# Presentation: 083304

## Mitch Goldberg

Satellite Meteorology and Climatology Division
NESDIS Office of Research and Applications

March 23, 2005 – MODIS Science Team Meeting

**Mitch Goldberg**
**Satellite Meteorology and Climatology Division**
**NESDIS Office of Research and Applications**
**March 23, 2005 – MODIS Science Team Meeting**

## Integrated Products

- Goal of integrating products is to provide better information and understanding, which in turn empowers the public, private sector and government with informed decision making.
- GEOSS - Global Earth Observation System of Systems
- GEOSS recognizes that we have a  growing global system of systems which needs coordination to  1) establish standards/protocols for quality, data formats, data exchange , 2)  avoid redundacy, and 3) determine future requirements

## GEOSS will focus on Nine Societal Benefit Areas

**Notes:**
Societal Benefit Focus Areas for U.S. Integrated Earth Observation System (note: GEO benefits differ slightly on Oceans and Ecological Forecasting – GEO benefit areas are Coastal, Terrestrial and Marine Ecosystems and Biodiversity instead.)

Weather Forecasting   
Reduce Loss of Life and Property from Disasters
Protect and Monitor Our Ocean Resources 
Understand, Assess, Predict, Mitigate, and Adapt to  Climate Variability and Change
Support Sustainable Agriculture and Combat Land Degradation
Understand the Effect of Environmental Factors on Human Health and Well-Being
Develop the Capacity to Make Ecological Forecasts Protect and Monitor Water Resources
Monitor and Manage Energy Resources

Social, Economic, & Science Concerns
More than half the world’s population lives within 60 km of the shoreline
Could rise to 3/4 by the year 2020 
More than 90% of natural disaster-related deaths occur in developing countries
25% of Earth’s biological productivity & an estimated 80-90% of global commercial fish catch is concentrated in coastal zones
Worldwide agricultural benefits of better El Niño forecasts are conservatively estimated at $450-$550M/year
Foundation for sound decision-making: global, regional, & local level

## NOAA Objectives and Goals

- Cooperation and collaboration in building an integrated global earth observation system
- Expansion of earth observation science, monitoring technology and applications
- Collaborative data exchange
- Integrated Processing Systems

## Comprehensive
Sustained
Integrated

- Sustained
- Integrated

- Attributes of an Integrated Global Observing System

## Consists of physical, chemical, biological systems
Encompasses in situ, mobile, airborne and satellite observations
Includes broad range of spatial and temporal scales
Global to local
Years to minutes

- Encompasses *in situ,* mobile, airborne and satellite observations
- Includes broad range of spatial and temporal scales
- Global to local
- Years to minutes

- Comprehensive

## Consists of future, current, and predecessor systems
Includes sustained R&D program feeding into evolving long-term operational program

- Includes sustained R&D program feeding into evolving long-term operational program

- Sustained

## Integrated Multiple Platforms Orchestrated to Serving One or More Missions

- Integrated **Multiple Platforms Orchestrated to Serving One or More Missions**

**Climate**

**Disasters**

**Weather**

## Integrated One Platform Serving Multiple Missions

**Water**

- Integrated **One Platform Serving Multiple Missions**

**Health**

**Land**

**Ocean**

**Climate**

**Disasters**

**Weather**

**Solar Weather**

**Coastal**

**Ecosystems**

## More efficient
More effective
Ensures sustainability

- More effective
- Ensures sustainability

**Benefits of implementing an Integrated, Comprehensive, Sustained Global Observing System in supporting National missions**

## Goal - Transition Products from Individual Satellites to a “System of Systems”

**Goal - Transition Products from Individual Satellites to a “System of Systems”**

**Formulate and Integrate Environmental products using GOES-R series, NPOESS series, and MetOp satellites along with Other Structured Data Sources**

**GOES R**

**NPOESS**

**Other Data Sources**

**Integrated System**

- Environmental Products that are mostly generated from observations that are independent of one another

