Pius Lee

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.

Air Quality Forecast Verification Using Satellite Data

Abstract NOAA’s operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service developmental (research mode) particulate matter (PM2.5) predictions tested during the summer 2004 International Consortium for Atmospheric Research on Transport and Transformation/New England Air Quality Study (ICARTT/NEAQS) field campaign. The forecast period included long-range transport of smoke from fires burning in Canada and Alaska and a regional-scale sulfate event over the Gulf of Mexico and the eastern United States. Over the 30-day time period for which daytime hourly forecasts were compared with observations, the categorical (exceedance defined as AOD > 0.55) forecast accuracy was between 0% and 20%. Hourly normalized mean bias (forecasts − observations) ranged between −50% and +50% with forecasts being positively biased when observed AODs were small and negatively biased when observed AODs were high. Normalized mean errors are between 50% and 100% with t...

Quantifying the contribution of thermally driven recirculation to a high-ozone event along the Colorado Front Range using lidar

A high-ozone (O3) pollution episode was observed on 22 July 2014 during the concurrent “Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality” (DISCOVER-AQ) and “Front Range Air Pollution and Photochemistry Experiment” (FRAPPE) campaigns in northern Colorado. Surface O3 monitors at three regulatory sites exceeded the Environmental Protection Agency (EPA) 2008 National Ambient Air Quality Standard (NAAQS) daily maximum 8-hr average (MDA8) of 75 ppbv. To further characterize the polluted air mass and assess transport throughout the event, measurements are presented from O3 and wind profilers, O3-sondes, aircraft, and surface monitoring sites. Observations indicate thermally-driven upslope flow was established throughout the Colorado Front Range during the pollution episode. As the thermally-driven flow persisted throughout the day, O3 concentrations increased and affected high-elevation Rocky Mountain sites. These observations, coupled with modeling analyses, demonstrate a westerly return flow of polluted air aloft, indicating the mountain-plains solenoid circulation was established and impacted surface conditions within the Front Range.

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.

Merged dust climatology in Phoenix, Arizona based on satellite and station data

In order to construct climate quality long-term dust storm dataset, merged dust storm climatology in Phoenix is developed based on three data sources: regular meteorological records, in situ air quality measurements, and satellite remote sensing observations. The result presented in this paper takes into account the advantages of each dataset and integrates individual analyses demonstrated and presented in previous studies that laid foundation to reconstruct a consistent and continuous time series of dust frequency. A key for the merging procedure is to determine analysis criteria suitable for each individual data source. A practical application to historic records of dust storm activities over the Phoenix area is presented to illustrate detailed steps, advantages, and limitations of the newly developed process. Three datasets are meteorological records from the Sky Harbor station, satellite observed aerosol optical depth data from moderate resolution imaging spectroradiometer, and the U.S. Environmental Protection Agency Air Quality System particulate matter data of eight sites surrounding Phoenix. Our purpose is to construct dust climatology over the Phoenix region for the period 1948–2012. Data qualities of the reconstructed dust climatology are assessed based on the availability and quality of the input data. The period during 2000–2012 has the best quality since all datasets are well archived. The reconstructed climatology shows that dust storm activities over the Phoenix region have large interannual variability. However, seasonal variations show a skewed distribution with higher frequency of dust storm activities in July and August and relatively quiet during the rest of months. Combining advantages of all the available datasets, this study presents a merged product that provides a consistent and continuous time series of dust storm activities suitable for climate studies.

