An Analysis of CMAQ Gas Phase Dry Deposition over North America Through Grid-Scale and Land-Use Specific Diagnostics in the Context of AQMEII4

The fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4) is conducting a diagnostic intercomparison and evaluation of deposition simulated by regional-scale air quality models over North America and Europe. In this study, we analyze annual AQMEII4 simulations performed with the Community Multiscale Air Quality Model (CMAQ) version 5.3.1 over North America. These simulations were configured with both the M3Dry and Surface Tiled Aerosol and Gas Exchange (STAGE) dry deposition schemes available in CMAQ. A comparison of observed and modeled concentrations and wet deposition fluxes shows that the AQMEII4 CMAQ simulations perform similarly to other contemporary regional-scale modeling studies. During summer, M3Dry has higher ozone (O3) deposition velocities (Vd) and lower mixing ratios than STAGE for much of the eastern U.S. while the reverse is the case over eastern Canada and along the West Coast. In contrast, during winter STAGE has higher O3 Vd and lower mixing ratios than M3Dry over most of the southern half of the modeling domain while the reverse is the case for much of the northern U.S. and southern Canada. Analysis of the diagnostic variables defined for the AQMEII4 project, i.e. grid-scale and land-use (LU) specific effective conductances and deposition fluxes for the major dry deposition pathways, reveals generally higher summertime stomatal and wintertime cuticular grid-scale effective conductances for M3Dry and generally higher soil grid-scale effective conductances (for both vegetated and bare soil) for STAGE in both summer and winter. On a domain-wide basis, the stomatal grid-scale effective conductances account for about half of the total O3 Vd during daytime hours in summer for both schemes. Employing LU-specific diagnostics, results show that daytime Vd varies by a factor of 2 between LU categories. Furthermore, M3Dry vs. STAGE differences are most pronounced for the stomatal and vegetated soil pathway for the forest LU categories, with M3Dry estimating larger effective conductances for the stomatal pathway and STAGE estimating larger effective conductances for the vegetated soil pathway for these LU categories. Annual domain total O3 deposition fluxes differ only slightly between M3Dry (74.4 Tg/year) and STAGE (76.2 Tg/yr), but pathway-specific fluxes to individual LU types can vary more substantially on both annual and seasonal scales which would affect estimates of O3 damages to sensitive vegetation. A comparison of two simulations differing only in their LU classification scheme shows that the differences in LU cause seasonal mean O3 mixing ratio differences on the order of 1 ppb across large portions of the domain, with the differences generally largest during summer and in areas characterized by the largest differences in the fractional coverages of the forest, planted/cultivated, and grassland LU categories. These differences are generally smaller than the M3Dry vs. STAGE differences outside the summer season but have a similar magnitude during summer. Results indicate that the deposition impacts of LU differences are caused both by differences in the fractional coverages and spatial distributions of different LU categories as well as the characterization of these categories through variables like surface roughness and vegetation fraction in look-up tables used in the land-surface model and deposition schemes. Overall, the analyses and results presented in this study illustrate how the diagnostic grid-scale and LU-specific dry deposition variables adopted for AQMEII4 can provide insights into similarities and differences between the CMAQ M3Dry and STAGE dry deposition schemes that affect simulated pollutant budgets and ecosystem impacts from atmospheric pollution.

Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling

The U.S. EPA is leveraging recent advances in meteorological modeling to construct an air quality modeling system to allow consistency from global to local scales. The Model for Prediction Across Scales-Atmosphere (MPAS-A or MPAS) has been developed by the National Center for Atmospheric Research (NCAR) as a global complement to the Weather Research and Forecasting model (WRF). Patterned after a regional coupled system with WRF, the Community Multiscale Air Quality (CMAQ) modeling system has been coupled within MPAS to explore global-to-local chemical transport modeling. Several options were implemented into MPAS for retrospective applications. Nudging-based data assimilation was added to support continuous simulations of past weather to minimize error growth that exists with a weather forecast configuration. The Pleim-Xiu land-surface model, the Asymmetric Convective Model 2 boundary layer scheme, and the Pleim surface layer scheme were added as the preferred options for retrospective air quality applications with WRF. Annual simulations were conducted using this EPA-enhanced MPAS configuration on two different mesh structures and compared against WRF. MPAS generally compares well with WRF over the conterminous United States. Errors in MPAS surface meteorology are comparable to WRF throughout the year. Precipitation statistics indicate MPAS performs slightly better than WRF. Solar radiation in MPAS is higher than WRF and measurements, suggesting fewer clouds in MPAS than WRF. Upper-air meteorology is well-simulated by MPAS, but errors are slightly higher than WRF. These comparisons lend confidence to use MPAS for retrospective air quality modeling and suggest ways it can be further improved in the future.

