Abstract
Using historical weather forecast data downloaded from the National Oceanic and Atmospheric Administration’s [NOAA] National Weather Service Digital Library, we performed statistical analysis on the forecast accuracies of temperature, probability of precipitation, quantitative precipitation and wind speed. The major findings of this study are: (1) There are significant variations in forecast accuracies at different geographical locations in the United States; (2) The overall accuracies of 3-day or longer temperature forecasts are similar in magnitude to the standard deviations of historical daily changes in temperature; (3) There are statistically significant biases in the forecasts of either large positive or negative changes in temperatures; (4) The observed probabilities of precipitation are significantly lower than forecasted probabilities for 2-day or longer horizons; (5) On the average, the 3-day or longer forecasts for quantitative precipitation tend to significantly under estimate actual amount for periods of heavy precipitation; and (6) Forecasters generally under predict wind speeds by a large margin for days when wind speeds exceed 20 mph. An improved weather forecast model can be constructed based on some of the empirical statistical parameters from this study.
Highlights
Despite advances in computing and satellite technologies and improvements in various atmospheric models scientists use to predict weather [1], there are still significant uncertainties in weather forecasts
Some past studies have focused on the accuracies of forecasting severe weather conditions, such as tropical cyclones [2,3], but there have been fewer studies focused on quantifying the uncertainties of general every day weather forecasts, or comparing forecast accuracies across different geographical regions within the United States [4-6]
To compare geographical variations in forecast accuracies, we selected 60 geographical locations almost evenly spaced throughout the continental United States in terms of latitude and longitude1
Summary
Despite advances in computing and satellite technologies and improvements in various atmospheric models scientists use to predict weather [1], there are still significant uncertainties in weather forecasts. Even though some of our analysis will be focusing on the occurrences of heat waves of over 10 degree temperature change or heavy precipitation of more than half an inch, the focus of our studies can still be characterized as high probability weather events. This is in contrast to studies of rare weather events such as tornados that could cause multi-billion dollar damages, or a flood with a 100-year recurrence interval. For comparison purposes we will list the average daytime maximum temperature for different geographical locations, as well as their overall variations as measured by the standard deviations For both of these measures, we will not list their sampling errors as the number of measurements is sufficiently large for this study. We offer some ideas for future work and some potential practical applications of our findings
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