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.