Nowcasting Convective Storms for Aviation in NCAR/RAL Convective Weather Group

Cai Huaqing

National Center for Atmospheric Research

Boulder, CO, USA

Outline

• Very-short term storm forecast (0-2 hr) ----NCAR Auto-Nowcaster

• Short-term storm forecast (0-8 hr) ---- FAA CoSPA (Consolidated Storm Prediction for Aviation)

• Very-short term storm forecast over the ocean (Oceanic Convective Diagnosis and Nowcsting

Why We Still Need Nowcasting?

Detection and extrapolation of surface convergence boundaries ….

….that trigger thunderstorm initiation and impact storm evolution.

The Auto-nowcaster Systemis unique in its ability to provide nowcasts of storm initiation by…..

• Weather Forecast Office Washington DC • Sydney Australia Forecast Office

• U. S. Army White Sands Missile Range

• Central U. S. for the FAA

Where has the Auto-nowcaster been demonstrated ?

Has being transferred to:• Bureau Meteorology Beijing China

• U.S National Weather Service – Dallas Weather Forecast Office

• AWIPS

Data Sets

Radar WSR-88DSatelliteMesonetProfilerSoundingNumerical ModelLightning

Analysis Algorithms Predictor Fields

Forecaster Input

Fuzzy Logic Algorithm - Membership functions - weights - Combined likelihood field

Final Prediction

Flow Chart for the Auto-Nowcaster System

Predictor Fields

Large-Scale Environment

B-L characteristicsSatellite Cloud Typing

Boundary characteristics

Cumulusdevelopment

Storm motion and trends

Example of Auto-Nowcaster Initiation Forecast

1 hour forecast Verification

Initiationnowcasts

extrapolationnowcasts

CoSPA

• 0-8 hr blended forecasting system developed by MIT/LL, NOAA GSD and NCAR.

• It provides VIL and echo top forecasts for Federal Aviation Administration.

• It uses Hrrr (High resolution rapid refresh) model developed by NOAA GSD.

• NCAR is responsible for blending the extrapolated forecast provided by MIT/LL and Hrrr forecasts produced by NOAA GSD.

CoSPA Functional/Data FlowInput Data

HRRRNest

ForecastCIWS

0-2 Hour

MIT/LL Extrapolation

Filter and Track

2-6 Hour

Multiscale Advection2-6 Hour

RUCForecast

NOAA NWP Models

AnalysisFields

NCAR Blending

IntensityCalibration

PhaseCorrection Weighting

FilterContour

Merge withCIWS

Products

MIT/LL Display

CoSPAWebsite

MIT/LLNOAA ESRLNCAR Virtual

Processing Location:

Verification

CoSPA Displays

Slide from Depree et al. 2009

Examples of CoSPA Forecasts

Forecasting Convection Over the Ocean

• Why we care storms over the ocean?

• Diagnosis of oceanic convection• Nowcasting of oceanic convection• Uplink weather information into

the cockpit

Air France 447 (0145 UTC 1 June 2009)

• The wide-area view provided by real-time experimental Global Convection/Turbulence uplinks may have improved pilot situational awareness

(approx.) Last verbal contact, 0133 UTC

(approx.) Last ACARS message, 0214 UTC

(approx.) Last verbal contact, 0133 UTC

(approx.) Last ACARS message, 0214 UTC

Longwave IR (0145 UTC)

Cloud Top Height (0145 UTC)

Convection Diagnosis Oceanic (CDO) 0145 UTC

+

+

Motivation : Air France 447

“Wx Ahead” Uplink Message valid 0130 UTC 1 June 2009

/EXP CLOUD TOP FI AF447/AN NXXXAF 01 Jun 09-- '/' Cloud tops 30,000 to 40,000 ft//////CCC///// 'C' Cloud tops above 40,000 ft///////////CC///// *4.0N,30.0W///////// *////////C////////// //*//CC///CCC///////// ///*CCCC/C/CC////////// ////*CCCCCCC//C///////// ///CC*CCCCC///CC//////// ///CCC*CCCCC///////////// //CCCCC*CCCC////////////// //CCCCCCC*CCC////////////// /CCCCCCCCC*CCC///// / //CCCCCCCCCC*CC///// // /// //CCCCCCCCCCC*C///////// ////// /////CCCCCCCCCC*C// // // ////// //////CCCCCCCC//*/ // / ////// CC//////CCCCCCC//* ///////// CCC////////////// * ///////// /C//////////// *1.3N,31.4W //// /////// */ *// / /*/// // /*/// / /*// /*// * // * /// * // // * //// * ///// * ////// * ///// /// * ///Pos Rpt / // * /0133 // X 1.4S,32.8W //Valid for // /0130-0200z //Pilot feedback at url: http://[site deleted]

Graphical view (EFB concept) Text-based view for ACARS printer

30-39Kft>40Kft

/=30-39KftC=>40Kft

Oceanic Diagnosis and Nowcasting SystemConvective Diagnosis Oceanic (CDO)

identifies convective cells

CDOInterest

CDOBinaryProduct

Convective Nowcasting Oceanic (CNO-Titan) makes 1-hr and 2-hr nowcasts of storm

location using an object tracker (Titan)