- Products are formulated and produced as one integrated system

**MetOp**

## Steps to Integrated Products and Systems

**      ****2004****	****             2008              2012              2016            2020**

- Full
- Integrated
-  System

- NPOESS

- GOES-R Series

- NPP

- MetOP Program

- AQUA, NOAA-15/16/17/18, GOES-M,L

-  **Integrated Sensors**: Initially need to simplify the complexity of building products between instruments on one platform
-  **Integrated Calibration**: Then we need to evaluate the calibration between multiple satellites at different orbits
-  **Integrated Satellites**: Finally we can utilize these cross-calibrated, collocated data sets to build enhanced products

- GOES-N,O,P

**    ****Advanced Individual Capabilities**

- NOAA-N / N’

## Integrating MODIS with AIRS and AMSU

## AMSU is a critical component of AIRS-  provides retrievals in overcast conditions-  drives cloud clearing

**AIRS acquires 2,916,000 spectra = 35 GByte/day**

## AIRS/AMSU Products for a ≈50 km footprint (varies w/ view angle), 324,000 footprints/day  

- Cloud Cleared Radiance
- Temperature
- Moisture
- Ozone
- Land/Sea Surface Temperature
- Surface Spectral Emissivity
- Surface Reflectivity
- Cloud Top Pressure

- Cloud Liquid Water (AMSU product)
- Cloud Fraction (per 15 km footprint).
- Carbon Monoxide
- Carbon Dioxide
- Methane
- Cirrus Cloud Optical Depth and Particle Size

## AIRS Update

- NESDIS has implemented the AIRS/AMSU processing system – quasi-operational
- Processing system is based on the retrieval methodologies developed by the AIRS Science Team
- Science Algorithms developed by NASA, NOAA, UW, MIT, UMBC  - truly a collaborative effort
- Science improvements are continuing

## Science Improvements

- Adding  MODIS to improve cloud clearing and soundings
- Adding trace gas retrieval algorithms to derive CO2, CO, and CH4
- Improving surface emissivity/bidirectional reflectance (non-ocean)

##          AIRS & MODIS PRODUCTS ARE DISTRIBUTED THROUGH THREE MAIN CHANNELS

## What have we learned?

- AIRS instrument is extremely stable and accurate
- Only 5% of the globe is clear at a 14 km fov
- AIRS has resulted in positive impacts in NWP, however only clear channels are assimilated and larger impacts are still expected.
- Cloud-clearing increases yield to 50 - 70%
- Retrievals from cloud-cleared radiances are significantly more accurate than AMSU-only.
- Demonstrated 1 K/Km precision

- ATOVS

- AIRS

## AIRS NWP Challenge 

- Assimilate cloud-cleared  radiances to improve the yield of observations in lower troposphere.
- Challenges
- NWP forecast accuracy is highly sensitive to accuracy of input data
- Need to provide very accurate cloud-cleared radiances
- Strategy
- Use MODIS  to improve accuracy of cloud-cleared radiances

## ALL diff < +- 0.5 K

- 735.69 cm-1  (peak ~ 700 mb)

- Cloud-cleared minus clear simulated
-  brightness temperatures (ECMWF)
- Observed minus clear simulated
-  brightness temperatures (ECMWF)

## ALL diff < +- 0.5 K

- 965 cm-1  (window)

- Cloud-cleared minus ECMWF
- Raw minus ECMWF

## Cloud Clearing

- R1 = (1-a1)*Rclear + a1*Rcloud
- R2 = (1-a2)*Rclear + a2*Rcloud

- Rclear(i) = R1(i) + η *[R1(i)-R2(i)]
-                η = a1/(a2-a1)
- η =  (Rclear-est – R1)/(R1-R2)

**Notes:**
290 – 250/(280 – 250)

## MODIS in addition to AMSU will improve cloud clearing

- AMSU has limitations in predicting lower tropospheric AIRS channels.
- Therefore, AIRS cloud clearing will not be as accurate for lower tropospheric clouds
- AMSU is still important especially when there are no clear MODIS fovs in the AIRS fov
- Our approach is to use both AMSU and MODIS for cloud clearing

