experiments in 1-6 h forecasting of convective storms using radar extrapolation and
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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
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Overarching Goal
Blend
Numerical Forecasting Methods
and
Observational methods
To improve 1-6 h nowcasting
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Predictability
Forecast Length
Extrapolation
NWP
Fo
reca
st S
kill
Blended
Be
st
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Challenge - How to blend extrapolation and model nowcast
ExtrapolationForecast
NumericalModelForecast
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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
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Study areaJune 2005
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Example Initiation case
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Extrapolation• Probabilities• Extrapolation plus smart trending (synoptic situation and time of day)
Observational Techniques Examined
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• 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
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Subjective evaluation of forecast quality
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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
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Forecast
Observed
Quality = 2.0 Quality = 3.0
Quality = 4.0 Quality = 5.0
Examples of Forecast Quality
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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
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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
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Quality = 4.0
Forecast
observed
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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
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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
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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
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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
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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
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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
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Overarching Goal
Blend
Numerical Forecasting Methods
and
Observational methods
To improve 1-6 h nowcasting
Summary
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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.
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Thank You
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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
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Forecast
Observed
Quality = 1.5
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Example Initiation case