1. Multi-, and Hyper-Spectral Remote Sensing Technology, Techniques, and Applications
Tim Schmit
Advanced Satellite Products Brach
NOAA Satellite and Information Services
With input from W. P. Menzel, G. S. Wade, J. Daniels, M. Pavolonis, and J. Li, M. Gunshor, W. Smith, T. Zapotocny (CIMSS), etc.

2. Outline Multi-spectral Hyper-spectral Current GOES Sounder GOES-R ABI
Overview
Products
GOES-R ABI
Improved temporal, spatial and spectral resolutions
Hyper-spectral
overview
applications

3. Description: GOES-8/P Sounders
19 channels (18 Infrared; 1 Visible)
Spatial resolution: ~ 10km
Hourly scanning over CONUS and adjacent waters
Products include standard imagery and derived, Level-2 products

4. Current GOES Sounder Uses by NWS
Sounder Product
Operational Use within the NWS
Clear-sky Radiances
Assimilation into NCEP operational regional & global NWP models over water
Layer & Total Precipitable Water
Assimilation into NCEP operational regional & global NWP models; display and animation within NWS AWIPS for use by forecasters in forecasting precipitation and severe weather
Cloud-top retrievals (pressure, temperature, cloud amount)
Assimilation into NCEP operational regional NWP models; display and animation within NWS AWIPS for use by forecasters; supplement to NWS/ASOS cloud measurements for generation of total cloud cover product at NWS/ASOS sites
Surface skin temperature
Image display and animation within NWS AWIPS for use by forecasters at NWS WFOs
Profiles of temp & moisture
Display (SKEW-Ts) within NWS AWIPS for use by forecasters at NWS WFOs in forecasting precipitation and severe weather
Atmospheric stability indices
Image display and animation within NWS AWIPS for use by forecasters at NWS WFOs in forecasting precipitation and severe weather
Water Vapor Winds

5. Clear-Sky Radiances
Sounder clear-sky radiance observations (all channels) at full resolution; not cloud-cleared
Operational Applications
NWP
Assimilated (ocean only) into NCEP’s
North American Mesoscale (NAM) model/NDAS
RUC
GFS/GDAS
To Be Done
Animation of GOES-12 Sounder radiances

6. Total Precipitable Water
Physical Retrieval (Ma et al, 1999)
PW computed for three layers and for entire atmospheric column
Pixel level retrievals
Distributed to AWIPS, NCEP
Operational Applications
Nowcasting
Gulf of Mexico return flow
Southwest US monsoon
QPF (heavy rain, flash flooding)
Convective potential and morphology
Fog potential
Situational awareness in pre-convective environments for potential watch/warning scenarios
NWP
Assimilated into NCEP North American Mesoscale (NAM) model/NDAS & RUC over land

7. Lifted Index Operational Applications
Computed from retrieved temperature and moisture profiles
Parcel lifted mechanically from 100 hPa mixed layer up tp 500 hPa level
Pixel level retrievals
Distributed to AWIPS
Operational Applications
Nowcasting
Convective potential
Convective morphology
Situational awareness in pre-convective environments for potential watch/warning scenarios

8. Cloud Top Products Cloud Height Algorithms Operational Applications
CO2 slicing for mid to upper-level non-opaque clouds (Chahine, 1974; Smith and Platt, 1978; Menzel et al, 1983)
IR window for low, opaque clouds
Pixel level retrievals
Distributed to AWIPS, NCEP
Operational Applications
Nowcasting
Aviation Terminal Aerodrome Forecasts (TAFs)
Supplements Automated Surface Observing System (ASOS) with upper-level cloud information
Effective Cloud Amount (ECA) selected as proxy for NWS RTMA hourly sky cover
NWP
Cloud initialization
Assimilated into NCEP North American Mesoscale (NAM) model/NDAS & RUC

9. Assimilating GOES Sounder products into a numerical model and generating forecast imagery
Forward radiative transfer equations (RTE) are used to compute the expected image radiative temperatures, at a given wavelength, from the forecast temperature and moisture profiles predicted by the a regional model.
McIDAS display on web
{18 UT 05 Oct 2006} OR
CRAS = CIMSS Regional Assimilation System
On AWIPS, comparing GOES observed imagery with CRAS forecast imagery, both valid at the same time.
{18 UT 06 Oct 2006}

10. NWS Forecast Office Assessment of GOES Sounder Atmospheric Instability
Summer ‘99 Forecaster assessment of usefulness of changes in hourly LI, CAPE, & CINH product for predicting location/timing of thunderstorms
Out of 248 valid weather cases:
- Significant Positive Impact (30%)
- Slight Positive Impact (49%)
- No Discernible Impact (19%)
- Slight Negative Impact (2%)
- Significant Negative Impact (0)
Figure from the National Weather Service, Office of Services

11. The Advanced Baseline Imager:
ABI Current
Spectral Coverage
16 bands 5 bands
Spatial resolution
0.64 mm Visible km Approx. 1 km
Other Visible/near-IR 1.0 km n/a
Bands (>2 mm) 2 km Approx. 4 km
Spatial coverage
Full disk per hour Every 3 hours
CONUS per hour ~4 per hour
Mesoscale Every 30 sec n/a
Visible (reflective bands)
On-orbit calibration Yes No

12. ABI Visible/Near-IR Bands
Schmit, T. J., M. M. Gunshor, W. P. Menzel, J. J. Gurka, J. Li, and A. S. Bachmeier, 2005: Introducing the next-generation Advanced Baseline Imager on GOES-R. Bull. Amer. Meteor. Soc., 86,
Schmit et al, 2005

13. ABI IR Bands Schmit et al, 2005
Schmit, T. J., M. M. Gunshor, W. P. Menzel, J. J. Gurka, J. Li, and A. S. Bachmeier, 2005: Introducing the next-generation Advanced Baseline Imager on GOES-R. Bull. Amer. Meteor. Soc., 86,
Schmit et al, 2005

14. Nocturnal Fog/Stratus Over the Northern Plains
Schmit, T. J., M. M. Gunshor, W. P. Menzel, J. J. Gurka, J. Li, and A. S. Bachmeier, 2005: Introducing the next-generation Advanced Baseline Imager on GOES-R. Bull. Amer. Meteor. Soc., 86,
“ABI” 4 minus 11 μm Difference
ABI image (from MODIS) shows greater detail in structure of fog.