## Predicting AIRS from MODIS, AMSU

- 100-300 mb

- 800 mb

- 50-80 mb

- 700 mb

- η = ( Rclear-est – R1)/(R1-R2)

## Predicting AIRS from MODIS, AMSU

- η = ( Rclear-est – R1)/(R1-R2)

## MODIS Cloud-Clearing Strategies

- MODIS 1 km resolution can be used to find clear holes
- Clear MODIS channels can be compared with cloud-cleared AIRS convolved to MODIS spectral resolution for QC
- Clear MODIS can be used to provide the clear estimate for cloud clearing

## Aqua MODIS SRF Overlay on AIRS Spectrum

**Aqua MODIS SRF Overlay on AIRS Spectrum**

- ~ VIIRS channel

## Using MODIS to QC AIRS Cloud-Cleared Radiances

## AIRS convolved to MODIS

## AIRS convolved to MODIS

## Cloud-Cleared AIRS convolved to MODIS

## 50% of 46%

- Cloud-Cleared AIRS convolved to MODIS

- Use of MODIS to QC AIRS     (Abs(AIRS-MODIS)<  0.5 K)

## Currently using MODIS to quality control 

- AIRS Cloud-Cleared Radiances

-  Cloud-clear minus clear simulated (ECMWF)

##  All-sky minus clear simulated (ECMWF)

-  All-sky minus clear simulated (ECMWF)

- Consequence of not cloud-clearing

## Improved QC of Cloud Cleared Radiances Improves Retrieval Accuracy

## Retrieval errors are significantly

- reduced after MODIS is used to
- QC  AIRS cloud-cleared radiances

- W/O QC

## Retrieval errors are significantly

- reduced after MODIS is used to
- QC  AIRS cloud-cleared radiances

- W/O QC

## Summary

- We are beginning to build integrated processing systems by focusing on AQUA.
- We plan to experiment with sensors from  the A-Train
- We are adapting our AIRS/AMSU/MODIS processing system  to generate operational products from IASI/AMSU/AVHRR (2006) and from CrIS/ATMS/VIIRS (2008)
- Same Science (e.g. same transmittance model, same cloud detection/clearing, etc) and Software will process AIRS, IASI and CrIS

**Notes:**
Contribution between absorption lines

## Additional Slides

## AIRS Spatial Features Seen from MODIS

**AIRS Spatial Features Seen from MODIS**

**Clear AIRS FOV**

**Cloudy AIRS FOV**

**Cloud Top Pressure**

**Cloud Top Emissivity**

**Brightness Temp.**

**Cloudy AIRS FOV**

## AIRS convolved to MODIS spectral resolution and

-    MODIS spatially convolved to AIRS spatial resolution

- BIAS

## AIRS convolved to MODIS spectral resolution and

-    MODIS spatially convolved to AIRS spatial resolution

-  RMS

## Temperature errors as a function of sounder instrumentation

- To use real AIRS data need to treat clouds
- Assimilate cloudy radiances
- Cloud-clear

## NOAA-Unique AIRS Products

- Thinned radiance datasets for NWP data assimilation, including PC scores
- Products into BUFR format
- Use of MODIS to improve AIRS cloud-cleared radiances.
- Noise-filtered radiances based on eigenvector decomposition
- Thinned datasets for scientific studies, including reprocessing for climate.

