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Yu Zhang
,
Yang Hong
,
Xuguang Wang
,
Jonathan J. Gourley
,
Xianwu Xue
,
Manabendra Saharia
,
Guangheng Ni
,
Gaili Wang
,
Yong Huang
,
Sheng Chen
, and
Guoqiang Tang

Abstract

Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.

Full access
Jonathan J. Gourley
,
Humberto Vergara
,
Ami Arthur
,
Robert A. Clark III
,
Dennis Staley
,
John Fulton
,
Laura Hempel
,
David C. Goodrich
,
Katherine Rowden
, and
Peter R. Robichaud
Free access
Steven M. Martinaitis
,
Jonathan J. Gourley
,
Zachary L. Flamig
,
Elizabeth M. Argyle
,
Robert A. Clark III
,
Ami Arthur
,
Brandon R. Smith
,
Jessica M. Erlingis
,
Sarah Perfater
, and
Benjamin Albright

Abstract

There are numerous challenges with the forecasting and detection of flash floods, one of the deadliest weather phenomena in the United States. Statistical metrics of flash flood warnings over recent years depict a generally stagnant warning performance, while regional flash flood guidance utilized in warning operations was shown to have low skill scores. The Hydrometeorological Testbed—Hydrology (HMT-Hydro) experiment was created to allow operational forecasters to assess emerging products and techniques designed to improve the prediction and warning of flash flooding. Scientific goals of the HMT-Hydro experiment included the evaluation of gridded products from the Multi-Radar Multi-Sensor (MRMS) and Flooded Locations and Simulated Hydrographs (FLASH) product suites, including the experimental Coupled Routing and Excess Storage (CREST) model, the application of user-defined probabilistic forecasts in experimental flash flood watches and warnings, and the utility of the Hazard Services software interface with flash flood recommenders in real-time experimental warning operations. The HMT-Hydro experiment ran in collaboration with the Flash Flood and Intense Rainfall (FFaIR) experiment at the Weather Prediction Center to simulate the real-time workflow between a national center and a local forecast office, as well as to facilitate discussions on the challenges of short-term flash flood forecasting. Results from the HMT-Hydro experiment highlighted the utility of MRMS and FLASH products in identifying the spatial coverage and magnitude of flash flooding, while evaluating the perception and reliability of probabilistic forecasts in flash flood watches and warnings.

NSSL scientists and NWS forecasters evaluate new tools and techniques through real-time test bed operations for the improvement of flash flood detection and warning operations.

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Steven M. Martinaitis
,
Benjamin Albright
,
Jonathan J. Gourley
,
Sarah Perfater
,
Tiffany Meyer
,
Zachary L. Flamig
,
Robert A. Clark
,
Humberto Vergara
, and
Mark Klein

Abstract

The flash flood event of 23 June 2016 devastated portions of West Virginia and west-central Virginia, resulting in 23 fatalities and 5 new record river crests. The flash flooding was part of a multiday event that was classified as a billion-dollar disaster. The 23 June 2016 event occurred during real-time operations by two Hydrometeorology Testbed (HMT) experiments. The Flash Flood and Intense Rainfall (FFaIR) experiment focused on the 6–24-h forecast through the utilization of experimental high-resolution deterministic and ensemble numerical weather prediction and hydrologic model guidance. The HMT Multi-Radar Multi-Sensor Hydro (HMT-Hydro) experiment concentrated on the 0–6-h time frame for the prediction and warning of flash floods primarily through the experimental Flooded Locations and Simulated Hydrographs product suite. This study describes the various model guidance, applications, and evaluations from both testbed experiments during the 23 June 2016 flash flood event. Various model outputs provided a significant precipitation signal that increased the confidence of FFaIR experiment participants to issue a high risk for flash flooding for the region between 1800 UTC 23 June and 0000 UTC 24 June. Experimental flash flood warnings issued during the HMT-Hydro experiment for this event improved the probability of detection and resulted in a 63.8% increase in lead time to 84.2 min. Isolated flash floods in Kentucky demonstrated the potential to reduce the warned area. Participants characterized how different model guidance and analysis products influenced the decision-making process and how the experimental products can help shape future national and local flash flood operations.

