Cooperative Research with STAR
by
Al Powell
CICS Science Meeting 6 Nov 2013
STAR Cooperative Research Progam
• STAR works on remote sensing of the environment in key focus areas: – Algorithms, products and applications for satellite
data – Observation simulations – Calibration, intercalibration and validation of
satellite data – Observing system design
Interesting Topics for Collaborations (Not comprehensive)
• Earth System Monitoring from Satellites • Future Satellite Programs • Calibration / Validation • Data Fusion and Algorithm Development • Land and Hydrology • Data Assimilation • Climate Research and Modeling • Education, Literacy, and Outreach
Science Research and Applications
POES JPSS
NASA Decadal Survey
INT’L METOP, GCOM, JASON
DMSP COSMIC
STAR
Long Term Monitoring and Maintenance
Sensor Calibration and Validation Algorithm Selection, Development & Cal/Val
Algorithm Integration
OSD/OSPO LTM, Anomaly
Resolution Instrument specs,
Algorithm requirements, enhancements
NCDC Metadata &
archival
NASA Spacecraft & instrument
status, Climate
Pre-operational algorithms
WMO Xcal and retrieval
algorithms and cal/val data to
other Space Agencies
NWS/NWP RTM & cal/val, Assimilation,
Product Science
EUMETSAT Common Instrument
Data base, Calibration,
Algorithm Retrievals, Field campaigns
Academia Sensor Science
Product Science
DOD Common
Instrument Data base, Calibration,
Retrieval algorithms
NOS Sensor Science
Product Science
ESA, EUMETSATCNES
DSCOVR GOES
GOES-R
Calibration/Validation at STAR JPSS post-launch calibration/validation for ATMS, VIIRS, CrIS, and OMPS, collaborating with NASA, cooperative institutes, and other organizations
GOES-R pre-launch calibration leveraging advanced technologies from NIST
Operational calibration support with the Integrated Cal/Val system and long-term monitoring
Re-calibrate historical satellite data to support climate studies (MSU/AMSU, HIRS, Jason/TOPEX)
International collaboration through GSICS and CEOS
HIRS
S-NPP/NOAA18
S-NPP/METOP
VIIRS/MODIS
Aquarius/SMOS
S-NPP/NOAA19
Jason/TOPEX
Dome C
MOBY
Jason/METOP
Desert
Bia
s (%
)
Advancing calibration science and technology to foster consistency in Earth observation time series for weather and climate applications
Data Fusion for Rainfall Rate Retrievals • Current Work: PMM-supported collaboration between NESDIS /
STAR and NWS / OHD to integrate information from gauges, radar, satellite (GOES IR algorithm calibrated using MW rain rates), and NWP model forecasts using the current Multi-sensor Precipitation Estimator (MPE) framework at NWS – Use gauges to bias-correct radar and satellite – Merge corrected radar, satellite, and gauge-only fields based on error
characteristics – Blend in NWP model forecasts where validation shows greater skill than
radar or gauges (e.g., snowfall, complex terrain) • Proposed Work: Enhance the integration of radar and satellite data
by using radar to help calibrate the GOES IR algorithm • Long-Term: Develop a suite of rainfall products covering
instantaneous to climate scales using the best available information (heavier reliance on IR satellite and radar for instantaneous; heavier reliance on MW satellite and gauges for climate scale)
Drought-related vegetation stress in July from NOAA-19
•In 2011-2013, USA was affected by drought-related severe vegetation stress (VS)
•The impacts include water level reduction, crop/pasture losses, wildfires, shipping etc
•VS was monitored by the Vegetation Health technology from NOAA-19 operational satellite applied for every 4*4 km global land
•The method employs indices and products based on NDVI & Brightness Temperature
•They are delivered every week to NOAA Web •http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_browse.php
•The products include drought detection & monitoring intensity & duration, vegetation health, moisture and thermal conditions, malaria & fire risk and others for the world, continents 192 countries & nearly 4,000 regions
• Rapid Refresh (11 km) data are now being used over Alaska.
