G. Leptoukh, IGARSS08, Boston

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Gregory Leptoukh, NASA Goddard Space Flight CenterSuhung Shen, George Mason University/NASAIvan Csiszar, University of MarylandPeter Romanov. University of MarylandTatiana Loboda, University of MarylandIrina Gerasimov, ni.gsfc.nasa.govJuly 2008G. Leptoukh, IGARSS08, Boston1

Outline What is NEESPI? NASA NEESPI Data Center: Background Goals and Approach of NASA NEESPI DataCenter Products in the NASA NEESPI Data Center Giovanni NEESPI Giovanni Examples of NEESPI Giovanni usage Future plansJuly 2008G. Leptoukh, IGARSS08, Boston2

What is NEESPI?NEESPI Northern Eurasian Earth SciencePartnership InitiativeWhat is this Initiative about? NEESPI is designed to establish an international, large-scale,interdisciplinary program aimed at developing a better understandingof the interactions between the terrestrial ecosystem, the atmosphere,and human dynamics in Northern Eurasian.What are NEESPI goals? To conduct a large-scale, interdisciplinary program of fundedresearch aimed at developing a better understanding of theinteractions between the terrestrial ecosystem and the atmosphere,with a special emphasis on the human impacts and feedbacks innorthern Eurasia in support of international Earth science programswith particular relevance to global climate change research interests(including carbon) and international sponsoring agency fundingpriorities.July 2008G. Leptoukh, IGARSS08, Boston3

What is the NEESPI study area? The NEESPI study area is loosely defined as the region lyingbetween 15 E Lon in the west, the Pacific Coast in the east, 40 NLat in the south, and the Arctic Ocean coastal zone in the north. Includes territories of the former USSR, Fennoscandia, EasternEurope, Mongolia, and Northern China. All landscapes and components of the terrestrial biosphere,including the hydrology and atmosphere, that are interactive forpurposes of Earth science investigation (to include the humanimpacts) are considered a part of NEESPI study area.July 2008G. Leptoukh, IGARSS08, Boston4

What ecosystem types are in northern Eurasia?The vast territory encompasses: peat bog-tundra, forest tundra and borealforests in the north forests and agriculture at the mid-latitudes forest-steppes, steppe, agriculture and aridzones in the south lakes, ice, and coastal zones throughout theregionJuly 2008G. Leptoukh, IGARSS08, Boston5

NEESPI Science and Data Support CentersWithin the United States:For hydrometeorological information:National Climatic Data Center, Asheville, NCFor remote sensing information:Goddard Space Flight Center, Greenbelt, MDWithin the Russian Federation:For hydrometeorological information:Research Institute For Hydrometeorological InformationFor remote sensing information:SCANEX Corp., MoscowWithin China with focus on East Asia:Beijing Climate CenterJuly 2008G. Leptoukh, IGARSS08, Boston6

NASA NEESPI Data ory LeptoukhIvan Csiszar (UMD)Peter Romanov (UMD/NOAA)Suhung Shen, Tatiana Loboda, Irina GerasimovThe project is supported by NASA through ROSES 2005 NNH05ZDA001N-ACCESSJuly 2008G. Leptoukh, IGARSS08, Boston7

NASA NEESPI Data Center Infrastructure DiagramWater and EnergyCycleClimate Variabilityand ChangeNEESPI Science Focus AreasAtmosphericCompositionCarbon Cycle MODISAIRSOMINERINNCDCDataNASA-NEESPIGES DISCarchivesJuly 2008S4PAOnlinearchiveS4PAG. Leptoukh, IGARSS08, BostonRemoteonlinearchives8

Goals and Approach of NASA NEESPI Data CenterNASA NEESPI Data Center focus is on collectingremote sensed data, providing tools and services insupporting NEESPI scientific objectives: Provide online data access through advanced data management system Reformatt data into common data format, common projection Preprocess data into same spatial resolution that enables intercomparison or relationship studies Provide parameter and spatial subsetted data Online data visualization and analysis toolJuly 2008G. Leptoukh, IGARSS08, Boston9

Products processed for NASA NEESPI Data Center Fire Products: MODIS/Terra and MODIS/Aqua, derived fromMOD14CM1 and MYD14CM1 using UMD algorithm Vegetation index: MODIS/Terra and MODIS/Aqua, derived fromMODVI and MYDVI Land Cover: MODIS/Terra, derived from MOD12CM1 Land/Water mask: MODLWM Land Surface Temperature: MODIS/Terra, derived from MOD11CM1 Soil Moisture: AMSR-E, derived from AMSR E L3 DailyLand Snow and Ice: NOAA, derived from daily snow and cover in atNOAA/NESDIS within Interactive Multisensor Snow and Ice MappingSystem (IMS)July 2008G. Leptoukh, IGARSS08, Boston10

