Satellite Data Resources for

Atmospheric Science Applications

• A large volume of weather and environmental satellite data are now archived and widely available for a variety of atmospheric science applications.

• Both the environmental satellite systems and their associated data systems are in a dramatic state of flux and critical changes are underway.

• Environmental satellites started with polar orbiting weather satellites in the mid 1960’s and have progressed to a suite of polar and geostationary satellites with specific applications including their traditional weather and climate applications.

• The ultimate repository for these satellite data in the U.S. is NOAA/NESDIS which has finally built the Comprehensive Large Array Stewardship System (CLASS) at NCDC in Ashville, NC. This system took over from the Satellite Active Archive (SAA) which was built in the early 1990’s in response to NASA’s EOSDIS program.

• EOSDIS was created as the data and information system for the Earth Observing System a NASA program of both polar orbiting and geostationary satellites.

• It spawned a “Version 0” and a series of Distributed Active Archive Centers (DAACs) which still function today.

• Other than these two successes EODIS was a very expensive failure in terms of delivering satellite data to the science users.

• We hope that CLASS will learn from the mistakes made by EOSDIS and that like SAA it will succeed where EOSDIS failed.

National Polar-orbiting Operation Satellite System (NPOESS)

• The first of the big changes!!!

• In the late 1990’s congress and the president decided that the 3 polar-orbiting satellite systems should be merged into one.

05301330

0930

METOP

Future (2009-2019)• 3-Orbit System

– 3 US Converged

– 1 EUMETSAT/METOP

– Specialized Satellites

Tomorrow (2005)• 4-Orbit System

– 2 US Military

– 1 US Civilian

– 1 EUMETSAT/METOP

Local Equatorial Crossing

Time

METOP

0530

08301330

0930

DMSP

DMSP

POES

Evolution

Today• 4-Orbit System

– 2 US Military

– 2 US Civilian

U.S. civil and defense programs, working in partnership with EUMETSAT, will ensure improved global coverage and long-term continuity of observations at less cost!

Local Equatorial

Crossing Time

0530

07301330

0830

DMSP

DMSP

POES

POES

DMSP/POES NPOESS

Local Equatorial

Crossing Time

Specialized Satellites

NPOESS NPOESS

NPOESS

Mozambique Floods

NPOESS Mission StatementNPOESS is required to provide an OPERATIONAL remote sensing capability to

acquire and receive in real-time at field terminals, and to acquire, store and disseminate to processing centers, GLOBAL and regional environmental imagery and specialized

METEOROLOGICAL, CLIMATIC, TERRESTRIAL, OCEANOGRAPHIC and SOLAR-GEOPHYSICAL and other data in support of CIVILIAN and NATIONAL

SECURITY missions

DMSP

POES

NPOESS

DMSP/POES to NPOESS Convergence / Evolution of Satellite Systems

DMSP/POES to NPOESS Convergence / Evolution of Missions

DMSP

POES

NPOESS

-Imaging-Sounding-Space Environment

-Imaging-Sounding-Climate-Ozone-Space Environment

-Imaging-Sounding-Climate-Ozone-Space Environment

NPOESS/NPPClimate Contributions

• NPOESS will be the source for much of the satellite derived climate data in the future (2009-2025)

• NPOESS planned capabilities are well suited to climate use– Can produce and deliver short-term observations and predictions on

Earth processes– Can produce and deliver long-term observations for climate

monitoring and climate research [“systematic & process measurements”]

• NPP will provide the bridge between EOS & NPOESS for research users (2007-2012)

• NPP and NPOESS will contribute to continued algorithm improvements with the new generation of sensors

• NPP and NPOESS will produce long-term data sets (CDRs and measurements for CDRs) for some of the important climate variables

• Currently, the GOES system consists of GOES-12 operating as GOES-East and GOES-10 operating as GOES-West.

• The next satellites in the series, GOES-N/O/P and R, are currently under development. GOES-N is scheduled to launch in April 2005, GOES-O in 2007 and GOES-N in 2008.

• The GOES-R series will follow with the first launch in 2012**.