• In-person forecaster training/refresher training will be conducted next week in Fairbanks and Anchorage
• An updated training module in PowerPoint and VisitView formats is now available
• A live training session with the NWS Central Region was conducted (July 24, 2012)
• WFO’s from every region (sans Pacific) are currently evaluating the products
• A blog dedicated solely to the GOES-R fog/low cloud products was created to keep training current
http://fusedfog.blogspot.com/
GOES-R Fog/Low Cloud Activities
Data Assimilation Activities in STAR
• Training and Outreach – Support the Colloquium on satellite data assimilation – Support JCSDA workshop – Support JCSDA Seminars – Support the re-hiring of the DA faculty position at UMD – Support the JCSDA newsletter
10
Science to Enable Satellite Data Assimilation CRTM: Radiative Transfer Model. New sensors,… CLBLM: Line-By-Line Model CSEM: Surface Emissivity Model Cloudy Radiance Assimilation Generalized Quality-Control &Pre-Processing Tool Ocean, Land data assimilation
Assimilation of New Sensors & Products SNPP/ATMS GCOM-W AMSR2 OSCAT wind vector Jason GOES
Accelerate Readiness for future Sensors GPM SMAP GOES-R Etc
OSSE and Data Impact Experiments SNPP impact on NOAA forecast (hurricane & global) Afternoon orbit data loss impact assessment Support the Hurricane Sandy gap mitigation activities
O2R/R2D (Research-To-Demonstration) Environment S4 Supercomputer O2R for GOES-R and JPSS funded projects Access 2 Researchers from NOAA, Partners &CIs Data availability –and BUFR tool- (CMFT)
Engaging with Community & Partners Work closely with JCSDA (coordination of
activities, collaboration with partners) FFO support (AMV, Spectroscopy, etc) Visiting Scientist Program open to all
Fig.9 Pacific Ocean reconstructed height (shaded) and wind field anomaly over the Pacific ocean at 1000hPa model level for the planetary wave number 1 ~ 6. Shaded area indicate the height anomaly exceeding the significant tests at the 95% confidence level. (Negative anomaly : Blue-purple shading; Positive anomaly : Green-yellow-red). (a) 1948-56; (b) 1957-64; (c) 1965-77; (d) 1978-88, (e) 1989-98, (f) 1999-2005. Coastline in orange.
(c)
(d)
(e)
(f)
(a)
(b)
Fig.10 Pacific Ocean. Left panel indicates the wave kinetic energy anomaly 1/2(u2(n)+ v2 (n) ) with wave number (x-axis). Right panel indicates the average normalized fish landings for the Pacific Ocean for each species in the periods coincident with abrupt climate regime shifts. (a) 1950-56, (b) 1957-64, (c) 1965-77, (d) 1978-88, (e) 1989-98, (f) 1999-2005. The x-axis code number indicates both geographical ocean subregion (shown in Figure 1) and the FAO fish species category, the codes are summarized in Appendix Table 1.
(c)
(d)
(e)
(f)
(a)
(b)
00.10.20.3
1 2 3 4 5 6
50-56
0
0.1
0.2
1 2 3 4 5 6
57-64
0
0.05
0.1
1 2 3 4 5 6
65-77
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0.05
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1 2 3 4 5 6
78-88
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1 2 3 4 5 6
89-98
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1 2 3 4 5 6
99-05
GOES-R Proving Ground CIMSS Nearcasting Evaluation at the Storm Prediction Center
4-hour Nearcast of Ɵe lapse rate 5-hour Nearcast of Ɵe lapse rate
Two cases where forecasters found the CIMSS nearcasting products useful: At left, the nearcasted Ɵe lapse rate identified a potentially active area of development in NE Arkansas 4 hours in advance. At right, an area of suppressed convection was identified 5 hours in advance over central Florida.
