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Climate Test Bed Seminar Series 24 June 2009

Climate Test Bed Seminar Series 24 June 2009. Bias Correction & Forecast Skill of NCEP GFS Ensemble Week 1 & Week 2 Precipitation & Soil Moisture Forecasts. Yun Fan & Huug van den Dool. Acknowledgement: Jae Schemm, John Janowiak, Doug Lecomte , Jin Huang , Pingping Xie,

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Climate Test Bed Seminar Series 24 June 2009

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  1. Climate Test Bed Seminar Series 24 June 2009 Bias Correction & Forecast Skillof NCEP GFS Ensemble Week 1 & Week 2Precipitation & Soil Moisture Forecasts Yun Fan & Huug van den Dool Acknowledgement: Jae Schemm, John Janowiak, Doug Lecomte, Jin Huang, Pingping Xie, Viviane Silva, Peitao Peng, Vern Kousky, Wayne Higgins

  2. Outline • Motivation • Methodology • Performance of NCEP GFS Week1 & Week2 Ensemble Precipitation Forecasts • Analysis of Week1 & Week2 Biases & Errors • Application: land model forced with bias corrected week1 week2 P & T2m forecast • Future Work

  3. History of Soil Moisture “Dynamical” OutlookCPC Leaky Bucket Hydrological ModelForced With Week 1 & Week 2 GFS Forecasts Single member HR MRF (started around 1997 & CONUS) Ensemble GFS (started late 2001 & CONUS) Bias corrected Ensemble GFS (started late 2003 & CONUS) Bias corrected Ensemble GFS (started late 2007 & global land) :The prediction skill of soil moisture crucially depends on our ability to predict precipitation Early stage (both good and bad comments)  Recent years (more & more good comments) So its time to verify & quantify: daily GFS ensemble week 1 & week 2 precip forecast skills & statistics

  4. The quality of soil moisture prediction largely or almost entirely depends on the quality of precipitation prediction

  5. Daily bias correction based on last 30 (or 7) day forecast errors Today Last 30 day Week1 Week2 Past Future 1/N Σ [ Pf (week1) – Po (week1) ] = Bias1 1/N Σ [ Pf (week2) – Po (week2) ] = Bias2 Pf : GFS ensemble week1 & week2 precip forecast Po: Observed week1 & week2 precip from CPC daily global Unified Precip N = ( 30, 7..….)

  6. North America Seasonal cycle with Large day to day fluctuation On 0.5x0.5 obs grid

  7. South America On 0.5x0.5 obs grid

  8. Asia-Australia On 0.5x0.5 obs grid

  9. Africa On 0.5x0.5 obs grid

  10. How good is GFS? Seasonal cycle with Large day to day fluctuation On 0.5x0.5 obs grid

  11. How good is GFS? Seasonal cycle with Large day to day fluctuation On 0.5x0.5 obs grid

  12. Comparison(based on last 30-day forecast errors)Obs grids(regrid model grids to 0.5x0.5 obs grids) Model grids (regrid obs grids to 2.5x2.5 model grids) Question: Does grid matter for skills assessment?

  13. Skill does not depend much on the grid

  14. Today Comparisonbias corrected skills(based on last 30-day forecast errors) bias corrected skills (based on last 7-day forecast errors) Question: Does the bias estimate influence skill? Last 30 day Week1 Week2 Past Future 1/N Σ [ Pf (week1) – Po (week1) ] = Bias1 1/N Σ [ Pf (week2) – Po (week2) ] = Bias2 Pf : GFS ensemble week1 & week2 precip forecast Po: Observed week1 & week2 precip from CPC daily global Unified Precip N = ( 30, 7..….)

  15. 1) Skill depends on the definition of bias 2) 30-day bias correction better than 7-day bias correction

  16. Comparisonbias corrected skills(based on last 30-day forecast errors) raw forecast skills (no bias correction applied)Question: Does bias correction improve skill in terms ofSpatial CorrelationandRMSE?

  17. Bias correction is time & location dependent

  18. Bias correction helps everywhere

  19. Table 1. Averaged (May 1, 2008 – June 7, 2009) spatial correlations over different monsoon regions Increased by 80% The effectiveness of bias correction is mainly space dependent. Bias correction can correct spatial distribution of Pf& reduce its error. Increased by 67% Table 2. Averaged (May 1, 2008 – June 7, 2009) RMSE over different monsoon regions (unit: mm/week) Similarity of Pf & Po Reduced by 23% Reduced by 28% Distance of Pf & Po

  20. 30-day running mean CONUS

  21. In terms of Spatial Anomaly Correlation, bias correction helps: 1) very little over North America 2) considerably over South America & Africa 3) a little over Asia-Australia In terms of RMSE: Bias correction helps everywhere Questions: Why bias correction works but varies in space and time? What biases look like? Are biases removable & to what extent are they removable?

