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Daniel Gombos
,
James A. Hansen
,
Jun Du
, and
Jeff McQueen

Abstract

A minimum spanning tree (MST) rank histogram (RH) is a multidimensional ensemble reliability verification tool. The construction of debiased, decorrelated, and covariance-homogenized MST RHs is described. Experiments using Euclidean L 2, variance, and Mahalanobis norms imply that, unless the number of ensemble members is less than or equal to the number of dimensions being verified, the Mahalanobis norm transforms the problem into a space where ensemble imperfections are most readily identified. Short-Range Ensemble Forecast Mahalanobis-normed MST RHs for a cluster of northeastern U.S. cities show that forecasts of the temperature–humidity index are the most reliable of those considered, followed by mean sea level pressure, 2-m temperature, and 10-m wind speed forecasts. MST RHs of a Southwest city cluster illustrate that 2-m temperature forecasts are the most reliable weather component in this region, followed by mean sea level pressure, 10-m wind speed, and the temperature–humidity index. Forecast reliabilities of the Southwest city cluster are generally less reliable than those of the Northeast cluster.

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Pius Lee
,
Daiwen Kang
,
Jeff McQueen
,
Marina Tsidulko
,
Mary Hart
,
Geoff DiMego
,
Nelson Seaman
, and
Paula Davidson

Abstract

This study investigates the impact of model domain extent and the specification of lateral boundary conditions on the forecast quality of air pollution constituents in a specific region of interest. A developmental version of the national Air Quality Forecast System (AQFS) has been used in this study. The AQFS is based on the NWS/NCEP Eta Model (recently renamed the North American Mesoscale Model) coupled with the U.S. Environmental Protection Agency Community Multiscale Air Quality (CMAQ) model. This coupled Eta–CMAQ modeling system provided experimental air quality forecasts for the northeastern region of the United States during the summers of 2003 and 2004. The initial forecast over the northeastern United States was approved for operational deployment in September 2004. The AQFS will provide forecast coverage for the entire United States in the near future. In a continuing program of phased development to extend the geographical coverage of the forecast, the developmental version of AQFS has undergone two domain expansions. Hereinafter, this “developmental” domain-expanded forecast system AQFS will be dubbed AQFS-β. The current study evaluates the performance of AQFS-β for the northeastern United States using three domain sizes. Quantitative comparisons of forecast results with compiled observation data from the U.S. Aerometric Information Retrieval Now (AIRNOW) network were performed for each model domain, and interdomain comparisons were made for the regions of overlap. Several forecast skill score measures have been employed. Based on the categorical statistical metric of the critical success index, the largest domain achieved the highest skill score. This conclusion should catapult the implementation of the largest domain to attain the best forecast performance whenever the operational resource and criteria permit.

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Brian Eder
,
Daiwen Kang
,
S. Trivikrama Rao
,
Rohit Mathur
,
Shaocai Yu
,
Tanya Otte
,
Ken Schere
,
Richard Wayland
,
Scott Jackson
,
Paula Davidson
,
Jeff McQueen
, and
George Bridgers

The National Air Quality Forecast Capability (NAQFC) currently provides next-day forecasts of ozone concentrations over the contiguous United States. It was developed collaboratively by NOAA and Environmental Protection Agency (EPA) in order to provide state and local agencies, as well as the general public, air quality forecast guidance. As part of the development process, the NAQFC has been evaluated utilizing strict monitor-to-gridcell matching criteria, and discrete-type statistics of forecast concentrations. While such an evaluation is important to the developers, it is equally, if not more important, to evaluate the performance using the same protocol as the model's intended application. Accordingly, the purpose of this article is to demonstrate the efficacy of the NAQFC from the perspective of a local forecaster, thereby promoting its use. Such an approach has required the development of a new evaluation protocol: one that examines the ability of the NAQFC to forecast values of the EPA's Air Quality Index (AQI) rather than ambient air concentrations; focuses on the use of categorical-type statistics related to exceedances and nonexceedances; and, most challenging, examines performance, not based on matched grid cells and monitors, but rather over a “local forecast region,” such as an air shed or metropolitan statistical area (MSA). Results from this approach, which is demonstrated for the Charlotte, North Carolina, MSA and subsequently applied to four additional MSAs during the summer of 2007, reveal that the quality of the NAQFC forecasts is generally comparable to forecasts from local agencies. Such findings will hopefully persuade forecasters, whether they are experienced with numerous tools at their disposal or inexperienced with limited resources, to utilize the NAQFC as forecast guidance.

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Walter F. Dabberdt
,
Mary Anne Carroll
,
Darrel Baumgardner
,
Gregory Carmichael
,
Ronald Cohen
,
Tim Dye
,
James Ellis
,
Georg Grell
,
Sue Grimmond
,
Steven Hanna
,
John Irwin
,
Brian Lamb
,
Sasha Madronich
,
Jeff McQueen
,
James Meagher
,
Talat Odman
,
Jonathan Pleim
,
Hans Peter Schmid
, and
Douglas L. Westphal

The U.S. Weather Research Program convenes expert working groups on a one-time basis to identify critical research needs in various problem areas. The most recent expert working group was charged to “identify and delineate critical meteorological research issues related to the prediction of air quality.” In this context, “prediction” is denoted as “forecasting” and includes the depiction and communication of the present chemical state of the atmosphere, extrapolation or nowcasting, and numerical prediction and chemical evolution on time scales up to several days. Emphasis is on the meteorological aspects of air quality.

The problem of air quality forecasting is different in many ways from the problem of weather forecasting. The latter typically is focused on prediction of severe, adverse weather conditions, while the meteorology of adverse air quality conditions frequently is associated with benign weather. Boundary layer structure and wind direction are perhaps the two most poorly determined meteorological variables for regional air quality prediction. Meteorological observations are critical to effective air quality prediction, yet meteorological observing systems are designed to support prediction of severe weather, not the subtleties of adverse air quality. Three-dimensional meteorological and chemical observations and advanced data assimilation schemes are essential. In the same way, it is important to develop high-resolution and self-consistent databases for air quality modeling; these databases should include land use, vegetation, terrain elevation, and building morphology information, among others. New work in the area of chemically adaptive grids offers significant promise and should be pursued. The quantification and effective communication of forecast uncertainty are still in their early stages and are very important for decision makers; this also includes the visualization of air quality and meteorological observations and forecasts. Research is also needed to develop effective metrics for the evaluation and verification of air quality forecasts so that users can understand the strengths and weaknesses of various modeling schemes. Last, but not of least importance, is the need to consider the societal impacts of air quality forecasts and the needs that they impose on researchers to develop effective and useful products.

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