1 RTP, NC 1 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Goddard Interactive Online Visualization ANd aNalysis Infrastructure NASA Goddard Space Flight.

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1 RTP, NC 1 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Goddard Interactive Online Visualization ANd aNalysis Infrastructure NASA Goddard Space Flight Center Assessing U.S Air Quality with Remote Sensing Data via

2 RTP, NC 2 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Content What is Giovanni? Giovanni for Air Quality Trace gases in Giovanni Interoperability of Giovanni Case studies Giovanni and Google Earth

3 RTP, NC 3 Gregory Leptoukh & Frank Lindsay, AQ Data Summit About Giovanni Giovanni is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Giovanni provides a simple and easy way to explore, visualize, analyze, and access vast amounts of Earth science remote sensing and model data.

4 RTP, NC 4 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Aerosol from MODIS and GOCART modelParticulate Matter (PM 2.5) from AIRNow Ozone Hole from OMI Aerosol from GOCART model ppmv Carbon Monoxide from AIRS Water Vapor from AIRSMODIS vs SeaWiFS Chlorophyll Giovanni Instances CloudSat HIRDLS MLS OMI TRMM SeaWiFS AMSR-E HALOE TOMS Models Parasol CALIOP Data Inputs MODIS AIRS MISR and more…

5 RTP, NC 5 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Capabilities Basic (one-parameter): Area plot – averaged or accumulated over any data period for any rectangular area (various map projections) Time plot – time series averaged over any rectangular area Hovmöller plots –longitude-time or latitude-time cross sections ASCII output – for all plot types (can be used with GIS apps) Image animation – for area plot Vertical profiles Vertical cross-sections, zonal means Beyond basics: Area plot - geographical intercomparison between two parameters Time plot - an X-Y time series plot of several parameters Scatter plot of parameters in selected area and time period Scatter plot of area averaged parameters - regional (i.e., spatially averaged) relationship between two parameters Temporal correlation map - relationship between two parameters at each grid point in the selected spatial area Temporal correlation of area averaged parameters - a single value of the correlation coefficient of a pair of selected parameters Difference plots Anomaly plots Acquiring parameter and spatial subsets in a batch mode through Giovanni

6 RTP, NC 6 Gregory Leptoukh & Frank Lindsay, AQ Data Summit The Power of Simplicity  Only a Web browser is needed.  No need to learn data formats and programming.  No need to download large amounts of data.  Customized data and analyses can be obtained with only a few mouse clicks. Caution: Giovanni is an exploration tool!

7 RTP, NC 7 Gregory Leptoukh & Frank Lindsay, AQ Data Summit AOT for June 2006 Terra MODIS Aqua MODIS Envisat MERIS Parasol POLDER

8 RTP, NC 8 Gregory Leptoukh & Frank Lindsay, AQ Data Summit AOT Differences for June 2006 Terra MODIS – Aqua MODISAqua MODIS - POLDER Terra MODIS – MERISMERIS – POLDER

9 RTP, NC 9 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Scatter plots

10 RTP, NC 10 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Time series

11 RTP, NC 11 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Maps of NO 2

12 RTP, NC 12 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Maps of CO

13 RTP, NC 13 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Profile Data Comparisons HIRDLS MLS HIRDLS and MLS ozone (top) and temperature (bottom) profiles acquired March 12, 2007, over France during the passage of a weather front. Note the tropopause fold (arrow) in the ozone profiles. MLS vertical resolution is ~3 km, HIRDLS vertical resolution is ~1 km.

14 RTP, NC 14 Gregory Leptoukh & Frank Lindsay, AQ Data Summit California fires by MODIS

15 RTP, NC 15 Gregory Leptoukh & Frank Lindsay, AQ Data Summit PM2.5 from AirNow in Giovanni

16 RTP, NC 16 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Visualizing California’s Wildfires from Space Tropospheric NO 2 UV Aerosol Index Total Column CO Aerosol Small Mode Fraction Cloud Optical Thickness Aerosol Mass over Land OMI AIRS MODIS October 2007 Data from NASA’s Aura OMI (Tropospheric NO 2 and UV Aerosol Index), Aqua AIRS (Total Column CO) and Terra MODIS (Aerosol Small Fraction, Cloud Optical Thickness and Aerosol Mass Concentration Over Land)

17 RTP, NC 17 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Dust event, May 23, 2007 Data Fusion ( prototype ) in Terra Aqua Terra + Aqua

18 RTP, NC 18 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Interoperability

19 RTP, NC 19 Gregory Leptoukh & Frank Lindsay, AQ Data Summit

20 RTP, NC 20 Gregory Leptoukh & Frank Lindsay, AQ Data Summit SATs in Giovanni MODISCALIPSO Sea WIFS OMITOMS Aerosol Data NASA GES DISC Inter- opera- bility MAPSSICAREAMAPSSERVIRetc. BAMG- OMAS Data Fed ESDC Format BinaryASCIIKMLMATLABetc.IDL HDF 4/5 NetCDF already in Giovanni 3.06

