basics of developing a convective storm nowcasting … · basics of developing a convective storm...

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

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


• Know your forecast challenges and data availability

Data availability – Everything you can get your hands on!!

SatelliteRadarRadarSurface weather stationsRawindsondesLightingGPS Precipitable Water VaporAircraft TAMDAR dataProfilers


• 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


• 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


• 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


• 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


• 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


• 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


• 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


• 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


• 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


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





3) Surface stations show moisture same as 1300 local sounding

4) Satellite shows no cumulus over plains clouds even along sea breeze





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

basics of developing a convective storm nowcasting … · basics of developing a convective storm...

Documents