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Toward a 4D Cube of the Toward a 4D Cube of the Atmosphere via Data Atmosphere via Data
AssimilationAssimilation
Kelvin DroegemeierKelvin DroegemeierUniversity of OklahomaUniversity of Oklahoma
13 August 200913 August 2009
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Bringing all the Data Bringing all the Data Together: AssimilationTogether: Assimilation
Old School Old School – Graphically overlay – Graphically overlay different types of data (the GIS different types of data (the GIS approach)approach)
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Modern Modern Approach – Assemble a variety Approach – Assemble a variety of data sets into a single, coherent, of data sets into a single, coherent, dynamically consistent picture – data dynamically consistent picture – data assimilationassimilation
Bringing all the Data Bringing all the Data Together: AssimilationTogether: Assimilation
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Data Assimilation Data Assimilation
Dat
a A
ssim
ilat
ion
Sys
tem
RadarsRadars Radial Wind, Reflectivity
Other ObservationsOther Observations A Bit of Everything Some Places
ForecastForecastModel OutputModel Output
All Variables, But From a Forecast
3D Gridded AnalysisThat Contains all
Variables, is Dynamically
Consistent, and has Minimum Global
Error w/r/t theObservations
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Detecting Weather Hazards Detecting Weather Hazards
3D Gridded AnalysisThat Contains all
Variables, is DynamicallyConsistent, and has Minimum Global Error
w/r/t theObservations
Detection Algorithms Applied to Gridded Fields
Features and Relationships
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WSR-88DWSR-88D
WSR-88DAlgorithms
Application: Traditional Use of Application: Traditional Use of Radar Data for Detecting Weather Radar Data for Detecting Weather
Hazards Hazards
TDWR TDWR
TDWRAlgorithms
WDSS
ITWS
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The Problem: Where is the Real The Problem: Where is the Real Tornado?Tornado?
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Observed Reflectivity
Assimilated Reflectivity(ensemble Kalman Filter)
Retrieved Temperature
R. Fritchie, K. Droegemeier, M. Xue, M. Tong
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Observed Reflectivity
Assimilated Reflectivity(ensemble Kalman Filter)
Retrieved Pressure
R. Fritchie, K. Droegemeier, M. Xue, M. Tong
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1010
Virtual 4D Weather CubeVirtual 4D Weather Cube
Virtual 4D Virtual 4D Weather Weather
CubeCube
4th
dimensiontime
HazardHazard
ObservationObservation
0 – 15 mins0 – 15 mins
15-60mins15-60mins
1- 24 hrs1- 24 hrs
Aviation weather informationin 3 dimensions
( latitude/longitude/height)
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Real Time Wind Analysis (400 m grid Real Time Wind Analysis (400 m grid spacing)spacing)
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Numerical Prediction Numerical Prediction
3D Gridded AnalysisThat Contains all
Variables, is DynamicallyConsistent, and has Minimum Global Error
w/r/t theObservations
Detection Algorithms Applied to Gridded Fields
Features and Relationships
Forecast Models
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Prediction: March 2000 Fort Prediction: March 2000 Fort Worth TornadoWorth Tornado
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Tornado
Local TV Station RadarLocal TV Station Radar
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NWS 12-hr Computer Forecast Valid at 6 pm NWS 12-hr Computer Forecast Valid at 6 pm CDT (near tornado time)CDT (near tornado time)
No No Explicit EvidenceExplicit Evidence of Precipitation in North of Precipitation in North TexasTexas
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Reality Was Quite Different!Reality Was Quite Different!
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6 pm 7 pm 8 pmR
adar
Fcs
t W
ith
Rad
ar D
ata
2 hr 3 hr 4 hr
Xue et al. (2003)
Fort Worth
Fort Worth
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Fcs
t w
/o R
adar
Dat
a
2 hr 3 hr 4 hr
Rad
ar6 pm 7 pm 8 pm
Fort Worth
Fort Worth
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Observation-Based Statistical Observation-Based Statistical Nowcasting (smart echo Nowcasting (smart echo
extrapolation)extrapolation)
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Comparing Model- and Observation-Comparing Model- and Observation-Based/Statistical Nowcasting Based/Statistical Nowcasting
ApproachesApproaches
Numerical Prediction with Radar Data Assimilation
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As a Forecaster As a Forecaster Worried About Worried About This Reality… This Reality…
7 pm
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As a Forecaster As a Forecaster Worried About Worried About This Reality… This Reality…
How Much How Much Trust Would Trust Would You Place in You Place in This Model This Model Forecast? Forecast?
3 hr
7 pm
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Actual RadarActual Radar
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Ensemble Member #1Ensemble Member #1 Ensemble Member #2Ensemble Member #2
Ensemble Member #3Ensemble Member #3 Ensemble Member #4Ensemble Member #4Control ForecastControl Forecast
Actual RadarActual Radar
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Probability of Intense PrecipitationProbability of Intense Precipitation
Model Forecast Radar Observations
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Research to Operational Research to Operational Practice: NOAA Hazardous Practice: NOAA Hazardous
Weather Test BedWeather Test Bed Experimental Forecasts Experimental Forecasts
Since 2005Since 2005 High Resolution EnsemblesHigh Resolution Ensembles High Resolution High Resolution
DeterministicDeterministic Dynamically Adaptive/On Dynamically Adaptive/On
DemandDemand
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Composite Reflectivity 18 UTC on 24 May Composite Reflectivity 18 UTC on 24 May 20072007
Observed 21 hr, 2 km Grid ForecastObserved 21 hr, 2 km Grid Forecast
Xue et al. (2008)
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21 hr, 4 km Grid Spacing Ensemble 21 hr, 4 km Grid Spacing Ensemble ForecastsForecasts
Mean Spread
Observed 2 km GridXue et al. (2008)
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21 hr, 4 km Grid Spacing Ensemble 21 hr, 4 km Grid Spacing Ensemble ForecastsForecasts
Prob Ref > 35 dBZ Spaghetti
Observed 2 km GridXue et al. (2008)
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Application to CCFPApplication to CCFP
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Centers of On-Demand Forecast Grids Centers of On-Demand Forecast Grids Launched at NCSA During 2007 Spring Launched at NCSA During 2007 Spring
ExperimentExperiment
Launched automatically in response to hazardous weather messages (tornado watches, mesoscale discussions)
Launched based on forecaster guidance
Graphic Courtesy Jay Alameda and Al Rossi, NCSA
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The Value of Adaptation: Forecaster-The Value of Adaptation: Forecaster-Initiated Predictions on 7 June 2007Initiated Predictions on 7 June 2007
Brewster et al. (2008)
Radar Observations Standard 20-hr Forecast 5 hr LEAD Dynamic Forecast
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Real Time Testing TodayReal Time Testing Today
1 km grid, 9-hour Forecast