Atmospheric Sciences [A]

A21G
 MC:2009  Tuesday  0800h

Aerosol Multisensor and Model Intercomparison and Synergy I


Presiding:  G Leptoukh, NASA GSFC; R Kahn, NASA GSFC

A21G-01 INVITED

The Aerosol Measurement and Processing System: New Capabilities and Results

* Braverman, A Amy.Braverman@jpl.nasa.gov, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Kalashnikova, O Olga.Kalashnikova@jpl.nasa.gov, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Manipon, G Geraldjohn.Manipon@jpl.nasa.gov, Raytheon Corporation, Suite 500 299 N. Euclid Ave., Pasadena, CA 91101, United States
Paradise, S Susan.Paradise@jpl.nasa.gov, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Penner, J penner@umich.edu, Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, 2455 Hayward Street, Ann Arbor, MI 48109, United States
Wilson, B Brian.Wilson@jpl.nasa.gov, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Xing, Z Zhangfan.Xing@jpl.nasa.gov, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Xu, L lixum@umich.edu, Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, 2455 Hayward Street, Ann Arbor, MI 48109, United States

The Aerosol Measurement and Processing System (AMAPS) is a grid based, distributed computing environment for aerosol science. AMAPS is motivated by the community's call for a modern infrastructure to access, manipulate and analyze aerosol data (see the Bulletin of the American Meteorological Society, October 2003). AMAPS offers access, subsetting, and data analysis functions for level 2 aerosol data products from MISR, MODIS, and AERONET, including the new AERONET Maritime Network. The system is available in two modes: service user mode and power user mode. Service users access data and computational capabilities through pre-constructed web pages that call workflows: web service functions chained together in XML documents. Power users access computational capabilities from the command line of AMAPS-enabled computers, by embedding web service calls directly in their python programs. The AMAPS python package also offers streamlined functions to read, extract and manipulate data over the internet. In this talk, we review the latest improvements and enhancements including the addition of the MODIS level 2 cloud product, and discuss recent science findings enabled by the AMAPS system.

http://amaps.jpl.nasa.gov/

A21G-02 INVITED

Pre-operational aerosol analysis and forecasts in the ECMWF IFS as part of the GEMS project

* Boucher, O olivier.boucher@metoffice.gov.uk, Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, United Kingdom
Morcrette, J jean-jacques.morcrette@ecmwf.int, European Centre for Medium Range Weather Forecasts, Shinfield Park, Reading, RG2 9AX, United Kingdom
Benedetti, A angela.benedetti@ecmwf.int, European Centre for Medium Range Weather Forecasts, Shinfield Park, Reading, RG2 9AX, United Kingdom
rest of the GEMS aerosol team, t

The GEMS project is developing comprehensive monitoring and forecasting systems for trace atmospheric constituents relevant to climate and air quality. The systems will provide the basis for value-added data and information services to be developed as part of Europe's Global Monitoring for Environment and Security (GMES) initiative. A prognostic representation of aerosols (dust, sea-salt, black carbon, organic matter and sulphate aerosols from precursor emissions) has been developed in the ECMWF Integrated Forecast System (IFS) in both its analysis and forecast modules. Aerosol optical depths from the MODIS satellite instrument are being assimilated using a 4D-VAR technique and appropriate error statistics. A near-real-time aerosol forecast (without data assimilation) and a 2003-2007 reanalysis (with data assimilation of satellite aerosol optical depths) are now available. The system is being evaluated both routinely using aerosol data from available surface networks and more thoroughly on various case studies. This presentation will describe the GEMS aerosol model, the data assimilation module and their validation. Plans for the future will also be presented.

