John N. McHenry

Assessment of an ensemble of seven real-time ozone forecasts over eastern North America during the summer of 2004

The real-time forecasts of ozone (O 3 ) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 monitoring stations throughout the eastern United States and southern Canada. One of the first ever real-time ensemble O 3 forecasts, created by combining the seven separate forecasts with equal weighting, is also evaluated in terms of standard statistical measures, threshold statistics, and variance analysis. The ensemble based on the mean of the seven models and the ensemble based on the median are found to have significantly more temporal correlation to the observed daily maximum 1-hour average and maximum 8-hour average O 3 concentrations than any individual model. However, root-mean-square errors (RMSE) and skill scores show that the usefulness of the uncorrected ensembles is limited by positive O 3 biases in all of the AQFMs. The ensembles and AQFM statistical measures are reevaluated using two simple bias correction algorithms for forecasts at each monitor location: subtraction of the mean bias and a multiplicative ratio adjustment, where corrections are based on the full 53 days of available comparisons. The impact the two bias correction techniques have on RMSE, threshold statistics, and temporal variance is presented. For the threshold statistics a preferred bias correction technique is found to be model dependent and related to whether the model overpredicts or underpredicts observed temporal O 3 variance. All statistical measures of the ensemble mean forecast, and particularly the bias-corrected ensemble forecast, are found to be insensitive to the results of any particular model. The higher correlation coefficients, low RMSE, and better threshold statistics for the ensembles compared to any individual model point to their preference as a real-time O 3 forecast.

Evaluating the performance of regional-scale photochemical modeling systems: Part I—meteorological predictions

In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperature, water vapor, and wind speed were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the method of spectral decomposition can help provide additional insight into the models’ performance. The results indicate that both meteorological models under-represent the variance of fluctuations on the intra-day time scale. Correlations between model predictions and observations for temperature and wind speed are insignificant on the intra-day time scale, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and longer-term components. This better model performance on longer time scales suggests that current regional-scale models are most skillful for characterizing average patterns over extended periods. The implications of these results to using meteorological models to drive photochemical models are discussed. r 2001 Elsevier Science Ltd. All rights reserved.

A Real-Time Eulerian Photochemical Model Forecast System: Overview and Initial Ozone Forecast Performance in the Northeast U.S. Corridor

This article reports on the first implementation of a real-time Eulerian photochemical model f o recast system in the United States. The forecast system consists of a tripartite set of one-way coupled models that run routinely on a parallel micro process or supercomputer. The component models are the fifth-generation Pennsylvania State University (PSU)–NCAR Mesoscale Model (MM5), the Sparse-Matrix Operator Kernel for Emissions (SMOKE) model, and the Multiscale Air Quality Simulation Platform—Real Time (MAQSIPRT) photochemical model. Though the system has been run in real time since the summer of 1998, forecast results obtained during August of 2001 at 15-km grid spacing over New England and the northern mid-Atlantic—conducted as part of an “early start” NOAA air quality forecasting initiative—are described in this article. The development and deployment of a real-time numerical air quality prediction (NAQP) system is technically challenging. MAQSIP-RT contains a full photochemical oxidant gas-phase chemic...

The New England Air Quality Forecasting Pilot Program: Development of an Evaluation Protocol and Performance Benchmark

Abstract The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a “test bed” for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implementation of an evaluation protocol. This Pilot Program enlisted three regional-scale air quality models, serving as prototypes, to forecast ozone (O3) concentrations across the northeastern United States during the summer of 2002. A suite of statistical metrics was identified as part of the protocol that facilitated evaluation of both discrete forecasts (observed versus modeled concentrations) and categorical forecasts (observed versus modeled exceedances/nonexceedances) for both the maximum 1-hr (125 ppb) and 8-hr (85 ppb) forecasts produced by each of the models. Implementation of the evaluation protocol took place during a 25-day period (August 5–29), utilizing hourly O3 concentration data obtained from over 450 monitors from the U.S. Environment Protection Agency’s Air Quality System network.