GOES-R

GOES R Instruments GOES R Instruments • Advanced Baseline Imager (ABI)• Hyperspectral Environmental Suite (HES)

– Disk Sounding– Severe Weather Mesoscale– Coastal Imager

• GOES Lightning Mapper (GLM)• Solar Instrument Suite (SIS)• Space Environment In Situ Suite (SEISS)

HES-IR Tasks• HES - Disk Sounding (HES-DS)

– Provide vertical moisture and temperature information, and other environmental data that will be used by NOAA and other public and private agencies to produce routine meteorological analyses and forecasts

– Provide data that may be used to extend knowledge and understanding of the atmosphere and its processes in order to improve short/long-term weather forecasts.

• HES - Severe Weather / Mesoscale (HES-SW/M)– Provide environmental data that can be used to expand knowledge of

mesoscale and synoptic scale storm development and provide data that may be used to help in forecasting severe weather events.

– Backup mode in the event of a GOES-R ABI failure (both).

HES-Disk Sounding (HES-DS) task

• Spatial Resolution– IR: Threshold=10 km, Goal=2 km, – Vis: Threshold=1.0 km, Goal= 0.5 km

• Coverage rate (Threshold)– 62 degree LZA / hour

at 10 km resolution• Coverage area must

be flexible and selectable.

HES-Severe Weather/Mesoscale task• Spatial Resolution

– IR: Threshold=4 km, Goal=2 km, – Vis: Threshold=1.0 km, Goal= 0.5 km

• Coverage rate– 1000 km x 1000 km (locations vary) in 4.4 minutes– Coverage area must be flexible and selectable.

• Spectral coverage:– Specific examples are cited in the MRD, same as HES-DS

• Spectral resolution: – 15 um CO2 band: 0.6 cm-1, Windows: 0.6-1.0 cm-1, Ozone: 1 cm-1,

H2O: 1-2 cm-1, near 4 um: 2.5 cm-1, Visible: 0.18 um

IR Spectral Coverage (DS or SW/M)

5Note that

0.625cm-1 1.25cm-1 2.5cm-1

0.625 cm-1 0.625 cm-1

0.6 cm-1 0.6 cm-1

CO2

(T)

Important lines for cloud emissivity and cloud type

Ozone “Traditional Side of

H2O absorption”

CO2 weak H2OCO N2O Temperature

Example 2

Example 1

IR Spectral Coverage (DS or SW/M)

5

HES

HES’

Expected scan times for the DS task sensor emissive bands (HES bands 1-3).

Coverage Region

Coverage Area (km2)

GSR (Hz)

GSD (km)

Within Frame Scan Efficiency

Coverage Time

Full Disk 1.00E+08 300 10 0.6 1 hr 32.6 min 62-degree LZA 7.00E+07 300 10 0.65 0 hr 59.8 min

CONUS 1.50E+07 300 10 0.9 0 hr 9.3 min Mesoscale 1.00E+06 300 10 0.8 0 hr 0.7 min

Coastal Waters 2.40E+06 300 10 0.95 0 hr 1.4 min

Expected scan times for the SW/M task sensor emissive bands (HES bands 1-3).

Coverage Region

Coverage Area (km2)

GSR (Hz)

GSD (km)

Within Frame Scan Efficiency

Coverage Time

Full Disk 1.00E+08 300 4 0.6 9 hr 38.7 min 62-degree LZA 7.60E+07 300 4 0.65 6 hr 46.0 min

CONUS 1.50E+07 300 4 0.9 0 hr 57.9 min Mesoscale 1.00E+06 300 4 0.8 0 hr 4.3 min

Coastal Waters 2.40E+06 300 4 0.95 0 hr 8.8 min

Hyperspectral Environmental SuiteHyperspectral Environmental SuiteCoastal WatersCoastal Waters

• Coastal Water (CW) Imaging– Coastal Waters within 400 km– Vis Spatial resolution 300 m – Spectral Range 0.4 m - 1.0 m– 180 minutes hour refresh rate– Can look over land also