Free access
Sheng Chen
,
Jonathan J. Gourley
,
Yang Hong
,
P. E. Kirstetter
,
Jian Zhang
,
Kenneth Howard
,
Zachary L. Flamig
,
Junjun Hu
, and
Youcun Qi

Abstract

Quantitative precipitation estimation (QPE) products from the next-generation National Mosaic and QPE system (Q2) are cross-compared to the operational, radar-only product of the National Weather Service (Stage II) using the gauge-adjusted and manual quality-controlled product (Stage IV) as a reference. The evaluation takes place over the entire conterminous United States (CONUS) from December 2009 to November 2010. The annual comparison of daily Stage II precipitation to the radar-only Q2Rad product indicates that both have small systematic biases (absolute values > 8%), but the random errors with Stage II are much greater, as noted with a root-mean-squared difference of 4.5 mm day−1 compared to 1.1 mm day−1 with Q2Rad and a lower correlation coefficient (0.20 compared to 0.73). The Q2 logic of identifying precipitation types as being convective, stratiform, or tropical at each grid point and applying differential ZR equations has been successful in removing regional biases (i.e., overestimated rainfall from Stage II east of the Appalachians) and greatly diminishes seasonal bias patterns that were found with Stage II. Biases and radar artifacts along the coastal mountain and intermountain chains were not mitigated with rain gauge adjustment and thus require new approaches by the community. The evaluation identifies a wet bias by Q2Rad in the central plains and the South and then introduces intermediate products to explain it. Finally, this study provides estimates of uncertainty using the radar quality index product for both Q2Rad and the gauge-corrected Q2RadGC daily precipitation products. This error quantification should be useful to the satellite QPE community who use Q2 products as a reference.

Full access
Jonathan J. Gourley
,
Zachary L. Flamig
,
Humberto Vergara
,
Pierre-Emmanuel Kirstetter
,
Robert A. Clark III
,
Elizabeth Argyle
,
Ami Arthur
,
Steven Martinaitis
,
Galateia Terti
,
Jessica M. Erlingis
,
Yang Hong
, and
Kenneth W. Howard

Abstract

This study introduces the Flooded Locations and Simulated Hydrographs (FLASH) project. FLASH is the first system to generate a suite of hydrometeorological products at flash flood scale in real-time across the conterminous United States, including rainfall average recurrence intervals, ratios of rainfall to flash flood guidance, and distributed hydrologic model–based discharge forecasts. The key aspects of the system are 1) precipitation forcing from the National Severe Storms Laboratory (NSSL)’s Multi-Radar Multi-Sensor (MRMS) system, 2) a computationally efficient distributed hydrologic modeling framework with sufficient representation of physical processes for flood prediction, 3) capability to provide forecasts at all grid points covered by radars without the requirement of model calibration, and 4) an open-access development platform, product display, and verification system for testing new ideas in a real-time demonstration environment and for fostering collaborations.

This study assesses the FLASH system’s ability to accurately simulate unit peak discharges over a 7-yr period in 1,643 unregulated gauged basins. The evaluation indicates that FLASH’s unit peak discharges had a linear and rank correlation of 0.64 and 0.79, respectively, and that the timing of the peak discharges has errors less than 2 h. The critical success index with FLASH was 0.38 for flood events that exceeded action stage. FLASH performance is demonstrated and evaluated for case studies, including the 2013 deadly flash flood case in Oklahoma City, Oklahoma, and the 2015 event in Houston, Texas—both of which occurred on Memorial Day weekends.

Full access
Jonathan J. Gourley
,
Yang Hong
,
Zachary L. Flamig
,
Ami Arthur
,
Robert Clark
,
Martin Calianno
,
Isabelle Ruin
,
Terry Ortel
,
Michael E. Wieczorek
,
Pierre-Emmanuel Kirstetter
,
Edward Clark
, and
Witold F. Krajewski

Despite flash flooding being one of the most deadly and costly weather-related natural hazards worldwide, individual datasets to characterize them in the United States are hampered by limited documentation and can be difficult to access. This study is the first of its kind to assemble, reprocess, describe, and disseminate a georeferenced U.S. database providing a long-term, detailed characterization of flash flooding in terms of spatiotemporal behavior and specificity of impacts. The database is composed of three primary sources: 1) the entire archive of automated discharge observations from the U.S. Geological Survey that has been reprocessed to describe individual flooding events, 2) flash-flooding reports collected by the National Weather Service from 2006 to the present, and 3) witness reports obtained directly from the public in the Severe Hazards Analysis and Verification Experiment during the summers 2008–10. Each observational data source has limitations; a major asset of the unified flash flood database is its collation of relevant information from a variety of sources that is now readily available to the community in common formats. It is anticipated that this database will be used for many diverse purposes, such as evaluating tools to predict flash flooding, characterizing seasonal and regional trends, and improving understanding of dominant flood-producing processes. We envision the initiation of this community database effort will attract and encompass future datasets.

