Basics of Developing a Convective Storm Nowcasting System
Jim Wilson and Rita Roberts
NOWCASTING TIMELINE
Herb Ligda
Extrapolation of
Radar Echoes
WSR-57 Radar
Detection of Colliding
Sydney Olympics
Forecast
Demonstration
Program
Extrapolation of
Radar EchoesDetection of Colliding
Sea Breezes
First U.S. weather satellite
launched from Cape Canaveral, FL
James Purdom: Some uses of GOES imagery
in mesoscale convection forecasting
Wilson and Wilk:
Nowcasting Applications
of Doppler Radar
1953 1965
19841960
197620001981
Training Outline
• A nowcast example•The nowcast process
oData quality oAssemble climatology statisticsoKnow your pre-storm environment! oDocument the different weather regimes for convective weatheroIdentify the important predictor information oDevelop nowcast rulesoCombine information together in a forecaster-computer system tooCombine information together in a forecaster-computer system to
produce rapidly updated nowcasts oProvide meaningful products that meet the needs of the end-user
Area of mountain thunderstorms moving from NW toward
B08FDP Nowcast challenge on 2 Aug. Opening ceremony rehearsal fo Beijing 2008
NW toward Beijing
Area of mountain thunderstorms moving from NW toward
B08FDP Nowcast challenge on 2 Aug. Opening ceremony rehearsal fo Beijing 2008
NW toward Beijing
Will there be rain at the Olympic stadium during the rehearsal? Yes or No
WRF/RUC forecast for 16, 17 and 18 local
Forecasting verylittle precipitationand does not resemble actualsituation.
However it doesnot forecast anyprecipitation onplains
Grapes-Swift 2-hr Extrapolation for 1800 local
Advection rain is very slowtoward stadium
Stadium
Grapes-Swift 2-hr Blend for 1800 local
Stadium
Rapidly movedRain past the stadium
Extrapolation Nowcasts for 16, 17 and 18 local
Forecasts rainover stadiumbetween 17 and 18 local
Human modification of extrapolation forecast for 1800 local
Dissipates stormsover plains.No rain over stadium.stadium.
Human says No rain during the rehearsal
Why did the human forecast no rain?
The forecaster was correct!
Because a nowcasting process had already been developed for Beijing.
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability
Data availability – Everything you can get your hands on!!
SatelliteRadarRadarSurface weather stationsRawindsondesLightingGPS Precipitable Water VaporAircraft TAMDAR dataProfilers
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability • Data quality
Stability Calculations are Sensitive to Small Changes in the Boundary Layer
Difference between 1st sonde point and independent surface observation
Mean = -11.4 Mean = -3.54
Mean = -9.48 Mean = -13.31
Mean = -0.49
Mean = -0.33
Mean = -0.33
Mean = -1.8
RH (%) T (C)Moist Moist
Moist Moist
Dry Dry
Higher
Lower
HigherHigher
Higher
Be aware of sounding biases
Mean = 4.19 Mean = -2.17 Mean = -0.12 Mean = -0.61
Small Temperature Bias in soundings(Vaisala RS-92 sondes)
Moist Moist
MoistMoist
Dry
Dry Dry
Dry
Small RH Bias in soundings(Vaisala RS-92 sondes)
HigherHigher
Lower
Lower
Lower
Lower
Higher Higher
Impact of Thresholding Radar Data at 5 dBZLoss of Clear Air Information
Horizontal rolls
No thresholding With thresholding
Cold Front
Radar 1980-’s
Detection of Clear Air Features in the Boundary Layer
Dry Line
SecondaryDry Lines
Gravity Waves
Cumulus Clouds
Early Radar Detection of Cumulus Clouds
S-Pol Radar Reflectivity at 2.6 deg elevation 1 km GOES Visible Image
Must have sensitive radars to detect these low reflectivity values.
