Tianfeng Chai

Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011

We employed an optimal interpolation (OI) method to assimilate AIRNow ozone/PM2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) data into the Community Multi-scale Air Quality (CMAQ) model to improve the ozone and total aerosol concentration for the CMAQ simulation over the contiguous United States (CONUS). AIRNow data assimilation was applied to the boundary layer, and MODIS AOD data were used to adjust total column aerosol. Four OI cases were designed to examine the effects of uncertainty setting and assimilation time; two of these cases used uncertainties that varied in time and location, or “dynamic uncertainties.” More frequent assimilation and higher model uncertainties pushed the modeled results closer to the observation. Our comparison over a 24-hr period showed that ozone and PM2.5 mean biases could be reduced from 2.54 ppbV to 1.06 ppbV and from –7.14 µg/m3 to –0.11 µg/m3, respectively, over CONUS, while their correlations were also improved. Comparison to DISCOVER-AQ 2011 aircraft measurement showed that surface ozone assimilation applied to the CMAQ simulation improves regional low-altitude (below 2 km) ozone simulation. Implications: This paper described an application of using optimal interpolation method to improve the model’s ozone and PM2.5 estimation using surface measurement and satellite AOD. It highlights the usage of the operational AIRNow data set, which is available in near real time, and the MODIS AOD. With a similar method, we can also use other satellite products, such as the latest VIIRS products, to improve PM2.5 prediction.

Building and Testing Atmospheric Chemistry Reanalysis Modeling System

This study is a first step towards building an atmospheric chemistry reanalysis modeling system. We aim to provide the air quality science community with three-dimensional (3D) reanalysis atmospheric chemical fields over the conterminous U.S. (CONUS). This initial 3D gridded reanalysis product is available at 12 km horizontal grid spacing with 22 uneven vertical levels extending from surface to 100 hPa. The principal components of the modeling system are the Weather Research and Forecasting meteorological model, a chemical data assimilation model based on an optimal interpolation scheme, and the U.S. EPA Community Multi-scale Air Quality modeling system (CMAQ). Only the Moderate Resolution Imaging Spectro-radiometer (MODIS) Aerosol Optical Depth observations are assimilated as we focus on the aerosol reanalysis at this early stage. CMAQ predictions before and after the assimilation are evaluated against the AIRNow surface PM2.5 (Particulate Matter smaller than 2.5 μm in diameter) measurements. Based on the preliminary results, the future directions to improve the chemistry reanalysis modeling system are discussed.

A Conservative Downscaling of Satellite-Detected Chemical Compositions: NO 2 Column Densities of OMI, GOME-2, and CMAQ.

A conservative downscaling technique was applied when comparing nitrogen dioxide (NO2) column densities from space-borne observations and a fine-scale regional model. The conservative downscaling was designed to enhance the spatial resolution of satellite measurements by applying the fine-scale spatial structure from the model, with strict mass conservation at each satellite footprint pixel level. With the downscaling approach, NO2 column densities from the Ozone Monitoring Instrument (OMI; 13 × 24 km nadir footprint resolution) and the Global Ozone Monitoring Experiment-2 (GOME-2; 40 × 80 km) show excellent agreement with the Community Multiscale Air Quality (CMAQ; 4 × 4 km) NO2 column densities, with R = 0.96 for OMI and R = 0.97 for GOME-2. We further introduce an approach to reconstruct surface NO2 concentrations by combining satellite column densities and simulated surface-to-column ratios from the model. Compared with the Environmental Protection Agency’s (EPA) Air Quality System (AQS) surface observations, the reconstructed surface concentrations show a good agreement; R = 0.86 for both OMI and GOME-2. This study demonstrates that the conservative downscaling approach is a useful tool to compare coarse-scale satellites with fine-scale models or observations in urban areas for air quality and emissions studies. The reconstructed fine-scale surface concentration field could be used for future epidemiology and urbanization studies.