21 RTP, NC 21 Gregory Leptoukh & Frank Lindsay, AQ Data Summit OGC and other m2m protocols WMS: –Via Map Server –Via Giovanni WCS: –Via WebGIS –Via Giovanni OPeNDAP Hyrax 4 serving data on 12 machines. Sample URLs: WMS sample URL to get WMS data for 'LAYER=AIRX3STD_TOTO3_A‘: bin/wms_ogc?TARGET_SRS=EPSG:4326&Service=WMS&VERSION=1.1. 1&REQUEST=GetMap&SRS=EPSG:4326&WIDTH=768&HEIGHT=512&BB OX=-180,-90,180,90&LAYERS=AIRX3STM_TOTO3_A,coastline bin/wms_ogc?TARGET_SRS=EPSG:4326&Service=WMS&VERSION=1.1. 1&REQUEST=GetMap&SRS=EPSG:4326&WIDTH=768&HEIGHT=512&BB OX=-180,-90,180,90&LAYERS=AIRX3STM_TOTO3_A,coastline GDS (Grads-DODS):

22 RTP, NC 22 Gregory Leptoukh & Frank Lindsay, AQ Data Summit WMS and WCS in Serving MODIS data via WMS : Maps (decorated and undecorated), time-series, Hovmoller, time-averaged maps, difference maps Example of Maps: wms.cgi?SERVICE=WMS&WMTVER=1.0.0&REQUEST=GetMap&SRS=EPSG:4326&EXCEPT IONS=INIMAGE&FORMAT=GIF&BBOX=-130,24,-60,52&TIME= T00:00:00Z&WIDTH=800&HEIGHT=400&LAYERS=MOD08_D3.005::Optical_Depth_Land_A nd_Ocean_Mean Serving data via WCS: Here is a get capabilities url: wcs.cgi?SERVICE=WCS&WMTVER=1.0.0&REQUEST=GetCapabilities

23 RTP, NC 23 Gregory Leptoukh & Frank Lindsay, AQ Data Summit WCS support outside of OMI NO2 (Level 3): getCapabilities and describeCoverage requests getCoverage example request: bin/wcsNO2?service=WCS&version=1.0.0&request=getCoverage&CRS=WGS84&resx=0.5&resy=0.5&cov erage=NO2Total&bbox= ,-89.75,179.75,89.75&TIME= / &format=netCDF bin/wcsNO2?service=WCS&version=1.0.0&request=getCoverage&CRS=WGS84&resx=0.5&resy=0.5&cov erage=NO2Total&bbox= ,-89.75,179.75,89.75&TIME= / &format=netCDF AIRS X2RET (Level 2 collection 5): getCapabilities and describeCoverage requests bin/ceopAIRX2RET?service=wcs&version=1.0.0&request=describeCoverage bin/ceopAIRX2RET?service=wcs&version=1.0.0&request=describeCoverage getCoverage request: bin/ceopAIRX2RET?service=WCS&version=1.0.0&request=getCoverage&coverage=H2OMMRStd&crs=W GS84&bbox= ,-89.75,179.75,89.75&resX=0.5&resY=0.5&time= &format=netCDF bin/ceopAIRX2RET?service=WCS&version=1.0.0&request=getCoverage&coverage=H2OMMRStd&crs=W GS84&bbox= ,-89.75,179.75,89.75&resX=0.5&resY=0.5&time= &format=netCDF The server now supports 28 variables, including both 2D and 3D fields: TSurfAir, TAirStd, GP_Height, GP_Surface, PSurfStd, TSurfStd, totH2OStd, H2OMMRStd, H2OMMRSat, H2OMMRSat_liquid, O3VMRStd, totO3Std, PCldTopStd, TCldTopStd, olr, clrolr, CO_total_column, CO_VMR_eff, CO_eff_press, CH4_total_column, CH4_VMR_eff, CH4_eff_press, GP_Height_MWOnly, sfcTbMWStd, EmisMWStd, totH2OMWOnlyStd, totCldH2OStd, and numCloud. OMI UVB and O3 (Level 3) coming shortly

24 RTP, NC 24 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Air Quality Tools and Datasets on aerosols Available Science Data Sets (examples) PM 2.5 station data - EPA AirNow  (via WCS)  DataFed (aggregated and gridded)  (via WCS)  Giovanni MODIS TERRA and AQUA total and Fine mode Aerosol Optical Depth CALIOP Aerosol Feature Mask curtain plots OMI NO 2 Tropospheric column and Aerosol Index Useful Tools for Air Quality Applications AOD/ PM 2.5 scatter plots, correlation maps, time series and difference plots AOD and PM 2.5 loops for examining long range transport of aerosols