Reflectivity Radial velocity
Ground Clutter – no filtering
Observation of clear air features requires special attention to data quality control
Clutter Mitigation Decision (CMD) Filter Applied
Observation of clear air features requires special attention to data quality control
Reflectivity Radial velocity
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability• Data quality• Assemble climatology statistics
Storm Track and Precipitation Climatologies
0
50
100
150
200
250
15-M
ar17
-Mar
19-M
ar21
-Mar
23-M
ar25
-Mar
27-M
ar29
-Mar
31-M
ar2-
Apr4-
Apr6-
Apr8-
Apr10
-Apr
12-A
pr14
-Apr
16-A
pr18
-Apr
20-A
pr22
-Apr
24-A
pr26
-Apr
28-A
pr30
-Apr
2-May
4-May
6-May
8-May
10-M
ay12
-May
14-M
ay
Nu
mb
er o
f S
torm
Tra
cks
Daily number of storms
Diurnal frequency of rainstorms
March 15 to May 15, 2005
0
50
100
150
200
250
300
350
400
450
500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time (UTC)
Num
ber
of
Sto
rms
Diurnal frequency of rainstorms
Build a Climatology
Taiwan 4 Year Radar Climatology
Days with Weak synoptic
14 LST 16 LST15 LST
Forcing
Frequency of Radar reflectivity> 40 dBZ
Pin-Fang Lin et al, 2010
19 LST18 LST17 LST
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability• Data quality• Assemble climatology statistics• Know your pre-storm environment! Know your area!!
ConvectiveAvailablePotential
Conduct sounding analysis throughout the day using updated surface station information is critical
DryAdiabat
Mixing Ratio
MoistAdiabat
ConvectiveInhibition
(CIN)
PotentialEnergy
(CAPE)
Know Your Forecast Area
Sea and Lake Breezes
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability• Data quality• Assemble climatology statistics• Know your pre-storm environment! • Document the different weather regimes for convective weather
Scenarios Leading to Heavy Rainfall
Convective Bands & Local InitiationSynoptically-Driven
Weather• Front (cold season)
• Mei-Yu Front (spring)
• Southwesterly (southern) monsoon
Maximum raingauge accum:97 mm
Mei-Yu Front/MCS: 14 June 2008
• Mesoscale Convective System (MCS)/ Mesoscale Convective Vortex (MCV)
• Short-wave Trough
• Typhoons
• Easterly Wave
Numerical Weather Prediction Models Provide Important Information on the pre-storm environment and different weather
regimes
Must have Rapid Update Cycle
(RUC) Numerical Weather
Prediction fields, with hourly
forecasts, for nowcasting
convective weather.
Real-time Rapid Refresh Domain
Current RUC-13 CONUS Domain
HRRR
2008
convective weather.
HRRR
2009
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability• Data quality • Assemble climatology statistics• Know your pre-storm environment! • Document the different weather regimes for convective weather• Develop nowcast conceptual models
Radar
Circular cold pools
Satellite
Circular cold pools
Storm evolution in the Amazon
Conceptual model of storm initiation and evolution over the Amazon
Prepared by Andrea Lima
or convective rolls
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability• Data quality • Assemble climatology statistics• Know your pre-storm environment! • Document the different weather regimes for convective weather• Develop nowcast conceptual models• Identify the important predictor information
Radar Reflectivity
5h elapsed
Predictor Identification
5h elapsedtime
Storm Duration
� Storm growth and intensification:storms move with boundary
� Storm dissipation: Boundary moves away from storm
Predictor Indentification
Time 1 Time 2
Convective Weather Predictors
• Boundary layer structure– convergence line position– colliding boundaries– strength of the
convergence
• Cloud characteristics-cloud type-cloud growth-cloud top temperatures-new cloud motion
The list of predictors is based on research and experience.
convergence– low-level shear– boundary-relative steering
flow– stability
-new cloud motion
• Storm Characteristics- position and motion- growth rate - storm structure
- storm merger - storm-boundary interaction- storm decay
research and experience
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability• Data quality • Assemble climatology statistics• Know your pre-storm environment! • Document the different weather regimes for convective weather• Develop nowcast conceptual models• Identify the important predictor information• Develop nowcast rules
Storms moving from Mountains to the PlainsJune 12, 2006
Convective Storm Nowcast Rules For Beijing
Wilson et al. 2010
Rules for Storm moving from Mountains to Plains
• Organized storms with a gust front
• Cumulus clouds or storms on plains
• Modified sounding is unstable
Move storms to Plains if:
Rules for Storm moving from Mountains to Plains
• Storms unorganized and no gust front
• Modified sounding is stable
Dissipate Storms if:
• No cumulus clouds on plains
No Cumulus
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability• Data quality • Assemble climatology statistics• Know your pre-storm environment! • Document the different weather regimes for convective weather• Develop nowcast conceptual models• Identify the important predictor information • Develop nowcast rules• Combine information together in a forecaster-computer system to
produce rapidly updated nowcasts
Need to Develop aThunderstormNowcasting System
• Computers can be used to process all the data.
