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Robert A. Maddox
,
Jian Zhang
,
Jonathan J. Gourley
, and
Kenneth W. Howard

Abstract

Terrain and radar beam-elevation data are used to examine the spatial coverage provided by the national operational network of Doppler weather radars. This information is of importance to a wide variety of users, and potential users, of radar data from the national network. Charts generated for radar coverage at 3 and 5 km above mean sea level show that radar surveillance near 700 and 500 hPa is very limited for some portions of the contiguous United States. Radar coverage charts at heights of 1, 2, and 3 km above ground level illustrate the extent of low-level radar data gathered above the actual land surface. These maps indicate how restricted the national radar network coverage is at low levels, which limits the usefulness of the radar data, especially for quantitative precipitation estimation. The analyses also identify several regions of the contiguous United States in which weather phenomena are sampled by many adjacent radars. Thus, these regions are characterized by very comprehensive radar information that could be used in many kinds of research studies.

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Alan Gerard
,
Steven M. Martinaitis
,
Jonathan J. Gourley
,
Kenneth W. Howard
, and
Jian Zhang

Abstract

The Multi-Radar Multi-Sensor (MRMS) system is an operational, state-of-the-science hydrometeorological data analysis and nowcasting framework that combines data from multiple radar networks, satellites, surface observational systems, and numerical weather prediction models to produce a suite of real-time, decision-support products every 2 min over the contiguous United States and southern Canada. The Flooded Locations and Simulated Hydrograph (FLASH) component of the MRMS system was designed for the monitoring and prediction of flash floods across small time and spatial scales required for urban areas given their rapid hydrologic response to precipitation. Developed at the National Severe Storms Laboratory in collaboration with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and other research entities, the objective for MRMS and FLASH is to be the world’s most advanced system for severe weather and storm-scale hydrometeorology, leveraging the latest science and observation systems to produce the most accurate and reliable hydrometeorological and severe weather analyses. NWS forecasters, the public, and the private sector utilize a variety of products from the MRMS and FLASH systems for hydrometeorological situational awareness and to provide warnings to the public and other users about potential impacts from flash flooding. This article will examine the performance of hydrometeorological products from MRMS and FLASH and provide perspectives on how NWS forecasters use these products in the prediction of flash flood events with an emphasis on the urban environment.

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Jonathan J. Gourley
,
David P. Jorgensen
,
Sergey Y. Matrosov
, and
Zachary L. Flamig

Abstract

Advanced remote sensing and in situ observing systems employed during the Hydrometeorological Testbed experiment on the American River basin near Sacramento, California, provided a unique opportunity to evaluate correction procedures applied to gap-filling, experimental radar precipitation products in complex terrain. The evaluation highlighted improvements in hourly radar rainfall estimation due to optimizing the parameters in the reflectivity-to-rainfall (ZR) relation, correcting for the range dependence in estimating R due to the vertical variability in Z in snow and melting-layer regions, and improving low-altitude radar coverage by merging rainfall estimates from two research radars operating at different frequencies and polarization states. This evaluation revealed that although the rainfall product from research radars provided the smallest bias relative to gauge estimates, in terms of the root-mean-square error (with the bias removed) and Pearson correlation coefficient it did not outperform the product from a nearby operational radar that used optimized ZR relations and was corrected for range dependence. This result was attributed to better low-altitude radar coverage with the operational radar over the upper part of the basin. In these regions, the data from the X-band research radar were not available and the C-band research radar was forced to use higher-elevation angles as a result of nearby terrain and tree blockages, which yielded greater uncertainty in surface rainfall estimates. This study highlights the challenges in siting experimental radars in complex terrain. Last, the corrections developed for research radar products were adapted and applied to an operational radar, thus providing a simple transfer of research findings to operational rainfall products yielding significantly improved skill.

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Jonathan J. Gourley
,
Yang Hong
,
Zachary L. Flamig
,
Li Li
, and
Jiahu Wang

Abstract

Rainfall products from radar, satellite, rain gauges, and combinations have been evaluated for a season of record rainfall in a heavily instrumented study domain in Oklahoma. Algorithm performance is evaluated in terms of spatial scale, temporal scale, and rainfall intensity. Results from this study will help users of rainfall products to understand their errors. Moreover, it is intended that developers of rainfall algorithms will use the results presented herein to optimize the contribution from available sensors to yield the most skillful multisensor rainfall products.

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Jonathan J. Gourley
,
Scott E. Giangrande
,
Yang Hong
,
Zachary L. Flamig
,
Terry Schuur
, and
Jasper A. Vrugt

Abstract

Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary.

To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.

