Communicating Uncertainties for Microwave-Based ESDRs

1. Communicating Uncertainties for Microwave-Based ESDRs Frank J. Wentz, Carl A. Mears, and Deborah K. SmithRemote Sensing Systems, Santa Rosa CA Supported by: NASA MEaSUREsProgram 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. Large, Heterogeneous User Base 5000-10000 Distinct Users Applications ranging from tracking seals and albatrosses to high precision climate monitoring 500 Peer-Reviewed Journal Papers used DISCOVER data. Geographic Distribution ofUsers

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 • 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. 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 RSS RSS - Petty Petty RSS – Petty vs RSS

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

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

12. Communicating Uncertainties in ESDRs: Summary • 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