The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation

The Community Multiscale Air Quality (CMAQ) model version 5.3 (CMAQ53), released to the public in August 2019 and followed by version 5.3.1 (CMAQ531) in December 2019, contains numerous science updates, enhanced functionality, and improved computation efficiency relative to the previous version of the model, 5.2.1 (CMAQ521). Major science advances in the new model include a new aerosol module (AERO7) with significant updates to secondary organic aerosol (SOA) chemistry, updated chlorine chemistry, updated detailed bromine and iodine chemistry, updated simple halogen chemistry, the addition of dimethyl sulfide (DMS) chemistry in the CB6r3 chemical mechanism, updated M3Dry bidirectional deposition model, and the new Surface Tiled Aerosol and Gaseous Exchange (STAGE) bidirectional deposition model. In addition, support for the Weather Research and Forecasting (WRF) model's hybrid vertical coordinate (HVC) was added to CMAQ53 and the Meteorology-Chemistry Interface Processor (MCIP) version 5.0 (MCIP50). Enhanced functionality in CMAQ53 includes the new Detailed Emissions Scaling, Isolation and Diagnostic (DESID) system for scaling incoming emissions to CMAQ and reading multiple gridded input emission files. Evaluation of CMAQ531 was performed by comparing monthly and seasonal mean daily 8 h average (MDA8) O₃ and daily PM_2.5 values from several CMAQ531 simulations to a similarly configured CMAQ521 simulation encompassing 2016. For MDA8 O₃, CMAQ531 has higher O₃ in the winter versus CMAQ521, due primarily to reduced dry deposition to snow, which strongly reduces wintertime O₃ bias (2-4 ppbv monthly average). MDA8 O₃ is lower with CMAQ531 throughout the rest of the year, particularly in spring, due in part to reduced O₃ from the lateral boundary conditions (BCs), which generally increases MDA8 O₃ bias in spring and fall ( 0.5 μg m^-3). For daily 24 h average PM_2.5, CMAQ531 has lower concentrations on average in spring and fall, higher concentrations in summer, and similar concentrations in winter to CMAQ521, which slightly increases bias in spring and fall and reduces bias in summer. Comparisons were also performed to isolate updates to several specific aspects of the modeling system, namely the lateral BCs, meteorology model version, and the deposition model used. Transitioning from a hemispheric CMAQ (HCMAQ) version 5.2.1 simulation to a HCMAQ version 5.3 simulation to provide lateral BCs contributes to higher O₃ mixing ratios in the regional CMAQ simulation in higher latitudes during winter (due to the decreased O₃ dry deposition to snow in CMAQ53) and lower O₃ mixing ratios in middle and lower latitudes year-round (due to reduced O₃ over the ocean with CMAQ53). Transitioning from WRF version 3.8 to WRF version 4.1.1 with the HVC resulted in consistently higher (1.0-1.5 ppbv) MDA8 O₃ mixing ratios and higher PM_2.5 concentrations (0.1-0.25 μg m^-3) throughout the year. Finally, comparisons of the M3Dry and STAGE deposition models showed that MDA8 O₃ is generally higher with M3Dry outside of summer, while PM_2.5 is consistently higher with STAGE due to differences in the assumptions of particle deposition velocities to non-vegetated surfaces and land use with short vegetation (e.g., grasslands) between the two models. For ambient NH₃, STAGE has slightly higher concentrations and smaller bias in the winter, spring, and fall, while M3Dry has higher concentrations and smaller bias but larger error and lower correlation in the summer.

