CPC Outlooks for Major U.S. Cities
Translation of
CPC Forecasts to Individual Cities
The CPC monthly and
seasonal outlooks are for forecast divisions. Two files are available to translate
temperature forecasts for the CPC forecast divisions into forecasts for temperature or
degree days for individual cities within the U.S.
Data
Files
Translation
of CPC Forecasts to Individual Cities
The following file
contains the information required to downscale the temperature outlook information to
temperature outlooks for individual cities.
File Explanation
Filename: |
citytemptran.dat |
Description: |
Gives the information
required to translate the CPC temperature outlooks to individual cities. |
Derivation: |
Regression relationships
between downtown airport temperatures and the associated forecast division, enhanced by an
analysis of recent trends. Derived from data from 1951-1997. |
Contents: |
Column 1: |
The city number. The
city name and location are written in ASCII on the last (rightmost) columns of text, and
also in a separate dictionary file. |
Column 2: |
Forecast Division
Number. This gives forecast division in which the city resides. |
Column 3: |
Season Number, defined
by the month number of the CENTER month of a three month season, 1=DJF, 2= JFM, ...
12=NDJ. |
Column 4: |
Seasonal mean
temperature of the downtown airport. |
Column 5: |
Seasonal standard
deviation of the downtown airport. |
Column 6: |
Constant term, a, of the
regression equation that translates the forecast division temperature (t(FD)) (See Col. 2
for division number) to the downtown airport temperature (t(City)). t(City) = a + b*t(FD).
The a term is listed in Column 6. |
Column 7: |
Multiplicative term, b,
of the regression equation that translates the forecast division temperature (See Col. 2
for division number) to the downtown airport temperature. t(City) = a + b*t(FD). The
b term is listed in Column 7. |
Column 8: |
The skill of the
equation: t(City) = a + b*t(CD). This value, r, is actually the correlation coefficient
between t(FD) and t(City) for the given season and city. This value, can be used together
with b in column 7 to find useful characteristics of the relationship between
the city airport and its forecast division. |
Column 9: |
The average difference
between seasonal mean temperature at the downtown airport (Column 4) and the seasonal mean
minimum temperature (MN-MIN) at that station. Note that the temperature range is twice the
value presented in Col. 9, and the seasonal mean maximum temperature can be estimated by
adding the result displayed in Col. 9 to the mean. |
Column 10: |
Mean difference between
the downtown airport mean temperature (AP) and that of the Forecast Division(FD) Column 10
gives the (AP-FD). |
Column 11: |
Mean difference between
the urban area average temperatures (U) and the forecast division average temperatures. (U-FD) |
Column 12: |
Mean difference between
the urban area average temperatures and the mean temperature at surrounding rural stations
(R) within 100 km of the city center (U-R). |
Data Set
Usage
This data set is used to obtain forecast for individual cities from forecast division
data.
A: Probability anomaly
method.
Assume the probability anomaly for below, near, and above median values of
the seasonal temperature and precipitation to be the same for the city as it is in the
forecast division in which the city resides. The probability anomalies are available in
digital form on the cpcllft.dat forecast file. Forecasts can
also be obtained by manually interpolating values from the forecast maps to the exact city
location and used and forecast distribution rules to determine the probability anomaly for
other categories. The anomaly is then applied to the city's climatology.
Information provided in the city temperature translation file can be used to obtain the
class limits.
Class Limits for Below Normal Temperatures = Seasonal mean - .431*sd
Class Limits for Above Normal Temperatures = Seasonal mean + .431*sd
B: Forecast distribution method.
Locate the appropriate forecast division for the desired city in the city
temperature translation file. The CPC outlook for that division is given in the
probability of exceedence forecast file sets: (cpcllftd.YYYY.dat).
Specific exceedence percentile values for the forecast probabilities can be converted to
exceedence values for city temperatures by applying the the following formula:
variable
definition: |
t(city) |
- The mean seasonal
temperature at a downtown airport. |
t(FD) |
- The mean seasonal
temperature for the forecast division appropriate for the city. |
a, b |
- empirical constants
found in in columns 6 and 7 of the city temperature translation file. |
|
|
Formula:(FORTRAN
syntax used for formula) |
t(City) = a
+ b*t(FD) |
EXAMPLE: The one-month lead CPC seasonal forecast
issued in February, 1995 for MAM 1996 indicated that there was a 50% chance that the
3-month seasonal mean temperature for Forecast Division 4 would exceed 46.59 degrees F.
Find the temperature with a similar exceedence percentage for New Yorks LaGuardia
Airport.
From the city temperature translation file:
T(LGA)= 5.46+1.01FD(4) = 5.46 + 1.01*(46.59) = 52.52 degrees F.
Because the distribution for seasonal mean temperatures are are very
close to Gaussian for the majority of seasons and locations in the U.S., the entire
forecast temperature distributions for given cities are easy to find. The forecast
means and standard deviations for the forecast division temperatures can be found in
columns 19 and 21, respectively, of the probability of exceedence forecast file. The
information for the divisions can be translated into forecasts for cities with information
in the city temperature translation file as follows:
variable
definition: |
|
t(city),t(FD) |
- forecast mean seasonal
temperature at the downtown city airport and its forecast division, respectively.
|
sd(city), sd(FD) |
- forecast standard
deviation of the city airport and forecast division mean temperatures. |
a, b |
- constants found in the
city temperature translation file in columns 6 and 7, respectively. |
|
|
Formula:
(FORTRAN syntax used for formula) |
t(city) = a
+ b* t(FD) |
sd(city) =
b * sd(FD) |