• A nowcast system can generate rapidly updated forecast products every 5-10 minutes.every 5-10 minutes.
• Forecasters can assimilate all the information more quickly and use their knowledge to improve the automated products.
• Automated products can be rapidly disseminated to end users for avoidance of high impact weather.
Convective Weather Predictors
• Boundary layer structure– convergence line position– colliding boundaries– strength of the
convergence
• Cloud characteristics-cloud type-cloud growth-cloud top temperatures-new cloud motionconvergence
– low-level shear– boundary-relative steering
flow– stability
-new cloud motion
• Storm Characteristics- position and motion- growth rate - storm structure
- storm merger - storm-boundary interaction- storm decay
Storm Trends
VDRAS WindRetrievals
ExtrapolationConvergence line
detection & characterization
NowcastSystem
Cloud Monitoring
NWP Derived forecast fields
Products
Climatology
The Nowcasting ProcessFrom Start to End
• Know your forecast challenges and data availability• Data quality • Assemble climatology statistics• Know your pre-storm environment! • Document the different weather regimes for convective weather• Develop nowcast conceptual models• Identify the important predictor information • Develop nowcast rules• Combine information together in a forecaster-computer system to
produce rapidly updated nowcasts• Provide meaningful products that meet the needs of the end-user
Why did the human forecast no rain ?
1) Storms only moderately organized, frequently will dissipate in moving to plains
The forecaster was correct!
2) Sounding stable CIN -87, CAPE 91 2) Sounding stable CIN -87, CAPE 91
Beijing sounding released at 1300 local
CIN -87j/kg
CAPE 91 j/kg
Sounding Corrected forDry bias
Why did the human forecast no rain ?
1) Storms only moderately organized, frequently will dissipate in moving to plains
The forecaster was correct!
2) Sounding stable CIN -87, CAPE 91 2) Sounding stable CIN -87, CAPE 91
3) Surface stations show moisture same as 1300 local sounding
4) Satellite shows no cumulus over plains clouds even along sea breeze
Why did the human forecast no rain ?
1) Storms only moderately organized, frequently will dissipate in moving to plains
The forecaster was correct!
2) Sounding stable CIN -87, CAPE 91 2) Sounding stable CIN -87, CAPE 91
3) Surface stations show moisture same as 1300 local sounding
4) Satellite shows no cumulus over plains clouds even along sea breeze
5) Radar shows no cumulus over plains
02 Aug 200802-14 UTC
Radar tracks of >35 dBZ cells
•Education and training workshops focused on convective storm nowcasting
o2000: Sydney, Australia, WWRP[1], WMO[2]
o2003: Brasilia, Brazil, WMOo2003: Ankara, Turkey, Mitsubishio2005: Pretoria, South Africa, WMOo2007: Palm Cove, Australia, WMOo2007: Sahel Region, Ouagadougou, Burkina Faso, West Africao2008: Beijing, China, Beijing Meteorological Bureau (April)o2008: Beijing, China, Chinese Meteorological Agency (July)o2009: Beijing, China, Chinese Meteorological Agencyo2010: Taipei, Taiwan, Central Weather Bureauo2010: Beijing, China, Chinese Meteorological Agencyo2010: Sibiu, Romania, EUMETSATo2010: Taipei, Taiwan, Central Weather Bureauo2010: Taipei, Taiwan, Central Weather Bureauo2011: Hong Kong, China, Hong Kong Observatoryo2011: Beijing and Chengdu, China, Chinese Meteorological Agency
[1] WWRP = World Weather Research Program[2] WMO = World Meteorological Organization