1. Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1, and Henry Revercomb 1 1 University of Wisconsin – Madison 2 Hampton University CLARREO SDT Meeting The National Institute of Aerospace April 10-12, 2012

2. Potential Climate Trend Specification Satellite Instrument Characteristics CLARREO SDT April 10-12, 2012 4 - 100

3. CLARREO SDT April 10-12, 2012

4. HIRS Vs IASI/AIRS/CrIS Retrieval Resolution Retrieval Vertical Response to Vertical Structure Impulse (after: Rabier et al., ECMWF) CLARREO SDT April 10-12, 2012

5. Filters to Spectrometers Climate Investigation CLARREO SDT April 10-12, 2012 Objective: To determine whether or not useful climate trend parameters can be obtained from the continuous record of HIRS data dating back to the Nimbus-6 HIRS of 1975. (note: HIRS-4 on Metop-A can be cross calibrated with IASI on Metop-A and then used through Simultaneous Nadir Overpasses to cross calibrate all the NOAA satellite HIRS- 3/HIRS-2 and Nimbus-6 satellite HIRS-1 observations) Procedure: A.Determine the accuracy of HIRS monthly mean climate variables relative to CLARREO project determined AIRS monthly mean values (1) Simulate HIRS from IASI (IASI absolute calibration is comparable to AIRS) (2) Retrieve monthly mean climate variables using same retrieval algorithm that was used for CLARREO AIRS climate parameters (3) Compare Differences of HIRS and AIRS with respect to GDAS specifications as a measure of relative accuracy (i.e., accounts for sampling differences between Metop-HIRS (09:30 orbit) and Aqua-AIRS (13:30 orbit) Next Steps: (1) If results of “A” above are favorable, repeat “A” using actual Metop HIRS rather than Metop IASI simulated HIRS and full resolution IASI radiances. Compare results. (2) Extend processing to all cross-calibrated HIRS data extending back to 1975.

6. Desirable Features of Climate Retrieval Algorithm Linear dependence on radiance spectra -Variation depends only on radiance (i.e., no other input variables) All sky -clear and cloudy (0 - 100%) Independent of Field-of-View (FOV) size -Can be applied to different instruments Retrieval Variables -Surface : temperature & spectral emissivity -Atmosphere : T, H 2 O, and O 3 profiles & CO 2 ppm -Cloud : height and optical thickness

7. Dual Regression Retrieval Algorithm Classified linear Dual-Regression (DR) -Very fast (real-time) all-sky temperature, water vapor, ozone profiles plus surface skin temperature and spectral emissivity, cloud pressure and optical depth and total CO 2 concentration retrieval algorithm Non-linear dependence on cloud pressure and humidity accounted for by classification (9 cloud height / H 2 O classes within 5 CO 2 classes) Training Data Sets for Robust Retrievals - Large (15,704 clear sky and 19948 cloudy sky) global all season radiosonde / remote region ECMWF analysis data set - Cloud altitudes diagnosed from humidity profile - Surface skin temperature and emissivity and cloud microphysical properties based on empirical data sets with Gaussian random perturbations - UMBC SARTA and Texas A&M / U. Wisconsin Cloud RTM for radiances CLARREO SDT April 10-12, 2012

8. Technique – Dual Regression 1 1 Initial cloud-class selected from 8 200-hPa overlapping cloud layer class regressions (solution is one closest to layer mean) 2 Retrieval below cloud set equal to missing if Max(Tclr-Tcld) >25 K 3 For HIRS, GDAS profile is used in place of “Cloud-Trained Profile” to define cloud class Linearizes Cloud and Moisture Dependence through classification Based on single 40-yr Global Profile Data Set & Calculated Radiances CLARREO SDT April 10-12, 2012 Cloud Top Altitude / Cloud Class

9. Spectral Channels Used For Profile Retrievals AIRS (1450/2378), IASI(7021/8461),CrIS (1245/1305), & HIRS (16/20) CLARREO SDT April 10-12, 2012

10. Climate Variables Retrieved Temperature Profile (K) Water Vapor Mixing Ratio Profile(g/kg) Relative Humidity Profile (%) Ozone Profile (ppmv) Surface Skin Temperature (K) Total Precipitable Water (cm) CO 2 Concentration (ppm) Cloud-top Altitude (hPa) Thin Cirrus Cloud-top Altitude (hPa) Effective Cloud Optical Depth Atmospheric Stability (Lifted Index) AIRS Climatology based on retrievals from nadir-only full resolution (13-km) observations binned into 10-degree latitude-longitude grid cells CLARREO SDT April 10-12, 2012

11. Cloud Comparison of MetOp “HIRS” Vs IASI CLARREO SDT April 10-12, 2012 IASI HIRS

12. Temperature Comparison of MetOp “HIRS” Vs IASI IASI 500 hPa HIRS 500 hPa IAS 850 hPaI HIRS 850 hPa

13. Monthly Mean Cloud Comparisons (August, 2009) As can be seen there are large differences between HIRS and AIRS derived cloud parameters. In general HIRS retrievals show higher altitude and lower optical thickness clouds than does AIRS Cloud Pressure (hPa) Cloud Optical Thickness CLARREO SDT April 10-12, 2012

14. Monthly Mean 500 hPa Temperature (August, 2009) HIRS temperature retrieval “errors” are larger than the AIRS “errors”, particularly over the conventional data rich land areas. HIRS retrieved temperatures are generally colder than the HIRS retrieved temperatures

15. Monthly Mean 500 hPa Humidity (August, 2009) HIRS humidity “errors” are comparable to the AIRS “errors” The spatial distribution of HIRS humidity deviations from GDAS compare favorably with the spatial distribution AIRS humidity deviations from GDAS

16. AIRS & HIRS Vs GDAS Global Comparisons August 1, 2009 CLARREO SDT April 10-12, 2012 Comparisons with the NCEP Global Data Assimilation System (GDAS) product shows the HIRS DR retrieval errors are about twice as large as those for the AIRS on a global basis. (Note the factor of 2 abscissa scale difference between HIRS and AIRS “error” plots) AIRS minus GDAS HIRS minus GDASAIRS minus GDASHIRS minus GDAS 1 K 2 K 4 % 20 % Mean Stde Mean Stde

17. Conclusions Bad News: The HIRS retrieval errors appear to be too large to provide climate accuracy measurements of atmospheric state for the early detection and magnitude of climate change Good News: The results presented here validate the need for satellite ultraspectral radiance measurement (e.g., from CLARREO) retrievals for providing atmospheric state measurements with suitable accuracy for the early detection and magnitude of climate change CLARREO SDT April 10-12, 2012