Limei Ran 1, Uma Shankar 1, Ellen Cooter 2, Aijun Xiu 1, Neil Davis 1 1 Center for Environmental Modeling for Policy Development, Institute for the Environment,

Slides:

Advertisements

Similar presentations

Transitioning unique NASA data and research technologies to the NWS 1 Stephanie L Haines and Frank LaFontaine June 12, 2007 Compositing of MODIS Products.

Advertisements

Slide 1 Dust Modelling Workshop, 26 Feb08 Dust Modelling Workshop, Reading U., 26 February 2008 Modelling dust aerosols for the ECMWF IFS J.-J. Morcrette,

Chapter 3: Where to Find Satellite Data and Images on the Internet 1:00 – 1:45.

A Tutorial on MODIS and VIIRS Aerosol Products from Direct Broadcast Data on IDEA Hai Zhang 1, Shobha Kondragunta 2, Hongqing Liu 1 1.IMSG at NOAA 2.NOAA.

MODIS Land Products Workshop April 18, 2003 Instructors: Matt Reeves Numerical Terradynamics Simulation Group University of Montana Dave Verbyla Dept.

OMI follow-on Project Toekomstige missies gericht op troposfeer en klimaat Pieternel Levelt, KNMI.

Use of Lidar Backscatter to Determine the PBL Heights in New York City, NY Jia-Yeong Ku, Chris Hogrefe, Gopal Sistla New York State Department of Environmental.

Enhancement of Satellite-based Precipitation Estimates using the Information from the Proposed Advanced Baseline Imager (ABI) Part II: Drizzle Detection.

Global transport and radiative forcing of biomass burning aerosols Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,

MODIS The MODerate-resolution Imaging Spectroradiometer (MODIS ) Kirsten de Beurs.

A Web-based Community Approach to Model Evaluation using AMET Saravanan Arunachalam 1, Nathan Rice 2 and Pradeepa Vennam 1 1 Institute for the Environment.

NLCD and MODIS Landuse Processing Tools and Projection Issues in Modeling Limei Ran and Alison Eyth Center for Environmental Modeling for Policy Development.

FEST-C for Bi-directional CMAQ: Update Ellen Cooter U.S. EPA, National Exposure Research Lab, Research Triangle Park, NC Verel Benson Benson Consulting,

Comparison of the CAM and RRTMG Radiation Schemes in WRF and coupled WRF-CMAQ models Aijun Xiu, Zac Adelman, Frank Binkowski, Adel Hanna, Limei Ran Center.

Development of an Agricultural Fertilizer Modeling System for Bi-directional Ammonia Fluxes in the Community Multiscale Air Quality (CMAQ) Model Limei.

Limei Ran 1,2, Robert Gilliam 3, Jonathan Pleim 3, Adel Hanna 1, William Benjey 3 1 Center for Environmental Modeling for Policy Development, Institute.

Status, Topics, and Questions regarding the Federal Land Manager Environmental Database (FED) March 20, 2011.

 Provides integrated online access to:  Monitoring data: from over 3 dozen networks  Modeling data: from a variety of modeling scenarios  Emissions.

Air Quality-Climate Interactions Aijun Xiu Carolina Environmental Program.

Update on biogenic and soil emissions modeling for DYNAMO Daniel Cohan, Rui Zhang, Quazi Rasool, and Arastoo Pour Biazar AQAST 9 meeting, St. Louis University.

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Integrating Cropland Management into.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Air Quality Products from.

Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.

Infusing satellite Data into Environmental Applications (IDEA): PM2.5 forecasting tool hosted at NOAA NESDIS using NASA MODIS (Moderate Resolution Imaging.

CMAQ (Community Multiscale Air Quality) pollutant Concentration change horizontal advection vertical advection horizontal dispersion vertical diffusion.

A Modeling Investigation of the Climate Effects of Air Pollutants Aijun Xiu 1, Rohit Mathur 2, Adel Hanna 1, Uma Shankar 1, Frank Binkowski 1, Carlie Coats.

Chapter 4: How Satellite Data Complement Ground-Based Monitor Data 3:15 – 3:45.

Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.

Center for Community Modeling and Analysis System (CMAS) Adel Hanna Director, CMAS October 6, th Annual CMAS Conference, Chapel Hill, NC.