25 RTP, NC 25 Gregory Leptoukh & Frank Lindsay, AQ Data Summit PM2.5 (EPA  DataFed  Giovanni) Deep Blue MODIS Aerosol Optical Depth The standard MODIS AOTGOCART AOT (Goddard  DataFed  Giovanni) The standard MODIS AOT GOCART AOT Prototyping PM25 data in

26 RTP, NC 26 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Giovanni Air Quality Data (July 7 th, 2006) Level-3 MODIS AQUA AODEPA AirNow PM 2.5 (ug/m 3 ) MODIS and OMI imagery show smoke aerosols over the northeast, southeast and Great Lakes. CALIOP Aerosol Flag (yellow) confirms that aerosols are above the boundary layer EPA AirNow PM 2.5 doesn’t show anything around Great Lakes, i.e. aerosols are primarily above the boundary layer OMI Aerosol Index CALIOP Aerosol Flag (yellow)

27 RTP, NC 27 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Giovanni Air Quality Services: AOD/PM 2.5 Correlation Maps and Time Series May 2007 AOD/PM 2.5 correlation map over the U.S Moderate to good correlation in the eastern U.S No significant differences were found when using the Fine Mode MODIS AOD. May AOD and PM 2.5 Time series over the southeast

28 RTP, NC 28 Gregory Leptoukh & Frank Lindsay, AQ Data Summit CALIPSO: Elevated Smoke Layers over the US Midwest Smoke Giovanni MODIS Terra AOD map Giovanni PM 2.5 Map Smoke in Great Lakes region moving east

29 RTP, NC 29 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Level-3 MODIS AOD EPA PM 2.5 (ug/m3) May 22 nd, 2007: Smoke over North Carolina. High AOD and low PM 2.5 (r=0.54). There is also haze in the southeast Improved correlation over this region when excluding smoke areas (r=0.80) Giovanni data sets and tools help provide a more complete understanding of the origin, evolution, and vertical distribution of aerosol pollution over the continental U.S. Giovanni Air Quality tools: Understanding AOD/PM 2.5 correlations

30 RTP, NC 30 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Level-3 MODIS AOD EPA PM 2.5 (ug/m3) CALIOP Aerosol Flag (yellow) for examining the vertical aerosol distribution. Aerosols in Georgia and Alabama from the surface to 4 km, AOD/PM 2.5 correlation is moderately good Aerosols in the northeast are above boundary layer, AOD/PM2.5 correlation is poor Giovanni Air Quality tools: Understanding AOD/PM 2.5 correlations

31 RTP, NC 31 Gregory Leptoukh & Frank Lindsay, AQ Data Summit July 31 st, 2007 In Canada and the north-central US, MODIS and OMI show thick aerosols plumes. CALIOP overpass has a plume above the boundary layer

32 RTP, NC 32 Gregory Leptoukh & Frank Lindsay, AQ Data Summit July 31, 2007: Haze over the south eastern US In the southeast (Tennessee, Mississippi, Alabama and Arkansas) MODIS and PM 2.5 show good spatial agreement and have moderately good correlation (see scatter plot) Low OMI Aerosol Index and CALIPSO Aerosol flag (see previous slide) also indicate aerosols are primarily confined to the boundary layer in these states

33 RTP, NC 33 Gregory Leptoukh & Frank Lindsay, AQ Data Summit July 31, 2007: Haze over the south eastern US OMI contours over MODIS AOD. White lines indicated CALIPSO overpass

34 RTP, NC 34 Gregory Leptoukh & Frank Lindsay, AQ Data Summit

35 RTP, NC 35 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Importing Giovanni Data into Google Earth

36 RTP, NC 36 Gregory Leptoukh & Frank Lindsay, AQ Data Summit A-Train in Google Earth via Giovanni: Calipso Lidar

37 RTP, NC 37 Gregory Leptoukh & Frank Lindsay, AQ Data Summit Examined AOD and PM 2.5 maps, correlation maps and time series plots. Fine Mode AOD also available High AOD/PM 2.5 correlation indicates the MODIS algorithm is capturing aerosols at the surface in addition to elevated aerosols (if any) High AOD and low PM 2.5 may indicate the presence of aerosol plumes above the boundary layer CALIPSO overpass (if available) together with AOD/PM 2.5 correlations and scatter plots to qualitatively assess the vertical distribution of aerosols OMI measurements are less sensitive to aerosols in the boundary layer, so if OMI doesn’t show high aerosol while MODIS does, it may indicate aerosol being in the boundary layer MODIS algorithm issues (e.g. retrieval problems over bright surfaces) may affect correlations Analysis of U.S Air Quality Via

Test Case - NO 2 Air Pollution Data from Aura OMI Iamges Courtesy of Mark O. Wenig, Eric J. Bucsela, Edward A. Celarier, James F. Gleason, NASA J. Pepijn Veefkind, K. Folkert Boersma, Ellen Brinksma, KNMI