NESDIS Office of Research and Applications NOAA Hyperspectral Activities Mitch Goldberg Chief, Satellite Meteorology and Climatology Division NESDIS Center for Satellite Applications and Research (ORA) April 27, 2004

NESDIS Office of Research and Applications Topics Lessons learned from AIRS and needed research New focus areas – Satellite-derived Air Quality and Carbon Products

NESDIS Office of Research and Applications Lessons Learned from AIRS

NESDIS Office of Research and Applications Advanced IR Sounders NASA AIRS - Atmospheric Infrared Sounder (2002 – 2008?) EUMETSAT IASI - Infrared Atmospheric Sounding Interferometer ( ?) NPP/NPOESS CrIS - Cross-track Infrared Sounder ( ?) GOES-R Hyperspectral Environmental Sounder (HES) ( ?)

NESDIS Office of Research and Applications Risk Reduction Benefits Early demonstration of operational processing of high spectral resolution infrared sounder data prior to CrIS, IASI and GOES-R Validation of retrieval performance Early opportunity for forecast centers to learn how to assimilate advanced IR data Early opportunity for operational agencies like NOAA and EUMETSAT to use “matured” algorithms to generate high quality soundings for CriS and IASI

NESDIS Office of Research and Applications What have we learned? AIRS instrument is extremely stable and accurateAIRS instrument is extremely stable and accurate Only 5% of the globe is clear at a 15 km fovOnly 5% of the globe is clear at a 15 km fov Impact on NWP is currently smallImpact on NWP is currently small Cloud-clearing increases yield to 60%Cloud-clearing increases yield to 60% Retrievals from cloud-cleared radiances are significantly more accurate than AMSU-only.Retrievals from cloud-cleared radiances are significantly more accurate than AMSU-only. Retrievals from cloud-contaminated radiances are also significantly more accurate than AMSU-onlyRetrievals from cloud-contaminated radiances are also significantly more accurate than AMSU-only Recommendation: assimilate cloud-cleared radiancesRecommendation: assimilate cloud-cleared radiances

NESDIS Office of Research and Applications Cloud Clearing

NESDIS Office of Research and Applications Cloud clearing infrared radiances requires a clear estimate for a handful of IR channels. Clear estimate can come from: AMSUForecastMODIS Cloud clearing using AMSU has limitations – works only for mid - upper level clouds.

NESDIS Office of Research and Applications Cloud-contaminated Cloud-Cleared

NESDIS Office of Research and Applications

Our retrieval studies have demonstrated accurate AIRS retrievals from cloud-cleared radiances. 50 % coverage instead of 5% Total Precipitable Water 1 K RMS differences with Model Analysis

NESDIS Office of Research and Applications Our retrieval studies have demonstrated accurate AIRS retrievals in clear (solid) and even in cloudy conditions (dash curve) AIRS performance is much better than AMSU even in cloudy conditions 50 % coverage

NESDIS Office of Research and Applications COLLOCATED RADIOSONDES

NESDIS Office of Research and Applications COLLOCATED RADIOSONDES

NESDIS Office of Research and Applications Critical Research Areas How do we assimilate cloud contaminated radiances? Cloud-cleared radiances? Cloud-contaminated radiances? Integrating imager and sounder information –Improved cloud cleared radiances

NESDIS Office of Research and Applications Data Access for Research and Application Data Compression using PCA Data Compression using PCA Gridded data sets for research Gridding reduces file size from 35 GB/day to 1.2 GBs.; PCA reduce file size to 0.7 resolution

NESDIS Office of Research and Applications Ozone Band PCA compression seems to be working quite well The residuals are at noise levels and can be compressed and stored in a separate file for lossless compression Most people will not want the residuals. The picture to the left can be also used as a form of metadata which will convince the user that the lossy compression is favorable Users can decide whether they want the residual file 50x data compression

NESDIS Office of Research and Applications Air Quality

NESDIS Office of Research and Applications Background Background Congress mandates… NOAA must develop and deploy air quality forecast model at NCEP which produces 24 hour ozone and particulate matter forecasts nationwide NOAA acts… Memorandum of understanding signed between EPA and NOAA to develop and implement an accurate air quality forecast program NESDIS Role to Meet this Goal Utilize satellite observations of aerosols and ozone to monitor air quality and improve air quality forecast by assimilation of satellite derived aerosol products

NESDIS Office of Research and Applications Operational GOES Imager Monitoring Aerosol optical depths over United States at high spatial (4 km) and temporal (30 minutes) resolution. GOES aerosol optical depths correlate fairly well with surface measurements of PM2.5

NESDIS Office of Research and Applications Transport of Smoke from Canadian Fires Transport of Smoke from Canadian Fires July 6, 2002 July 7, 2002 July 8, 2002 Transport of smoke to the New York/Pensylvannia region Smoke covers most of the new England region reaching as far down as North Carolina. Burning eyes and dirty air quality reported over much of B-W area Smoke blown off of the coast over the Atlantic

NESDIS Office of Research and Applications Transport of Smoke from Canadian Fires July 8, 2002

NESDIS Office of Research and Applications Limitations of Current GOES Imager Single Visible Channel Retrieval Identification of aerosol size/type not possibleIdentification of aerosol size/type not possible Uncertainties in estimation of surface contributionUncertainties in estimation of surface contribution No on-board calibration sourceNo on-board calibration source GOES Aerosol Retrieval Algorithm Dependence on a priori informationDependence on a priori information Assumptions of aerosol modelAssumptions of aerosol model However future GOES-R/ABI will improve capability