Advances in Support of the CMAQ Bidirectional Science Option for the Estimation of Ammonia Flux from Agricultural cropland Ellen Cooter U.S. EPA, National.

Jonathan Pleim 1, Robert Gilliam 1, and Aijun Xiu 2 1 Atmospheric Sciences Modeling Division, NOAA, Research Triangle Park, NC (In partnership with the.

Community Multiscale Air Quality Modeling System CMAQ Air Quality Data Summit February 2008.

Heidy Plata 1, Ezinne Achinivu 1, Szu-Ting Chou 1, Sheryl Ehrman 1, Dale Allen 2, Kenneth Pickering 2♦, Thomas Pierce 3, James Gleason 3 1 Department of.

Center for Environmental Modeling for Policy Development University of North Carolina at Chapel Hill Center for Community Modeling and Analysis System.

Three Dimensional Air Quality System (3D-AQS) Air Quality Data Summit U.S. EPA, Research Triangle Park, NC February 12, 2008 Jill Engel-Cox Battelle Memorial.

Evaluating ammonia (NH 3 ) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in situ aircraft measurements William Battye,

Development and Preliminary Results of Image Processing Tools for Meteorology and Air Quality Modeling Limei Ran Center for Environmental Modeling for.

Remote Sensing of Evapotranspiration with MODIS

DEVELOPING HIGH RESOLUTION AOD IMAGING COMPATIBLE WITH WEATHER FORECAST MODEL OUTPUTS FOR PM2.5 ESTIMATION Daniel Vidal, Lina Cordero, Dr. Barry Gross.

NASA and Earth Science Applied Sciences Program

NASA Earth Observing System Visualization Tools ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences Introduction.

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Using Dynamical Downscaling to Project.

Adel Hanna Director, CMAS The Community Modeling and Analysis System CMAS 14 Years Serving the Community 14 th Annual CMAS Conference, Octobera 2015.

Ambiguity of Quality in Remote Sensing Data Christopher Lynnes, NASA/GSFC Greg Leptoukh, NASA/GSFC Funded by : NASA’s Advancing Collaborative Connections.

Further Steps for Improving Soil NOx Estimates in CMAQ Quazi Ziaur Rasool, Rui Zhang, Benjamin Lash, Daniel S. Cohan Rice University, Houston, TX *Adapted.

An Improved Ammonia Inventory for the WRAP Domain ENVIRON International Corporation and University of California, Riverside WRAP Emission Forum Meeting.

Diagnostic Study on Fine Particulate Matter Predictions of CMAQ in the Southeastern U.S. Ping Liu and Yang Zhang North Carolina State University, Raleigh,

Center for Community Modeling and Analysis System (CMAS) Adel Hanna Director, CMAS October 1, th Annual CMAS Conference, Chapel Hill, NC.

U.S. Environmental Protection Agency Office of Research and Development Implementation of an Online Photolysis Module in CMAQ 4.7 Christopher G. Nolte.

Representing the optical properties of black carbon in the integrated WRF-CMAQ system Francis S. Binkowski, UNC David C. Wong, US EPA.

Limei Ran 1, Ellen Cooter 2, Verel Benson 3, Dongmei Yang 1, Robert Gilliam 2, Adel Hanna 1, William Benjey 2 1 Center for Environmental Modeling for Policy.

Chiara Badaloni Roma, 15 Oct EXPOSURE TO TRAFFIC AIR POLLUTION IN A CASE-CONTROL STUDY OF CHILDHOOD LEUKAEMIA.

Advances in Support of the CMAQ Bidirectional Science Option for the Estimation of Ammonia Flux from Agricultural cropland Ellen Cooter U.S. EPA, National.

Robin L. Dennis, Jesse O. Bash, Kristen M. Foley, Rob Gilliam, Robert W. Pinder U.S. Environmental Protection Agency, National Exposure Research Laboratory,

John M. Huddleston, PhD Research Associate “Cooperative Institute for Research in the Atmosphere” June 11, 2009 The Killer App.

Spatial Allocator Version 4.3

Ellen Cooter1, Limei Ran1, Verel Benson2, Dongmei Yang3,

Land Use in Regional Climate Modeling

Use of Near-Real-Time Data for the Global System

Improving an Air Quality Decision Support System through the Integration of Satellite Data with Ground-Based, Modeled, and Emissions Data Demonstration.

