COMET NWP Symposium
December 1999
Ensemble Forecasting Lab
1.
Goals of this Lab:
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2.
Facilitate the use of ensemble data in the forecast process
Introduce the forecasters to different ensemble displays
Examine and use ensemble probability forecasts
Give you ideas you can impart on your staff
Schedule
 Before Lab – Ensemble forecasting techniques background
 Introduction and scenarios briefing
10 minutes
 Complete Part 1 – February Cold front
20 minutes
 Complete Part II – November event
20 minutes
 Answer and discussion questions
20 minutes
Case I Background
For this case, the forecaster will use available MRF and ensemble data to
forecast the temperatures and frontal passage timing along the East Coast of
the United States. Temperature forecasts, clouds, and the chance of
precipitation. The forecast should be very generic following the NWS
standard day 3-7 forecast formats. (I.e. HIGH IN THE 30s, 40s, 50s etc).
The front may produce locally heavy rains and the timing of precipitation is
critical. The key is timing the front and its impact on the weather.
The ensemble data is limited to spaghetti plot forecasts of 500-mb height, a
mean 500-mb height forecast, and the probability of the exceedance of 5580
m over North America.
Lab Assignments:
Each team will do the same things.
Start with the data on 07 Feb 1999 (there is no data on 9 Feb)
End with the data from 11 Feb 1999
For each Day answer the following questions:
 When will the cold front pass through the cities of the eastern
United States?
 Is there potential for any record temperatures over the eastern
United States during this forecast period?
 Where is the most likely location where it will rain over the eastern
United States?
 Create a standard NWS extended range forecast for: (use KMDT
as the approximate location)
 Thursday 11 Feb
 Friday 12 Feb
 Saturday 12 Feb
Case II Background
For this case, the forecaster will use available MRF and ensemble data to
forecast the temperatures and sensible weather for the plain States
For this case, the ensemble data includes the standard 500-mb spaghetti
plots, 850 MB and surface spaghetti plots and several probability charts.
The probability charts include:
 The probability of rain >= 0.01
 The probability of rain >= 0.10
 The probability of rain >= 0.25
 The probability of rain >= 0.50
 The probability T 850 =< 0C
 The probability T 850 >=16C
Each spaghetti chart contains the following data:
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Each ensemble member
The mean forecast of each isoline
The dispersion of the forecasts
A mean forecast in the lower panel comprised of all members equally
weighted
 The departure of this mean from the 30 year Climatology
 This departure is divided by the 30 year standard deviation showing
standardized anomalies
Lab Assignments:
Each team will do the same things.
Start with the data on 05 Nov 1999 through 07 November 1999
For each Day answer the following questions:
 Create a standard NWS extended range forecast for your assigned
location
 08 Nov
 09 Nov
 10 Nov
 What is the likelihood of rain in the plains?
 Are you confident?
 Why?
 What is the likelihood it will be colder than normal in the plains?
 Are you confident?
 Why?
 What charts provide confidence that:
 It will likely be rain free?
 What charts suggest no heavy rain is likely?
 What charts suggest it may be warmer than normal?
 Did the Temperature at 850 exceeding 16C add any
information?
Discussion
1.
What should be done at your office to improve the use of ensemble
data into the forecast process at longer ranges?
2.
What should be done at your office to improve the use of ensemble
data into the forecast process at shorter ranges?
3.
What value does the spaghetti and ensemble dispersion provide? Is it
an improvement over the basic spaghetti plot?
4.
What value does the probability forecasts provide? Can you think of
other useful products?
5.
What value does having the climatological data provide the
forecaster? Should these kinds of data be in AWIPS?
6.
With multiple models and multiple solutions, how specific or generic
should our 3-7 day forecasts be?