Parameters in NEESPI ter NameSensor NameAerosol Optical Depth at 0.55 micronAtmospheric Water Vapor (QA-weighted)Aerosol Small Mode FractionCloud Fraction (Day and Night)Cloud Fraction (Day only/Night only))Cloud Optical Depth – Total (QA-w)Cloud Optical Depth – Ice (QA-w)Cloud Optical Depth – Liquid (QA-w)Cloud effective radius – Total (QA-W)Cloud effective radius – Ice (QA-W)Cloud effective radius – Liquid (QA-W)Cloud Top Pressure (Day and Night)Cloud Top Pressure (Day only/Night only)Cloud Top temperature (Day and Night)Cloud Top temperature (Day only/Night only)Column Amount OzoneNO2 Total Vertical Column DensityNO2 Tropospheric Vertical Column DensityGPCP precipitationCloud and Overpass Corrected Fire Pixel CountOverpass Corrected Fire Pixel CountMean Cloud Fraction over Land for Fire DetectionMean Fire Radiative PowerEnhanced Vegetation Index (EVI)Normalized Difference Vegetation Index (NDVI)Land Surface Temperature (daytime/nighttime)Surface Air TemperatureSurface Skin TemperatureSoil Moisture MeanIce Occurrence FrequencySnow Occurrence ura OMIAura OMIAura OMIGPCP SDIS/IMSNESDIS/IMSAvailablesince: 01Statusmonth SWKOPS operational, TS in testing, WK working on, NA Data not availableJuly 2008G. Leptoukh, IGARSS08, Boston11

NEESPI Data Access Methods ftp: Mirador: online search and access Giovanni instances: OPS: neespi Available to partners: neespi daily In testing: landcover, nightlight, IPCCmodelsJuly 2008G. Leptoukh, IGARSS08, Boston12

What is Giovanni? Online portal for multi-sensor and multi-disciplinaryexploration tool Visualization and statistical analysis A customizable Web-based interface No need to install software No need to download, learn data formats, and process data Select, click, explore Download image or data in different formats Product lineage (data processing and algorithm steps)July 2008G. Leptoukh, IGARSS08, Boston13

Big picture of GiovanniAerosol from GOCART modelData Inputs10-6 ppmvAIRSMODISMISRParasolParticulate Matter (PM 2.5) from AIRNowAerosol from MODIS and GOCART modelCarbon Monoxide from SHALOETRMMAMSR-ESeaWiFSModelsand more July 2008Water Vapor from AIRSMODIS vs SeaWiFS ChlorophyllG. Leptoukh, IGARSS08, BostonOzone Hole from OMI14

Main Giovanni page: 2008G. Leptoukh, IGARSS08, Boston15

Giovanni-NEESPISelect area (Lat/Lon value) Enter Lat/lon or draw box on map Map zoom in/out Sliding map left/right to draw boxacross datelineSelect parameters One or more parameters Description of parameters Product name Sensor/model name Time coverageSelect temporal rangeSelect visualization typeSubmitJuly 2008G. Leptoukh, IGARSS08, Boston16

Results pageProduct LineageDownload DataPlot Preferences Image size Color Projection SmoothJuly 2008G. Leptoukh, IGARSS08, Boston17

Download Data PageJuly 2008G. Leptoukh, IGARSS08, Boston18

Product Lineage PageJuly 2008G. Leptoukh, IGARSS08, Boston19

Input/output data formats¾Input data format: hdf, hdfeos, netCDF, binary¾Input data type: gridded, swath¾Output data format: hdf, netCDF, ascii¾Output image format: gif, png, KMZJuly 2008G. Leptoukh, IGARSS08, Boston20

Giovanni and GISGiovanni can be accessed in a machine-to-machineway via Web Mapping Service (WMS) and WebCoverage Service (WCS) protocols. Giovanni can act as WMS or WCS server, thusallowing any GIS clients to add layers or get subsetteddata from Giovanni. Giovanni also can act as WCS client by gettingremotely located data via WCS.July 2008G. Leptoukh, IGARSS08, Boston21

Examples of using Giovanni NEESPIJuly 2008G. Leptoukh, IGARSS08, Boston22

Decrease of Ice Occurrence?20002006Jan-AprBarents SeaJuly 2008Sea of OkhotskG. Leptoukh, IGARSS08, Boston23

Exploration of the role of lagged effectsof ecological processes on catastrophicfire occurrence in various regions ofNorthern Eurasia.

