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Fong Ngan
,
Hyuncheol Kim
,
Pius Lee
,
Khalid Al-Wali
, and
Bright Dornblaser

Abstract

The overprediction of surface wind speed during nighttime by the Advanced Research core of the Weather Research and Forecasting (WRF-ARW) model was investigated for a period of the Second Texas Air Quality Study (28 May–3 July 2006). In coastal regions of southeastern Texas, the model had a significant increase of wind speed biases on the surface in the evening throughout the period, especially between 4 and 12 June. The synoptic pattern was a high pressure system centered over the Louisiana–Mississippi area that was subjected to a weak easterly–southeasterly flow in the lower troposphere. The weather conditions favorable for sea-breeze development brought a southerly–southwesterly onshore flow to the near-surface levels. In comparison with measurements, the downward sensible heat flux was overpredicted at night, which resulted in a warm bias in surface temperature. For the vertical wind profile on days with an evening wind bias, sea-breeze-driven nocturnal low-level jets (southerly–southwesterly) were present at around 300 m while another wind maximum was observed at higher levels (around 1.5–2 km), which were associated with a high pressure system centered on southeastern states. The vertical gradient of wind speed in the lowest 150 m was smoother in the model than it was in the observations; this could be attributed to excessive downward mixing. Sensitivities using different land surface and PBL parameterizations showed that the model's overprediction of nocturnal wind was still present despite improvements in the predictions of surface temperature and sensible heat flux.

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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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Jianping Huang
,
Jeffery McQueen
,
James Wilczak
,
Irina Djalalova
,
Ivanka Stajner
,
Perry Shafran
,
Dave Allured
,
Pius Lee
,
Li Pan
,
Daniel Tong
,
Ho-Chun Huang
,
Geoffrey DiMego
,
Sikchya Upadhayay
, and
Luca Delle Monache

Abstract

Particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) is a critical air pollutant with important impacts on human health. It is essential to provide accurate air quality forecasts to alert people to avoid or reduce exposure to high ambient levels of PM2.5. The NOAA National Air Quality Forecasting Capability (NAQFC) provides numerical forecast guidance of surface PM2.5 for the United States. However, the NAQFC forecast guidance for PM2.5 has exhibited substantial seasonal biases, with overpredictions in winter and underpredictions in summer. To reduce these biases, an analog ensemble bias correction approach is being integrated into the NAQFC to improve experimental PM2.5 predictions over the contiguous United States. Bias correction configurations with varying lengths of training periods (i.e., the time period over which searches for weather or air quality scenario analogs are made) and differing ensemble member size are evaluated for July, August, September, and November 2015. The analog bias correction approach yields substantial improvement in hourly time series and diurnal variation patterns of PM2.5 predictions as well as forecast skill scores. However, two prominent issues appear when the analog ensemble bias correction is applied to the NAQFC for operational forecast guidance. First, day-to-day variability is reduced after using bias correction. Second, the analog bias correction method can be limited in improving PM2.5 predictions for extreme events such as Fourth of July Independence Day firework emissions and wildfire smoke events. The use of additional predictors and longer training periods for analog searches is recommended for future studies.

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Zafer Boybeyi
,
Nash'at N. Ahmad
,
David P. Bacon
,
Thomas J. Dunn
,
Mary S. Hall
,
Pius C. S. Lee
,
R. Ananthakrishna Sarma
, and
Tim R. Wait

Abstract

The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) is a multiscale nonhydrostatic atmospheric simulation system based on an adaptive unstructured grid. The basic philosophy behind the OMEGA development has been the creation of an operational tool for real-time aerosol and gas hazard prediction. The model development has been guided by two basic design considerations in order to meet the operational requirements: 1) the application of an unstructured dynamically adaptive mesh numerical technique to atmospheric simulation, and 2) the use of embedded atmospheric dispersion algorithms. An important step in proving the utility and accuracy of OMEGA is the full-scale testing of the model using simulations of real-world atmospheric events and qualitative as well as quantitative comparisons of the model results with observations. The main objective of this paper is to provide a comprehensive evaluation of OMEGA against a major dispersion experiment in operational mode. Therefore, OMEGA was run to create a 72-h forecast for the first release period (23–26 October 1994) of the European Tracer Experiment (ETEX). The predicted meteorological and dispersion fields were then evaluated against both the atmospheric observations and the ETEX dispersion measurements up to 60 h after the start of the release. In general, the evaluation showed that the OMEGA dispersion results were in good agreement in the position, shape, and extent of the tracer cloud. However, the model prediction indicated that there was a limited spreading of the predictions around the measurements with a small tendency to underestimate the concentration values.

