1. Communicating Uncertainties for Microwave-Based ESDRs Frank J. Wentz, Carl A. Mears, and Deborah K. Smith Remote Sensing Systems, Santa Rosa CA Supported by: NASA MEaSUREs Program Carl Mears Poster: IN21A-1405: Uncertainty Estimates for MSU/AMSU Derived Atmospheric Temperatures Kyle Hilburn Poster: IN21A-1418: Decadal Trends and Variability in Special Sensor Microwave / Imager (SSM/I) Brightness Temperatures and Earth Incidence Angle Presented at AGU, San Francisco, 2011 December 6, 2001; IN24A

2. DISCOVER Project Distributed Information Services: Climate/Ocean Products and Visualizations for Earth Research A collaboration between Remote Sensing Systems and University of Alabama, Huntsville Supported by two NASA Programs:  MEaSUREs ( Making Earth Science Data Records for Use in Research Environments )  Earth System Data Records Uncertainty Analysis 23 Satellite Microwave Sensor (Radiometer and Scatterometers) Products: Sea-Surface Temperature and Winds, Water Vapor, Cloud Water, Rain Rate

3. Applications ranging from tracking seals and albatrosses to high precision climate monitoring 500 Peer-Reviewed Journal Papers used DISCOVER data. Large, Heterogeneous User Base 5000-10000 Distinct Users Geographic Distribution of Users

4. Providing Information on Accuracy of Products is Essential An estimate of a variable without an assigned uncertainty is, in some sense, meaningless Usually there is an implied uncertainty, i.e.: SST error = 0.5 C, wind error = 1 m/s, etc. However, for satellite retrievals the real uncertainties are usually dynamic and complex. This variability in the uncertainty must be communicated to the Users Multiple approaches required  Quality flags: the traditional approach  Formal errors to assess algorithm input errors  Simultaneous retrievals from multiple algorithms to assess algorithm assumption errors  For climate trends, compare results from different satellites

5. Traditional Quality Flags Indicate the occurrence of certain events that may effect quality Anomalous spacecraft attitude Anomalous on-board calibration Close to land Possible sea ice Etc. Usually include summary bits (or values) as a guide for data inclusion/exclusion Highest quality datasuitable for climate research Good quality datasuitable for operational weather analysis Poor quality datamay be OK for qualitative applications Bad datado not use  A useful, simple approach but lacks quantitative information  Need to provide Users with information on percent of data excluded  Need to development common notation/definitions among data providers  Error characterization is much more complex than can be captured by a few bits

6. Computation of Formal Error Estimates Assessment of Algorithm Input Errors Determine sensitivity of retrieval algorithm to errors in inputs: Brightness Temperatures (TB)  Retrieval Algorithm  SST, wind, vapor, cloud, rain Ancillary data  Retrieval Algorithm  SST, wind, vapor, cloud, rain Incidence Angle  Retrieval Algorithm  SST, wind, vapor, cloud, rain Hot load temperature  Retrieval Algorithm  SST, wind, vapor, cloud, rain For every observation, retrieval algorithm is run many times to determine these sensitivities (EP denotes environmental parameter): Enter AMSR-E Mission is being completely reprocessed with a formal error assigned to each retrieval

7. Formal Error Estimates for AMSR-E Water Vapor: A very dynamic quantity Errors increase in very MOIST AIR and also in HEAVY RAIN.

8. Verification of Formal Error Estimates SST Error determined from: Buoys Formal Estimate

9. RSS Petty RSS - Petty RSS – Petty vs RSS Provide Simultaneous Retrievals from Multiple Algorithms Assessment of Algorithm Assumption Errors Example: Rain Rate, much of the error is due to the assumptions built into the algorithm

10. AMSR-E minus WindSat ?? Inter-Comparison of Satellite Wind Time Series F13 SSMI, F16 & F17 SSM/IS, WindSat, and AMSR-E F16 SSM/I Problem

11. 10% flagged  = 0.51 1% flagged  = 0.73 0.1% flagged  = 0.93 Data Exclusion Based on Yield Along with error estimate, provide the cumulative distribution function

12. Providing Information on Accuracy of Products is Essential No easy one answer  Uncertainties are more complex than the retrievals themselves  Quite dynamic; highly dependent on the environment Multiple approaches required  Quality Flags  Formal Errors  Percentage Yield  Simultaneous retrievals from multiple algorithms  For climate trends, compare results from different satellites Data Provider and User must work together to determine best approach  Pro-Active: Users must be encourage to use uncertainty information  Moderated Blog: Users and Providers share common issues and problems  Web-based support documentation on uncertainties  AMSR-E will serve as a test-bed Communicating Uncertainties in ESDRs: Summary