17th Annual CMAS Conference, Chapel Hill, NC: October 22-24, 2018

University of Maryland, AOSC Brown Bag Seminar

Limei Ran and Alison Eyth

NASA ROSES 2007: Decision Support through Earth Science Research Results Improving an Air Quality Decision Support System through the Integration of Satellite.

J. Burke1, K. Wesson2, W. Appel1, A. Vette1, R. Williams1

Satellite data that we’ve acquired

JEHN-YIH JUANG, Donna Schwede, and Jon Pleim

Improving an Air Quality Decision Support System through the Integration of Satellite Data with Ground-Based, Modeled, and Emissions Data Demonstration.

Current Research on 3-D Air Quality Modeling: wildfire!

Presentation transcript:

Limei Ran 1, Uma Shankar 1, Ellen Cooter 2, Aijun Xiu 1, Neil Davis 1 1 Center for Environmental Modeling for Policy Development, Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 2 Atmospheric Modeling and Analysis Division, U.S. Environmental Protection Agency, Research Triangle Park, NC Contact: INTRODUCTION The Spatial Allocator (SA) is a set of geospatial tools we have developed over the past few years to help users manipulate and generate geospatial data files related to emissions and air quality modeling. The current release of the SA contains three parts of tool sets: Vector, Raster and Surrogate Tools. The current release of the SA contains the tools to process 2001 National Land Cover Database (NLCD) and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data for WRF modeling. Since then, with supports from NASA ROSES and US EPA projects we have enhanced the SA Raster Tools to process the following data: 1. MODIS cloud and Aerosol optical depth (AOD) products, 2. OMI AOD and NO2 products at both swath (L2 ) and gridded (L2G and L3) level, 3.Geostationary Operational Environmental Satellite (GOES) data, and 4. The Environmental Policy Integrated Climate (EPIC) fertilizer modeling system data for CMAQ ammonia bidirectional flux modeling. OBJECTIVES 1. Show the new tools developed in the coming release of the SA Raster Tools system, 2. Demonstrate gridded satellite images and some satellite data comparisons with CMAQ output, 3. Display processed data for EPIC fertilizer modeling, and 4. Some updates in the Vector Tools for the coming SA release. CURRENT DEVELOPMENT Tool to generate new biogenic emission landcover database (BELD4) from current 30-m NLCD, 500-m MODIS land cover data, Forest Inventory and Analysis data, and National Agricultural Statistical Service (NASS) data MODIS L2 Swath Cloud and Aerosol Product Re- griding Tool Obtain data (hdf4) from: data/search.html For MODIS cloud product: download L2 cloud product and L1 geo-location files (for 1-km variable location) For MODIS aerosol product: download L2 aerosol product files A run script file to define domain, input data directory, variables to be re-gridded, date range, and output NetCDF file OMI L2 Swath Aerosol and NO 2 Product Re-griding Tool Obtain data from: Re-grid the following L2 swath products with 13x24km spatial resolution at nadir: 1.OMI Near-UV Aerosol Extinction and Absorption Optical Depth 2.OMI-Aura Nitrogen Dioxide (NO2) Total and Tropospheric Column EPIC Fertilizer Modeling Tools for CMAQ Bi-directional NH 3 Modeling MCIP/CMAQ to EPIC tool: 1.Radiation (MJ m^2, daily total) 2.Tmax, Tmin (C, daily) 3.Precipitation (mm, daily total) 4.Relative humidity (fraction, daily average) 5.Windspeed (m s^-1, daily average) 6.Dry N from CMAQ (g/ha, daily total) 7.Wet N from CMAQ (g/ha, daily total) EPIC to CMAQ tool to create three NetCDF files for EPIC sites: 1.Soil property output (e.g., bulk density, wilting point…) 2.Time step output ( e.g. NO3 loss, denitrification, N- volatilized, Fertilize-NH3, LAI, crop height…) 3.Fertilizer application output ( e.g. Application-Date, Application-Depth, mineral and organic N and P) Sample EPIC Site Weather (June 14, 2002) MODIS Terra Aerosol :17:00 Gridded EUS 12km CMAQ domain and original Data MODIS Aqua Cloud :08:15 Gridded EUS 12km CMAQ domain and original Data