Correcting RADM's sulfate underprediction: Discovery and correction of model errors and testing the corrections through comparisons against field data

Abstract A serious underprediction of ambient sulfate (SO42−) by two comprehensive, Eulerian models of acid deposition, the Regional Acid Deposition Model (RADM) and the Acid Deposition and Oxidant Model (ADOM), was found in the National Acid Precipitation Assessment Program phase of the Eulerian Model Evaluation Field Study (EMEFS) model evaluation. Two hypotheses were proposed to explain the cause of the underprediction in RADM: insufficient SO42− production by nonprecipitating convective clouds and insufficient primary SO42− emissions. Modifications of the RADM cloud and scavenging module to simulate nonprecipitating cumulus clouds better are described in detail. Three contrasting pairs of tests using data from the EMEFS were applied to these hypotheses: source vs downwind regions, mid-summer vs late summer seasons and sunny-dry vs cloudy-wet synoptic types. The SO42− emissions hypothesis, tested by artificially boosting SO42− emissions, fared better than expected but was rejected because of its poor performance on the regional and seasonal contrast tests. The RADM nonprecipitating cumulus modification successfully captured the seasonal and the late summer synoptic contrasts but improvement is still needed for the regional and mid-summer synoptic contrasts.

The relative importance of oxidation pathways and clouds to atmospheric ambient sulfate production as predicted by the regional acid deposition model

The development and use of a version of the Regional Acid Deposition Model/Engineering Model (RADM/EM) called the Comprehensive Sulfate Tracking Model (COMSTM) is reported. The COMSTM is used to diagnose the relative contributions of each sulfate production pathway to the total atmospheric ambient sulfate predicted by RADM. Thirty meteorological cases are used to aggregate the results into annual estimates. For the operational RADM (denoted RADM 2.6), nonprecipitating cloud production of ambient sulfate dominates over precipitating cloud production, and the hydrogen peroxide pathway dominates over four other aqueous formation routes. Gas-phase production of sulfate contributes less than 40% of the ambient budget. Further, the COMSTM is used to explore the sensitivity of the RADM`s cloud water and rainwater pH`s and ambient sulfate predictions to uncertainties in the ammonia emissions inventory. By developing correction factors to improve in-cloud and deposited ammonia, and utilizing them in the COMSTM, it is shown that the uncertainties should have a minimal effect on predicted cloud water and rainwater pH`s and on the overall ambient sulfate budget in the operational RADM 2.6. 27 refs., 8 figs., 3 tabs.

The tagged species engineering model (TSEM)

Abstract The Tagged Species Engineering Model (TSEM), a three-dimensional Eulerian model, has been developed to examine source-receptor relationships and the response of sulfate deposition to hypothetical control strategies for sulfur dioxide source emissions. Archived output files from a comprehensive Eulerian model (the Regional Acid Deposition Model) are used to provide necessary input values of ozone, hydrogen peroxide, hydroxyl and hydroperoxy radicals, and other oxidants. Sulfur dioxide is then oxidized to sulfate by both gas-phase and aqueous-phase reactions. Results from comparisons of the simplified model with the comprehensive model show that TSEM represents sulfate deposition very well both for base case emissions and for a hypothetical 50% reduction in all sulfur-emitting sources. Source-receptor relationships are exhibited by means of tagged sulfur emissions, as illustrated in two examples.

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing.

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new “Deep Blue” retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented. Implications: Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.

Development of regional corrosion maps for galvanized steel by linking the RADM engineering model with an atmospheric corrosion model

Abstract Annual corrosion rates for galvanized steel standard panels were estimated for eastern North America and part of southern Canada using the Regional Acid Deposition Model Engineering Model Model (ACM). The galvanized steel ACM examines the contributions of wet and dry deposition, including anthropogenic and naturally occurring atmospheric species to galvanized steel structure corrosion. The results show agreement between model-predicted and field-measured annual corrosion rates of galvanized steel panel except for an exposure site located in up-state New York. Further comparison of corrosion rates showed some spatial disagreement of the relative contributions to the individual corrosion processes, particularly for the New York site. In addition, RADM EM MM-4 was used to predict the change in ambient sulfur (S) concentrations and hydrogen ion deposition from a hypothetical uniform 50°, reduction in S emissions. Using the ACM, the effects of the emission reduction on the annually estimated corrosion rates were modeled. The results show a beneficial reduction in regional corrosion rates estimated annually. However, due to nonlinearities associated with wet and dry deposition, the corrosion rates decline in a less than 1:1 proportion to the emissions reduction.