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Accuracy Weather Analysis

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Accuracy Weather Analysis

30-Day Forecast Accuracy Weather Analysis for Houston County by the Weather Channel

 Introduction

It has been argued that an improvement pertaining to the accuracy of weather forecasts, even if it is to a significant extend could save a significant amount of cash to the cost to managers, individuals, businesses and even the government. Different weather forecasters and companies provide varied weather forecasts. Therefore, it becomes critical to select the right weather forecaster in order to save time and space. It needs to be considered that not all weather forecasts are established the same. Private agencies including the Weather Channel, WSI or Custom do not simply outsource information from the National Weather Service (Forecast Watch, 2018). Instead, they employ their own models which they supplement with their meteorologists' analysis to create a unique weather forecast.

This analysis of the stated weather forecast is made after comparing the Weather Channel forecast with the actual and persistent occurrence data collected within the 30 day period. The data for this comparison was collected from 10 regions in Houston County at specific times of the day. For instance, daily temperature forecasts were collected at 19hours (Western Standard Time) in the region of the United Stated and continued until the period was over.

Methodology

There are six major attributes of a weather forecast that constitutes the overall quality. This includes uncertainty, sharpness, resolution accuracy, skil and reliability. However, Stanski et al. (1989) explains that there is no single verification measure that can effectively offer complete information pertaining to the quality of a weather forecast.  This analysis will employ reliability, accuracy, skill and resolution in making the assessment. These measures settled on are not only the easiest the measure but also the simplest. An analysis of the

Resolution is quite critical in making forecasts about precipitation since it has the capability of distinguishing between drizzle, hail, freezing rain, snow and rain. Skil relates to the accuracy of a given forecast as compared to some baseline or reference prediction such as a forecast which is compared to persistence of occurrence of the current conditions (Sanders, 1973). Therefore, an analysis of the accuracy of a weather forecast is quite important in ascertaining the validity and accuracy of the chosen weather forecast.

Analysis of Temperature Forecasts

 

There is a tendency of low temperature forecasts being higher compared to the temperature in high forecasts. This is largely attributed to collection and definition methodology: High temperatures are collected and defined from 7:00 a.m. to 1900hours while low temperatures are collected and defined from 1900hours to 8.00am. This implies that the one-day low temperature forecasts normally happen during the night after a one –day out high temperatures.

The forecast error for temperature, whether low or high, rises when the forecast period moves further out. On the other hand, the observations of a low temperature happen approximately 12 hours after the occurrence of high temperatures.  However, this is exclusive of the general difference in accuracy between low and high temperature forecasts.  In general perspective, there is a tendency for low temperatures to be less predictable compared to high temperatures.  This is manifested in the Houston Weather forecast by the Weather Channel.

From the figures presented in the February weather forecast by the Weather Channel, the mean absolute error for low temperature was 3.08 degrees Fahrenheit.  The report of the combined Day 1- and Day 28 of the February low and high temperature forests has a low error when compared to the actual occurrence of the low and high temperatures.  Using the persistence of occurrence of a day-to-day weather conditions, it was found that the Weather Channel’s forecasts have underestimated the maximum temperatures while also overestimating the minimum temperatures.  This indicates a tendency of being overly conservative. From the 30 day log, it is evident that the biases for both maxima and minima are the greatest on the first day and continue decreasing as the lead time increases. Particularly, most maxima and minima are underestimated by approximately 1degC. The exception to this is on day 7, day, day 8, day 9 and day 12 where the maxima are all negative while the minima indicates negative biases.

In this case study, the MAFE rises with lead time, for instance from 1±5 degrees Celsius on the first day to 2±5 degrees Celsius on the last forecast day.  As per the mean absolute deviation of the presented data by the Weather Channel, MAC is apparently larger compared to the highest MAFE. This indicates that the forecasts for all the elements stated are accurate compared to the climatologically normal.

References

Forecast Watch (2018). Select Weather Forecast Providers Based on Accuracy. Available at

            https://www.forecastwatch.com/products/riskmanagers/  (Accessed on 20th March, 2018)

The Weather Channel. (2018). Houston, TX Monthly Weather. Available from

https://weather.com/weather/monthly/l/Houston+TX+USTX0617:1:US Accessed on 20th March, 2018

Stanski, H., Wilson, L., & Burrows, W.(1989). Survey of common verification methods in

 



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