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

Abstract

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 verification, and aviation product development. Through about ten years of R&D, a real-time NMQ system has been implemented (http://nmq.ou.edu). Since June 2006, the system has been generating high-resolution 3D reflectivity mosaic grids (31 vertical levels) and a suite of severe weather and QPE products in real-time for the conterminous United States at a 1-km horizontal resolution and 2.5 minute update cycle. The experimental products are provided in real-time to end users ranging from government agencies, universities, research institutes, and the private sector and have been utilized in various meteorological, aviation, and hydrological applications. Further, a number of operational QPE products generated from different sensors (radar, gauge, satellite) and by human experts are ingested in the NMQ system and the experimental products are evaluated against the operational products as well as independent gauge observations in real time. The NMQ is a fully automated system. It facilitates systematic evaluations and advances of hydrometeorological sciences and technologies in a real-time environment and serves as a test bed for rapid science-to-operation infusions. This paper describes scientific components of the NMQ system and presents initial evaluation results and future development plans of the system.

Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm

“…The NWS MPE was conceived in the early 2000s to provide a full range of capabilities for radar QPE correction and multi-sensor blending [1,2,16]. The earlier versions of MPE ingested radar-only precipitation estimates, including the Digital Precipitation Array (DPA) generated by the WSR-88D Precipitation Processing System (PPS), rain gauge records, and externally defined radar climatology to create bias-corrected multi-radar and multi-sensor QPEs.…”

“…The values of E[Z 0 |z R , z S , z 0 >0] and Var[Z 0 |z R , z S , z 0 >0] are estimated as in [16], but have different weighting coefficients which are assigned to the radar and satellite dataset covariance matrix/vector according to the value of RRI in the domain A.…”

Coupled prediction of flood response and debris flow initiation during warm- and cold-season events in the Southern Appalachians, USA

“…The local gauge-corrected hourly radar-based QPE product (Q2RAD_HSR_GC, Q2 in short) at high spatial resolution (0.01 • × 0.01 • ) (Vasiloff et al, 2007;Zhang et al, 2011) was used in this study. We first evaluate the Q2 datasets using the GSMRGN rain gauge observations to characterize the spatial-temporal error structures in Q2, and then apply bias correction to improve the accuracy of Q2 based on the error structures identified.…”

The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network

“…From an observational perspective, tools such as rain gauges (Schneider et al, 2011), weather radars (Huuskonen et al, 2013;Zhang et al, 2011) and satellite sensors (Adler et al, 2003) can be employed to monitor and document precipitation behaviour. In recent years, considerable efforts have been dedicated to produce climate-quality observational data records of precipitation based solely on one of these tools or a combination of them.…”