• Air quality applications• Locate many tornado tracks• Flood extent• Hurricanes

Expected scan times for the CW task sensor Reflected Solar band

Coverage Region

Coverage Area (km2)

GSR (Hz)

GSD (km)

Coverage Time

Full Disk 1.00E+08 7400 0.3 65 hr 56.9 min 62-degree LZA 7.60E+07 7400 0.3 46 hr 16.0 min

CONUS 1.50E+07 7400 0.3 6 hr 35.7 min Mesoscale 1.00E+06 7400 0.3 0 hr 29.7 min

Coastal Waters 2.40E+06 7400 0.3 0 hr 60.0 min

HES CW TasksHES CW TasksHES CW TasksHES CW TasksWavelength Application

0.412 µm Dissolved organic carbon

0.443 µm Chlorophyll A absorbance, yellow substances

0.477 µm Fluorescence Chlorophyll A, Chlorophyll B absorbance

0.490 µm Water depth (reflection from sea grass)

0.510 µm Accessory pigments

0.530 µm Suspended sediment

0.550 µm Chlorophyll A baseline, backscatter sediments

0.570 µm Vegetative health

0.645 µm Vegetation (MODIS)

0.667 µm Chlorophyll A absorption, Vegetation, coccolith

0.678 µm Fluorescence, Vegetation

0.750 µm Atmospheric correction

0.763 µm Oxygen A band

0.865 µm Vegetation reflectance, atmospheric correction, red algae

0.905 µm Column water

GOES-R Coastal Water Imager• CW will provide first ocean color capability from geo orbit

– Can make measurements in constant tidal conditions• CW enables more frequent views of U.S. coastal ocean

– Necessary to resolve rapid changes due to tides and coastal currents• CW provides more opportunities for cloud-free viewing

– Better detect/monitor/track rapidly changing phenomena such as Harmful Algal Blooms, sediment plumes, and chaotic coastal zone currents magnitude that could be underestimated due to diurnal behavior

• CW offers higher spatial resolution ( 300 meters)– Fisheries researchers are limited by spatial resolution of current systems

—better than 1 km needed to improve measurement and modeling of small scale phenomena such as migration pathways for salmon fisheries

Visible Infrared Imaging Radiometer Suite (VIIRS)

• Being built by Raytheon SBRS– SeaWiFS and MODIS

heritage• First flight on NPOESS

Preparatory Project (NPP) in 2007 then NPOESS satellites starting in 2009

• Seven ocean color channels and 2 SST channels

ChannelName

channel Center

Channel Width

Ltypical ocean

Required SNR/NEΔT

VIIRS/SNR NEΔT

1M 412 nm 20 nm 44.9 352 670 2M 445 nm 18 nm 40 380 506 3M 488 nm 20 nm 32 415 515 4M 555 nm 20 nm 21 361 446 5M 672 nm 20 nm 10 242 ~ 400 6M 751 nm 15 nm 9.6 199 ~ 400 7M 865 nm 39 nm 6.4 215 314 15M 10.8 μm 1.0 μm 300K .070 .041 16M 12.0 μm 1.0 μm 300K .072 .041

M

• Approximately 1 km GSD ocean color– 742 m GSD and Nadir, 1092 m at +/- 850 km, 1597m at End of

Scan (+/- 1500 km)– Designed to meet global ocean imaging requirements at 1 km

GSD – Maximum revisit frequency of twice a day at 1030 and 1530

• Approximately 1 km GSD ocean color– 742 m GSD and Nadir, 1092 m at +/- 850 km, 1597m at End of

Scan (+/- 1500 km)– Designed to meet global ocean imaging requirements at 1 km

GSD – Maximum revisit frequency of twice a day at 1030 and 1530

Why HES-CW given VIIRS?• Tides, diel winds (such as the land/sea breeze), river runoff,

upwelling and storm winds drive coastal currents that can reach several knots. Furthermore, currents driven by diurnal and semi-diurnal tides reverse approximately every 6 hours.

• VIIRS daily sampling at the same time cannot resolve tides, diurnal winds, etc.