Successful 21st annual CIMSS Student Workshop Madison, WI - June 2013
The Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin Space Science and Engineering Center (SSEC) held its annual Student Workshop on Atmospheric, Satellite, and Earth Sciences on 23-27 June 2013. CIMSS staff, along with UW Atmospheric and Oceanic Sciences (AOS) faculty and students, other UW researchers, and local community scientists provided a wide perspective on the current state and challenges of interdisciplinary earth science.
Twelve students engaged in small group discussions and science activities with working experts. Exercise of satellite data display and analysis programs (such as UW-SSEC’s McIDAS) ,as well as local field trips to a television weather office (WKOW), the National Weather Service (NWS) office (in Sullivan), the Aldo Leopold Nature Center, the UW Washburn Observatory, the UW Geology Museum, and the geological wonders of Devil’s Lake State Park were interspersed with classroom talks, demonstrations, and hands-on activities.
CIMSS contributes to NOAA ‘s Cross-Cutting Priority for Environmental Literacy, Outreach, and Education.
UW AOS Professor Ankur Desai explains the chemistry behind infrared remote sensing of the atmosphere in the Ecometeorology Laboratory.
Brian Olson and TV Channel 27 weather set
NOAA/NWS forecast office in Sullivan, WI
Explore a World of Data with
Dan Pisut NOAA Environmental Visualization Lab [email protected] http://www.nnvl.noaa.gov/view/
the NOAA View Data Imagery Portal
EDUCATION, LITERACY AND OUTREACH
the1%
Scientist
• Metadata • Provenance • Parsing • Formats
the 99%
Us
• Images • Simple access • No lingo
Data for Different People with Different Needs
At Home • Interactive • Answers questions
Museums/Science on a Sphere • Enhanced imagery • Easily understood
Producer • Raw imagery • Customizable
Teacher • Data • Inquiry-based tools
Television/Meteorologist • Google Earth • Timely
the Assumptions for the 99%
You don’t know the difference between OI SST, Pathfinder SST, GHRSST or ERSST
You don’t know that a dataset only has a certain lifespan
You don’t want to wait for data to process into imagery
You will come up with more interesting uses for our data than us
Data Real-Time
Weekly Monthly Annual
Outputs B/W Images Color Images Google Earth Excel Format Resolutions
Storage Web FTP WMS
Access Browse Download Incorporate
Over 170 scripts running in unison managing data flow
the Data
• Sea surface temperature • Heat content • Temperature at depths (0-5,000m) • Salinity at depths (0-5,000m) • Nitrate at depths (0-5,000m) • Silicate at depths (0-5,000m) • Phosphate at depths (0-5,000m) • Dissolved Oxygen at depths (0-5,000m) • Coral bleaching • Coral reef locations • Chlorophyll concentration • Sea surface height • Bathymetry • Surface currents
• Ozone concentration • Aerosol optical depth • Rain accumulation • Moisture • Outgoing longwave energy • Ocean surface winds • Infrared clouds
• Surface temperature • Vegetation NDVI • Active fire locations • Soil moisture • Drought • Nighttime lights • Change in nighttime lights
• Snow and ice cover • Sea ice concentrations • Median sea ice cover
• Sea surface temperature anomaly
• Precipitation anomaly • Surface temperature anomaly • Temperature of the lower
stratosphere • Temperature of the middle
troposphere • Temperature of the upper
troposphere • Ocean pH model • Ocean aragonite saturation state
model • Sea ice concentration predictions
(RCP 2.6-8.5) • Surface temperature predictions
(RCP 2.6-8.5) • Precipitation predictions
(RCP 2.6-8.5) • Ocean temperature predictions
(RCP 2.6-8.5)
Ocean Atmosphere Land Cryopshere Climate
Over 31,000 images and growing each day
http://www.nnvl.noaa.gov/view/