Experiments in 1-6 h Forecasting of Convective Storms Using Radar Extrapolation andNumerical Weather Prediction

Acknowledgements Mei Xu - MM5 Morris Weisman – WRF James Pinto –WRF, NCWF-6, computer support Steve Weygandt - RUC Tom Saxen – NCWF-6, Extrapolation Cindy Mueller – NCWF-6, Extrapolation, management Jenny Sun – Forecast VDRAS Dan Megenhardt – computer support Rita Roberts – Scientific advise Frank Hage – Display support

Overarching Goal

Blend

Numerical Forecasting Methods

and

Observational methods

To improve 1-6 h nowcasting

Predictability

Forecast Length

Extrapolation

NWP

Fo

reca

st S

kill

Blended

Be

st

Challenge - How to blend extrapolation and model nowcast

ExtrapolationForecast

NumericalModelForecast

8 methods that produce 1-6h forecasts4 numerical and 4 observational

Forecasts evaluated with the objective of developingideas for blending numerical and observational

To meet this challenge – NCAR conducted a forecast extravaganza this summer

Study areaJune 2005

Example Initiation case

Extrapolation• Probabilities• Extrapolation plus smart trending (synoptic situation and time of day)

Observational Techniques Examined

• Probabilities• 20 km grid• 3 h forecast cycle• ACARS, VAD, profiler, GOES precip water)

NWP Techniques Examined

• nested grid• 3h forecast cycle• observational nudging• radar data assimilation (conus mosaic of reflectivity)

• 4 km grid• 24h forecast cycle• initialized with 40km ETA

The point is-State of the art techniques were available

Subjective evaluation of forecast quality

1 – forecast and observed almost perfect overlap.

2 – majority of observed and forecast echoes overlap or offsets <50 km.3- forecast and observed look similar but there are a number of echo offsets and several areas maybe missing or extra.

4 – the forecasts and observed are significantly different with very little overlap; but some features are suggestive of what actually occurred.

5- There is no resemblance to forecast and observed.

Forecast Quality DefinitionsWilson subjective categories

Forecast

Observed

Quality = 2.0 Quality = 3.0

Quality = 4.0 Quality = 5.0

Examples of Forecast Quality

1.Quality of forecasts for echo Existing at forecast time.

2. Quality of NWP forecasts of initiation

3. Quality of NWP forecasts of change in area size

1

2

3

4

5

0 2 4 6

Forecast Period (Hours)

Qu

alit

y

1. Echo present at forecast time

Forecast Quality

Extrapolation

NWP

Bes

t

Quality = 4.0

Forecast

observed

0 1 2 3 4 5 6 Forecast Length, hours

.2

.4

.6

.8

1.0

Accuracy of Rainfall Nowcasts>1 mm/h

GRID MESH 20 km Jun-Oct 2002

Courtesy of Shingo Yamada JMA

Extrapolation

NWP

Cri

tica

l S

ucc

ess

Ind

ex (

CS

I)

Cross over region

Best NWP Results

2-hourforecast

4-hourforecast

6-hourforecast

Initiation(number cases)

17 17 17

Initiations fxcorrect (percent)

71 71 65

Forecast quality(category)

3.6 3.8 3.9

Offset median (hours) 1.0 1.0 0.0

False alarms(number)

5

2. Initiation Forecasts

2, 4 and 6 hr forecasts of trend in area size

3. Area Size Trend Forecasts

g+ large growthg medium growth g- small growthnc no changed- small dissipationd medium dissipationd+ large dissipation

7 Trend Categories

forecast

observed

Error 2 categories

2, 4 and 6 hr forecasts of trend in area size

3. Area Size Trend Forecasts

g+ large growthg medium growth g- small growthnc no changed- small dissipationd medium dissipationd+ large dissipation

7 Trend Categories

forecast

observed

Error 2 categories

2, 4 and 6 hr forecasts of trend in area size

3. Area Size Trend Forecasts

g+ large growthg medium growth g- small growthnc no changed- small dissipationd medium dissipationd+ large dissipation

7 Trend Categoriesforecast

observed

Error 6 categories

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6

Error in Forecasting trend in Area Size (number of categories)

Acc

um

ula

ted

Per

cen

tag

e

6 h

Best NWP results

3. Area Size Trend Forecasts

Best Worse

Overarching Goal

Blend

Numerical Forecasting Methods

and

Observational methods

To improve 1-6 h nowcasting

Summary

Summary

1. Model – frequent cycling (3hr), assimilate radar reflectivity

2. Initiation – Give full weight to model

3. Existing storms – Extrapolate and trend area size based on model trend (more weight for dissipation trend)

Unfinished – examine model and extrapolation predictability stratified by precipitation organization, synoptic situation and time of day.

Thank You

05

10

15

2025

30

35

40

g+ g g- nc d- d d+

2-hour trend

0

5

10

15

20

25

g+ g g- nc d- d d+

4-hour trend

g+ large growthg medium growthg- small growthnc no changed- small dissipationd medium dissipationd+ large dissipation

0

5

10

15

20

25

g+ g g- nc d- d d+

6-hour trend

Trend Category

Num

ber

case

s

Area Size Trends

Forecast

Observed

Quality = 1.5

Example Initiation case