A21G-03

The Three-Dimensional Air Quality System (3D-AQS) as a Data Synthesis Toolbox

* Hoff, R M hoff@umbc.edu, Joint Center for Earth Systems Technology, University of Maryland, Baltimore County,, Suite 320 5523 Research Park Drive, Baltimore, MD 21228, United States
Zhang, H hazhang@umbc.edu, Joint Center for Earth Systems Technology, University of Maryland, Baltimore County,, Suite 320 5523 Research Park Drive, Baltimore, MD 21228, United States
Jordan, N njordan1@umbc.edu, Joint Center for Earth Systems Technology, University of Maryland, Baltimore County,, Suite 320 5523 Research Park Drive, Baltimore, MD 21228, United States
Prados, A I ana.i.prados@nasa.gov, Joint Center for Earth Systems Technology, University of Maryland, Baltimore County,, Suite 320 5523 Research Park Drive, Baltimore, MD 21228, United States
Engel-Cox, J engelcoxj@battelle.org, Battelle Memorial Institute, 2101 Wilson Boulevard, Suite 800, Arlington, VA 22201, United States
Huff, A huffa@battelle.org, Battelle Memorial Institute, 2101 Wilson Boulevard, Suite 800, Arlington, VA 22201, United States
Weber, S webers@battelle.org, Battelle Memorial Institute, 2101 Wilson Boulevard, Suite 800, Arlington, VA 22201, United States
Zell, E zelle@battelle.org, Battelle Memorial Institute, 2101 Wilson Boulevard, Suite 800, Arlington, VA 22201, United States
Kondragunta, S shobha.kondragunta@noaa.gov, NOAA NESDIS, 5200 Auth Road, Suitland, MD 20746-4304, United States
Szykman, J J james.j.szykman@nasa.gov, Environmental Protection Agency, 109 TW Alexander Drive, Durham, NC 27709, United States
Johns, B johns.brad@epa.gov, Environmental Protection Agency, 109 TW Alexander Drive, Durham, NC 27709, United States
Dimmick, F dimmick.fred@epa.gov, Environmental Protection Agency, 109 TW Alexander Drive, Durham, NC 27709, United States
Wimmers, A wimmers@ssec.wisc.edu, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, 1225 W. Dayton St., Madison, WI 53706, United States
Al-Saadi, J j.a.al-saadi@nasa.gov, NASA Langley Research Center, MS 475, Hampton, VA 23681, United States
Kittaka, C Chieko.Kittaka-1@nasa.gov, SSAI, 1 Enterprise Parkway, Suite 200, Hampton, VA 23666, United States

A system has been developed to combine remote sensing and ground-based measurements of aerosol concentration and aerosol light scattering parameters into a three-dimensional view of the atmosphere over the United States. Utilizing passive and active remote sensors from space and the ground, the system provides tools to visualize particulate air pollution in near-real time and archives the results for retrospective analyses. The main components of the system (IDEA, the Smog Blog, Smog Stories, AIRQuest and RSIG) are described and the relationship of how data moves from one system to another is outlined. In order to provide examples of how the results can be used to analyzed cases of pollution, three events (two fires and one wintertime low-PBL haze) are discussed. Not all tools are useful at all times and the sparsity of some data, the limitations caused by overlying clouds, etc. are shown. Nevertheless, multiple sources of data help paint a more thorough picture of haze events than what one would obtain with only surface based sensors.

A21G-04

Development of the data assimilation quality MODIS and MISR aerosol products for data integration and assimilation

* Shi, Y yingxi.shi@und.nodak.edu, University of North Dakota, Department of Atmospheric Sciences Clifford Hall R400 University & Tulane, Grand Forks, ND 58202-9006, United States
Zhang, J jzhang@aero.und.edu, University of North Dakota, Department of Atmospheric Sciences Clifford Hall R400 University & Tulane, Grand Forks, ND 58202-9006, United States
Reid, J S jeffrey.reid@nrlmry.navy.mil, Naval Research Laboratory, Aerosol and Radiation Section Marine Meteorology Division Naval Research Laboratory 7 Grace Hopper Ave., Stop 2, Monterey, CA 93943-5502, United States

Satellite aerosol products are widely used in climate and weather related research. Indeed, recent studies have shown that satellite aerosol products, such as the operational MODIS product, could be successfully used in CTM models for improving visibility and air quality forecasts. However, as pointed by several studies, that while satellite over ocean aerosol products are mostly acceptable, large uncertainties still exist, especially in regional scale. Bias and noise are related to the observing environment such as boundary conditions, cloud contaminations, and aerosol micro-physics. These retrieval sensitivities need to be well studied and documented for a further use of such products in models or other aerosol related researches. Extended from our previous studies, we carefully examined uncertainties in the reported aerosol optical properties for the newer version of MODIS collection 5 and MISR over ocean aerosol products. Major differences are explored and a new quality control procedures including empirical correction and QA check are presented. The benefits of the quality assured MODIS and MISR aerosol products to data assimilation and data integration are also examined through the use of the NRL aerosol data assimilation system NAVDAS-AOD.