Diagnostic Evaluation of NOx Emission Upgrade on Air Quality Forecast

The U. S. National Air Quality Forecasting Capability (NAQFC) provides air quality forecast for the nation by disseminating numerical model predicted surface concentration of O3 and PM2.5 to the public. However, the fidelity of NAQFC is dependent on the accuracies of the emission projection factors employed to estimate the various emissions. This study focuses on comparing variability of surface NOx and O3 concentrations for two emission modeling scenarios for July of 2011: the Base Case and a New Emission Case. The Base Case used the U.S. EPA 2005 National Emission Inventory (NEI2005), its projection procedures adhered to a standard practice used by NAQFC since its inception in 2003. The New Emission Case adopted a scaling procedure based on more recent EPA data demonstrated a significant reduction of the mobile source’s share of NOx emission among the major contributors. It reduced from 33.6 % in the Base Case to 25.2 % in the New Emission Case. This is even more significant if one takes into account the large increase in vehicle miles traveled since 2005. The NOx SIP Call had achieved significant reduction of NOx emission from power plants, but still lagged behind that achieved by the reduction in the on-road vehicular (mobile) exhausts. Geographically population change trends in the last decade do not necessarily translates into proportional changes in NOx emission.

Assimilation of Satellite Oceanic and Atmospheric Products to Improve Emission Forecasting

Satellite data presents an unprecedented opportunity to improve emission inventories at a near-real-time pace. Here we demonstrate how to utilize satellite oceanic and atmospheric products to improve emission forecasting. First, we present the development and validation of a global high resolution marine isoprene emission product. Isoprene emission is calculated from NOAA global weather forecasting data and Chlorophyll-a and light attenuation rate at 490 nm (K490) data derived from the Moderate Resolution Imaging Spectrometer (MODIS) aboard Aqua. The emission product is validated with isoprene measurements from field campaigns. In the second case, nitrogen dioxide (NO2) data from the EPA Air Quality System (AQS) and the Ozone Monitoring Instrument (OMI) are used to examine the long-term trends in nitrogen oxides (NOx) emissions for the NOAA National Air Quality Forecasting Capability (NAQFC). Comparing of summertime NOx data from OMI, NAQFC and AQS over New York between 2005 and 2011 shows a similar reduction level from all datasets (33 % reduction from 2005 to 2011), but OMI and AQS agree better while NAQFC emission inventories fail to catch the gradual progression of emission reduction. These case studies, in addressing various aspects of emission uncertainty, collectively demonstrate that satellite remote sensing can play an important role in improving emission forecasting and, hopefully, air quality predictions.

Incremental Development of Air Quality Forecasting System with Off-Line/On-Line Capability: Coupling CMAQ to NCEP National Mesoscale Model

The National Air Quality Forecast Capability (NAQFC) is based on the EPA Community Multiscale Air Quality (CMAQ) model driven by meteorological data from the NOAA North American Mesoscale (NAM) Non-hydrostatic Meso-scale Model (NMM). Currently, NMM meteorological data on Arakawa E-grid are interpolated on a CMAQ’s Arakawa C-grid using the processors PRODGEN and PREMAQ to handle map-projection transform, vertical layer collapsing, and other emission and meteorological data feed issues. The FY11 pre-implementation version of NAM has undergone significant changes in the vertical layering, horizontal grid projection and improved science components for its FY11 upcoming major upgrade release. This provides an opportunity to improve the coupling methodology between NMM and CMAQ that reduces uncertainties both in the meteorological and emission inputs for the off-line air quality modeling and helps development of on-line NMM-CMAQ version. Three major tasks are needed to achieve a tighter coupling between them: (1) Adapt to NAM’s vertical hybrid pressure and grid structure; (2) Change CMAQ to use the same rotated latitude longitude B staggered horizontal grid structure as NAM, (3) Modify emission model to provide generic inputs for the B staggered grid and hybrid vertical structure of NAM. The first task achieves consistent matching of dynamics between the two systems, despite the possible necessity of layer-collapsing to fit within operational time-lines. The second task removes unnecessary interpolation of meteorology data for air quality simulations. The third task involves modification of the U.S. EPA Sparse Matrix Object Kernel Emission (SMOKE) model to handle the staggered B grid. At this time only the first of these three steps has been accomplished, and the test result from this test focusing on the selected test period has been compared to that produced by the operational NAQFC. Further work with all these three modifications concurrently in place is underway.