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Richard T. McNider
,
Arastoo Pour-Biazar
,
Kevin Doty
,
Andrew White
,
Yuling Wu
,
Momei Qin
,
Yongtao Hu
,
Talat Odman
,
Patricia Cleary
,
Eladio Knipping
,
Bright Dornblaser
,
Pius Lee
,
Christopher Hain
, and
Stuart McKeen

Abstract

High mixing ratios of ozone along the shores of Lake Michigan have been a recurring theme over the last 40 years. Models continue to have difficulty in replicating ozone behavior in the region. Although emissions and chemistry may play a role in model performance, the complex meteorological setting of the relatively cold lake in the summer ozone season and the ability of the physical model to replicate this environment may contribute to air quality modeling errors. In this paper, several aspects of the physical atmosphere that may affect air quality, along with potential paths to improve the physical simulations, are broadly examined. The first topic is the consistent overwater overprediction of ozone. Although overwater measurements are scarce, special boat and ferry ozone measurements over the last 15 years have indicated consistent overprediction by models. The roles of model mixing and lake surface temperatures are examined in terms of changing stability over the lake. From an analysis of a 2009 case, it is tentatively concluded that excessive mixing in the meteorological model may lead to an underestimate of mixing in offline chemical models when different boundary layer mixing schemes are used. This is because the stable boundary layer shear, which is removed by mixing in the meteorological model, can no longer produce mixing when mixing is rediagnosed in the offline chemistry model. Second, air temperature has an important role in directly affecting chemistry and emissions. Land–water temperature contrasts are critical to lake and land breezes, which have an impact on mixing and transport. Here, satellite-derived skin temperatures are employed as a path to improve model temperature performance. It is concluded that land surface schemes that adjust moisture based on surface energetics are important in reducing temperature errors.

Open access
David P. Bacon
,
Nash’at N. Ahmad
,
Zafer Boybeyi
,
Thomas J. Dunn
,
Mary S. Hall
,
Pius C. S. Lee
,
R. Ananthakrishna Sarma
,
Mark D. Turner
,
Kenneth T. Waight III
,
Steve H. Young
, and
John W. Zack

Abstract

The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) and its embedded Atmospheric Dispersion Model is a new atmospheric simulation system for real-time hazard prediction, conceived out of a need to advance the state of the art in numerical weather prediction in order to improve the capability to predict the transport and diffusion of hazardous releases. OMEGA is based upon an unstructured grid that makes possible a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from a few tens of meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning because its unstructured grid permits the addition of grid elements at any point in space and time. In particular, unstructured grid cells in the horizontal dimension can increase local resolution to better capture topography or the important physical features of the atmospheric circulation and cloud dynamics. This means that OMEGA can readily adapt its grid to stationary surface or terrain features, or to dynamic features in the evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first model to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and hence real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with data.

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S. G. Gopalakrishnan
,
David P. Bacon
,
Nash'at N. Ahmad
,
Zafer Boybeyi
,
Thomas J. Dunn
,
Mary S. Hall
,
Yi Jin
,
Pius C. S. Lee
,
Douglas E. Mays
,
Rangarao V. Madala
,
Ananthakrishna Sarma
,
Mark D. Turner
, and
Timothy R. Wait

Abstract

The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is an atmospheric simulation system that links the latest methods in computational fluid dynamics and high-resolution gridding technologies with numerical weather prediction. In the fall of 1999, OMEGA was used for the first time to examine the structure and evolution of a hurricane (Floyd, 1999). The first simulation of Floyd was conducted in an operational forecast mode; additional simulations exploiting both the static as well as the dynamic grid adaptation options in OMEGA were performed later as part of a sensitivity–capability study. While a horizontal grid resolution ranging from about 120 km down to about 40 km was employed in the operational run, resolutions down to about 15 km were used in the sensitivity study to explicitly model the structure of the inner core. All the simulations produced very similar storm tracks and reproduced the salient features of the observed storm such as the recurvature off the Florida coast with an average 48-h position error of 65 km. In addition, OMEGA predicted the landfall near Cape Fear, North Carolina, with an accuracy of less than 100 km up to 96 h in advance. It was found that a higher resolution in the eyewall region of the hurricane, provided by dynamic adaptation, was capable of generating better-organized cloud and flow fields and a well-defined eye with a central pressure lower than the environment by roughly 50 mb. Since that time, forecasts were performed for a number of other storms including Georges (1998) and six 2000 storms (Tropical Storms Beryl and Chris, Hurricanes Debby and Florence, Tropical Storm Helene, and Typhoon Xangsane). The OMEGA mean track error for all of these forecasts of 101, 140, and 298 km at 24, 48, and 72 h, respectively, represents a significant improvement over the National Hurricane Center (NHC) 1998 average of 156, 268, and 374 km, respectively. In a direct comparison with the GFDL model, OMEGA started with a considerably larger position error yet came within 5% of the GFDL 72-h track error. This paper details the simulations produced and documents the results, including a comparison of the OMEGA forecasts against satellite data, observed tracks, reported pressure lows and maximum wind speed, and the rainfall distribution over land.