Impact of the 2008 Global Recession on Air Quality over the United States: Implications for Surface Ozone Levels from Changes in NOx Emissions

Satellite and ground observations detected large variability in nitrogen oxides (NOx) during the 2008 economic recession, but the impact of the recession on air quality has not been quantified. This study combines observed NOx trends and a regional chemical transport model to quantify the impact of the recession on surface ozone (O3) levels over the continental United States. The impact is quantified by simulating O3 concentrations under two emission scenarios: business-as-usual (BAU) and recession. In the BAU case, the emission projection from the Cross-State Air Pollution Rule is used to estimate the “would-be” NOx emission level in 2011. In the recession case, the actual NO2 trends observed from Air Quality System ground monitors and the Ozone Monitoring Instrument on the Aura satellite are used to obtain “realistic” changes in NOx emissions. The model prediction with the recession effect agrees better with ground O3 observations over time and space than the prediction with the BAU emission. The results show that the recession caused a 1–2 ppbv decrease in surface O3 concentration over the eastern United States, a slight increase (0.5–1 ppbv) over the Rocky Mountain region, and mixed changes in the Pacific West. The gain in air quality benefits during the recession, however, could be quickly offset by the much slower emission reduction rate during the post-recession period.

The Performance and Issues of a Regional Chemical Transport Model During Discover-AQ 2014 Aircraft Measurements Over Colorado

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction operates the U.S. Air Quality Forecasting Capability (NAQFC) which uses primarily the U.S Environmental Protection Agency’s Community Multi-Scale Air Quality (CMAQ) model. NAQFC focuses on surface ozone and PM2.5 (particle matter with diameter <2.5 µm), which impacts human-health. Near surface ozone mainly comes from photochemical reactions of NOx and volatile organic compounds (VOCs). Its sources in upper layers could come from either long-range transport or stratospheric ozone. Most PM2.5 comes from near-surface primary emissions or secondary generation from photochemical reactions. During the summer 2014 NASA Discover-AQ-Colorado program, the NOAA Air Resources Laboratory (ARL) provided a real-time forecast in support of aircraft measurements with 12 km CONUS (Contiguous United States) and 4 km nested domains. Here we compare the model results with the aircraft data to investigate our predictions.

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.

Observing System Simulation Experiments (OSSEs) Using a Regional Air Quality Application for Evaluation

Satellite-based and high-altitude airborne remotely sensed air quality data complement land-based and routinely commercial-flight and other measurement-campaign acquired remotely sensed and in situ observations. It is important to optimize the combination and placement of these wide ranges of measurements and data acquisition options for cost-effectiveness. Under this initiative we attempt to quantify the gain by a regional state-of-the-science chemical data assimilation and chemical transport modeling system when incremental sets of observation are acquired into the system. This study represents a first step in a series of steps to ingest such proposed incremental additions of observation. The efficacy of such proposals is quantified systematically by Observation Simulation System Experiments (OSSEs). We compared two end-to-end regional air quality forecasting simulations using: (a) the Weather Forecasting and Research (WRF) regional application initialized by the U.S. national Weather Service (NWS) Global Forecasting System (GFS) coupled with the U.S. Environmental Protection Agency Community Multi-scale Air Quality (CMAQ) chemical model (Byun and Schere 2006), and (b) the same as above but with a new GFS enhanced by assimilating a fictitious addition of Atmospheric Infrared Sounder (AIRS) retrieved radiances at 13 km spatial resolution at nadir from a proposed geostationary satellite positioned over 75oW staring over the U.S. Both sensitivity runs were performed in 12 km horizontal grid resolution and with daily initialization for 12 days between July 29 and August 9 2005. Noticeable forecast skill improvement in surface concentration for O3 and particulate matter smaller than 2.5 µm in diameter (PM2.5) was achieved.

Satellite‐Based Daily PM2.5 Estimates During Fire Seasons in Colorado

The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM2.5) from wildfires are limited by the lack of accurate high-resolution PM2.5 exposure data over fire days. Satellite-based aerosol optical depth (AOD) data can provide additional information in ground PM2.5 concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM2.5 concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM2.5 concentrations over fire seasons (April to September) in Colorado for 2011-2014. Our model had a 10-fold cross-validated R 2 of 0.66 and root-mean-squared error of 2.00 μg/m3, outperformed the multistage model, especially on the fire days. Elevated PM2.5 concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short-term and long-term epidemiological studies of wildfire PM2.5.