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Analysis of Short-Term Probability of
Precipitation Forecasts
(October 2006 through June
2007)
By ForecastWatch.com, a Service
of Intellovations, LLC
July 10, 2007
Executive Summary
One-day-out probability of precipitation (POP) forecasts
were evaluated over approximately 800 locations in the United States between October
1, 2006 and June 30, 2007. Publicly available POP forecasts were collected
from CustomWeather,
The National Weather Service,
The Weather Channel,
and a non-public feed from DTN Meteorlogix (collected at the same time as the
public forecasts). All private forecasting companies did much better than the
National Weather Service. DTN Meteorlogix had the best scores for the
nine-month POP accuracy period, as well as for the winter months only.
Importance of POP Forecasts
Many organizations rely on good precipitation forecasts.
Concrete pouring and asphalting decisions depend on reliable rain forecasts.
Missing the rain can result in costly re-dos, while forecasting rain when there
ends up being none results in lost revenue opportunities. Public works
departments and state DOTs rely on accurate snow and ice forecasts to know when
to call out crews and pre-treat roads. For them reliable forecasts are
critical for public safety, and for avoiding unnecessary and costly crew callouts.
Accurate precipitation forecasts are similarly important
to electrical utilities, airports, golf courses, outdoor sports and recreation,
and police/emergency management. Accurate precipitation forecasts add to the
bottom line of weather-dependent businesses. And they help cities, counties
and other organizations better meet their mission.
How POP Forecasts Are Evaluated
There are two components to measuring the accuracy of a
probability-of-precipitation forecast. The first is accuracy. If, over the forecasts
being measured, there was precipitation the same percentage of time as
forecast, the forecast is said to be accurate. For example, if it
rained 10% of the time the POP forecast called for a 10% chance of rain, the
POP forecasts would be accurate. If, on average, there is precipitation three
out of ten days at a given location, and the forecaster always predicted a 30%
chance of precipitation every day, the forecaster would be accurate. While
accurate, the forecast isn't useful.
The second measure of a POP forecast is resolution.
A perfectly resolved POP forecaster would always predict no chance of
precipitation for dry days, and 100% precipitation for days on which there was
rain or snow. The forecaster above who always forecast a 30% chance of
precipitation would be said to be fully unresolved. However, a
forecaster who predicted 100% chance of precipitation on dry days, and zero
percent on wet ones is still perfectly resolved, but completely inaccurate.
While resolved, the forecast isn't useful.
Evaluating a POP forecast fully, therefore, must take both
the accuracy and the resolution of the forecast into account. The calculation
used to evaluate POP forecasts is called the Brier score. The Brier score
takes both accuracy and resolution into account. A Brier score ranges from
zero to one, with zero being perfectly accurate and resolved (0% POP forecast
on dry days, 100% POP forecast on days with precipitation).
Methodology of the Comparison
Brier scores are much more useful the larger number of
forecasts and observations there are to calculate. This study evaluated POP
forecasts for approximately 800 locations within the United States over the
period of October 1, 2006 through June 30, 2007. Forecasts were collected at
approximately 6pm ET each day. The next-day, or one-day-out forecast, was
entered in at that point. The forecasts were compared against precipitation
measured by the National Weather Service. If more than 0.01 inches of
precipitation fell, it was considered to have been a precipitation event.
About 200,000 one-day-out POP forecasts were collected
over the period for each forecaster. The number used is less that the total collected
because occasionally a weather observation station was down for maintenance or
a weather forecast was invalidated because of errors (for example, rarely the
National Weather Service reported precipitation probabilities greater than
100%).
Results of Short-Term POP Forecast
Comparison
The following tables detail the Brier scores for each
weather forecast provider for one-day-out probability of precipitation
forecasts. Table 1 shows Brier scores for the nine month period of October 1,
2006 through June 30, 2007. The second column is the number of forecasts that
were evaluated in calculating the one-day-out forecasts. The third column is
the number of possible forecasts that could have been evaluated had every
actual and forecast been collected and considered valid. The fourth column is
the calculated Brier score for the period.
|
Provider
|
Number of Forecasts
|
Percent of Possible Forecasts
|
Brier Score
|
|
DTN Meteorlogix
|
174743
|
78.7%
|
0.1219
|
|
CustomWeather
|
195862
|
87.8%
|
0.1271
|
|
The Weather Channel
|
195681
|
87.9%
|
0.1382
|
|
NWS
|
166737
|
77.7%
|
0.1903
|
Table 1: Results
of nine month short-term POP forecast analysis (lower is better)
Many businesses, governments, and individuals are particularly interested in
winter forecasts. Preparations for snow, such as changing business processes,
salting roads in advance, and keeping employees on standby are real costs. Better
prediction of winter precipitation means that businesses, governments, and
individuals can plan better, save money, and provide better service. POP
scores for the winter months of December 2006 through February 2007 have been
broken out in Table 2.
|
Provider
|
Number of Forecasts
|
Percent of Possible Forecasts
|
Brier Score
|
|
DTN Meteorlogix
|
44092
|
62.4%
|
0.1104
|
|
CustomWeather
|
64847
|
90.3%
|
0.1228
|
|
The Weather Channel
|
64645
|
90.2%
|
0.1351
|
|
NWS
|
59561
|
85.1%
|
0.1846
|
Table 2: Results
of short-term POP forecast analysis for winter 2006-2007 (lower is better)
About ForecastWatch.com
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