GFS MOS Wind Guidance: Problem Solved? Eric Engle and Kathryn Gilbert MDL/Statistical Modeling Branch 15 May 2012
Reason for Refresh Development Sample Predictand Definition Predictors and Equation Development Independent Test Systems Verification Impact on Gridded MOS Overview
GFS Model “bugfix” implemented in May 2011 Set new thermal roughness length to address a low level warm bias over land Affected the behavior of the low level wind fields Usually chosen as predictors (10-m u, v, speed) Reason for Refresh
GFS Vegetation Type Vegetation Type Description 7: Groundcover only (perennial) 8: Broadleaf shrubs with perennial groundcover 9: Broadleaf shrubs with bare soil 11: Bare soil Roughness length change has most impact in these vegetation type areas. Verifications show this is true (not shown) VEGETATION TYPES (DORMAN AND SELLERS, 1989; JAM)
GFS Model “bugfix” implemented in May 2011 Set new thermal roughness length to address a low level warm bias over land Affected the behavior of the low level wind fields Usually chosen as predictors (10-m u, v, speed) Had a direct impact on GFS MOS wind speed guidance Large guidance errors (strong positive biases) Western CONUS (low vegetation/desert areas) Most pronounced in warm season, during daytime hours Reason for Refresh
Implementation 32.2 knot error
2009 Credit: Dr. Yun Fan Jan-Apr 2011 May-July
GFS Model “bugfix” implemented in May 2011 Set new thermal roughness length to address a low level warm bias over land Affected the behavior of the low level wind fields Usually chosen as predictors (10-m u, v, speed) Had a direct impact on GFS MOS wind speed guidance Large forecast busts (strong positive biases) Western CONUS (low vegetation/desert areas) Most pronounced in warm season, during daytime hours Complaints from NWS forecasters and private sector GFS MOS wind guidance “unusable” Reason for Refresh
“Our workload has increased due to this problem” “…we continue to deal with serious fire weather conditions…” “The forecaster stated that this issue makes the point and gridded MOS…typically used to populate GFE…unusable and times.” “The situation with the MAV guidance winds has become a source of frustration and a workload issue for our office.” Responses from Users
Turning off partial inflation (PI) Many stations benefit from PI Development without boundary layer model predictors Many stations benefit from these Bias correction Big project…significant MOS production overhead The solution…collect sufficient mixed sample and redevelop Most timely Solutions Investigated
Previous two warm seasons available April through September, 2010 and Comprised of three different versions of GFS model. Development Sample Date RangeVersion April – June 2010Pre (operational) July – September (reforecast) April – May (operational) May 10 – September (operational) Balance the influence of “new” model data (version 9.0.1) with a sufficient sample size 64% “new” / 36% “old”
Predictands: 10-m U-wind 10-m V-wind 10-m Wind Speed Derived from hourly observed 10-m wind speed and direction Wind data are quality controlled via MDL software Regression equations for predictands are developed simultaneously Predictors selected that best fit all 3 predictands Different coefficient 3-hourly guidance to 192-h 6-hourly guidance from 204-h to 264-h Predictand Definition
U, V, Speed at 1000, 925, 850, 700, and 500 hPa; and 10-m Mass divergence, relative vorticity, vertical velocity at 925, 850, 700, and 500 hPa Mean Layer RH ; hPa; and sigma Temperature Difference between , , and hPa levels K index Sine and cosine DOY (harmonic functions) PBL mixing parameter Bilinear interpolation Observed predictors offered out to 15-h (persistence) Predictors Offered
Single station equation development Multiple linear regression (forward selection) Maximum number of predictors: 10 (no forcing) 100 “cases” required equation to be developed Three independent test systems were developed Determine the best mix of “old” and “new” GFS model sample Regression and Equation Development
TEST1 Only GFS v9.0.1 “new” used July-Sept (reforecast); May 10 – Sept TEST2 Same as TEST1, but includes April 1 – May 9, 2011 (GFS v9.0.0) TEST3 All data from previous 2 warm seasons included April-Sept and 2011 Independent Test Systems
Cross-validation “leave one out” technique used Each of the 3 test systems comprised of 8 equation sets 7 out of 8 months of “new” GFS model data included in each equation set Withheld month is used to verify the equation set in which it was held back. End up with 8 fully independent months of guidance to verify against Independent Testing
Compare Equations for KPHX (24-h) 00Z Operational (as of 5/14/2012) PredictorCoeff. 10-m U-wind m V-wind m Wind Speed sigma Mean Layer RH hPa Mass Divergence hPa Mean Layer RH hPa Temp difference Cosine 2*DOY hPa Wind Speed hPa Vert. Velocity (GB) New (awaiting implementation) PredictorCoeff. 925 hPa U-wind hPa Wind Speed m V-Wind Cosine DOY hPa Wind Speed hPa Mean Layer RH PBL Mixing (low) hPa Vert. Velocity (GB) hPa Mass Divergence K Index (GB) ( ≥ 40)
Verifications show TEST3 being the most accurate/skillful system Attribute this to a longer sample New equations have similar skill in wind direction guidance as the old equations Guidance in Alaska degraded with test developments Existing equations will remain in place Verification Summary
MOS-2000 software uses ASCII files that contain regression equation information for a particular suite of elements Grouped by season and model cycle Wind: 1 file for 00Z, warm season that contains all equations for all stations and projections Decompose these files and merge with “new” equation files. Merging Equation Files
Impact on Gridded MOS
GMOS Example
Change in GFS thermal roughness length Negative impact on GFS MOS Wind equations redeveloped using previous 2 warm seasons Except for Alaska Large wind speed guidance errors removed Positive impact on Gridded MOS Future work? Bias correction Redevelop again with longer sample Implementation in June 2012 Summary
See the New Equations in Action…
Questions?