Preserving Cloud Information Bruce R. Barkstrom & John J. Bates NCDC

Outline ► Fundamental Preservation Commandments ► Questions  Variability Quantification  Error Analysis and Physics ► Costs ► What Can We Do Now?

Four Commandments for Preserving Information 1. Thou shalt not be forced to preserve information before it is ready 2. Thou shalt not lose information – if possible 3. Thou shalt not cost more than necessary 4. Thou must make data accessible and valuable  To current users  To future users

When is Data Ready for Preservation? ► When we have a good model of the underlying “natural variability” and “expected climate change” of the fields being measured  Not just mean and standard deviation – current applications need description of extreme events  Need regional time variations ► When we have a physical basis for estimating errors and their impact on climate change detectability  Need more than just measurement statistics  Must include probability distribution of possible biases

Quantification of Field Variability ► The “variability Turing test”:  Can you generate an ensemble of computer generated fields with statistics that is indistinguishable from those of the real field? ► The “climate Turing test”:  Can you generate a model of “trends” whose statistics are indistinguishable from those of the expected climate changes?

Current State ► Measurement “Requirements” for Climate usually stated as global values of means and standard deviations ► Corresponding statistics can be generated by appropriate white noise ► Is this adequate?  Probably not – clouds variations are more complex than a global mean and simple latitudinal variations ► Can we come up with a common basis for stating variability across Earth science?  Regional?  Regional with moving systems?

No Preservation Without Understandable Error Assessments ► Error assessments for climate data records are difficult  Need physical basis for estimating uncertainties, not just internally consistent measurement statistics  Error assessments must be tied to algorithm code – data editing is as important as coefficients or outlines of algorithms  Errors are not believable if entire data production process is not publicly understandable

Current State ► Algorithm Theoretical Basis Documents do not necessarily represent the “as-built” algorithms with their data editing ► EOS data production systems are “overwhelmingly complex”  May need new documentation tools to provide understanding – 100,000 lines of code is not readable in a Sunday afternoon ► As Science Teams disperse, community knowledge will be lost unless we take steps to prevent it  May need to develop “data scholars”

Action Items 1. Can this workshop produce an understandable, quantitative description of cloud variability – and of expected cloud property changes? 2. Is it possible to develop a community- accepted standard checklist of errors for cloud properties?

Sample Error Checklist ► Are the “as-built” instrument drawings available? ► Is the ground calibration data available? ► Is there a computational math model of the instrument that includes all of the physics of the measurement? ► How was the gain determined? ► How was the spectral response determined? ► How was the Point Spread Function measured? ► …

Models for Preservation Funding ► The Cemetery Model:  Pay when the body is deposited; live off the interest ► The Advanced Cemetery Model:  Pay for the previous bodies, as well as the one you’re depositing; make sure to add new bodies (the Cemetery as Pyramid) ► The Cemetery as Theme Park:  Make the cemetery interesting to visit; charge admission ► The Public Broadcasting Approach:  Beg for support annually – and ask for volunteers

Actions That Can Reduce Preservation Costs and Risk ► Arrange a “Submission Agreement” (data will) with your designated archive ► Gather required original documents and make sure your archive can accept them  Drawings  Calibration plans and procedures  Science Team minutes  Source Code ► Arrange peer review of documentation

Summary ► Our data will not survive without careful thought to ensure  Physical insight into the measured variables and the measurement process  Adequate public access to the measurement process  Cost-effective archival ► Archives know less than you do about your data; if you don’t act to preserve that information, archives can’t preserve it!