David Kitzmiller

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

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 Administrations National Severe Storms Laboratory, the Federal Aviation Administrations 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...

IMPROVING QPE AND VERY SHORT TERM QPF An Initiative for a Community-Wide Integrated Approach

Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) are critical to accurate monitoring and prediction of water-related hazards and water resources. While tremendous progress has been made in the last quarter-century in many areas of QPE and VSTQPF, significant gaps continue to exist in both knowledge and capabilities that are necessary to produce accurate high-resolution precipitation estimates at the national scale for a wide spectrum of users. Toward this goal, a national next-generation QPE and VSTQPF (Q2) workshop was held in Norman, Oklahoma, on 28–30 June 2005. Scientists, operational forecasters, water managers, and stakeholders from public and private sectors, including academia, presented and discussed a broad range of precipitation and forecasting topics and issues, and developed a list of science focus areas. To meet the nations needs for the precipitation information effectively, the authors herein propose a community-wide int...

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

AbstractRapid 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.

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

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 NWSs 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.

Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

AbstractThis study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QP...

Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States

AbstractThis study evaluated 24-, 6-, and 1-h radar precipitation estimated from the National Mosaic and Multisensor Quantitative Precipitation Estimation System (NMQ) and the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) over the conterminous United States (CONUS) for the warm season April–September 2009 and the cool season October 2009–March 2010. Precipitation gauge observations from the Automated Surface Observing System (ASOS) were used as the ground truth. Gridded StageIV multisensor precipitation estimates were applied for supplementary verification. The comparison of the two systems consisted of a series of analyses including the linear correlation coefficient (CC) and the root-mean-square error (RMSE) between the radar precipitation estimates and the gauge observations, large precipitation amount detection categorical scores, and the reliability of precipitation amount distribution. Data stratified for the 12 CONUS River Forecast Centers (RFCs) and for th...

Effects of Retrospective Gauge-Based Readjustment of Multisensor Precipitation Estimates on Hydrologic Simulations

This paper presents methodologies for mitigating temporally inconsistent biases in National Weather Service (NWS) real-time multisensor quantitative precipitation estimates (MQPEs) through rain gauge‐ based readjustments, and examines their effects on streamflow simulations. In this study, archived MQPEs over 1997‐2006 for the Middle Atlantic River Forecast Center (MARFC) area of responsibility were readjusted at monthly and daily scales using two gridded gauge products. The original and readjusted MQPEs were applied as forcing to the NWS Distributed Hydrologic Model for 12 catchments in the domain of MARFC. The resultant hourly streamflow simulations were compared for two subperiods divided along November 2003, when a software error that gave rise to a low bias in MQPEs was fixed. It was found that readjustment at either time scale improved the consistency in the bias in streamflow simulations. For the earlier period, independent monthly and daily readjustments considerably improved the streamflow simulations for most basins as judged by bias and correlation. By contrast, for the later period the effects were mixedacrossbasins.Itwasalsofoundthat1)readjustmentstendedtobemoreeffectiveinthecoolratherthan warm season, 2) refining the readjustment resolution to daily had mixed effects on streamflow simulations, and3)at thedaily scale,redistributing gaugerainfallis beneficial forperiodswithsubstantial missingMQPEs.

Comparative Strengths of SCaMPR Satellite QPEs with and without TRMM Ingest versus Gridded Gauge-Only Analyses

AbstractThis paper assesses the accuracy of satellite quantitative precipitation estimates (QPEs) from two versions of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm relative to that of gridded gauge-only QPEs. The second version of SCaMPR uses the QPEs from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and Microwave Imager as predictands whereas the first version does not. The assessments were conducted for 22 catchments in Texas and Louisiana against National Weather Service operational multisensor QPE. Particular attention was given to the density below which SCaMPR QPEs outperform gauge-only QPEs and effects of TRMM ingest. Analyses indicate that SCaMPR QPEs can be competitive in terms of correlation and CSI against sparse gauge networks (with less than one gauge per 3200–12 000 km2) and over 1–3-h scale, but their relative strengths diminish with temporal aggregation. In addition, the major advantage of SCaMPR QPEs is its relatively low false alarm rates...

Utility of SCaMPR Satellite versus Ground-Based Quantitative Precipitation Estimates in Operational Flood Forecasting: The Effects of TRMM Data Ingest

AbstractThis study examines the utility of satellite-based quantitative precipitation estimates (QPEs) from the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for hydrologic prediction. In this work, two sets of SCaMPR QPEs, one without and the other with Tropical Rainfall Measurement Mission (TRMM) version 6 data integrated, were used as input forcing to the lumped National Weather Service hydrologic model to retrospectively generate flow simulations for 10 Texas catchments over 2000–07. The year 2000 was used for the model spinup, 2001–04 for calibration, and 2005–07 for validation. The results were validated using observed streamflow alongside similar simulations obtained using interpolated gauge QPEs with varying gauge network densities, and still others using the operational radar–gauge multisensor product (MAPX). The focus of the evaluation was on the high-flow events. A number of factors that could impact the relative utility of SCaMPR satellite QPE and gauge-only analysis...

Multisensor Precipitation Estimation in the NOAA National Weather Service: Recent Advances

The NOAA National Weather Services current capabilities for producing real-time, multisensor precipitation estimates have been focused on providing input to hydrologic prediction models for mainstem rivers and larger headwaters. New functions are now being implemented to produce analyses and short-range forecasts for flash flood prediction operations, using a grid mesh of 1 km and a 5-minute update cycle with minimal time lag. The functionality produces rainrate and rainfall analyses and forecasts from multiple radars, covering entire County Warning Areas. The characteristics of the new High-resolution Precipitation Estimator and High-resolution Precipitation Nowcaster and their output products are described below.