A21G-05

Comparisons of MODIS and CALIPSO level 2 aerosol products and their combined use for calculating direct aerosol radiative effects

* Redemann, J Jens.Redemann-1@nasa.gov, BAERI / NASA Ames Research Center, 4742 Suffolk Ct., Ventura, CA 93003, United States
Vaughan, M A Mark.A.Vaughan@nasa.gov, NASA Langley Research Center, Mail Stop 475, Hampton, VA 23681-2199, United States
Zhang, Q zhang@baeri.org, BAERI / NASA Ames Research Center, 4742 Suffolk Ct., Ventura, CA 93003, United States
Russell, P B Philip.B.Russell@nasa.gov, NASA Ames Research Center, MS 245-5, Moffett Field, CA 94035-1000,
Livingston, J M john.livingston@sri.com, SRI International, G-179 333 Ravenswood Ave., Menlo Park, CA 94025, United States
Remer, L A Lorraine.A.Remer@nasa.gov, NASA Goddard Space Flight Center, code 613.2, Greenbelt, MD 20771, United States
Christopher, S A sundar@nsstc.uah.edu, University of Alabama in Huntsville, 320 Sparkman Drive, NSSTC, Huntsville, AL 35805-1912, United States

The CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument aboard the CALIPSO (Cloud- Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite has been acquiring data as part of the A-Train constellation of satellites since June of 2006. As of December 2007, the level 2 data products have been augmented to include aerosol optical depths and extinction profiles. In conjunction, the MODIS-Aqua level-2 data set, MYD04, is readily available for essentially all days in orbit since June of 2002 and provides column integrated aerosol observations at a spatial resolution of 10x10km at nadir. In this paper we present comparisons of the standard MODIS-Aqua level-2 aerosol data to the initial release of the CALIPSO level-2 aerosol data set. For four months in 2007, we present comparisons of mid-visible aerosol optical depth (AOD) calculated from the CALIPSO derived aerosol extinction profiles to MODIS-Aqua aerosol optical depth retrievals that cover the same spatial domain. We focus on AOD comparisons over water and show how the use of appropriate quality flags in the CALIPSO product and a restriction to scenes with cloud fractions below 1 % (as defined in the MODIS aerosol retrievals) results in generally good correlation between the two data sets and rms differences in AOD of 0.1 or less. A geographic breakdown of our data shows a paucity of comparative observations at high latitudes (greater than 40 degrees), but a few regions of remarkably similar monthly mean AOD. We will conclude by describing our strategy for combining the MODIS and CALIPSO aerosol measurements for the purpose of calculating direct aerosol radiative effects.

A21G-06

Providing Space-Based Constraints on the Chemical Composition of Aerosols

* Veefkind, J P veefkind@knmi.nl, Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, De Bilt, NL-3730 AE, Netherlands
Veihelmann, B ben.veihelmann@knmi.nl, Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, De Bilt, NL-3730 AE, Netherlands
Boersma, K F boersma@knmi.nl, Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, De Bilt, NL-3730 AE, Netherlands
Levelt, P F levelt@knmi.nl, Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, De Bilt, NL-3730 AE, Netherlands

Human activities around the globe cause the emission of gases such as SO2, NOx and VOCs. In the atmosphere, these gases are transformed to secondary fine mode aerosol particles. Understanding sources of anthropogenic aerosols and their precursor gases is relevant for climate change as well as for air quality. To predict how the aerosol load responds on changing emissions, knowledge of the chemical composition of aerosol particles is necessary. Over the last decade, accurate satellite data on aerosol optical thickness (AOT) and tropospheric NO2 columns have become available. In this contribution we use AOT from MODIS on the NASA EOS Aqua satellite and NO2 data from OMI on the NASA EOS Aura satellite, which are both part of the A-Train satellite constellation. Whereas tropospheric NO2 is a direct measure of the concentration of this gas, the AOT is an optical parameter, which is not directly linked to the physical and chemical properties of the aerosol particles. To establish the link between the AOT and its precursor gases, we have investigated the spatial correlation of AOT and tropospheric NO2 in multi-year averages for different regions and seasons. In regions where the aerosol is dominated by anthropogenic sources, there is a strong spatial correlation between AOT and tropospheric NO2. We show that the corresponding slope between the AOT and the NO2 varies strongly between source regions and contains information on the chemical composition of the aerosol particles. For example, we find that the AOT- NO2 slope is comparable between Northwestern Europe and the US East coast, but deviates from the slope over Eastern Europe. This difference in slope can be explained by difference in the sulfate to nitrate ratio of the aerosol particles.

