A52D-01 INVITED
Air Quality Applications of Data from the Aura Satellite
Data from the Ozone Monitoring Instrument (OMI) and Tropospheric Emission Spectrometer (TES) instruments on NASA's Aura satellite are being applied for air quality analysis and modeling purposes. We summarize the types of applications that are being attempted, discuss the successes that have been achieved, and outline the limitations of the data. Column amounts of the trace gases O3, NO2, SO2, HCHO and aerosol optical depths from OMI are being used for estimating emissions, for assimilation into air quality forecast models, and in analyzing the impacts of major source regions. Profiles of CO and O3 from TES are being used in analyses of long range transport of air pollutants and the air quality impacts of megacities and boreal fires. Assimilation of OMI ozone data into an air quality prediction system has proven successful. Limitations of the Aura data include inadequate temporal sampling, relative insensitivity to boundary layer pollution, limited vertical profile information, and cloud interference. All of these limitations must be carefully considered when applying the Aura data for air quality purposes. Possible future expansion of Aura data applications will be discussed.
A52D-02 INVITED
Air Quality Research and Applications Using AURA OMI Data
The Ozone Monitoring Instrument (OMI) on EOS Aura is a new generation of satellite remote sensing instrument designed to measure trace gas and aerosol absorption at the UV and blue wavelengths. These measurements are made globally at urban scale resolution with no inter-orbital gaps that make them potentially very useful for air quality research, such as the determination of the sources and processes that affect global and regional air quality, and to develop applications such as air quality forecast. However, the use of satellite data for such applications is not as straightfoward as satellite data have been for stratospheric research. There is a need for close interaction between the satellite product developers, in-situ measurement programs, and the air quality research community to overcome some of the inherent difficulties in interpreting data from satellite-based remote sensing instruments. In this talk we will discuss the challenges and opportunities in using OMI products for air quality research and applications. A key conclusion of this work is that to realize the full potential of OMI measurements it will be necessary to combine OMI data with data from instruments such as MLS, MODIS, AIRS, and CALIPSO that are currently flying in the "A-train" satellite constellation. In addition similar data taken by satellites crossing the earth at different local times than the A-train (e.g., the recently MetOp satellite) would need to be processed in a consistent manner to study diurnal variability, and to capture the effects on air quality of rapidly changing events such as wild fires.
A52D-03 INVITED
Evolving Interface between Air Pollution Assessments and Atmospheric Characterizations
Air pollution assessments typically focus on the "current" pollutant of concern, often shifting through categories (e.g., acid gases, particles, ozone) with an attendant change in spatial focus (e.g., neighborhood, urban, regional, global). Despite the lag between emerging scientific evidence and incorporation into policies, the most recent shift in air program management aligns well with our understanding that many of the processes controlling origin, transformation fate and removal of pollutants are well integrated across varying scales of space, time, chemical composition and environmental media. The combination of air pollution complexity and multiplicity of effects demands more comprehensive characterizations through a multi-faceted collaborative assessment effort. This presentation explores key observational and simulation linkages toward optimizing the broader characterization capability across space and ground based systems.
A52D-04
Biomass burning source characterization requirements in air quality models with and without data assimilation: challenges and opportunities
Quantitative models of the transport and evolution of atmospheric pollution have graduated from the laboratory to become a part of the operational activity of forecast centers. Scientists studying the composition and variability of the atmosphere put great efforts into developing methods for accurately specifying sources of pollution, including natural and anthropogenic biomass burning. These methods must be adapted for use in operational contexts, which impose additional strictures on input data and methods. First, only input data sources available in near real-time are suitable for use in operational applications. Second, operational applications must make use of redundant data sources whenever possible. This is a shift in philosophy: in a research context, the most accurate and complete data set will be used, whereas in an operational context, the system must be designed with maximum redundancy. The goal in an operational context is to produce, to the extent possible, consistent and timely output, given sometimes inconsistent inputs. The Naval Aerosol Analysis and Prediction System (NAAPS), a global operational aerosol analysis and forecast system, recently began incorporating assimilation of satellite-derived aerosol optical depth. Assimilation of satellite AOD retrievals has dramatically improved aerosol analyses and forecasts from this system. The use of aerosol data assimilation also changes the strategy for improving the smoke source function. The absolute magnitude of emissions events can be refined through feedback from the data assimilation system, both in real- time operations and in post-processing analysis of data assimilation results. In terms of the aerosol source functions, the largest gains in model performance are now to be gained by reducing data latency and minimizing missed detections. In this presentation, recent model development work on the Fire Locating and Monitoring of Burning Emissions (FLAMBE) system that provides smoke aerosol boundary conditions for NAAPS is described, including redundant integration of multiple satellite platforms and development of feedback loops between the data assimilation system and smoke source.
