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

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)

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

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

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

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

The Problem: Where is the Real The Problem: Where is the Real Tornado?Tornado?

Observed Reflectivity

Assimilated Reflectivity(ensemble Kalman Filter)

Retrieved Temperature

R. Fritchie, K. Droegemeier, M. Xue, M. Tong

Observed Reflectivity

Assimilated Reflectivity(ensemble Kalman Filter)

Retrieved Pressure

R. Fritchie, K. Droegemeier, M. Xue, M. Tong

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)

Real Time Wind Analysis (400 m grid Real Time Wind Analysis (400 m grid spacing)spacing)

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

Prediction: March 2000 Fort Prediction: March 2000 Fort Worth TornadoWorth Tornado

Tornado

Local TV Station RadarLocal TV Station Radar

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

Reality Was Quite Different!Reality Was Quite Different!

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

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

Observation-Based Statistical Observation-Based Statistical Nowcasting (smart echo Nowcasting (smart echo

extrapolation)extrapolation)

Comparing Model- and Observation-Comparing Model- and Observation-Based/Statistical Nowcasting Based/Statistical Nowcasting

ApproachesApproaches

Numerical Prediction with Radar Data Assimilation

As a Forecaster As a Forecaster Worried About Worried About This Reality… This Reality…

7 pm

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

Actual RadarActual Radar

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

Probability of Intense PrecipitationProbability of Intense Precipitation

Model Forecast Radar Observations

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

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)

21 hr, 4 km Grid Spacing Ensemble 21 hr, 4 km Grid Spacing Ensemble ForecastsForecasts

Mean Spread

Observed 2 km GridXue et al. (2008)

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)

Application to CCFPApplication to CCFP

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

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

Real Time Testing TodayReal Time Testing Today

1 km grid, 9-hour Forecast