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Verification

Explore advanced verification tools to enhance understanding of forecast uncertainty. Improve collaboration, forecast efficiency, and convey uncertainty effectively. Analyze forecast quality, skill scores, and error distributions for better decision-making. Example study on DIRC2 forecasts. Utilize data to improve forecasts and processes.

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Verification

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  1. Verification Lisa Holts Kevin Werner

  2. Outline • Motivation • Verification capabilities • Example

  3. Motivations • Improve understanding of uncertainty from forecast tools • How good are our forecasts? • How much value do human forecasters add? • Is one tool better than another? • How do the above questions depend on lead time? Amount of runoff? • Can we use what we learn to: • Improve forecasts? • Make forecast process more efficient? • Improve collaboration? • Convey uncertainty to our users?

  4. Verification Capabilities • Search By • Station ID • State, River, Location

  5. Verification Capabilities • Select Forecast Period • Forecasts Available • Climatology Available

  6. Verification Capabilities • Select Years • Appear on Period Selection • Select any set of years available

  7. Verification Capabilities • Select Data Source • All Selected By Default

  8. Verification Capabilities • Select Plot Type • Historical By Default

  9. Verification Capabilities • Load Statistics

  10. Verification Capabilities • Navigation Bar • Water Supply Forecasts • Verification • Data Checkout

  11. Verification Capabilities • Location • Change • Clear • Return to main menu

  12. Verification Capabilities • Plot Area • Auto Scroll • Clearly Labeled

  13. Verification Capabilities • About • Explains the displayed graph • Changes when graph is changed

  14. Verification Capabilities • Site Options • Saves five previous sites viewed • Print Graph • Display data in Data Checkout

  15. Verification Capabilities • Side Options • Statistic • Forecast Type/Data Source • Time Scale • Threshold

  16. Verification Capabilities • Statistic • 18 Statistic choices • Graph change on click • Mouse-over displays info about graph • Group Titles • Mouse and click to display other options

  17. Verification Capabilities • Forecast Type • Change Data Sources to be displayed • Graph change on click

  18. Verification Capabilities • Time Scale • Change Period • Modify Years • Graph change on click • Month • Only displayed when Contingency Table Statistic is chosen • Change the month the table displays

  19. Verification Capabilities • Threshold • Default is Climatology / Historical Average • Enter value in KAF and press enter • Type ‘mean’ to return to default • Valid option for all but Rank Histogram statistic options. • Graph change on ‘Enter’

  20. Assignment • Loose instructions provided • Results should drive study • Goal is not to see five sets of the same plots… rather something focused on the interesting parts for the basin involved

  21. Assignment (con’t) • Step 1: Identify forecast points with extensive data and interest • Step 2: Examine data for visual clues • Step 3: Error and skill scores • Step 4: Categorical statistics • Step 5: Forecast uncertainty • Step 6: Climate variability • Step 7: Snow / Precipitation connections

  22. Example • DIRC2 has forecast / observation pairs from 1991 on. • We’ll look at the available plots and then consolidate it into the case study…

  23. Step 2: Examine data

  24. Step 3: Error and Skill score

  25. Error by year • Comparison of forecast error to “average” error useful diagnostic tool

  26. Percent Difference

  27. Step 4: Categorical:Probability of Detection • High years much for difficult to detect in the early season • All forecasts during low years have been for low volumes

  28. False Alarm Rate • Similar story here as with POD

  29. Step 5: Forecast Uncertainty:Forecast Distribution • Some tendency to underforecast. • 26% of observed streamflow falls above the 10% exceedence forecast value • Note that results improve if you consider only 2000-2008

  30. Forecast Uncertainty by month

  31. Observed Lag-1 Analysis

  32. Results that might be presented …

  33. ESP is really, really good • ESP is much better than any other forecast method particularly in high years • With “real” ESP as opposed to ESP reforecasts, results should be even better

  34. Forecast Quality • January forecasts are essentially as good as climatology • Coordination process appears to add (marginal) value except in April. • Forecast tweaks much less than the error should not be entertained unless some overriding rational exists to support those tweaks.

  35. High flows hardLow flows no so hard • Forecast system is perfect (POD = 100% in all cases) for detecting below average flows. • Forecasts struggle with detecting high flows even through May.

  36. Reasonable max not so reasonable • Observed streamflow greater than the reasonable max nearly 30% of the time. • Problem in the 1990s… results since 1999 are much better

  37. Climate variability not much help • Very low predictability based on prior year hydrology or climate index • Weak tendency for low years to follow low years

  38. Possible Application(Discussion) • April DIRC2 forecast is 150kAc-ft. Average error for April is 35kAc-ft. How could you use this information to: • Improve your forecast? • Improve your forecast process? • Improve forecast application?

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