A52D-05
GEMS: Assimilation of Satellite and In-Situ Observations to Monitor and Forecast Global and Regional Air Quality
Under the umbrella of European Global Monitoring for Environment and Security (GMES) the Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in-situ data (GEMS) project has been running since March 2005. The aim of the project is to build a pre-operational system that will assimilate both satellite and in- situ observations to monitor atmospheric composition. Greenhouse gases, reactive gases, and aerosol are all monitored in a global 4-dimensional variational (4D-Var) data assimilation system running at about 1 degree resolution. These global fields are then used as boundary conditions for regional air quality modelling on the European scale. Both the global and regional component will improve our understanding of surface fluxes, long- range transport, and in general the causes of poor air quality. We will present an overview of the project and show how we combine state-of-the-art modelling, data assimilation and retrieval techniques to support scientific research, air quality forecasters, and environmental policy makers.
A52D-06
Combing OMI and MLS to Improve Tropospheric Ozone Retrievals for Air Quality Applications
OMI and MLS, both measuring ozone profiles, are two of the four instruments on board the EOS AURA satellite. MLS measures ozone profiles at a vertical resolution of about 3 km from 0.1 mb to 200 mb but with a limited spatial coverage. OMI measures ozone profiles from the stratosphere to the troposphere at very high spatial resolution but with a much coarser vertical resolution especially in the troposphere. So combing MLS and OMI data can improve the separation of stratospheric ozone from tropospheric ozone. In this study, we first use zonal MLS data combined with climatological ozone profiles in the troposphere to correct for across-track dependent biases (-2%-5%) in OMI level 1b radiance. We then use MLS data as a priori information in the stratosphere to retrieve ozone profiles from corrected OMI radiances using the optimal estimation technique. The combined retrievals are compared MLS retrievals and OMI retrievals that do not use MLS information. The inclusion of MLS data in OMI retrievals reduces the uncertainty in both stratospheric column ozone and tropospheric column ozone compared to using either instrument alone and improves the identification of tropospheric ozone due to pollution.
A52D-07
Improved Tropospheric Carbon Monoxide Profiles Using AIRS and TES Measurements
Atmospheric CO concentrations are simultaneously measured by EOS A-train satellite sensors, which include AIRS on Aqua, TES and MLS on Aura. Based on the heritage of the A-train system, the combined datasets from these sensors will provide the best available three-dimensional trace gas information that incorporates the uniqueness of AIRS large spatial coverage, TES high vertical resolutions, and MLS measurements at the upper troposphere and stratosphere. This presentation introduces a new technique that combines TES CO profile measurements with AIRS retrievals by using them as a priori profiles, which reflect near real-time observations taken within only 15 minutes. The combined datasets in this fashion will extend AIRS CO observational sensitivity to the lower atmosphere, which is especially important for air quality studies. The portion of this study for non- coincident profiles will provide the opportunity to propagate TES CO vertical measurement sensitivity horizontally. For AIRS pixels that are located away from nadir objective analysis techniques will be used to populate the observations. Weightings for TES and MLS L2 products will be based on AIRS observed variances and the averaging kernels of TES and MLS. The collocated AIRS and TES datasets are combined and overlapped with DACOM in situ CO measurements from INTEX-B field campaign for validation.