New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model

Ammonia surface flux is bidirectional; that is, net flux can be either upward or downward. In fertilized agricultural croplands and grasslands there is usually more emission than deposition especially in midday during warmer seasons. In North America, most of the ammonia emissions are from agriculture with a significant fraction of that coming from fertilizer. A new bidirectional ammonia flux modeling system has been developed in the Community Multiscale Air Quality (CMAQ) model, which has close linkages with the Environmental Policy Integrated Climate (EPIC) agricultural ecosystem model. Daily inputs from EPIC are used to calculate soil ammonia concentrations that are combined with air concentrations in CMAQ to calculate bidirectional surface flux. The model is evaluated against surface measurements of NH₃ concentrations, NH₄ ⁺ and SO₄ ^2- aerosol concentrations, NH₄ ⁺ wet deposition measurements, and satellite retrievals of NH₃ concentrations. The evaluation shows significant improvement over the base model without bidirectional ammonia flux. Comparisons to monthly average satellite retrievals show similar spatial distribution with the highest ammonia concentrations in the Central Valley of California (CA), the Snake River valley in Idaho, and the western High Plains. In most areas the model underestimates, but in the Central Valley of CA, it generally overestimates ammonia concentration. Case study analyses indicate that modeled high fluxes of ammonia in CA are often caused by anomalous high soil ammonia loading from EPIC for particular crop types. While further improvements to parameterizations in EPIC and CMAQ are recommended, this system is a significant advance over previous ammonia bidirectional surface flux models.

Long-term trends in the ambient PM2.5- and O3-related mortality burdens in the United States under emission reductions from 1990 to 2010

Concentrations of both fine particulate matter (PM2.5) and ozone (O3) in the United States (US) have decreased significantly since 1990, mainly because of air quality regulations. Exposure to these air pollutants is associated with premature death. Here we quantify the annual mortality burdens from PM2.5 and O3 in the US from 1990 to 2010, estimate trends and inter-annual variability, and evaluate the contributions to those trends from changes in pollutant concentrations, population, and baseline mortality rates. We use a fine-resolution (36 km) self-consistent 21-year simulation of air pollutant concentrations in the US from 1990 to 2010, a health impact function, and annual county-level population and baseline mortality rate estimates. From 1990 to 2010, the modeled population-weighted annual PM2.5 decreased by 39 %, and summertime (April to September) 1 h average daily maximum O3 decreased by 9 % from 1990 to 2010. The PM2.5-related mortality burden from ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, and stroke steadily decreased by 54% from 123 700 deaths year-1 (95% confidence interval, 70 800-178 100) in 1990 to 58 600 deaths year-1 (24 900-98 500) in 2010. The PM2.5-related mortality burden would have decreased by only 24% from 1990 to 2010 if the PM2.5 concentrations had stayed at the 1990 level, due to decreases in baseline mortality rates for major diseases affected by PM2.5. The mortality burden associated with O3 from chronic respiratory disease increased by 13% from 10 900 deaths year-1 (3700-17 500) in 1990 to 12 300 deaths year-1 (4100-19 800) in 2010, mainly caused by increases in the baseline mortality rates and population, despite decreases in O3 concentration. The O3-related mortality burden would have increased by 55% from 1990 to 2010 if the O3 concentrations had stayed at the 1990 level. The detrended annual O3 mortality burden has larger inter-annual variability (coefficient of variation of 12%) than the PM2.5-related burden (4%), mainly from the inter-annual variation of O3 concentration. We conclude that air quality improvements have significantly decreased the mortality burden, avoiding roughly 35 800 (38%) PM2.5-related deaths and 4600 (27%) O3-related deaths in 2010, compared to the case if air quality had stayed at 1990 levels (at 2010 baseline mortality rates and population).

Improving the simulation of convective dust storms in regional-to-global models

Convective dust storms have significant impacts on atmospheric conditions and air quality and are a major source of dust uplift in summertime. However, regional-to-global models generally do not accurately simulate these storms, a limitation that can be attributed to (1) using a single mean value for wind speed per grid box, i.e., not accounting for subgrid wind variability and (2) using convective parametrizations that poorly simulate cold pool outflows. This study aims to improve the simulation of convective dust storms by tackling these two issues. Specifically, we incorporate a probability distribution function for surface wind in each grid box to account for subgrid wind variability due to dry and moist convection. Furthermore, we use lightning assimilation to increase the accuracy of the convective parameterization and simulated cold pool outflows. This updated model framework is used to simulate a massive convective dust storm that hit Phoenix, AZ, on 6 July 2011. The results show that lightning assimilation provides a more realistic simulation of precipitation features, including timing and location, and the resulting cold pool outflows that generated the dust storm. When those results are combined with a dust model that accounts for subgrid wind variability, the prediction of dust uplift and concentrations are considerably improved compared to the default model results. This modeling framework could potentially improve the simulation of convective dust storms in global models, regional climate simulations, and retrospective air quality studies.