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Yagmur Derin
,
Pierre-Emmanuel Kirstetter
,
Noah Brauer
,
Jonathan J. Gourley
, and
Jianxin Wang

Abstract

To understand and manage water systems under a changing climate and meet an increasing demand for water, a quantitative understanding of precipitation is most important in coastal regions. The capabilities of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B product for precipitation quantification are examined over three coastal regions of the United States: the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. A novel uncertainty analysis of IMERG is proposed that considers environmental and physical parameters such as elevation and distance to the coastline. The IMERG performance is traced back to its components, i.e., passive microwave (PMW), infrared (IR), and morphing-based estimates. The analysis is performed using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference at the native resolution of IMERG of 30 min and 0.1°. IMERG Final (IM-F) quantification performance heavily depends on the respective contribution of PMW, IR, and morph components. IM-F and its components overestimate the contribution of light rainfall (<1 mm h−1) and underestimate the contribution of high rainfall rates (>10 mm h−1) to the total rainfall volume. Strong regional dependencies are highlighted, especially over the West Coast, where the proximity of complex terrain to the coastline challenges precipitation estimates. Other major drivers are the distance from the coastline, elevation, and precipitation types, especially over the land and coast surface types, that highlight the impact of precipitation regimes.

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Qing Cao
,
Yang Hong
,
Jonathan J. Gourley
,
Youcun Qi
,
Jian Zhang
,
Yixin Wen
, and
Pierre-Emmanuel Kirstetter

Abstract

This study presents a statistical analysis of the vertical structure of precipitation measured by NASA–Japan Aerospace Exploration Agency’s (JAXA) Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in the region of southern California, Arizona, and western New Mexico, where the ground-based Next-Generation Radar (NEXRAD) network finds difficulties in accurately measuring surface precipitation because of beam blockages by complex terrain. This study has applied TRMM PR version-7 products 2A23 and 2A25 from 1 January 2000 to 26 October 2011. The seasonal, spatial, intensity-related, and type-related variabilities are characterized for the PR vertical profile of reflectivity (VPR) as well as the heights of storm, freezing level, and bright band. The intensification and weakening of reflectivity at low levels in the VPR are studied through fitting physically based VPR slopes. Major findings include the following: precipitation type is the most significant factor determining the characteristics of VPRs, the shape of VPRs also influences the intensity of surface rainfall rates, the characteristics of VPRs have a seasonal dependence with strong similarities between the spring and autumn months, and the spatial variation of VPR characteristics suggests that the underlying terrain has an impact on the vertical structure. The comprehensive statistical and physical analysis strengthens the understanding of the vertical structure of precipitation and advocates for the approach of VPR correction to improve surface precipitation estimation in complex terrain.

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Manabendra Saharia
,
Pierre-Emmanuel Kirstetter
,
Humberto Vergara
,
Jonathan J. Gourley
,
Yang Hong
, and
Marine Giroud

Abstract

Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.

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Jonathan J. Gourley
,
Robert A. Maddox
,
Kenneth W. Howard
, and
Donald W. Burgess

Abstract

Implementation of the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network provides the potential to monitor rainfall and snowfall accumulations at fine spatial and temporal resolutions. An automated, operational algorithm called the Precipitation Processing System (PPS) uses reflectivity data to estimate precipitation accumulations. The utility of these estimates has yet to be quantified in the Intermountain West during winter months. The accuracy of precipitation estimates from the operational PPS during cool-season, stratiform-precipitation events in Arizona is examined. In addition, a method, with the potential for automation, is developed to improve estimates of precipitation by calibrating infrared data (10.7-μm band) from Geostationary Operational Environmental Satellite-9 using reflectivity-derived rainfall rates from WSR-88D radar. The “multisensor” approach provides more accurate estimates of rainfall across lower elevations during cool-season extratropical storms. After the melting layer has been manually identified using volumetric radar reflectivity data, reflectivity measured in or above it is discarded. Melting-layer heights also indicate the altitude of the rain–snow line. This information is used to delineate and map frozen versus liquid precipitation types. Rain gauges are used as an independent, ground-based source to assess the magnitude of improvements made over PPS rainfall products. Although the technique is designed and evaluated over a limited area in Arizona, it may be applicable to many mountainous regions.

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Elizabeth M. Argyle
,
Jonathan J. Gourley
,
Zachary L. Flamig
,
Tracy Hansen
, and
Kevin Manross

ABSTRACT

Hazard Services is a software toolkit that integrates information management, hazard alerting, and communication functions into a single user interface. When complete, National Weather Service forecasters across the United States will use Hazard Services for operational issuance of weather and hydrologic alerts, making the system an instrumental part of the threat management process. As a new decision-support tool, incorporating an understanding of user requirements and behavior is an important part of building a system that is usable, allowing users to perform work-related tasks efficiently and effectively. This paper discusses the Hazard Services system and findings from a usability evaluation with a sample of end users. Usability evaluations are frequently used to support software and website development and can provide feedback on a system’s efficiency of use, effectiveness, and learnability. In the present study, a user-testing evaluation assessed task performance in terms of error rates, error types, response time, and subjective feedback from a questionnaire. A series of design recommendations was developed based on the evaluation’s findings. The recommendations not only further the design of Hazard Services, but they may also inform the designs of other decision-support tools used in weather and hydrologic forecasting.

Incorporating usability evaluation into the iterative design of decision-support tools, such as Hazard Services, can improve system efficiency, effectiveness, and user experience.

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