Advancing Tropical Cyclone Precipitation Forecast Verification Methods and Tools

Author:

Newman Kathryn M.12,Brown Barbara12,Gotway John Halley12,Bernardet Ligia32,Biswas Mrinal12,Jensen Tara12,Nance Louisa12

Affiliation:

1. a National Center for Atmospheric Research, Boulder, Colorado

2. c Developmental Testbed Center, Boulder, Colorado

3. b Global Systems Laboratory, National Oceanic and Atmospheric Administration, Boulder, Colorado

Abstract

Abstract Tropical cyclone (TC) forecast verification techniques have traditionally focused on track and intensity, as these are some of the most important characteristics of TCs and are often the principal verification concerns of operational forecast centers. However, there is a growing need to verify other aspects of TCs as process-based validation techniques may be increasingly necessary for further track and intensity forecast improvements as well as improving communication of the broad impacts of TCs including inland flooding from precipitation. Here we present a set of TC-focused verification methods available via the Model Evaluation Tools (MET) ranging from traditional approaches to the application of storm-centric coordinates and the use of feature-based verification of spatially defined TC objects. Storm-relative verification using observed and forecast tracks can be useful for identifying model biases in precipitation accumulation in relation to the storm center. Using a storm-centric cylindrical coordinate system based on the radius of maximum wind adds additional storm-relative capabilities to regrid precipitation fields onto cylindrical or polar coordinates. This powerful process-based model diagnostic and verification technique provides a framework for improved understanding of feedbacks between forecast tracks, intensity, and precipitation distributions. Finally, object-based verification including land masking capabilities provides even more nuanced verification options. Precipitation objects of interest, either the central core of TCs or extended areas of rainfall after landfall, can be identified, matched to observations, and quickly aggregated to build meaningful spatial and summary verification statistics.

Funder

NOAA Research

National Science Foundation

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference53 articles.

1. Evaluation of satellite-retrieved extreme precipitation rates across the central United States;AghaKouchak, A.,2011

2. Baldwin, M. E., and K. E. Mitchell, 1997: The NCEP hourly multisensor U.S. precipitation analysis for operations and GCIP research. Preprints, 13th Conf. on Hydrology, Long Beach, CA, Amer. Meteor. Soc., 54–55.

3. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS;Beck, H. E.,2019

4. Biswas, M., and Coauthors, 2018: Hurricane Weather Research and Forecasting (HWRF) Model: 2017 scientific documentation. NCAR Tech. Note NCAR/TN-544+STR, 111 pp., https://doi.org/10.5065/D6MK6BPR.

5. The Model Evaluation Tools (MET): More than a decade of community-supported forecast verification;Brown, B.,2021

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