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Véronique Ducrocq
,
Isabelle Braud
,
Silvio Davolio
,
Rossella Ferretti
,
Cyrille Flamant
,
Agustin Jansa
,
Norbert Kalthoff
,
Evelyne Richard
,
Isabelle Taupier-Letage
,
Pierre-Alain Ayral
,
Sophie Belamari
,
Alexis Berne
,
Marco Borga
,
Brice Boudevillain
,
Olivier Bock
,
Jean-Luc Boichard
,
Marie-Noëlle Bouin
,
Olivier Bousquet
,
Christophe Bouvier
,
Jacopo Chiggiato
,
Domenico Cimini
,
Ulrich Corsmeier
,
Laurent Coppola
,
Philippe Cocquerez
,
Eric Defer
,
Julien Delanoë
,
Paolo Di Girolamo
,
Alexis Doerenbecher
,
Philippe Drobinski
,
Yann Dufournet
,
Nadia Fourrié
,
Jonathan J. Gourley
,
Laurent Labatut
,
Dominique Lambert
,
Jérôme Le Coz
,
Frank S. Marzano
,
Gilles Molinié
,
Andrea Montani
,
Guillaume Nord
,
Mathieu Nuret
,
Karim Ramage
,
William Rison
,
Odile Roussot
,
Frédérique Said
,
Alfons Schwarzenboeck
,
Pierre Testor
,
Joël Van Baelen
,
Béatrice Vincendon
,
Montserrat Aran
, and
Jorge Tamayo

The Mediterranean region is frequently affected by heavy precipitation events associated with flash floods, landslides, and mudslides that cause hundreds of millions of euros in damages per year and, often, casualties. A major field campaign was devoted to heavy precipitation and f lash f loods from 5 September to 6 November 2012 within the framework of the 10-yr international Hydrological Cycle in the Mediterranean Experiment (HyMeX) dedicated to the hydrological cycle and related high-impact events. The 2-month field campaign took place over the northwestern Mediterranean Sea and its surrounding coastal regions in France, Italy, and Spain. The observation strategy of the field experiment was devised to improve knowledge of the following key components leading to heavy precipitation and flash flooding in the region: 1) the marine atmospheric f lows that transport moist and conditionally unstable air toward the coasts, 2) the Mediterranean Sea acting as a moisture and energy source, 3) the dynamics and microphysics of the convective systems producing heavy precipitation, and 4) the hydrological processes during flash floods. This article provides the rationale for developing this first HyMeX field experiment and an overview of its design and execution. Highlights of some intensive observation periods illustrate the potential of the unique datasets collected for process understanding, model improvement, and data assimilation.

Full access
Suzanne Van Cooten
,
Kevin E. Kelleher
,
Kenneth Howard
,
Jian Zhang
,
Jonathan J. Gourley
,
John S. Kain
,
Kodi Nemunaitis-Monroe
,
Zac Flamig
,
Heather Moser
,
Ami Arthur
,
Carrie Langston
,
Randall Kolar
,
Yang Hong
,
Kendra Dresback
,
Evan Tromble
,
Humberto Vergara
,
Richard A Luettich Jr.
,
Brian Blanton
,
Howard Lander
,
Ken Galluppi
,
Jessica Proud Losego
,
Cheryl Ann Blain
,
Jack Thigpen
,
Katie Mosher
,
Darin Figurskey
,
Michael Moneypenny
,
Jonathan Blaes
,
Jeff Orrock
,
Rich Bandy
,
Carin Goodall
,
John G. W. Kelley
,
Jason Greenlaw
,
Micah Wengren
,
Dave Eslinger
,
Jeff Payne
,
Geno Olmi
,
John Feldt
,
John Schmidt
,
Todd Hamill
,
Robert Bacon
,
Robert Stickney
, and
Lundie Spence

The objective of the Coastal and Inland Flooding Observation and Warning (CI-FLOW) project is to prototype new hydrometeorologic techniques to address a critical NOAA service gap: routine total water level predictions for tidally influenced watersheds. Since February 2000, the project has focused on developing a coupled modeling system to accurately account for water at all locations in a coastal watershed by exchanging data between atmospheric, hydrologic, and hydrodynamic models. These simulations account for the quantity of water associated with waves, tides, storm surge, rivers, and rainfall, including interactions at the tidal/surge interface.

Within this project, CI-FLOW addresses the following goals: i) apply advanced weather and oceanographic monitoring and prediction techniques to the coastal environment; ii) prototype an automated hydrometeorologic data collection and prediction system; iii) facilitate interdisciplinary and multiorganizational collaborations; and iv) enhance techniques and technologies that improve actionable hydrologic/hydrodynamic information to reduce the impacts of coastal flooding. Results are presented for Hurricane Isabel (2003), Hurricane Earl (2010), and Tropical Storm Nicole (2010) for the Tar–Pamlico and Neuse River basins of North Carolina. This area was chosen, in part, because of the tremendous damage inflicted by Hurricanes Dennis and Floyd (1999). The vision is to transition CI-FLOW research findings and technologies to other U.S. coastal watersheds.

Full access