The HMT Multi-Radar Multi-Sensor Hydro Experiment

Martinaitis, Steven M.; Gourley, Jonathan J.; Flamig, Zachary L.; Argyle, Elizabeth M.; Clark, Robert A.; Arthur, Ami; Smith, Brandon; Erlingis, Jessica M.; Perfater, Sarah; Albright, Benjamin

doi:10.1175/bams-d-15-00283.1

Cited by 19 publications

(19 citation statements)

References 17 publications

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“…The Hydrometeorology Testbed (HMT) MRMS -Hydrology (HMT-Hydro) experiments used a structured framework to better understand how experimental NWP forecasts and hydrologic model guidance affected warnings for flash flood events (Martinaitis et al 2017(Martinaitis et al , 2020. Forecasters and hydrologists from NWS WFOs and River Forecast Centers evaluated the Similar to other EWP experiments such as the GLM demonstration, discussion with participants underscored the impact of having subject-matter experts present with forecasters and other end users (Martinaitis et al 2020).…”

Section: Flash Flood Experimentsmentioning

confidence: 99%

The Experimental Warning Program of NOAA’s Hazardous Weather Testbed

Meyer

Krocak²,

Smith

et al. 2021

Bulletin of the American Meteorological Society

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NOAA’s Hazardous Weather Testbed (HWT) is a physical space and research framework to foster collaboration and evaluate emerging tools, technology, and products for NWS operations. The HWT’s Experimental Warning Program (EWP) focuses on research, technology, and communication that may improve severe and hazardous weather warnings and societal response. The EWP was established with three fundamental hypotheses: 1) collaboration with operational meteorologists increases the speed of the transition process and rate of adoption of beneficial applications and technology, 2) the transition of knowledge between research and operations benefits both the research and operational communities, and 3) including end-users in experiments generates outcomes that are more reliable and useful for society. The EWP is designed to mimic the operations of any NWS Forecast Office, providing the opportunity for experiments to leverage live and archived severe weather activity anywhere in the United States. During the first decade of activity in the EWP, 15 experiments covered topics including: new radar and satellite applications, storm-scale numerical models and data assimilation, total lightning use in severe weather forecasting, and multiple social science and end-user topics. The experiments range from exploratory and conceptual research to more controlled experimental design to establish statistical patterns and causal relationships. The EWP brought more than 400 NWS forecasters, 60 emergency managers, and 30 broadcast meteorologists to the HWT to participate in live demonstrations, archive events, and data-denial experiments influencing today’s operational warning environment and shaping the future of warning research, technology, and communication for years to come.

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Section: Flash Flood Experimentsmentioning

confidence: 99%

The Experimental Warning Program of NOAA’s Hazardous Weather Testbed

Meyer

Krocak²,

Smith

et al. 2021

Bulletin of the American Meteorological Society

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“…Maximum unit streamflow is a deterministic hydrologic product that is used to diagnose areas of flash flooding potential, as well as to identify the relative severity of the potential flash flooding impacts ( Fig. 2d; Gourley et al 2017;Martinaitis et al 2017). The maximum unit streamflow (m 3 s 21 km 22 ) is computed by taking the maximum streamflow throughout the forecast period and then dividing this number by the upstream drainage area.…”

Section: ) Maximum Unit Streamflowmentioning

confidence: 99%

“…One example of this is the NSSL Flooded Locations and Simulated Hydrographs (FLASH; Gourley et al 2017) system that generates deterministic stream discharge products using the Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a forcing mechanism (Zhang et al 2016). The successful evaluation of the deterministic flash flood products from the FLASH system during the 2014-16 MRMS Hydrometeorology Testbed-MRMS Hydrology (HMT-Hydro) experiment (Martinaitis et al 2017) resulted in the transition of the FLASH suite to the National Centers for Environmental Prediction (NCEP) in 2018. The FLASH products are used routinely in NWS weather forecast offices and at the NWS Weather Prediction Center for flash flood monitoring, detection, and decision making.…”

Section: Introductionmentioning

confidence: 99%

The Coupling of NSSL Warn-on-Forecast and FLASH Systems for Probabilistic Flash Flood Prediction

Yussouf

Wilson

Martinaitis

et al. 2020

Journal of Hydrometeorology

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The goal of the National Oceanic and Atmospheric Administration’s (NOAA) Warn-on-Forecast (WoF) program is to provide frequently updating, probabilistic model guidance that will enable National Weather Service (NWS) forecasters to produce more continuous communication of hazardous weather threats (e.g., heavy rainfall, flash floods, damaging wind, large hail, and tornadoes) between the watch and warning temporal and spatial scales. To evaluate the application of this WoF concept for probabilistic short-term flash flood prediction, the 0–3-h rainfall forecasts from NOAA National Severe Storms Laboratory’s (NSSL) experimental WoF System (WoFS) were integrated as the forcing to the NWS operational hydrologic modeling core within the Flooded Locations and Simulated Hydrographs (FLASH) system. Initial assessment of the potential impacts of probabilistic short-term flash flood forecasts from this coupled atmosphere–hydrology (WoFS-FLASH) modeling system were evaluated in the 2018 Hydrometeorology Testbed Multi-Radar Multi-Sensor Hydrology experiment held in Norman, Oklahoma. During the 3-week experiment period, a total of nine NWS forecasters analyzed three retrospective flash flood events in archive mode. This study will describe specifically what information participants extracted from the WoFS-FLASH products during these three archived events, and how this type of information is expected to impact operational decision-making processes. Overall feedback from the testbed participants’ evaluations show promise for the coupled NSSL WoFS-FLASH system probabilistic flash flood model guidance to enable earlier assessment and detection of flash flood threats and to advance the current warning lead time for these events.

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“…The RF classifier was trained on reported flash flood events . To prevent overrepresentation of dynamic vulnerability in counties with possibly high values of static (e.g., flashiness) or semistatic predictors (e.g., commuters) but no actual flash flooding, the probabilities in counties with low daily unit peak discharge (<2 m 3 s −1 km −2 ) are mapped in the low likelihood category . The counties with vehicle‐related victims are extracted depending on the fatality day reported in the Storm Data fatality file and highlighted with red boundaries on the produced daily risk maps.…”

Section: Building a Prototype: May 2015 Flash Floodsmentioning

confidence: 99%

“…tual flash flooding, the probabilities in counties with low daily unit peak discharge (<2 m 3 s −1 km −2 ) are mapped in the low likelihood category. (79) The counties with vehicle-related victims are extracted depending on the fatality day reported in the Storm Data fatality file and highlighted with red boundaries on the produced daily risk maps. Local storm reports (LSRs) are mapped with red dots to illustrate flash flood emergency issues such as road flooding, closures, and rescues.…”

Section: Mapping Dynamic Human Risk Related To Vehiclesmentioning

confidence: 99%

Toward Probabilistic Prediction of Flash Flood Human Impacts

et al. 2017

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This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance-specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle-related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time- and space-dependent human vulnerability and risk assessment for short-fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact-based predictive models for flash flood casualties using machine-learning approaches in the future.

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The HMT Multi-Radar Multi-Sensor Hydro Experiment

Cited by 19 publications

References 17 publications

The Experimental Warning Program of NOAA’s Hazardous Weather Testbed

The Experimental Warning Program of NOAA’s Hazardous Weather Testbed

The Coupling of NSSL Warn-on-Forecast and FLASH Systems for Probabilistic Flash Flood Prediction

Toward Probabilistic Prediction of Flash Flood Human Impacts

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