Global training database for hyperspectral and multi-spectral atmospheric retrievals Suzanne Wetzel Seemann, Eva Borbas Allen Huang, Jun Li, Paul Menzel

Synthetic regression retrievals of atmospheric properties require a global dataset of temperature, moisture, and ozone profiles. Estimates of surface skin temperature and emissivity are also required to calculate radiances from each profile. Radiosonde temperature-moisture-ozone profile together with calculated MODIS radiances are used to create the synthetic regression relationship for atmospheric retrievals. We introduce a new data set consisting of global profiles drawn from NOAA-88, ECMWF, TIGR-3, CMDL ozonesondes, and FSL radiosondes. Application of the database to MODIS atmospheric retrievals will be presented for various combinations of profiles and different forward models. Skin temperature and emissivity values have been assigned to each profile. In earlier satellite regression retrieval algorithms, skin temperature and emissivity were assigned relatively randomly or held constant for each profile. A more physical basis for characterizing the surface is presented here, with emphasis on a new global ecosystem-based surface emissivity database.

PART I: Clear Sky Global Profiles NOAA-88 (subset) 5356 TIGR-3 (subset) 1125 CMDL ozonesondes, 8 sites 992 NOAA/FSL radiosondes: 1. Sahara desert 2003 568 2. All global stations, 2004 (4 days per month) 12,968 25,191 “C” To perform the retrieval, the regression relationship is applied to actual MODIS observations. ECMWF training set (subset) 4182 12223 “B” Total number of profiles: 8041 “A”

All profiles are for clear sky with saturation checks and other QC. Where ozone data were not available Paul VanDelst’s regression-based ozone profiles were used. Where surface pressure is not available, it is estimated based on station elevation and a standard atmosphere. Temperature and dew point temperature profile values below ground are set to the value at the surface level. Mixing ratio Temperature Ozone

A little background on the profiles… (Statistics shown here are for the full version “C”) Latitude Longitude TPW Skin temperature Temp at sfc Pressure Month Skin temperature

Clear-sky validation database The profile training dataset is routinely tested on Terra and Aqua MODIS MOD07 synthetic retrieval products. We have assembled a validation database of clear-sky cases at the SGP ARM site (Oklahoma) for both Terra and Aqua using manual cloud-clearing based on inspection of satellite imagery. All cases can be easily reprocessed with the latest MOD07 algorithm derived from any new training data.

Results from three different training data sets are shown here. Dry: bias= -0.04, rms= 1.86, n= 76 Wet: bias= 2.7, rms= 3.61, n= 48 All: bias= 1.02, rms= 2.68, n=124 Terra MODIS (red dots), GOES-8 and -12 (blue diamonds), and radiosonde (black X) TPW is compared to the ground-based ARM SGP microwave water radiometer for 124 clear sky cases April 2001 to September 2003. Results from three different training data sets are shown here. MODIS: bias= 0.32mm, rms= 2.53mm New SkinT and Emis using pCRTM model for forward calculation New SkinT and Emis using PFAAST model for forward calculation MODIS: bias=1.02mm, rms=2.69mm Original NOAA88 SkinT and Emis MODIS: bias=1.61mm, rms=3.99mm MODIS, GOES, Radiosonde TPW (mm) Microwave Radiometer TPW (mm)

Granule-based comparisons: AIRS/MODIS AIRS and collocated MOD07 profiles Temperature Mixing Ratio MOD07 TPW Operational AIRS TPW Only AIRS pixels with retrieval_type = 0 are displayed

Terra MODIS TPW (mm) for August 24, 2002 in the Sahara Desert region NCEP-GDAS MODIS: old (NOAA88) skin T & emis MODIS: new Skin T & emis

PART II: Surface Emissivity We need: A surface emissivity value for each profile in the training data set. We have: MODIS MOD11 emissivity, but only at 6 wavelengths (only 4 distinct wavelength regions): 3.7, 3.9, 4.0, 8.5, 11, 12 mm Laboratory measurements of emissivity at high spectral resolution but they are not necessarily representative of the emissivity of global ecosystems as viewed from space MOD11 Band 29 (8.5mm) emissivity

In the past, skin T and emis were assigned relatively randomly to each profile in satellite regression retrieval algorithms, including: MODIS retrievals (Seemann et al, 2003), ATOVS retrievals (Li et al., 2000). Compare with … Original NOAA88 emissivity assigned to training profiles in MODIS: two values (0.84 and 0.95) with std 0.15, 0.03. Linear interpolation for wavelengths in between.

