reduces bright band contamination at near-to-mid ranges from the radar, and enhances rainfall estimates at farther ranges, where the radar beam typically intercepts ice particles above the melting layer To deal with situations of embedded convection, a companion algorithm, the Convective-Stratiform Separation Algorithm (CSSA, Seo et al 2002, Ding et al 2003), has been implemented in conjunction with RCA If CSSA identifies those precipitation areas that are convective in nature; neither such areas contribute to the model of the stratiform VPR, nor is range correction applied there

A multi-site evaluation of the range correction and convective-stratiform separation algorithms for improving WSR-88D rainfall estimates

Since the summer of 2008, the Weather Surveillance Radar-1988 Doppler (WSR-88D) has had the capability to process base reflectivity data at eight times higher spatial resolution than previously (250 m x 0.5o vs. 1000 m x 1.0o). While this “super-resolution” is only being applied to some base radar products during the initial phase of implementation, we wish to determine what benefits may be achieved from its application to rainfall accumulation products such as those of the WSR-88D Precipitation Processing System (PPS). For example, higher radar spatial resolution may be expected to result in better detection of small-scale, heavy rain patterns and, in turn, better distribution of that rain to topographic features such as small-scale stream basins. However, these potential benefits might be offset by factors known to cause discrepancies between quantitative precipitation estimates (QPE) determined aloft and the amounts and distribution of rainfall realized at the ground, including subbeam advection, evaporation, and hydrometeor interactions. Indeed, the impact of these factors may be exacerbated when QPEs are analyzed at finer spatial scales.

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Dennis Miller

Papers

The WSR-88D Rainfall Algorithm

Real-time adjustment of range-dependent biases in WSR-88D rainfall estimates due to nonuniform vertical profile of reflectivity

Radar and Multisensor Precipitation Estimation Techniques in National Weather Service Hydrologic Operations

A multi-site evaluation of the range correction and convective-stratiform separation algorithms for improving WSR-88D rainfall estimates

High Resolution Radar Precipitation Evaluation