## How to integrate observations?

- Blend derived products  (Optimal Interpolation)
- Assimilation by a numerical weather prediction model  (Variational Analysis)
- Algorithm integration of multi-sensor time synchronous observations to improve product accuracy

## Algorithm integration of multi-sensor near-time synchronous observations

**Notes:**
Aqua launched May 4, 2002.  Aura launched 7/14/2004 and has TES (0.025 cm-1 (limb) + 0.1 cm-1 (nadir) interferometer).  Cloudsat & Calipso launch together in 2Q 2005.  Cloudsat is a radar and Calipso is a LIDAR.   Calipso can be used to verify cloud and aerosol algorithms within 1 MODIS pixel along track (same pixel in each scan line, but slightly off nadir) and 1 AIRS FOV in the AIRS sub-pixel space.   I think Calipso will be quite valuable for the 2 years it is in the Aqua orbit.  Cloudsat is a 94 GHz radar with a 3 km nadir FOV.  The orbit will precess over 2 years – not sure how to use this yet.   Parasol is 15 filter UV camera that can help tell us about the clouds – I don’t know much about it though.  OCO, due to be launched in late 2007 or early 2008 is a NASA ESSP mission to explore using the SW IR (1.61, 2.02 um) region to retrieve total column CO2 with a precision of 1 ppmv.   Mixing ratio requires measuring fixed gases using the O2 A-band at 0.76 um, so OCO has a total of 3 bands.   OCO has nadir and solar glint modes, both of which would be within the AIRS sub-pixel space.  It would be great if AIRS is still alive when OCO is operational.

## Elements of  Integrated Earth Observing System

- Research and operational observation instruments and platforms
- In situ and remote sensing observation networks
- Communication links and computing capacity
- Research and applications development
- Scientific and mathematical algorithms to combine multiple-source data
- Event-driven and model-driven target observations using in situ and very high spatial resolution imagers

## EO-1 Targets National Priority Wildfires

- (from NASA)

**Notes:**
NIFC
The National Interagency Fire Center in Boise, Idaho
Nations support center for wild land firefighting composed of 7 Federal and State agencies
NIFC’s National Interagency Coordination Center (NICC) produces and tracks large wildfire incidents maps
Wildfire incidents databases are Internet accessible

US Forest Service operates Remote Sensing Applications Center (RSAC) in Salt Lake City, Utah
RSAC provides advanced geospatial technology to improve monitoring and mapping of natural resources.
RSAC produces Active Fire Maps using MODIS imagery from Aqua and Terra using Rapid Fire Workstation. A joint effort between NASA/GSFC, UMD, NIFC, and US Forest Service Missoula Fire Science Laboratory.

Science Goal Monitor (SGM).
Developed by GSFC for multiple Earth and Space Science applications
SGM provided with human derived goals/objectives. SGM decomposes goals into application-specific command sequences or directives and monitors system to ensure goals are being satisfied.
This application - SGM accesses NIFC Large Incidents Database and the RSAC MODIS active fire maps to determine present fire location and extent.
SGM provides targeting request to EO-1 ground (MOPS) and on-orbit (JPL’s Casper) P&S systems to image active fires with EO-1 Advanced Land Imager instrument.
Depending upon time of scheduled request and orbital pass, this can be coordinated very quickly - as soon as one orbit.

EO-1 ALI data is received and preprocessed by EROS data center.
UMD receives data and performs value added processing (calibration, geolocation, remapped ERDAS image product and forwards to RSAC.
RSAC creates Burned Area Reflectance Classification (BARC) map and provides this to its Burned Area Emergency Rehabilitation (BAER) teams to mitigate potential subsequent impact to human life, property, and the environment.

Within 24-48 hours BAER tem gets information to do time-critical remediation such as:
- Install water or erosion control devices
- Plantings for erosion control or stability
- Install temporary barriers to protect treated or recovering areas

## 2025 Weather Forecast System ArchitectureTargeted Observations to Reduce Forecast Model Error Growth

**NOAA Forecast Operations**

***Underlying Communications Fabric and Data Grid***

**Weather Prediction System**

- Radar Data

**Numerical Forecast Model**

**Observations Data Analysis**

**Model initial state**

**Latest Obs**

**Predictions**

***Model***
***Predictions&***
***Ensemble***
***guidance***

**Global Observing System**

- Modeling and Data Assimilation

- (from NASA)