15. Nocturnal Fog/Stratus Over the Northern Plains
Schmit, T. J., M. M. Gunshor, W. P. Menzel, J. J. Gurka, J. Li, and A. S. Bachmeier, 2005: Introducing the next-generation Advanced Baseline Imager on GOES-R. Bull. Amer. Meteor. Soc., 86,
GOES minus 11 μm Difference
ABI image (from MODIS) shows greater detail in structure of fog.

16. 15-min time resolution “loop”
Loops made by T. Schmit Data has been remapped and re-navigated.

17. 1-min time resolution loop
Loops made by T. Schmit Data has been remapped and re-navigated.

18. Outline Multi-spectral Hyper-spectral Current GOES Sounder GOES-R ABI
Overview
Products
GOES-R ABI
Improved temporal, spatial and spectral resolutions
Hyper-spectral
overview
applications

19. Broad vs High Spectral Resolutions

20. Moisture Weighting Functions
100
100
Advanced Sounder
GOES
(18)
Pressure (hPa)
Pressure (hPa)
1000
1000
Moisture Weighting Functions
High spectral resolution advanced sounder will have more and sharper weighting functions compared to current GOES sounder. Retrievals will have better vertical resolution.
UW/CIMSS

21. Hourly AIRS measurements within an approximate geostationary disk coverage area.  
(an IR window image is shown)
A geostationary hyperspectral sounder could provide full hourly disk coverage to complement the partial coverage available with polar orbiting sounders.

22. Aircraft hyper-spectral data shows great promise for Temp and moisture
Andros Is. Bahamas 12 Sep 98
Raob
NAST
Altitude (km)
Relative Humidity (%)
Smith, 2004
3km
Distance (75 km)

23. Validation of AIRS profile retrievals at CART site
Guess
CIMSS Physical
Inversion
AIRS resolves absorption features in atmospheric windows enabling detection of temperature inversions – warming with height evident from spikes up
Monitoring capping inversion and cell top height critical for estimating storm development and likely storm intensity – if cell top is higher and drier uncapped moisture can rise rapidly without a low ceiling (e.g. stratosphere is stable area)
Li et al, 2006

24. GOES Data Improving Regional Forecasts
Hyperspectral Geo could do much more
The impact of five satellite data types and five in-situ data types in the Eta Data Assimilation/Forecast System (EDAS) was recently completed for 10-day time periods during three seasons (spring, summer and winter). A fall time period was not available due to a lack of consecutive data. The satellite data types included GOES clear air precipitable water (PW) as well as infrared cloud drift winds and water vapor cloud top winds, SSM/I marine vertically-integrated PW, and TOVS marine temperature retrievals down to cloud top. The in-situ data types included all the components of RAOB data, ACARS temperature and winds, and surface land observations. Overall, the results established that the satellite data types were of comparable importance to the in-situ data types for the time periods studied. During summer, satellite PW data were the most important data type in the EDAS, even for state variables other than moisture.
Having identified the impact of multiple satellite data types in the EDAS for several seasons, work began on identifying the impact of geostationary data versus polar orbiting data in the EDAS for multiple seasons. The case studies chosen include 15-day periods during the four seasons. Accumulated differences between the experimental runs (simulations with data denied) and control run (simulation with all available data) are then analyzed to demonstrate the forecast impact of these data types in the EDAS. The figure below illustrates the positive forecast impact (%) of GOES and POES data in the EDAS on standard meteorological state variables for the seasons thus far evaluated (fall 2001 and winter 2001/2002). The GOES data provides a more positive impact on the EDAS forecast for nearly all levels and fields.
Positive forecast impact (%) of both GOES and POES data in regional model (Eta Data Assimilation/Forecast System) on standard meteorological state variables for fall 2001 and winter 2001/2002.
Zapotocny et al, 2004

25. Upper-level SO2 plumes Broad-band ABI 7.34 μm - 6.95 μm
Water Vapor Band Difference convolved from AIRS data
sees SO2 plume from Montserrat Island, West Indies
SO2 Plume
Note that the high-spectral sounder (AIRS) gives a much greater brightness temperature difference (by almost a factor of 2) than the broad-band instruments. Plus the hyper-spectral measurements offer more contrast between the plume and it’s environment.
Current GOES can detect an ash cloud.
Broad-band ABI
7.34 μm μm
cm-1

26. USING GOES-R TO HELP MONITOR UPPER LEVEL SO2
Simulated GOES-R ABI
Image Difference
USING GOES-R TO HELP MONITOR UPPER LEVEL SO2
Simulated IR spectrums for “normal” and “SO2 enriched” atmosphere and spectral response functions
Plume
Difference and GOES-R ABI SRF
Anthony J. Schreiner*, Timothy J. Schmit#, Jun Li*, Gary P. Ellrod#, Mat Gunshor*
*CIMSS #NOAA/NESDIS
TOMS
Carn