a nasa -noaa collaboration in using geostationary ... · real-time land-atmosphere dynamics, amazon...
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GEONEXA NASA-NOAA collaboration in using
geostationary satellites for land monitoringNASA Ames Research Center
R. Nemani, W. Wang, J. Xiong, S Li, S. Ganguly and A. Michaelis
NASA Goddard Space Flight Center Alexei Lyapustin, Y. Wang
NASA Marshall Space Flight CenterP. Meyer, Gary Jedlovec
NOAA Satellite Meteorology and Climatology DivisionS. KalluriG. Stark
Atsushi Higuchi, Kazuhito Ichii, Chiba University, Japan, Hideaki Takenaka, JAXA, Japan
Ranga Myneni, Boston University, Steve Running, University of Montana
Tomoaki Miura, University of Hawaii, Jia Zhang, Carnegie Mellon University
MODIS/VIIRSScienceTeamMeeting,October17,2018
Outline• A generational shift in geostationary satellites• New opportunities for land monitoring• Leveraging NEX / OpenNEX compute
platforms• GEONEX Processing system• Adapting existing EOS/NOAA algorithms• Preliminary products from GEONEX• Community engagement on the cloud• Outreach• Summary
A generational shift in geostationary satellites
ABI – AdvancedBaselineImageronGOES-R/T
AHI – AdvancedHimawari ImageronHimawari
AMI – AdvancedMeteorologicalImageronGEO-KOMPSAT2
FCI – FlexibleCombinedImageronMTG
AGRI – AdvancedGeosynchronousRadiationImageronFengyun-4
New generation geostationary platforms offer capabilities similar to MODIS for Land Monitoring
A generational shift in geostationary satellites
1-2km coverage for large portions of the Earth at 5-15 minute interval
GEO-KOMPSAT2isscheduledtobelaunchinDecember2018MTG-FCIisscheduledfor2021MTG/IOCorKalpana follow-onarestillneeded
A generational shift in geostationary satellites
Improved Weather Forecasting
What can they do to improve land monitoring?
MODIS 16-day AHI 16-day
New opportunities for land monitoring
Increase in Cloud Free Observations
Geostationarysatellitesmayprovidedailycloud-freecoveragethatmaytake16ormoredaysforpolarorbiters.
Eye on the heat wave, Adelaide, Australia
New opportunities for land monitoringForafewhoursduringtheearlyafternoon,vehicleswerenotabletodriveonpavedroads
Diurnal wildfire dynamics, Montana, US
New opportunities for land monitoringByearlyafternoon,thefirescreatelocalcirculationsandbreakout.
Real-time land-atmosphere dynamics, Amazon
New opportunities for land monitoring
StronggradientsinSWDovershortdistancescreateidealconditionsforhorizontalwindconvergenceatthesurfaceleadingtoconvectivecloudsandlocalstorms.
• Restricted access• NASA funded• Focus on large-scale modeling
and analysis• Ideal for producing research
quality/reproducible results
• Builds on NEX• Open access• User-funded• Ideal for prototyping, exploration
and community engagement
NASA Private Cloud Commercial Public Cloud
NASA Earth Exchange (NEX) - OpenNEXA Private-Public Compute Infrastructure
Leveraging NEX-OpenNEX platforms
Giventhedatavolumes(0.5TB/daypersensor),wewillnotbeabletoexecuteGEONEXpipelinewithoutleveragingexistinginfrastructure
GEONEX Processing system
Current configuration of GEONEX processing system
Potential land surface products from geostationary sensors and corresponding algorithms
Prototyped GettingThere Workingon InitialStage
Adapting existing EOS/NOAA algorithms
Product Algorithm Current use Resolution/Frequency
Example products from GEONEX
OpenNEX (AWS)-adapted NOAA WFABBA (Fire) Algorithm
Mendocino complex fire in Northern California, detected by the algorithm over several 5 minute scans. For each scan, the overall product latency is 2.5 minutes from data acquisition.
Preliminary products from GEONEX
Adapting MAIAC Algorithm for GEONEX
Top-of-Atmosphere Surface Reflectance Aerosol Optical Depth
WeareabletoleverageexistingalgorithmandproducepromisingresultsfromAHI.MoreworkisneededforothersensorssuchasABIwithslightlydifferentbandconfiguration.
Vegetation Index from GEONEX/Angular Effects
Preliminary products from GEONEX
Thesun-sensorgeometryvarieswitheachpixelacrossthedomain.Consequently,extractingmeaningfulinformationshouldaccountfortheangulareffects.
Adapting MOD15 LAI/FPAR Algorithm for GEONEX
Preliminary products from GEONEX
MoreworkisneededincustomizingtheLook-Up-TablesandtoexploremachinelearningalgorithmstogenerateMODIS-consistentresults
HOUR
LYIN
PUTDA
TA
HOUR
LYOUP
UTDATA
GOES
RADA
RM
ESONE
TSNO
TELASO
S
RADIATIONVPD
TAIRPRECIP
MACHINE-LEARNINGBASEDDATAASSIMILATION
HISTORICALDATA:1979– 2018FREQUENCY:DAILY
OPERATIONAL:HOURLYWITH30-60MINUTESDELAY
NASAEARTHEXCHANGE(NEX)GRIDDEDHOURLYMETEOROLOGY(DHM)
Creating Near Real-time Surface Meteorology
Our long-term interest is supporting theNational Climate Assessment in the US Preliminary products from GEONEX
Adapting MOD17 GPP Algorithm for GEONEX
Potential synergy with OCO-2 and GEOCARB for calibrating GPP algorithmSIF under clear skies from OCO-2 is highly correlated with GPP from Flux towers
1km, hourly GPP estimates
Preliminary products from GEONEX
Sunetal.,Scien
ce201
7
Community engagement on the cloud
Cloud Deployment of GEONEX
ThesoftwarestackwebuiltforOpenNEX insupportoftheNationalClimateAssessmentwherecommunitymembersaccessed,analyzedandvisualizedthedownscaledclimateprojectionsproducedonNEX,isbeingre-usedforGEONEX.
Community
MATA
App Store (Knowledgebase)
Infrastructure
OpenNEX
Explore Datasets
Data AccessReal-Time Fire EventsClimate Change Analytics
Mobile App
Workflows
App/APIs
Models
Datasets
Analytics
Project Wiki
Capabilities
Products
fire | hydrology climate change
GEONEX | WELDNEX-DCP30 | NEX-GDDPNEX-TreeCover
Test AlgorithmsBuild Pipelines
Dockerize Applications
Web Services
Amazon Alexa, Siri, Google Assistant
Cluster ManagerJob Scheduler
Community engagement on the cloud
GEONEX website provides sample data and products
GEONEXproductsalongwithco-locatedMODISdataandtoolsforaccessing,compositingandvisualizingthedatawillbeprovidedtothecommunity.
www.geonex.org
Special Issue is open to receive manuscripts
Community engagementAll publications for which authors share the codes will be containerized and made available on OpenNEX
New opportunities for land monitoring
v Studies of diurnal land-atmosphere dynamics v Data-Model integration at a new levelv From states to fluxesv GEO-LEO integration (GOES, GEMS, GeoCARB, OCO, GCOM-C, MODIS/VIIRS)
Community Engagement / Cloud Computing
v Rapid prototyping and deploymentv Low latency product generationv Ready-to-deploy containers using building blocksv Data-driven products from machine learning
www.geonex.org