Multi-sensor view of dry land in mid-Asia,northwestern China, and MongoliaGPCP PrecipitationMODIS Land Cover (bare land)July 2008AMSR-E Soil MoistureMODIS NDVIG. Leptoukh, IGARSS08, Boston25

Interannual Variations of Fire Occurrenceover Mid-Asia Dry LandPrecipitationNDVIFire CountsMonthly precipitation, vegetation index, and fire counts over western Kazakhstanduring 2001-2002. Increased precipitation during spring of 2002 induced anincrease in plant productivity and the corresponding NDVI signal. The enhancedplant productivity potentially leads to a greater accumulation of fuels. Fuelaccumulation results in increased fire occurrence (observed through Fire Counts)during fall season.July 2008G. Leptoukh, IGARSS08, Boston26

Zooming onto Russian Far EastJuly 2008G. Leptoukh, IGARSS08, Boston27

Spatial patterns for different parameters forJuly (different years)NDVIFire countsSurfTemp D SurfTemp N Soil Moist200420032002EVINo significant difference in the July environment for 2002, 2003, and 2004July 2008G. Leptoukh, IGARSS08, Boston28

Exploring time-series for different parametersFire CountsEVISurface Temperature (day)NDVISoil MoistureDry Spring?July 2008G. Leptoukh, IGARSS08, Boston29

Zooming onto Fires in Russian Far EastJuly 2008G. Leptoukh, IGARSS08, Boston30

Analyzing time-series for various parametersFire CountsSurface Temperature (day)EVINDVISurface Temperature (night)Soil MoistureDry Spring!July 2008G. Leptoukh, IGARSS08, Boston31

Snapshots in May and uly 2008G. Leptoukh, IGARSS08, Boston32

Conclusion of the Russian Fire East firedanger exploration example A large number of fires detected in July of 2003 – a nearly 200-time increasein fire detections compared to other years during 2001-2006. despite thesummer monsoon suppression of large fire occurrence.Traditional vegetation indices (NDVI and EVI) included in operational firedanger assessment provide little information on the fuel state in this ecosystempre- or post-fire.No considerable differences in surface temperature and soil moisture in Julywere observed between the catastrophic year of 2003 and the two subsequentyears of low summer fire occurrence of 2004 and 2005.However, the temporal analysis indicates that dry spring conditions in 2003(detected through low soil moisture measurements in April and May) mayhave led to a stressed vegetative state and created conditions conducive tocatastrophic fire occurrence.July 2008G. Leptoukh, IGARSS08, Boston33

Observing Air Quality ChangesNov 4-6 2006 Beijing Car Restriction TestNO2 Tropospheric Column DensityNO2 Total Column DensityBeforeDuringAfterNO2 column density observed from Aura OMI before, during, and after car restriction testevent in Beijing. About 30% of the cars were reduced during Nov. 4-6 2006, coincided withthe Summit of the Forum on China-Africa Cooperation. The NO2 values were loweredsignificantly during the car-restricted days.July 2008G. Leptoukh, IGARSS08, Boston34

Future plans July 2008Add air-quality related remote sensing dataMake public the daily productsAdd climatology and anomaliesMove to 8-day productsAdd more model dataAdd socio-economical dataIntegrate “seamless” links to other NEESPIdata centers and projectsG. Leptoukh, IGARSS08, Boston35

Model dataIPCC: IntergovermentalPanel on ClimateChangeGFCM2GFCM2: GFDL-CM2GIAOM: NASA GMAOIAOMScenario: SRB1Base period: 1960-1990GIAOMSurface Temperature Anomaly in 2011-2030July 2008G. Leptoukh, IGARSS08, Boston36

Night Light Observed from Space19922002BeijingBeijingShanghaiHangzhouData source: Defense Meteorological Satellite Program (DMSP), NOAA NGDCJuly 2008G. Leptoukh, IGARSS08, Boston37

Related Publications Leptoukh, G., Csiszar, I., Romanov, P., Shen S., Loboda T.,Gerasimov, I., "Giovanni System Services for the NEESPIdomain,” iLEAPS Report Series, No 1. (2008) , submitted Berrick, S.W., Leptoukh, G., Farley, G., Rui, H., “Giovanni: AWeb Services Workflow-Based Data Visualization and AnalysisSystem,” Transactions on Geoscience and Remote Sensing, 2008,in review Leptoukh, G., Csiszar, I., Romanov, P., Shen S., Loboda T.,Gerasimov, I., “NASA NEESPI Data Center for Satellite RemoteSensing Data and Services,” Global and Planetary Change,Environment Research Letters, 2, 045009, 2007 Acker, J. and G. Leptoukh, “Online Analysis Enhances Use ofNASA Earth Science Data,” EOS, Transactions of AmericanGeophysical Union, 88, 14, 2007July 2008G. Leptoukh, IGARSS08, Boston38

Aerosol Optical Depth at 0.55 micron MODIS-Terra/Aqua 00.02/02.07 OPS TS Atmospheric Water Vapor (QA-weighted) MODIS-Terra/Aqua 00.02/02.07 OPS TS MODIS-Terra/Aqua 00.02/02.07 OPS TS Cloud Fraction (Day and Night) MODIS-Terra/Aqua 00.02/02.07 OPS TS Cloud Fraction (Day only/Night only)) MODIS-Terra/Aqua 00.02/02.07 OPS TS

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