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

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1 2 3 4 5 6

65-77

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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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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 dan.pisut@noaa.gov 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/