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.

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

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

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

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

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

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

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

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}

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

The Advanced Baseline Imager: ABI Current Spectral Coverage 16 bands 5 bands Spatial resolution 0.64 mm Visible 0.5 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 4 per hour Every 3 hours CONUS 12 per hour ~4 per hour Mesoscale Every 30 sec n/a Visible (reflective bands) On-orbit calibration Yes No

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, 1079-1096. Schmit et al, 2005

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, 1079-1096. Schmit et al, 2005

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, 1079-1096. “ABI” 4 minus 11 μm Difference ABI image (from MODIS) shows greater detail in structure of fog.

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, 1079-1096. GOES-10 4 minus 11 μm Difference ABI image (from MODIS) shows greater detail in structure of fog.

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

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

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

Broad vs High Spectral Resolutions

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

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.

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)

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

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

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 - 6.95 μm 1285-1345 cm-1

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