Historical Trends in PM2.5-Related Premature Mortality during 1990-2010 across the Northern Hemisphere

BACKGROUND

Air quality across the northern hemisphere over the past two decades has witnessed dramatic changes, with continuous improvement in developed countries in North America and Europe, but a contrasting sharp deterioration in developing regions of Asia.

OBJECTIVE

This study investigates the historical trend in the long-term exposure to PM2.5 and PM2.5-related premature mortality (PM2.5-mortality) and its response to changes in emission that occurred during 1990-2010 across the northern hemisphere. Implications for future trends in human exposure to air pollution in both developed and developing regions of the world are discussed.

METHODS

We employed the integrated exposure-response model developed by Health Effects Institute to estimate the PM2.5-mortality. The 1990-2010 annual average PM2.5 concentrations were obtained from the simulations using the WRF-CMAQ model. Emission mitigation efficiencies of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), and primary PM are estimated from the PM2.5-mortality responses to the emission variations.

RESULTS

Estimated PM2.5-mortalities in East Asia and South Asia increased by 21% and 85% respectively, from 866,000 and 578,000 in 1990, to 1,048,000 and 1,068,000 in 2010. PM2.5-mortalities in developed regions (i.e., Europe and high-income North America) decreased substantially by 67% and 58% respectively.

CONCLUSIONS

Over the past two decades, correlations between population and PM2.5 have become weaker in Europe and North America due to air pollution controls but stronger in East Asia due to deteriorating air quality. Mitigation of primary PM appears to be the most efficient way for increasing health benefits (i.e., providing the largest mortality reduction per unit emissions). However, reductions in emissions of NH3 are needed to maximize the effectiveness of NOx emission controls. Citation: Wang J, Xing J, Mathur R, Pleim JE, Wang S, Hogrefe C, Gan CM, Wong DC, Hao J. 2017. Historical trends in PM2.5-related premature mortality during 1990-2010 across the northern hemisphere. Environ Health Perspect 125:400-408; http://dx.doi.org/10.1289/EHP298.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O₃) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM_2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O₃ mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM_2.5 bias (PM_2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O₃ and PM_2.5 on average in January and July. Overall, the seasonal variation in simulated PM_2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM_2.5 concentrations decrease in the winter (when PM_2.5 is generally overestimated by CMAQ) and increase in the summer (when PM_2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O₃ mean bias, as O₃ tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O₃ is low); however, O₃ correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO _x (NO + NO₂), VOC and SO _x (SO₂ + SO₄) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O₃ due to large, widespread reductions in observed emissions.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O₃) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM_2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O₃ mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM_2.5 bias (PM_2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O₃ and PM_2.5 on average in January and July. Overall, the seasonal variation in simulated PM_2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM_2.5 concentrations decrease in the winter (when PM_2.5 is generally overestimated by CMAQ) and increase in the summer (when PM_2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O₃ mean bias, as O₃ tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O₃ is low); however, O₃ correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO _x (NO + NO₂), VOC and SO _x (SO₂ + SO₄) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O₃ due to large, widespread reductions in observed emissions.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O₃) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM_2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O₃ mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM_2.5 bias (PM_2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O₃ and PM_2.5 on average in January and July. Overall, the seasonal variation in simulated PM_2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM_2.5 concentrations decrease in the winter (when PM_2.5 is generally overestimated by CMAQ) and increase in the summer (when PM_2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O₃ mean bias, as O₃ tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O₃ is low); however, O₃ correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO _x (NO + NO₂), VOC and SO _x (SO₂ + SO₄) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O₃ due to large, widespread reductions in observed emissions.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO x (NO + NO2), VOC and SO x (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.