NESDIS Office of Research and Applications GOES-R Advanced Baseline Imager Better spectral, spatial, and temporal coverage than current GOES Imager 16 bands vs 5 bands16 bands vs 5 bands 0.5 km vs 1 km0.5 km vs 1 km 5 minutes vs 15 minutes5 minutes vs 15 minutes 0.46 – 0.49 um 0.59 – 0.69 um 0.84 – 0.88 um 0.52 – 0.75 um GOES GOES-R (visible bands similar to MODIS)

NESDIS Office of Research and Applications Visible and near-IR channels on the ABI The current GOES has only one visible band. Haze Clouds Veg. Cirrus Part. size Snow, Phase AVIRIS spectra

NESDIS Office of Research and Applications Correlations by US EPA Region for June PM event (Region 5-IL, IN, MI, MN, OH, WI & Region 3-DC, DE, MD, PA, VA, WV) (from NASA)

NESDIS Office of Research and Applications Aerosol Optical Depth Cloud Optical Thickness PM2.5 (ug/m3) 12 Sept A close-up of Houston shows many of the hourly PM2.5 monitors recorded 24 averages in excess of 40.5 ug/m3, (AQI>100). High AOD extends into a large portion of TX. Time Series shows agreement of hourly PM2.5 Concentrations (Surface Monitor) and Aerosol Optical Depth in Coincident MODIS pixel. Correlation Coefficient > (from NASA)

NESDIS Office of Research and Applications Clear sky Negative slope due to dust loading Sokolik, GRL, 2002 Wave Number (/cm) BT (K) Dust Detection

30 Larrabee Strow, UMBC

NESDIS Office of Research and Applications EPA Criteria Pollutants OzoneUV-VIS, IR* SO2UV*, IR* COIR* NO2UV-VIS* H2COUV* Aerosols (PM2.5 and PM10) DustIR*, VISDustIR*, VIS SmokeIR, VISSmokeIR, VIS SulfateVISSulfateVIS OrganicVISOrganicVIS CarbonUVCarbonUV * Hyper spectral measurements essential

NESDIS Office of Research and Applications UV-VIS Trace Gases and Aerosols Important for Air Quality

NESDIS Office of Research and Applications GOME NO2 Retrieval from Hyperspectral Measurements Note the urban/industrial plumes over eastern US and western Europe Image downloaded from NASA web page

NESDIS Office of Research and Applications NOAA Carbon Cycle Program GOALS Reduce Uncertainties in Global Carbon Cycle Fluxes Geographic distribution.Geographic distribution. Seasonal and inter-annual variation.Seasonal and inter-annual variation. Understand and monitor exchanges between atmosphere, land, and ocean.Understand and monitor exchanges between atmosphere, land, and ocean. Determine Climate Effects Variation and evolution of sources and sinks.Variation and evolution of sources and sinks. Monitor and model long term trends.Monitor and model long term trends. Provide Information For Policy (e.g., energy).Policy (e.g., energy). Mitigation (e.g., carbon sequestration).Mitigation (e.g., carbon sequestration). Ecosystem management (e.g. forests, coral reefs).Ecosystem management (e.g. forests, coral reefs).

NESDIS Office of Research and Applications ORA’s Contribution to the NOAA & NASA Carbon Programs GOAL > Satellite-derived global maps of CO 2, CO and CH 4 GOAL > Satellite-derived global maps of CO 2, CO and CH 4 Perform simulation studies to determine precision of trace gas measurements for AIRS, IASI, and CrIS. Develop satellite retrieval algorithms for CO, CH 4, and CO 2 Develop and validate carbon products from AIRS. Develop and evaluate techniques to incorporate satellite measurements into models. Coordinate with instrument developers to maximize carbon capabilities from planned (e.g., IASI and CrIS) and new missions (e.g., OCO).

NESDIS Office of Research and Applications

AIRS CO Retrievals GOES-R HES will have similar capabilities but every 15 minutes Image courtesy of Wallace McMillan, UMBC

NESDIS Office of Research and Applications Summary Hyperspectral imaging and soundings will provide vastly improved products which improve monitoring and predicting change in ecosystems, weather, climate and hazards. NOAA is actively using research satellites as pathfinders to future operational hyperspectral instruments. Two new focus areas: Air Quality and Carbon. Integrating Observations is a key component to NOAA’s mission. Highly accurate in situ observations are critical for validation Hyperspectral remote sensing of the environment is a new frontier – a lot more research is needed.

NESDIS Office of Research and Applications Additional Slides

NESDIS Office of Research and Applications Why high spectral resolution?  Improved spectral resolution results in Sharper weighting functions “Clean” channels (e.g. temperature channels not contaminated by water vapor lines)  Many channels with sharp weighting functions combined with low noise improves vertical resolution  Retrieval accuracy is greatly improved (temperature, moisture, skin temperature and surface emissivity) Resolving individual water vapor absorption lines allow detection of temperature inversions High spectral resolution allow the retrieval of trace gases

NESDIS Office of Research and Applications Current sounders do not meet user requirements Both WMO and NPOESS user requirements are temperatures with an average error of 1 K over 1 km layers in the troposphere and humidity with an average error of % Current sounder accuracy is 2 K and 20-30% with a vertical resolution of 3-6 km High spectral resolution infrared sounders will have 1 – 2 km resolution

NESDIS Office of Research and Applications Temperature & Moisture Accuracy Comparisons

NESDIS Office of Research and Applications um CO um CO26.3 um H20 CH 4 C0 AIRS IASI CrIS

NESDIS Office of Research and Applications