The GPM mission collects essential rain and snow data for scientific studies and societal benefit.

/pdf/the-global-precipitation-measurement-gpm-mission-for-science-3lwutytnuq.pdf

The Global Precipitation Measurement (GPM) Mission for Science and Society

The National Mosaic and Multi-sensor QPE (Quantitative Precipitation Estimation), or “NMQ”, system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory, the Federal Aviation Administration's Aviation Weather Research Program, and the Salt River Project. Further development has continued with additional support from the National Weather Service (NWS) Office of Hydrologic Development, the NWS Office of Climate, Water, and Weather Services, and the Central Weather Bureau of Taiwan. The objectives of NMQ research and development (R&D) are 1) to develop a hydrometeorological platform for assimilating different observational networks toward creating high spatial and temporal resolution multisensor QPEs for f lood warnings and water resource management and 2) to develop a seamless high-resolution national 3D grid of radar reflectivity for severe weather detection, data assimilation, numerical weather prediction model verif...

National Mosaic and Multi-Sensor QPE (NMQ) System: Description, Results, and Future Plans

Rapid advancements of computer technologies in recent years made the real-time transferring and integration of high-volume, multisource data at a centralized location a possibility. The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction demonstrates such capabilities by integrating about 180 operational weather radars from the conterminous United States and Canada into a seamless national 3D radar mosaic with very high spatial (1 km) and temporal (2 min) resolution. The radar data can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. This paper provides an overview of the initial operating capabilities of MRMS QPE products.

Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting...

The Evolution of the Goddard Profiling Algorithm to a Fully Parametric Scheme

[1] A conceptual model has been developed to create high-resolution precipitation analyses over land by merging gauge-based analysis and CMORPH satellite estimates using data over China for a 5 month period from April to September 2007. A two-step strategy is adopted to remove the bias inherent in the CMORPH satellite precipitation estimates and to combine the bias-corrected satellite estimates with the gauge analysis. First, bias correction is performed for the CMORPH estimates by matching the probability density function (PDF) of the satellite data with that of the gauge analysis using colocated data pairs over a spatial domain of 5°lat/lon centering at the target grid box and over a time period of 30 days, ending at the target date. The spatial domain is expanded wherever necessary over gauge-sparse regions to ensure the collection of a sufficient number of gauge-satellite data pairs. The bias-corrected CMORPH precipitation estimates are then combined with the gauge analysis through the optimal interpolation (OI) technique, in which the bias-corrected CMORPH is used as the first guess while the gauge data are used as the observations to modify the first guess over regions with station coverage. Error statistics are computed for the input gauge and satellite data to maximize the performance of the high-resolution merged analysis of daily precipitation. Cross-validation tests and comparisons against independent gauge observations demonstrate feasibility and effectiveness of the conceptual algorithm in constructing merged precipitation analysis with substantially removed bias and significantly improved pattern agreements compared with those of the input gauge and satellite data.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2011JD016118

A conceptual model for constructing high‐resolution gauge‐satellite merged precipitation analyses

Four-dimensional dynamic radar mosaic

An automated algorithm for radar beam occultation

Over the last five years the Central Weather Bureau of Taiwan and the United States NOAA/National Severe Storms Laboratory have been involved in a research and development initiative to improve the monitoring and prediction of flash floods, debris flows, and severe storms for the Taiwan environment. The initiative has produced a system that integrates observations from weather radars, rain gauges, satellites, and numerical weather prediction model fields to produce high resolution (1 km to 500 m) and rapid update (10-min) rainfall and severe storm monitoring products. These prototype products are assessed for potential use by government agencies and emergency managers for flood, flash flood, and mudslide warnings and water resource managements. The system also facilitates collaborations with academic communities for research and development of radar applications including QPE and nowcasting. This paper overviews the system structure and products, the research activities supporting the system, and the challenges faced in producing high resolution, accurate QPE for Taiwan.

Carrie Langston

Papers

National Mosaic and Multi-Sensor QPE (NMQ) System: Description, Results, and Future Plans

Four-dimensional dynamic radar mosaic

An automated algorithm for radar beam occultation

High-Resolution QPE System for Taiwan

MRMS Dual-Polarization Radar Synthetic QPE