A21G-07

First Results for a Global Aerosol Assimilation System using Multi-Sensor Datasets

* Schutgens, N schutgen@ccsr.u-tokyo.ac.jp, CCSR, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8568, Japan
Mukai, M , Japan Aerospace Exploration Agency (JAXA), 2-1-1 Sengen, Tsukuba, 305-8505, Japan
Miyoshi, T , NPD, Japan Meteorological Agency (JMA), 1-3-4 Otemachi, Tokyo, 100-8122, Japan
Takemura, T , RIAM, Kyushu University, 6-1 Kasuga-koen, Kasuga, Fukuoka, 816-8580, Japan
Nakajima, T , CCSR, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8568, Japan

We assimilate ground- and satellite observations of aerosol optical depth (AOD) into a global aerosol transport model using an Ensemble Kalman Filter. Global aerosol modelling is an important tool for understanding the climate impact of aerosol, both through direct and indirect effects. Aerosol modelling, however, is fraught with many uncertainties, one of the most obvious ones being the emission inventories. These emission inventories are either based on government tallies of emitted gases which then serve as proxies for the emission of secondary aerosol (carbon or sulfate), or on field campaigns whose results are generalized into simple parametrisations in e.g. windspeeds (sea salt and dust). The global aerosol transport model (SPRINTARS) is built on top of an AGCM and calculates aerosol loads for dust, sea salt, carbon and sulfate particles. The ensemble SPRINTARS calculations differ in the assumed emission inventories. This difference is used by the state-of-the-art Local Ensemble Kalman Filter (LETKF) to improve on simulated aerosol loads after comparison to actual observations. In the future, this approach will also allow us to establish new emission inventories directly from observed AOD. We will present assimilation studies for both simulated and real observations. The simulated observations allow us indepth studies of the response of the system to the spatial and temporal sampling of the observations (Observing System Simulation Experiments). Observations used in this study come from AERONET, MODIS or GOSAT. AERONET ground sites are often located near large emission sources and often make many observations per day. As a consequence, assimilation of AOD is succesful, but the AOD improvement is limited by the spatial coverage of AERONET. In contrast, satellite observations (MODIS or GOSAT) have a better spatial coverage although observed locations will only be visited once every 16 or 3 days. Aerosol events that last only a day are easily missed. We will discuss possible modifications to LETKF or the combination of different sensors (e.g. AERONET and GOSAT) to alleviate this problem). This work is done as part of the GOSAT mission by JAXA (Japan Aerospace Exploration Agency). GOSAT aims to observe the global distribution of CO2. Interpretation of GOSAT CO2 observations requires correction for the influence of aerosols.

A21G-08

Mineral dust characterization over the North Atlantic/ Saharan desert regions from combined satellite aerosol retrievals and AERONET observations for transport model applications.

* Kalashnikova, O Olga.V.Kalashnikova@jpl.nasa.gov, Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 93021, United States
Kahn, R ralph.kahn@nasa.gov, NASA GSFC, Goddard Space Flight Center, Greenbelt, MD 20771, United States
Chin, M mian.chin@nasa.gov, NASA GSFC, Goddard Space Flight Center, Greenbelt, MD 20771, United States
Zhang, J jzhang@aero.und.edu, University of North Dakota, University of North Dakota, Grand Forks, ND 58202, United States
Leptoukh, G Gregory.Leptoukh@nasa.gov, NASA GSFC, Goddard Space Flight Center, Greenbelt, MD 20771, United States

We demonstrate how MISR and MODIS space-based aerosol products provide complementary information, characterizing (1) transported desert dust plume extent over water, (2) aerosol optical thickness (AOT) evolution, and (3) particle size sensitivity and fraction spherical evolution for the thicker parts of these plumes. MODIS provides more extensive coverage, whereas MISR's multi-angle retrievals include dust properties, and fill in areas where glint precludes MODIS optical depth retrievals, increasing by up to 50% dust plume surface area coverage compared to MODIS-only observations. These results can be used to improve dust aerosol representations in climate, forecast, and transport models. For selected North Africa dust transport events, combined MISR and MODIS observations map systematic changes in retrieved plume surface area, based on AOT contours; these reflect differences in aerosol dispersion and removal rates that must be reproduced by models. We will show comparisons MISR and MODIS L3 aerosol daily data with GOCART-and NAAPS-predicted AOT and AOT dust fraction for the events selected, covering different stages of dust transport and at coincident with several available AERONET sites. In addition, we will present a comprehensive study of AOT and dust property spatial variability and temporal change over bright Saharan desert surfaces for the time-period of 2004-2008, as determined from MISR, OMI and AERONET observations. Regions selected for this study have significant dust loading throughout the year, and have been identified previously as dust sources. In addition to seasonal and inter-annual aerosol optical depth (AOT) variation, we investigate changes and trends in particle nonsphericity, absorption optical depth, angstrom exponent and AOT fractions of small, medium and large particles. We will demonstrate that though the total AOT shows significant variability and no clear trends over this four-year period, aerosol properties show some consistent inter-annual changes. We explore a range of meteorological, aerosol, and measurement-related factors to better understand the underlying causes of these observations.