CNO-Titan

Nowcast

CNO-Gridded produces gridded nowcasts that will

more closely resemble storm structures

CNO-GriddedNowcast

CNO-RFRandomForest

Nowcast

CNO-RF utilizes environmental and

model-based inputs to better predict storm initiation and decay[Cai et al. (2009)]

CTop CClass GCD

With

Growth/Decay

With

Growth/Decay

Without

Growth/Decay

CNO Based on TITAN (Dixon and Wiener, 1993)

TITAN for Radar Data An Example of 1 Hr CNO-TITAN

*1 hr nowcast of CDO valid at 1315 UTC on August 19, 2007 using TITAN technique is shown on the right; red lines on the right represent CDO = 2.5 verification.*Advantages of TITAN: computationally efficient; capability of addressing growth/decay.*Disadvantages of TITAN: polygons can only roughly represent storm shapes; tends to over-forecasting

CNO Based on Modified TITAN---- Gridded Forecast

An Example of 1 Hr CNO-Gridded Forecast TITAN Motion Vectors at t0

Gridded 0 hr TITAN Motion Vectors

Temporal & Spatial Smoothing

15-60 min Motion Vectors

15-60 min Forecastsby Advecting OriginalSatellite Data at t0

Gridded 1 hr TITAN Motion Vectors

Gridded 2 hr TITAN Motion Vectors

Merged with GFS Winds Closest in Time

Temporal & Spatial Smoothing

Temporal & Spatial Smoothing

75-120 min Motion Vectors

135-180 min Motion Vectors

75-120 min Forecastsby Advecting 60 minNowcasts

135-180 min Forecastsby Advecting 120 minNowcasts

Merged with GFS Winds Closest in Time

Merged with GFS Winds Closest in Time

1-3 Hr CNO-Gridded Forecast Flow Chart

*Advantages of CNO-Gridded: realistic looking storms; low bias.*Disadvantages of CNO-Gridded: could be computationally expensive; no explicit growth/decay capability

CNO Based on Random Forest Statistical Analysis and Data Fusion

• The random forest technique produces an ensemble of decision trees from labeled training instances– during training, RF generates estimates of predictor importance– RF trees “vote” on classification of new data points, comprising

a nonlinear empirical model that provides both deterministic predictions and probabilistic information

Vote: 1

=> 40 votes for “0”, 60 votes for “1”; consensus category “1”

Data pt.

Tree 1

Vote: 0

Data pt.

Tree 2

Vote: 0

Data pt.

Tree 3

Vote: 1

Data pt.

Tree 4

Vote: 0

Data pt.

Tree 100…

*Slide courtesy of John Williams and Dave Ahijevych

An Example of CNO-RF Forecast

Compared with CNO-TITAN ( 1 hr)

*1 hr forecasts valid at1315 UTC on August 19,2007 for both techniques; Red lines representCDO = 2.5 verification

*Advantages of random forest technique: more realisticlooking storms; taking into account of storm environment to address storm growth/decay.

*As a relatively new, novel technique for nowcasting, its potential needs to be fully explored

CNO

Hurricane DeanA

B

C

D

Hurricane DeanA

B

C

D

CNO-RF

CNO-TITAN

Statistical Evaluation of the Three Nowcasting Techniques

CSI BIAS

•5 days of data from Aug 19-23, 2007 over the Gulf of Mexico domain are used to calculate the statistics with a grid size of ~ 5 km and CDO threshold of 2.5•All three techniques show skill over persistence•RF and gridded forecast perform best at 1 hr lead time•TITAN is the best at 2-3 hr lead time•Gridded forecast is the best for 4-6 hr lead time

Examples of 1 hr Gridded Forecast over the Gulf of Mexico Domain

*White lines are CDO=2.5 verification, satellite data available every 30 min

A B

C

DIssue time: 1215 UTC 2009/09/05Valid time: 1315 UTC 2009/09/05

1 HR

Examples of 3 hr Gridded Forecast over the Gulf of Mexico Domain

*White lines are CDO=2.5 verification, satellite data available every 30 min

A B

C

DIssue time: 1215 UTC 2009/09/05Valid time: 1515 UTC 2009/09/05

3 HR

Examples of 6 hr Gridded Forecast over the Gulf of Mexico Domain

*White lines are CDO=2.5 verification, satellite data available every 30 min

A B

C

DIssue time: 1215 UTC 2009/09/05Valid time: 1815 UTC 2009/09/05

6 HR

Summary Statistics of CNO-Gridded Forecasts

•30 days of data from Sep 1-30, 2009 over the Gulf of Mexico domain are used to calculate the statistics with a grid size of ~ 5 km and CDO threshold of 2.5•The results showed here could serve as benchmark performance of extrapolation-based nowcasting techniques for oceanic convection•Similar verification for model forecasts need to be done so that a comparison of convective forecasting skills between model and extrapolation can be obtained

The black squares are

statistics from Aug 19-22,

2007

What are the GFS model scores for oceanic

convection???

Summary

• Multiple short-term convective forecasting products, both over land and over ocean, are being researched, developed and tested at NCAR/RAL for various agencies such as FAA, NOAA and NASA.

• Potential collaborations in the nowcasting areas would be beneficial to all participants.