1. Adam J. O’Shay and T. N. Krishnamurti FSU Meteorology 8 February 2001
Global Wind Forecasts from Improved Wind Analysis via the FSU-Superensemble suggesting possible impacts from Wind-LIDAR
Adam J. O’Shay and T. N. Krishnamurti
FSU Meteorology
8 February 2001

2. Agenda Impetus and motivation Superensemble (SE) Methodology
Data selection and data-assimilation issue
Results of forecasts
Summary

3. Motivation
Space-borne LIDAR could provide global modeler’s with wind data-sets useful for analysis in models
Provided with an additional wind-analysis the SE could greatly improve its global wind forecasts
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4. Motivation - Con’t
To show the benefit of improved wind analyses - the FSU SE was run using various multi-models’ observed data as training data - with ECMWF analysis being ground truth
Typically the FSU SE uses the FSU (ECMWF based) analyses as the training data-set as it has shown to be a superior initial state among global NWP models - instances where ‘superior initial states’ don’t always give improved forecasts do occur, although they are rare

6. FSU Superensemble Methodology
Division of time length to two time periods
Training period - Multi-model variables are regressed towards the observed data for each model. Multiple linear regression provides weights for the individual models and time period forecasts
Forecast period (test phase) - Weights are applied to the forecasts resulting in the SE forecast for each day/time period (Krishnamurti et al. 2000)

7. Superensemble Methodology - Con’t
This is a Multiple Linear Regression based ‘Least-Squares Minimization’ procedure, where the algorithm is based upon collective bias removal
Individual model bias removal assigns a weight of 1.0 to all (bias removed) models which results in inclusion of poor models - collective bias exhibits statistically better forecasts

8. Superensemble Methodology - Con’t
Each SE forecast was computed for each of the forecast days (48hr and 72hr) for a global domain upon each differing training run

9. Data Selection Time period - 1 January 2000 - 9 April 2000
80 day training period - with 20 days of forecast
U,V-winds at 200 & 850mb were examined - Results at 200mb presented here for time consideration
Several global models in addition to ECMWF analysis chosen based on continuity of data-record (interpolated to 1º x 1º if necessary)
Error calculations performed for global-domain

10. Data Assimilation point
All global NWP models do not use the same data-assimilation scheme
Multi-models examined here use the following data-assimilation schemes:
Perhaps the variation in each models’ data-assimilation accounts largely for the variation in forecasts that the SE expels when using a model other than ECMWF as t1
* This is NOT to say it is the only reason!!!

11. Data Assimilation - Con’t
Rabier et al. (2000) and Swanson et al. (2000) found that 4-D VAR has shown overall improvement in global modeling, particularly when coupled with a high resolution model (i.e. ECMWF -- T200+)
Given the above, models using data-assimilation schemes OTHER than 4-D VAR have been used as ‘training’ for the FSU SE

12. RMS ERRORS 48HR

13. RMS ERRORS 72HR

14. R(OBS,SE) = 0.70

15. R(OBS,SE) = 0.78

16. R(Obs,ETSE) ~ 0.5

17. R(Obs,ETSE) ~ 0.85

18. What is going on here?
Each of the multi-models uses a different data-assimilation scheme -- training the SE with each and obtaining the final SE product will result in disparate forecasts than if one had used ECMWF-based analysis as the training data

19. RMS Errors of Various Models
Courtesy of:

20. Summary
The SE shows a degradation in skill when the training data-set originates from a model with a poorer initial state (i.e. multi-models with various data-assimilation schemes)
The heart of the SE is the training data-set - the initiation of LIDAR into the data-assimilation scheme at FSU may lead to skill in global-wind forecasts far exceeding those of present day