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  • Author or Editor: Louisa Nance x
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Louisa B. Nance
and
Bradley R. Colman

Abstract

Severe downslope windstorms are a mesoscale, primarily wintertime, phenomenon that affect regions in the lee of large mountain ranges. The resolution of current weather prediction models is too coarse to explicitly predict downslope windstorms. Hence, additional operational tools are needed for making downslope windstorm forecasts. Current windstorm forecast techniques commonly utilize a tool referred to as a “decision tree.” Although decision trees provide valuable guidance, operational forecasters have not found this type of tool to be highly reliable. With recent advances in computer technology, a new type of operational tool is available for forecasting downslope windstorms: two-dimensional, nonlinear, mesoscale numerical models. This study investigates whether this type of model, initialized with upstream profiles taken from operational Eta Model forecasts, can produce accurate downslope windstorm forecasts.

Numerical simulations for high-wind events that affected seven regions in the United States between January 1993 and April 1997 indicate this tool is able to produce lee-slope wind speeds that meet the local peak gust threshold for a High Wind Warning for a majority of those cases where observed winds met this threshold. These simulations were initialized with upstream soundings taken from the 12- and 18-h Eta forecasts valid at the time of each high-wind event. A comparison for one region between the number of events for which High Wind Watches were posted and the number of events for which the two-dimensional model prediction met the peak gust threshold suggests this new tool would be a definite improvement over the current forecast technique. On the other hand, a preliminary test of the model’s ability to differentiate between windstorm and nonwindstorm events suggests the false warning rate for this tool may be high. Further testing of this tool is ongoing and will continue through the winter months of 2000/01.

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Jamie K. Wolff
,
Michelle Harrold
,
Tressa Fowler
,
John Halley Gotway
,
Louisa Nance
, and
Barbara G. Brown

Abstract

While traditional verification methods are commonly used to assess numerical model quantitative precipitation forecasts (QPFs) using a grid-to-grid approach, they generally offer little diagnostic information or reasoning behind the computed statistic. On the other hand, advanced spatial verification techniques, such as neighborhood and object-based methods, can provide more meaningful insight into differences between forecast and observed features in terms of skill with spatial scale, coverage area, displacement, orientation, and intensity. To demonstrate the utility of applying advanced verification techniques to mid- and coarse-resolution models, the Developmental Testbed Center (DTC) applied several traditional metrics and spatial verification techniques to QPFs provided by the Global Forecast System (GFS) and operational North American Mesoscale Model (NAM). Along with frequency bias and Gilbert skill score (GSS) adjusted for bias, both the fractions skill score (FSS) and Method for Object-Based Diagnostic Evaluation (MODE) were utilized for this study with careful consideration given to how these methods were applied and how the results were interpreted. By illustrating the types of forecast attributes appropriate to assess with the spatial verification techniques, this paper provides examples of how to obtain advanced diagnostic information to help identify what aspects of the forecast are or are not performing well.

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Kathryn M. Newman
,
Barbara Brown
,
John Halley Gotway
,
Ligia Bernardet
,
Mrinal Biswas
,
Tara Jensen
, and
Louisa Nance

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.

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Barbara G. Brown
,
Louisa B. Nance
,
Christopher L. Williams
,
Kathryn M. Newman
,
James L. Franklin
,
Edward N. Rappaport
,
Paul A. Kucera
, and
Robert L. Gall

Abstract

The Hurricane Forecast Improvement Project (HFIP; renamed the “Hurricane Forecast Improvement Program” in 2017) was established by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 with a goal of improving tropical cyclone (TC) track and intensity predictions. A major focus of HFIP has been to increase the quality of guidance products for these parameters that are available to forecasters at the National Weather Service National Hurricane Center (NWS/NHC). One HFIP effort involved the demonstration of an operational decision process, named Stream 1.5, in which promising experimental versions of numerical weather prediction models were selected for TC forecast guidance. The selection occurred every year from 2010 to 2014 in the period preceding the hurricane season (defined as August–October), and was based on an extensive verification exercise of retrospective TC forecasts from candidate experimental models run over previous hurricane seasons. As part of this process, user-responsive verification questions were identified via discussions between NHC staff and forecast verification experts, with additional questions considered each year. A suite of statistically meaningful verification approaches consisting of traditional and innovative methods was developed to respond to these questions. Two examples of the application of the Stream 1.5 evaluations are presented, and the benefits of this approach are discussed. These benefits include the ability to provide information to forecasters and others that is relevant for their decision-making processes, via the selection of models that meet forecast quality standards and are meaningful for demonstration to forecasters in the subsequent hurricane season; clarification of user-responsive strengths and weaknesses of the selected models; and identification of paths to model improvement.

Significance Statement

The Hurricane Forecast Improvement Project (HFIP) tropical cyclone (TC) forecast evaluation effort led to innovations in TC predictions as well as new capabilities to provide more meaningful and comprehensive information about model performance to forecast users. Such an effort—to clearly specify the needs of forecasters and clarify how forecast improvements should be measured in a “user-oriented” framework—is rare. This project provides a template for one approach to achieving that goal.

Open access