• HES-CW will have the ability to rapidly sample coastal waters providing a unique capability to observe the dynamic coastal ocean environment.

• HES-CW will provide higher spatial resolution (300 m vs. 1000 m)

• HES-CW will provide additional channels to measure solar stimulated fluorescence, suspended sediments, CDOM and improved atmospheric correction.

Example tidal cycle from Charleston, OR. Black arrows VIIRS sampling, red arrows HES-CW sampling.

Example tidal cycle from Charleston, OR. Black arrows VIIRS sampling, red arrows HES-CW sampling.

These improvements are critical for sampling coastal waters.

Coastal Remote Sensing Applications

A. Coastal Development 1. Mapping Floodplains 2. Hurricane Forecasts 3. Identifying At-Risk Properties 4. Managing Nuisance Plants 5. Mapping Oceanfront Setbacks

B. Habitat 6. Mapping Coral Reefs (Hawaii & U.S. Territories) 7. Monitoring the Spread of Brown Marsh 8. Mapping Kelp Beds 9. Preserving Wetlands and Riparian Areas 10. Determining Marine Protected Boundaries

C. Water Quality 11. Monitoring Storm Water Runoff 12. Monitoring Water Quality 13. Monitoring Red Tides 14. Mitigating Oil Spills 15. Monitoring Sewage Outfall Impacts

D. Living Resources 16. Mapping Sea Turtle Habitat 17. Damage to Submerged Aquatic Vegetation 18. Controlling Invasive Marsh Grasses 19. Mapping Oyster Beds 20. Impacts of Commercial Shellfish Harvesting

E. Waterways 21. Nautical Charts for Recreational Boaters 22. Stabilizing a Fluctuating Shoreline 23. Protecting Cultural Resources 24. Siting a Marine Outfall Pipeline 25. Identifying Channel Obstructions

(Courtesy Ed. Howard, NOAA)

Key Threshold and Goal RequirementsNominal Threshold

Channel Center Wavelength (um)

Nominal

Threshold Resolution

(um)

Nominal

Threshold Signal to

Noise

Nominal GOAL

Channel Center Wavelength (um)

Nominal

GOAL Resolution

(um)

Nominal Goal

Signal to Noise

0.412 0.02 0.407 through 0.987 0.01

0.443 0.02 0.57 0.010.477 0.02 1.38 0.03

0.49 0.02 1.61 0.060.51 0.02 2.26 0.05

0.53 0.02 11.2 0.80.55 0.02 12.3 1

0.645 0.02

Nominal

Threshold Horiz.

Resolution

Nominal Goal

Horiz. Resolution

0.667 0.01

0.678 0.010.75 0.02

0.763 0.020.865 0.02

0.905 0.035

300-meters all channels

(at Equator)

150-meters all channels

(at Equator)

300 to 1 all

channels

900 to 1 all

channels

Spatial Resolution

• The spatial resolution is at Nadir (over the Equator) so it degrades by latitude in U. S. coastal waters.

• The threshold requirement is 300 m at nadir; order 400-450 m in U. S. Coastal waters.– Considered adequate for water column properties

• The goal requirement is 150 m (200 m over U. S.). It will be very expensive to achieve this higher resolution.– Cost goes as the square of the spatial resolution improvement

– May not be possible for ocean SNR, etc.

– Will compete with frequency of coverage, SNR, and number of bands.

Spatial Resolution Comparison

6 km

20 km

30 m 150 m 300 m

0.65 μm

0.55 μm0.45 μm

Hyperion DataRGB Composite

The effect of spatial degradation from 30 m to 150 m and 300 m is illustrated

•Detailed coastal features are compromised at coarser resolutions

•Overall characteristics retained even at 300 m resolution

(Courtesy Ed. Howard, NOAA)

MODIS1 km water clarity

Modeled HES-CW (250 m)

HES-CW higher spatial resolution critical to monitor complex coastal waters

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

• The first satellite carries the Advanced Baseline Imager, the Solar Instrument Suite, and the Geostationary Lightning Mapper.

• The second carries the Hyperspectral Environmental Suite and Space Environmental In Situ Suite. Each satellite has a dry mass of approximately 1500 kilograms.

