Youhua Tang

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.

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.

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.

Toward a Unified National Dust Modeling Capability

This study aims to improve the NOAA Operational Dust Forecasting Capability. NOAA has developed and is operating the U.S. Dust Forecasting Capability (DFC) in concert with one of its core missions to build a “Weather Ready Nation”. The current DFC is based on the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler et al. 2010). The NOAA DFC has been in operations since November 2011. DFC gives dust forecast in the form of hourly surface fine particulate (particle small than 2.5 m in diameter (PM2.5)) concentration out to 48 h covering the continental United States (CONUS). It is based on the HYSPLIT simulations made at the National Centers for Environmental Prediction (NCEP) (forecast available at http://airquality.weather.gov). The DFC real-time dust forecast is widely used to help assessing and mitigating dust storm impact on the society and the environment such as on human health (e.g., Valley Fever), air and ground transportation safety, local economy such as estate value depreciation, and climate change. This study leverages the superiority of the High Resolution Rapid Refresh (HRRR) meteorological model. HRRR is a 3 km horizontal resolution regional numerical weather prediction (NWP) model for the CONUS, run operationally at NCEP. HRRR is proposed to provide the meteorology for the DFC. We propose to develop, test, and possibly select among several wind-blown dust emission schemes for the DFC dust-emission modeling. We considered the in-line emission modules in HRRR and the FENGSHA-CMAQ (the U.S. EPA Community Multiscale Air Quality model) windblown-dust module in the operational National Air Quality Forecasting Capability (NAQFC). The FENGSHA-CMAQ version 5.1’s wind-blown dust emission and diffusion module provides the initial wind-blown dust uptake and airborne suspension from the surface by using the surface wind from HRRR, and the HRRR low layer meteorology determines transport and turbulent mixing for the dust. These emission schemes are tested and evaluated over severe dust storms in the Western U.S. on May 11 2014.

Dynamic Coupling of the NMMB and CMAQ Models Through the U.S. National Unified Operational Prediction Capability (NUOPC)

An earth system modeling framework (ESMF) that enables unprecedented insight into the various aspects of the geophysical sciences of Planet Earth in an integrated and holistic manner is needed to study the physical phenomena of weather and climate. The ESMF concept has recently been promoted and elevated by multiple governmental agencies and institutions in the U.S.A. to unify a standard engineering practice and coding protocol in building geophysical model interfaces towards efficient dynamic coupling of earth models and deployment of earth modeling systems for operational services. This new capability is called the National Unified Operational Prediction Capability (NUOPC) (available at http://www.nws.noaa.gov/nuopc/). This project demonstrates the efficacy of using NUOPC as the software package to efficiently in-line, or 2-way couple at every synchronization time-step, the dust prediction capability of the U.S. National Air Quality Forecasting Capability (NAQFC). The NAQFC in the National Centers for Environmental Prediction (NCEP) operations comprises of an off-line coupled National Weather Service (NWS) North American Mesoscale-model (NAM) and the U.S. EPA Community Air Quality Multiscale Model (CMAQ). The limitation of the off-line coupled NAM-CMAQ is that NAM gives meteorological prediction to CMAQ hourly and uni-directionally. This project attempted a new coupling paradigm allowing NAM and CMAQ communicate with one another per synchronization time-step at roughly 5 min intervals uni-directionally or bi-directionally. In this project, the NUOPC protocol was tightly followed and the in-line NAM-CMAQ ability tested to forecast fine mode particulates concentration with earth-crustal origin. A strong dust storm occurred in the South Western U.S. on May 11 2014 was used as a test case for the NUOPC in-line NAM-CMAQ forecasting capability. The forecast performance for the test case was evaluated against measured surface concentration of fine particulate smaller than 2.5 μm in diameter (PM2.5).