Adam J. O’Shay and T. N. Krishnamurti FSU Meteorology 8 February 2001
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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
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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. Agenda. Impetus and motivation Superensemble (SE) Methodology - PowerPoint PPT Presentation
Transcript of 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
Agenda
Impetus and motivation Superensemble (SE) Methodology Data selection and data-assimilation issue
Results of forecasts Summary
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
from http://www-lite.larc.nasa.gov/
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
Multi-Model ForecastsEnsemble MeanBreeding ModesSingular Vector based Perturbations
Super Ensemble Forecasts
Observed State ‘Analysis’
CONTROL FORECAST
MULTI-MODEL FORECASTS
SUPER-ENSEMBLE FORECASTSSTATISTICS
‘OBSERVED’ANALYSIS FIELDS
TIME
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)
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
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
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
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!!!
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
RMS V-200mb 48HR FCST
0
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1 4 710 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97
Day
RMS (m/s)
RMS V-200 48HR FCST
0
2
4
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1 4 710131619 2225283134374043464952555861646770737679828588919497
Days
RMS (m/s)
RMS V-200mb 48HR FCST
0
2
4
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1 4 710 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97
Day
RMS (m/s)
RMS ERRORS 48HR
RMS ERRORS 72HR
RMS V-200mb 72HR FCST
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1 4 710 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97
Day
RMS (m/s)
RMS V-200mb 72HR FCST
0
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4
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1 4 710131619 2225283134374043464952555861646770737679828588919497
Day
RMS (m/s)
RMS V-200mb 72HR FCST
0
2
4
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1 4 710131619 2225283134374043464952555861646770737679828588919497
Day
RMS (m/s)
R(OBS,SE) = 0.70
R(OBS,SE) = 0.78
R(Obs,ETSE) ~ 0.5
R(Obs,ETSE) ~ 0.85
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
Courtesy of: http://sgi62.wwb.noaa.gov:8080/STATS/STATS.html
RMS Errors of Various Models
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