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Pius Lee
,
Jeffery McQueen
,
Ivanka Stajner
,
Jianping Huang
,
Li Pan
,
Daniel Tong
,
Hyuncheol Kim
,
Youhua Tang
,
Shobha Kondragunta
,
Mark Ruminski
,
Sarah Lu
,
Eric Rogers
,
Rick Saylor
,
Perry Shafran
,
Ho-Chun Huang
,
Jerry Gorline
,
Sikchya Upadhayay
, and
Richard Artz

Abstract

The National Air Quality Forecasting Capability (NAQFC) upgraded its modeling system that provides developmental numerical predictions of particulate matter smaller than 2.5 μm in diameter (PM2.5) in January 2015. The issuance of PM2.5 forecast guidance has become more punctual and reliable because developmental PM2.5 predictions are provided from the same system that produces operational ozone predictions on the National Centers for Environmental Prediction (NCEP) supercomputers.

There were three major upgrades in January 2015: 1) incorporation of real-time intermittent sources for particles emitted from wildfires and windblown dust originating within the NAQFC domain, 2) suppression of fugitive dust emissions from snow- and/or ice-covered terrain, and 3) a shorter life cycle for organic nitrate in the gaseous-phase chemical mechanism. In May 2015 a further upgrade for emission sources was included using the U.S. Environmental Protection Agency’s (EPA) 2011 National Emission Inventory (NEI). Emissions for ocean-going ships and on-road mobile sources will continue to rely on NEI 2005.

Incremental tests and evaluations of these upgrades were performed over multiple seasons. They were verified against the EPA’s AIRNow surface monitoring network for air pollutants. Impacts of the three upgrades on the prediction of surface PM2.5 concentrations show large regional variability: the inclusion of windblown dust emissions in May 2014 improved PM2.5 predictions over the western states and the suppression of fugitive dust in January 2015 reduced PM2.5 bias by 52%, from 6.5 to 3.1 μg m−3 against a monthly average of 9.4 μg m−3 for the north-central United States.

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Tanya L. Otte
,
George Pouliot
,
Jonathan E. Pleim
,
Jeffrey O. Young
,
Kenneth L. Schere
,
David C. Wong
,
Pius C. S. Lee
,
Marina Tsidulko
,
Jeffery T. McQueen
,
Paula Davidson
,
Rohit Mathur
,
Hui-Ya Chuang
,
Geoff DiMego
, and
Nelson L. Seaman

Abstract

NOAA and the U.S. Environmental Protection Agency (EPA) have developed a national air quality forecasting (AQF) system that is based on numerical models for meteorology, emissions, and chemistry. The AQF system generates gridded model forecasts of ground-level ozone (O3) that can help air quality forecasters to predict and alert the public of the onset, severity, and duration of poor air quality conditions. Although AQF efforts have existed in metropolitan centers for many years, this AQF system provides a national numerical guidance product and the first-ever air quality forecasts for many (predominantly rural) areas of the United States. The AQF system is currently based on NCEP’s Eta Model and the EPA’s Community Multiscale Air Quality (CMAQ) modeling system. The AQF system, which was implemented into operations at the National Weather Service in September of 2004, currently generates twice-daily forecasts of O3 for the northeastern United States at 12-km horizontal grid spacing. Preoperational testing to support the 2003 and 2004 O3 forecast seasons showed that the AQF system provided valuable guidance that could be used in the air quality forecast process. The AQF system will be expanded over the next several years to include a nationwide domain, a capability for forecasting fine particle pollution, and a longer forecast period. State and local agencies will now issue air quality forecasts that are based, in part, on guidance from the AQF system. This note describes the process and software components used to link the Eta Model and CMAQ for the national AQF system, discusses several technical and logistical issues that were considered, and provides examples of O3 forecasts from the AQF system.

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