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Uncertainty Representation and Quantification in Precipitation Data Records Yudong Tian Collaborators: Ling Tang, Bob Adler, George Huffman, Xin Lin, Fang.

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Presentation on theme: "Uncertainty Representation and Quantification in Precipitation Data Records Yudong Tian Collaborators: Ling Tang, Bob Adler, George Huffman, Xin Lin, Fang."— Presentation transcript:

1 Uncertainty Representation and Quantification in Precipitation Data Records Yudong Tian Collaborators: Ling Tang, Bob Adler, George Huffman, Xin Lin, Fang Yan, Viviana Maggioni and Matt Sapiano University of Maryland & NASA/GSFC Sponsored by NASA ESDR-ERR Program

2 2 1.What is uncertainty 2. Uncertainty quantification relies on error modeling 3.Finding a good error model 4.Uncertainties in precipitation data records 5. Conclusions Outline

3 Uncertainty quantification is to know how much we do not know 3 “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know.” -- Donald Rumsfeld “There are known knowns. These are things we know that we know.”There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know.” -- Donald Rumsfeld Information “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know.” -- Donald Rumsfeld Uncertainty But how much?

4 Uncertainty determines reliability of information What we do not know affects what we know Information Knowns Knowledge Signal Deterministic Systematic errors Uncertainty Unknowns Ignorance Noise Stochastic Random errors

5 Uncertainty Unknowns Ignorance Noise Stochastic Random errors Information Knowns Knowledge Signal Deterministic Systematic errors For ESDRs, uncertainty quantification amounts to determining systematic and random errors 5

6 Systematic and random error are defined by the error model 6 Error model determines the uncertainty definition and representation TiTi XiXi TiTi XiXi

7 Xi: measurements in data records T i : truth, error free. a, b: systematic error -- knowledge ε : random error -- uncertainty The multiplicative error model: or 7 The additive error model: Two types of error models can be used for precipitation data records

8 8 Which one is better? TiTi XiXi TiTi XiXi Different error models produce incompatible definition of uncertainty ε

9 1.It cleanly separates signal and noise 2. It has good predictive skills What is a good error model?

10 10 1.Mixes signal and noise 2. Lack of predictive skills TiTi XiXi TiTi XiXi A bad error model: Under-fitted model: systematic leaking into random errors Over-fitted model: random leaking into systematic errors

11 Test with NASA Precipitation Data Data: TMPA 3B42RT [ Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) Version 6 real-time product, 3B42RT ] Reference data: CPC-UNI [ Climate Prediction Center (CPC) Daily Gauge Analysis for the contiguous United Sates ] Study period: three years [ 09/ /2008 ] Resolution: daily, 0.25-degree 11

12 12 Additive error model: under-fitting makes systematic errors leak into random errors Additive Model Multiplicative Model 3B42RT Mean Daily Rainrate Uncertainty will be inflated due to the leakage

13 13 Error leakage produces random errors with a complex dependency and distribution Additive Model Multiplicative Model

14 14 The multiplicative error model predicts better Additive Model Multiplicative Model Model-predicted measurements Actual measurements Comparison of data distributions

15 Testing multiplicative model on more data records 15 σ(amplitude of random error -- uncertainty) TMPA 3B42 TMPA 3B42RT NOAA Radar

16 b Spatial distribution of the model parameters 16 a and b (systematic error) TMPA 3B42 TMPA 3B42RT NOAA Radar a

17 Uncertainty quantification in sensor data Time period: 3 years, 2009 ~ 2011 Reference: Q2 [ NOAA NSSL Next Generation QPE, bias-corrected with NOAA NCEP Stage IV (hourly, 4-km) ] Satellite sensor ESDRs: TMI and AMSR-E [ TMI: TRMM Microwave Imager; AMSR-E: Advanced Microwave Scanning Radiometer for EOS onboard Aqua ] Resolution: 5-minute, 0.25-degree Error Model: 17

18 Uncertainty in satellite sensor data 18 TMI AMSR-E σ(random error - uncertainty)

19 a b TMI AMSR-E Systematic error in satellite sensor data

20 20 1. Uncertainty in data record is defined by error model 2. A good error model -- simplifies uncertainty quantification [ σ vs. σ=f(T i ) ] -- produces accurate and consistent uncertainty info -- has predictive skills 3. Multiplicative model is recommended for high resolution precipitation data records 4. A standard error model unifies uncertainty definition and quantification, helps end users. Summary

21 21 Tian et al., 2012: Error modeling for daily precipitation measurements: additive or multiplicative? submitted to Geophys. Rev. Lett. Monday: M. R. Sapiano; R. Adler; G. Gu; G. Huffman: Estimating bias errors in the GPCP monthly precipitation product, IN14A-04, 4:45 Wednesday: Ling Tang; Y. Tian; X. Lin: Measurement uncertainty of satellite-based precipitation sensors. H33C-1314, 1:40 PM (poster). Viviana Maggioni; R. Adler; Y. Tian; G. Huffman; M. R. Sapiano; L. Tang: Uncertainty analysis in high-time resolution precipitation products. H33C- 1316, 1:40 PM (poster). Thursday: Uncertainties in Precipitation Measurements and Their Hydrological Impact Conveners: Yudong Tian and Ali Behrangi Posters (H41H), 8:00 AM -12:20 PM Oral (H44E), 4:00 PM – 6:00 PM, Room 3018 Website: References

22 Extra slides 22

23 What we do not know hurts what we know 23 Knowns | Unknowns Knowledge | Ignorance Signal | Noise Information | Uncertainty Uncertainty determines the information content

24 24 A nonlinear multiplicative measurement error model: T i : truth, error free. X i : measurements With a logarithm transformation, the model is now a linear, additive error model, with three parameters: A=log(α), B=β, x i =log(X i ), t i =log(T i ) The multiplicative error model

25 25 Additive model does not have a constant variance

26 For ESDR, uncertainty quantification amounts to determining systematic and random errors 26 Knowns | Unknowns Knowledge | Ignorance Signal | Noise Deterministic | Stochastic Predictable | Unpredictable Systematic errors | random errors Uncertainty determines the information content

27 27 Clean separation of systematic and random errors More appropriate for measurements with several orders of magnitude variability Good predictive skills Tian et al., 2012: Error modeling for daily precipitation measurements: additive or multiplicative? to be submitted to Geophys. Rev. Lett. The multiplicative error model has clear advantages

28 28 Probability distribution of the model parameters A B σ TMI AMSR-E F16 F17

29 Spatial distribution of the model parameters 29 TMI AMSR-E F16 F17 A B σ(random error)

30 Spatial distribution of the model parameters 30 TMI AMSR-E A B σ(random error)

31 31 Correct error model is critical in quantifying uncertainty TiTi XiXi TiTi XiXi TiTi XiXi

32 Optimal combination of independent observations (or how human knowledge grows) 32 Information content

33 “Conservation of Information Content” 33

34 Why uncertainty quantification is always needed 34 Information content

35 Summary and Conclusions Created bias-corrected radar data for validation Evaluated biases in PMW imagers: AMSR-E, TMI and SSMIS Constructed an error model to quantify both systematic and random errors 35


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