1. Remote Sensing Systems Climate Satellite Program Frank J. Wentz and Carl Mears Remote Sensing Systems, Santa Rosa, CA Supported in part by : NASA’s Earth Science Division NOAA’s Scientific Data Stewardship and Climate Program Office Presented at: CICS Mtg., Univ. of Maryland, September 8, 2010

2. Engineering Climate Data Records: The Challenges Large Volume: Over 150 satellite-years of observations from Microwave Radiometers Microwave Imagers (13) SSM/I: F08, F10, F11 F13,F14, F15 SSM/IS: F16, F17, F18 TMI AMSR-E and AMSR WindSat Microwave Sounders (16) MSU: Tiros-N, NOAA 6, 7, 8, 9, 10, 11, 12, and 14 AMSU: NOAA 15, 16,17 18, and 19, Aqua, MetOP A Difficult Calibration : Each sensor has its unique set of problems Sensor pointing and S/C attitude errors Antenna pattern knowledge error Scan-dependent errors Sun intrusion and thermal gradients in Hot Load Emissive Antenna High Precision Required: Climate Variability typical 1% of the Mean

3. Engineering Climate Data Records: The Method Maintain a High Level of Consistency The steadfast adherence to the same principles and methods in data processing all the way from radiometer counts  to climate time series, including: Geolocation Radiometer calibration Geophysical retrievals On-Going and Comprehension Validation Radiosondes temperature and vapor GPS vapor Ocean buoy winds and SST Scatterometer wind retrievals SST, Cloud and Rain retrievals from satellites IR sensors Much of this validation is done by the User community via peer reviewed papers. Sensor Calibration and Geophysical Retrievals Use Same Radiative Transfer Model = Same Retrieval Algorithm for all Sensors All MW imagers are calibrated to the same RTM All retrievals come from the inverse of the RTM (RTM -1 )

4. The Fundamental, Fundamental Data Record: Radiometer Counts Consistency in data processing requires the antenna temperatures or brightness temperature obtained from various data providers like TDRs from NOAA, NASA Level 1 dataset, JAXA L1A data be reversed engineered back to the original radiometer counts in the telemetry downlink. Also satellite position, velocity, and attitude vectors are required RSS maintains a complete archive of radiometer counts (both earth view and calibration), and associated spacecraft ephemeris and housekeeping data (thermistors).  Dataset is fully q/c and organized into time-ordered orbital files.  Volume size is relatively small  Non-Controversial  Stable  Basis for all product generation at RSS (Ta, Tb, CDR, gridded products)

5. Geophysical Retrievals Validation EP Adjustments ( i.e., clear sky bias, high vapor bias) Retrieval Algorithm Radiative Transfer Model Simulated Antenna Temperatures Sensor Antenna Temperatures Sensor AdjustmentsRTM Adjustments Automatic Calibration Methodology: Continuous Updating and Reprocessing Cycle Time ≈ ½ Year Radiometer Counts

6. Same ΔTa (simulated minus measured) plotted versus different parameters Same color scale: ΔTa goes from -3K to +3K Y=Wind, X=SST Y=Wind, X=Vapor Y=Vapor, X=SST Y=Orbit Position, South Pole to South Poel, X=Orbit number (5 years) Y=Sun Polar Angle, X=Sun Azimuth Angle RTM Error Diagnostics Sensor Calibration Error Diagnostics Distinguishing Sensor Errors from RTM Errors Sun intruding Into hot load

7. Comparing F17 and F13 Water Vapors (Monthly Averages) Version 7

8. Bias (SSMI minus GPS) = -0.07 mm Std. Dev. = 1.9 mm Num Obs = 53,730 Credit: Carl Mears (to be published) Geophysical Validation: Columnar Water Vapor

9. Version – 7 100 Satellite-Years of Earth System Data Records Consistently Processed with Common Algorithms Same RTM Used for Calibration and Retrievals for all Sensors The Products  SST (not all sensors)  Wind Speed  Columnar Water Vapor  Columnar Liquid Water  Rain Rate The Sensors  SSM/I: F08, F10, F11, F13, F14, F15  SSM/IS: F16, F17, F18  TMI  AMSR-E and AMSR-A  WindSat Availability Backbone: F13, WindSat, F16, and F17 now available Remaining Sensors will be made publicly available end of 2010. Products will be hosted at www.remss.com