Laboratory measurements of selected materials from UCSB (Dr. Wan, MODIS land team) Point out specifically desert areas missing low emis in 8-10micron region, and ocean/ice/trees could be assigned an emis as low as 0.7 at 4 microns. Original NOAA88 emissivity assigned to training profiles in MODIS: two values (0.84 and 0.95) with std 0.15, 0.03. Linear interpolation for wavelengths in between.

Approach: We use laboratory measurements to derive a “baseline emissivity” and relevant inflection points to the spectra. Then we use MODIS MOD11 to set the emissivity magnitudes. For this, we can use either MOD11 emissivities averaged by ecosystem, month and latitude band or apply separately to each MOD11 lat/lon pixel. Good for moderate spectral resolution (such as MODIS). Useful for higher spectral resolution by interpolating between inflection points, but apply with caution. Accuracy depends highly on the accuracy of MOD11 emissivity.

Used to derive “baseline emissivity” and inflection points Laboratory measurements of selected materials from UCSB (Dr. Wan, MODIS land team): Used to derive “baseline emissivity” and inflection points Inflection Points: 3.7, 4.3, 5, 7.6, 8.3, 9.3, 10.8, and 14.3mm

2) Set emissivity at 7.6mm constant at 0.98 Use ratio of the Find linear fit through first three MOD11 points, and extend from 3.7-4.3mm 2) Set emissivity at 7.6mm constant at 0.98 Use ratio of the Δ baseline emis from 4.3-5mm to 5-7.6mm to find emis at 5mm. 4) Hold emis constant from 8.3 to 9.3mm at MOD11 8.5mm value Keep slope from 10.8 to 14.3 mm the same as in baseline; set height based on best fit with MOD11 31 & 32 emis values. Black Line: “Baseline Emissivity” Inflection points chosen to best represent shape of spectra, not specific to any one instrument. Colored symbols: MOD11 emissivity averaged over IGBP ecosystem categories by month and latitude band

A few examples of the results for December 2003: Colored by latitude band for 4 selected ecosystems #5, Mixed Forests #6, Closed Shrubs #7, Open Shrubs #8, Woody Savanna Symbols indicate MOD11 points; Lines are based on laboratory baseline fit to MOD11 points divided into latitude bands (north polar (90-60), north midlat (60-30), Tropics (30N-30S), south midlat (30S-60S), and south polar (60S-90S) for each of 12 months… Note: we have one plot like this for each of the IGBP categories (15 nondesert land), for each month. Saved values into lookup table to apply to profiles as a function of lat/lon, month. Separate for desert, ice/snow, ocean.

Comparison of all land ecosystems for August 2002, Tropical latitude band Ecosystems with reduced 8.5mm emissivity are: Barren/Desert Land (pink *) Open Shrubs (black) Grasslands (pink x) Closed Shrubs (red)

Comparison of 4 years of data: Terra 2001-2004 for Ecosystem #16, Barren/Desert Land in August Was the desert less barren in 2004?

Other potential applications… weather/climate models: Global gridded emissivity (non-ecosystem based) Approach: Apply baseline emissivity fit to each MOD11 point instead of applying after averaging over ecosystem. Result: Spectrum of emissivity values at 8 inflection points for each month at each MOD11 latitude & longitude point.

Other potential applications… weather/climate models: Global gridded emissivity (non-ecosystem based)

Emissivity Spectra for 3 Sites: * Emissivity Spectra for 3 Sites: Sahara Desert (blue), WI (red), Antarctica (black)

Conclusions Future Plans Historically, synthetic regression retrievals have relied on training data sets that made little attempt to physically characterize the surface. We have developed a new global training data set that combines profiles from a number of sources. Associated with each profile in the data set is a physically-based characterization of the surface skin temperature and surface emissivity. Application of this SeeBor v.3 training data on MODIS MOD07 retrievals of total precipitable water show good improvement over the NOAA-88 training data set. With the new training data and an updated forward model, the RMS difference between MOD07 TPW and the ARM SGP MWR was reduced from 4mm to 2.5mm and significant reduction in retrieval noise was accomplished. Future Plans Experiment with new baseline emissivity curves and varied inflection points. Expand skin temperature parameterization. Update ozonesondes to include all launches to date. Update clear-sky ARM SGP validation data set to include all clear cases to date. Broaden validation data sets to include more sites, platforms, and instruments.