• These engineering designs are used for instrument, interface, design, and cost analyses.

Advanced Baseline Imager (ABI)

Hyperspectral Environmental Suite (HES)

Solar Imaging Suite (SIS)

Space Environment In-Situ Suite (SEISS)

MagnetometerGeostationary Lightning Mapper (GLM)

INSTRUMENTS

First launch planned fall 2012*

BUTTT!!!

NPOESS is 1 billion $$ over budget and the primary instrument VIIRS is delayed at least 18 months delaying the launches of all satellites including NASA’s NPOESS Preparatory Platform (NPP)

3 study contracts have been let for GOES-R

• The Comprehensive Large Array-data Stewardship System (CLASS) is an electronic library of NOAA environmental data.

• This web site provides capabilities for finding and obtaining those data.

• CLASS is NOAA's premiere on-line facility for the distribution of NOAA and US Department of Defense (DoD) Polar-orbiting Operational Environmental Satellite (POES) data, NOAA's Geostationary Operational Environmental Satellite (GOES) data, and derived data.

Scientific Data Stewardship Program ManagementNOAA TeamsNCDC,OAR, CCDD(ARC)NOAA ObservingSystem Council

NOAA Climate ProgramClimate Board

SDSWorking GroupCLASSWorking GroupClimateWorking GroupNOAA ScienceAdvisory Board

Research Climate Data Science TeamsObservationsScopingRequirementsSystems

R&DProductsServicesTheme Areas

FCDR TeamsTCDR TeamsOperational CDR Generation andData ManagementObservationsScopingRequirementsSystems

R&DProductsServicesTheme Areas

FCDR TeamsTCDR TeamsResearchToOperations

Revised chart showing SDS organizational elements. In the “NOAA Teams” box in the top row, NCDC refers to the National Climatic Data Center, OAR refers to the Office of Oceanic and Atmospheric Research, and CCDD (ARC) refers to NOAA’s Climate Change Data and Detection program element

and its Applied Research Center initiative.

The Subcommittee

John Bates, NOAA NCDCBob Evans, RSMAS, U. MiamiBruce Barkstrom, NASA LARCJeff Hawkins, NRL MontereySteve Worley, NCAR

John Bates, NOAA NCDC

John has his PhD from Wisconsin and is trained in atmospheric retrievals using satellite infrared data. He has worked for NOAA for a long time starting in Boulder with the Clmate Diagnostics Lab and then moving to NCDC. At NCDC his responsibilities are to be Tom Karl's satellite data person and he is a large part of the CLASS system that is being developed as the respository for all satellite data both NOAA and NASA.

Bob Evans, RSMAS, U. Miami

Bob is one of the Otis Brown-Bob Evans team that built up the satellite sensing and data system capabilities at RSMAS in Miami. He was one of the first persons I ever met in this business (at a Wisconsin workshop). He has worked in IR SST, ocean color, EOSDIS (data system) and in establishing receiving systems. Trained as an electrical engineer he knows a lot about the nitty gritty of the data system technologies while still doing a lot of the science.

Bruce Barkstrom, NASA LARC

Bruce was the ERBE project scientist and for a while the CERES project scientist. He was very involved in EOSDIS particularly concerning how the systems should be costed. He is also a radiation scientist but his interests go way beyond these areas and he is knowledgeable about many related satellite applications.

Jeff Hawkins, NRL Monterey

Jeff has been a lead on the development of satellite applications for the navy. He has worked with optical data (both vis and IR) and with polar orbiter and geostationary data. He has recently become very involved with microwave imagery as well. He is very good at using these data to give the navy the atmospheric sensing that they want for their applications. He has a group in Monterey that is very focused on developing these solutions.

Steve Worley, NCAR

Steve is an oceanographer who is taking over the data support section at NCAR. In this position he gets involved with a lot of different data types including satellite data. He must also know the data system technology to work with these data.

The Challenge

How to manage terrabytes to pedabytes of satellite data that are becoming increasingly important for a wide range of human activity.