10. MSU/AMSU Climate Data Records Trend Map, Middle Troposphere (1979-2009) Time Series for Each Channel

11. MSU/AMSU Uncertainty Analysis Two types of Analysis “Bottom Up” Estimated Errors Propagated Through Merging Procedure using Monte-Carlo Techniques Sampling Adjustments (local time, etc) Residual Calibration Errors “Top Down” Compare to other measurements of the same parameters Other sounder datasets Radisondes GPS-RO Range of simulated errors Satellite, Radiosonde and ReanalysisComparison

12. BACKUP SLIDES

13. Individual overpasses: RMS variation of SSMI minus bouy difference = 1.0 m/s Monthly Averages: RMS variation of SSMI minus bouy difference = 0.1 m/s ( lag-1 correlation =0.46) Estimate error bar is 0.05 m/s/decade at 95% confidence SSM/I trend minus the buoy trend is 0.02 m/s/decade. Yearly corrections (0.05-0.10 m/s) are applied to SSMI winds (equivalent to 50 mK T B adjustments) These results indicate we are doing better than 0.1 K/decade Geophysical Validation: Wind Speed Trends Figure from : Wentz, Ricciardulli, Hilburn, Mears: Science, July 13, 2007, How Much More Rain Will Global Warming Bring?

14. Climate Constraint: Constant Relative Humidity Figure from : Wentz and Schabel: Nature, 403,2000, Precise climate monitoring using complementary satellite data sets. Nearly all climate models predict a near constant Relative Humidity with global warming. This means total water vapor will increase with rising air temperatures at a rate ≈ 7%/K  Original MSU air temperatures showed little warming while SSM/I water vapors showed significant moistening.  This contradiction was due to errors in the original calibration of MSU.  Results here show with proper calibration MSU air temperature and SSM/I water vapors agree.  The “MSU controversy” was resolved.

15. Climate Constraint: Evaporation = Precipitation Figure from : Wentz, Ricciardulli, Hilburn, Mears: Science, July 13, 2007, How Much More Rain Will Global Warming Bring? On global, monthly time scales, Evaporation must equal Precipitation (Variability in storage term is extremely small)

16. Climate Constraint: Radiative Cooling Limit on Evaporation Climate Models: 4 mm/year/decade Wentz et al., Science, July 13, 2007: 13 mm/year/decade (C-C rate) Yu and Weller, BAMS, Vol 88, No 4, April 2007: 50 mm/year/decade J. R. Christy et al., Geophys. Res. Lett., 28, 183, 2001: 21 mm/year/decade Hamburg Ocean Atmos. Parameters & Fluxes from Satellite Data (IGARSS 2006 presentation) very large Explanation of Christy Result Found a trend in the Tropical Air-Sea Temperature Difference: -0.1 K/decade (1979-1999) Used to support their MSU Lower Tropospheric Temperature Retrieval, which indicated: In the tropics, the troposphere is not warming as fast as the surface. (Later was found to be incorrect) From the standpoint of evaporation: A trend of -0.1 K/decade in the air-sea temperature difference would greatly increase the trend in global evaporation Nearly all climate models predict an enhanced radiative cooling that is balance by an increase in latent heat from precipitation

17. Geolocation Analysis Attitude Adjustment Along-Scan Correction Absolute Calibration Hot Load Correction Antenna Emissivity Resampling Algorithm Rain Threshold OOB Q/C SSM/I F08, F10, F11 F13,F13, F15 NRL/RSSNoYesAPCYes0Opt. Intrep.0.18 mmOcean RTM TMIGoddardDynamicYesTA OffsetsNo3.5%Opt. Intrep.0.18 mmOcean RTM AMSR-ERSSFixedYesAPCYes0Opt. Intrep.0.18 mmOcean RTM AMSR-ARSSDynamicYesAPCYes0Opt. Intrep.0.18 mmOcean RTM WindSatNRL/RSSFixedYesAPCYes0Earth-Grid Weighted Average 0.18 mmOcean RTM SSM/IS F16, F17 RSSNoYesAPCYes1 - 4% Note: 1 Opt. Intrep.0.18 mmOcean RTM STEP-1: All Level-1 (NASA) or TDR (DMSP) datasets are reversed engineered back to radiometer counts Level-1 Processing: Counts-to-Antenna Temperature STEP-2: Apply a completely consistent set of Level-1 Routines to produce calibrated antenna temperatures Note 1: Over 19 – 92 GHz

18. Preliminary Results Over Land Calibration is done just over ocean. Land can be considered a withheld data set. How well does the ocean calibration extrapolate to land? Key factors: Radiometer linearity and accuracy of ocean RTM Histograms of 19 GHz V-pol at the high end: TA above and TB below. F13, F16, F17 consistent to about 0.25 K. Windsat has different spatial resolution