A simple lightning assimilation technique for improving retrospective WRF simulations

Convective rainfall is often a large source of error in retrospective modeling applications. In particular, positive rainfall biases commonly exist during summer months due to overactive convective parameterizations. In this study, lightning assimilation was applied in the Kain-Fritsch (KF) convective scheme to improve retrospective simulations using the Weather Research and Forecasting (WRF) model. The assimilation method has a straightforward approach: force KF deep convection where lightning is observed and, optionally, suppress deep convection where lightning is absent. WRF simulations were made with and without lightning assimilation over the continental United States for July 2012, July 2013, and January 2013. The simulations were evaluated against NCEP stage-IV precipitation data and MADIS near-surface meteorological observations. In general, the use of lightning assimilation considerably improves the simulation of summertime rainfall. For example, the July 2012 monthly averaged bias of 6 h accumulated rainfall is reduced from 0.54 to 0.07 mm and the spatial correlation is increased from 0.21 to 0.43 when lightning assimilation is used. Statistical measures of near-surface meteorological variables also are improved. Consistent improvements also are seen for the July 2013 case. These results suggest that this lightning assimilation technique has the potential to substantially improve simulation of warm-season rainfall in retrospective WRF applications.

A multisensor evaluation of the asymmetric convective model, version 2, in southeast Texas

There currently exist a number of planetary boundary layer (PBL) schemes that can represent the effects of turbulence in daytime convective conditions, although these schemes remain a large source of uncertainty in meteorology and air quality model simulations. This study evaluates a recently developed combined local and nonlocal closure PBL scheme, the Asymmetric Convective Model, version 2 (ACM2), against PBL observations taken from radar wind profilers, a ground-based lidar, and multiple daytime radiosonde balloon launches. These observations were compared against predictions of PBLs from the Weather Research and Forecasting (WRF) model version 3.1 with the ACM2 PBL scheme option, and the Fifth-Generation Meteorological Model (MM5) version 3.7.3 with the Eta PBL scheme option that is currently being used to develop ozone control strategies in southeast Texas. MM5 and WRF predictions during the regulatory modeling episode were evaluated on their ability to predict the rise and fall of the PBL during daytime convective conditions across southeastern Texas. The MM5 predicted PBLs consistently underpredicted observations, and were also less than the WRF PBL predictions. The analysis reveals that the MM5 predicted a slower rising and shallower PBL not representative of the daytime urban boundary layer. Alternatively, the WRF model predicted a more accurate PBL evolution improving the root mean square error (RMSE), both temporally and spatially. The WRF model also more accurately predicted vertical profiles of temperature and moisture in the lowest 3 km of the atmosphere. Inspection of median surface temperature and moisture time-series plots revealed higher predicted surface temperatures in WRF and more surface moisture in MM5. These could not be attributed to surface heat fluxes, and thus the differences in performance of the WRF and MM5 models are likely due to the PBL schemes.

IMPLICATIONS

An accurate depiction of the diurnal evolution of the planetary boundary layer (PBL) is necessary for realistic air quality simulations, and for formulating effective policy. The meteorological model used to support the southeast Texas 03 attainment demonstration made predictions of the PBL that were consistently less than those found in observations. The use of the Asymmetric Convective Model, version 2 (ACM2), predicted taller PBL heights and improved model predictions. A lower predicted PBL height in an air quality model would increase precursor concentrations and change the chemical production of O3 and possibly the response to control strategies.

Development and Testing of a Surface Flux and Planetary Boundary Layer Model for Application in Mesoscale Models

Although the development of soil, vegetation, and atmosphere interaction models has been driven primarily by the need for accurate simulations of long-term energy and moisture budgets in global climate models, the importance of these processes at smaller scales for short-term numerical weather prediction and air quality studies is becoming more appreciated. Planetary boundary layer (PBL) development is highly dependent on the partitioning of the available net radiation into sensible and latent heat fluxes. Therefore, adequate treatmentof surface properties such as soil moisture and vegetation characteristics is essential for accurate simulation of PBL development, convective and low-level cloud processes, and the temperature and humidity of boundary layer air. In this paper, the development ofa simple coupled surface and PBL model, which is planned for incorporation into the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM4/5), is described. The soil-vegetation model is based on a simple force-restore algorithm with explicit soil moisture and evapotranspiration. The PBL model is a hybrid of nonlocal closure for convective conditions and eddy diffusion for all other conditions. A one-dimensional version of the model has been applied to several case studies from field experiments in both dry desert-like conditions (Wangara) and moist vegetated conditions(First International Satellite Land Surface Climatology Project Field Experiment) to demonstrate the model's ability to realistically simulate surface fluxes as well as PBL development. This new surface-PBL model is currently being incorporated into the MM4-MM5 system.