  22. Temporal-spatial structures of last 30-daybiases:Daily Bias1 & Bias2 used to correct GFS ensemble week1 & week2 forecasts Today Last 30 day Week1 Week2 Past Future 1/30 Σ [ Pf (week1) – Po (week1) ] = Bias1 1/30 Σ [ Pf (week2) – Po (week2) ] = Bias2

  23. Annual Mean Bias or Raw Forecast Error Week-1 mean Bias Week-2 mean Bias

  24. Mean Bias of Daily R2 & Observed Precip (1979-2006)

  25. summer winter

  26. winter summer

  27. Temporal-spatial structures of last 30-daybiases: Daily Bias1 & Bias2 used to correct GFS ensemble week1 & week2 forecasts Today Last 30 day Week1 Week2 Past Future 1/30 Σ [ Pf (week1) – Po (week1) ] = Bias1 1/30 Σ [ Pf (week2) – Po (week2) ] = Bias2 Large-scale & low-frequency (annual or semi-annual cycles) are prominent First two EOF modes of Bias1 & Bias2 explain about 60% total variances GFS has prominent annual cycle errors (lesson for model development?)

  28. Temporal-spatial structures of real timeraw forecast errors:DailyGFS week1 & week2 forecast errorswithout bias correction Today Last 30 day Week1 Week2 Past Future 1/30 Σ [ Pf (week1) – Po (week1) ] = Bias1 1/30 Σ [ Pf (week2) – Po (week2) ] = Bias2 No bias correction applied Pf (week1) – Po (week1) = Error1 Pf (week2) –Po (week2) = Error2

  29. Temporal-spatial structures of real time raw forecast errors:DailyGFS week1 & week2 forecast errorswithout bias correction Today Last 30 day Week1 Week2 Past Future 1/30 Σ [ Pf (week1) – Po (week1) ] = Bias1 1/30 Σ [ Pf (week2) – Po (week2) ] = Bias2 Pf (week1) – Po (week1) = Error1 Pf (week2) –Po (week2) = Error2 No bias correction applied Raw forecast errors are dominated by the 1st, 2nd or 3rd EOFs in Bias1 & Bias2 First two EOF modes of Error1 & Error2 explain about 23~35% total variances At least this amount of error is removable. But so far bias correction was not done by EOF analysis

  30. Temporal-spatial structures of real timeforecast errors:GFS week1 & week2 forecast errorswith last 30-day bias correction Today Last 30 day Week1 Week2 Past Future 1/30 Σ [ Pf (week1) – Po (week1) ] = Bias1 1/30 Σ [ Pf (week2) – Po (week2) ] = Bias2 Bias correction: Error1= Error1 – Bias1 Error2= Error2 – Bias2 Pf (week1) – Po (week1)= Error1 Pf (week2) –Po (week2)= Error2

  31. Annual Mean ForecastError after bias correction 5 times smaller than mean bias or raw forecast error Week-1 mean forecast error Week-2 mean forecast error

  32. Temporal-spatial structures of real timeforecast errors:GFS week1 & week2 forecast errorswith last 30-day bias correction Today Last 30 day Week1 Week2 Past Future 1/30 Σ [ Pf (week1) – Po (week1) ] = Bias1 1/30 Σ [ Pf (week2) – Po (week2) ] = Bias2 Bias correction: Error1= Error1 – Bias1 Error2= Error2 – Bias2 Pf (week1) – Po (week1)= Error1 Pf (week2) –Po (week2)= Error2 Bias Corrected ForecastErrors are much more random (in time mainly, EOFs more “white”). Leading EOF modes of Bias1, Bias2, & Error1, Error2 Show that GFS has prominent large-scale & low-frequency errors or GFS has difficulty to reproduce those observed Precip patterns & their evolution. However, to some extent they can be corrected through bias correction, especially in winter season.

  33. Application Soil Moisture “Dynamical” Outlook CPC Leaky Bucket Hydrological ModelForced With Week-1 & Week-2 GFS EnsembleForecasts (Daily data from 01Nov2003 to present) All initial conditions & verification datasets are from leaky bucket model forced with daily observed P & T2m

  34. Some Thoughts: • Once this (SST, w) was the lower boundary…. • Both SST and w have (high) persistence • Old ‘standard’ in meteorology: If you cannot beat persistence ….. • For instance: dw/dt = P – E - R = F or w(t+1)=w(t) + F • Clearly if we do not know F with sufficient skill, the forecast loses against persistence (F=0).

  35. 30-day running mean P1=0.9511, C1=0.9512 PR1=16.27, FR1=18.02 P2=0.9015, C2=0.8957 PR2=23.67, FR2=26.56

  36. Precip 30-day running mean

  37. Even moderate forecast skill at right time still help a lot

  38. 30-day running mean for week-2 Hybrid persistence = week-1 forecast persists to week-2

  39. Summary • Moderate week-1 & week-2 GFS P forecast skills • Last 30-d biases dominated by low-frequency & large-scale errors • Bias corrections are time & location dependent • Soil moisture forecast skill hardly beats its persistence over CONUS • The inability to outperform persistence relates to the skill of precipitation not being above a threshold (AC>0.5 is required)

  40. Future Work • Is PDF bias correction better? • GFS Week3 & Week4 Precip Assessment • GFS hindcasts? • How about New CFSRR?

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