Forecast Accuracy Weather Analysis for Houston County by the Weather Channel
This paper presents an accuracy weather analysis for Houston County, the United States of America. According to this analysis, it is evident that skill is quite critical in forecasts of rainfall, cloud cover, temperature, , air pressure, precipitation, and visibility. Furthermore, this aspect is critical in making qualitative description of the forecasted weather out of the 30 days in advance. This analysis has as well unearthed the prevalence of a long term trend as it regards to the accuracy of the forecasts. For instance, the forecast of the expected rainfall for day 10 (average error ~ 1.5°C) are as skilful as those of day 2 or 5. Particularly, the current paper seeks to present a 30 analysis of a 30 day weather forecast for Houston County. The author will then proceed to document the trends, accuracy of the day-to-day medium range forecast for all the 30 days. The forecasts used are those prepared by the Weather Channel in partnership with Wunderground and the Weather Company, all of which are based in the United States.
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.
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.
The skill scores are used in rating the temperature forecasts in relation to the variability of the climatological temperature and not on the basis of error. Therefore, Houston has the lowest skill score except for days, 10, 18 and 19, as well as the lowest MAFEs for the lead time of the entire month with the exception of day 7 and 8. From day 15 backwards, the Houston temperature forecasts have a significantly lower skill (10% and lower). However, from day 15, the skill moves consistently exceeding the 60% for Day 1 maxima and 50% for the last day minima. Both maxima and minima temperatures for Houston have a skills core which exceeds 30%.
Most of the distribution errors for the maximum and minimum temperatures fall within the range of +12 and -12degC. The broadest distributions are found on day 15, 16, 23, 24, 27 and 82. Apparently, 50% of the forecast maxima for Day 1 and the 45 of the forecast minima for the last day have errors which are less than 1 degC.
The verification analysis for the Weather Channel rainfall forecast for Houston County was done for their skill. The actual and persistence occurrence data was collected in different parts of the country in each of the aforementioned dates. The economic value for different rainfall thresholds were used to analyze the skills. The processing of the forecasted data was done using a simple bias correction alongside the use of equitable threat score.
According to this analysis, the equitable threat score (ETS), the value of the ETC is positive for all the thresholds up to the last day of the forecast. Furthermore, the economic value for the rainfall forecast (that is a forecast for the probability that rainfall will occur) points out positive skills for the Weather Channel February Houston Weather forecast. Determinist forecasts for the entire period show negative skills for rainfall weather forecasts. This clearly suggests the possibility of false alarms increasing considering that forecasts are established for rainfall thresholds which are smaller.
The economic value for all rainfall thresholds for the entire period was computed for each day of the month of February. It is accordingly established that the skill scores differ from day 18- to 20 and from day 19 to 28. Area plots V against ∝ for yes rain periods were manifested during day 20 through to 28. False alarms were only identified in the first and seventhly day. On the other hand, the economic value for V rainfall forecast was positive until from day 10 to 25. Regarding relative ∝, the maximum importance to the forecast in the course of the month was for day 21 and 27 compared to other days. During the month of March, the value of V was higher during dates 22 and 25th with more significance for Day 5 regarding ∝. Nonetheless, the economic value of the general forecast is positive for a fewer days and hence; considered to be too low. Figure 1.1 below presents the economic value for yes rainfall forecast by the Weather Channel.
Figure 1.1 Economic Values for Yes Forecast from the Weather Channel
In effectively assessing the skill of the presented rainfall forecasts by use of skill score, the approximate HKS from the rainfall data processed by the WC was plotted for different days. In Houston during winter time, a significant amount of rainfall is experienced. This means that a significant quantity of rainfall may be experienced at least for every four days during the period. In better comprehending the quantitative improvement of the forecast quality for the Houston region, the economic and HKS scores are compared with the persistent daily occurrence collected from different regions of the country.
Findings revealed that the bias correction approach raised the level of the HKS during the last two weeks while the value was lower during the first two weeks. The scores were also lower during day 7, 19, and 26. The HKS values for rainfall thresholds for the last days of February were negative. The general implication of this analysis is that there was a higher HKS skills for all the rainfall threshold range during the 30 day period, except date 8 and 9 when there was false alarm. So far, the bias corrections indicate an improvement of the rainfall weather forecast data by the Weather Channel organization.
In verifying the predictions of the precipitation, worded forecasted are assigned to the following categories.
Thunder or rain ( heavy to medium precipitation expected in the course of the forecast period)
Showers (Intermitted or moderate precipitation anticipated during the forecast period).
Fine or need change( there is no precipitation which is referred to in the forecast, however, the wording suggests that this may be expected)
Drizzle (There is expectation of light precipitation at some point in the course of the forecast period)
Fog then fine and fine(There is no reference to precipitation nor is it anticipated in the forecast)
In day 1, a forecast of fog was associated with a 10% chance of precipitation on that day. Even on day 10, the chance of precipitation was reduced to 8% following a forecast of fog. Even for five days in advance, the results points a 5% chance of precipitation when the forecast is indicated as rain.
It is observable that Fog for day 11 to 19 is not associated with any rainfall despite heavy clouds. On the other hand, a forecast of thunder or rainfall is associated with approximately 5.0 mm of rain.
Regarding temperature, there is a long-term clear trend in the accuracy of the stated forecast. For instance, day 5 of the minimum temperature of 80 Degrees Celcius for the month of February are as accurate/skilful as day 24 of the minimum temperature during the same month. However, day 15 forecasts of the maximum temperature appear to be more skilful compared to day 8 of the maximum temperature during the same month.
A closer analysis of the forecasts and actual data points out that when the variability of the day-to-day maximum temperature is high; the forecast’s skill becomes slightly reduced. However, the variability regarding the accuracy of the formal forecasts by the agency is much lower compared to that of the persistent forecasts.
From the above 30 day weather log, it is apparent that the cloud cover is persistent in total cloud and all decks. The observation scores and persistence occurrence scored better at 12 h in total cloud. This indicates a probability of cross-over in superior prediction skill from diagnostic to trajectory method between 12 and 12 hours. The forecast by the Weather Channel has a consistent frequency for cloud cover for the 30 day period. In essence, the visual presentations of the hemispheric cloud cover provided important information for cloud cover when compared to the actual observations and persistence occurrences.
The terms “overcast” “broken clouds”, “scattered”, and “few”. Accordingly, the distribution of cloud cover by the Weather Channel is in tenths. In this analysis, the overcast reports were plotted against the respective occurrence for the 30 day period. Evidence suggests that although variations against actual data observations, the difference was significantly weak. Stated differently, the data by the Weather Channel is augmented or confirmed by human observation. These observations were recorded at different regions of the Houston County. The following three factors were considered in selecting observers for this course. The first qualification was that the observer had to be able to access different place based observations around his or her region of residency, b)an observer had to be able to report any form of cloud in their view point which are not sensed by ceilometers or directly overhead. In some cases, distance clouds which are observed by human observes may lead to reporting of “scattered” or “few” clouds. On the other hand, the local ceilometers may lead to reporting of “clear” atmospheric conditions. The third and last factor pertains to the safety concerns of the observer, whereby; an observer living in a risky situation could lead to generation of conservative reports. These factors were effectively employed in ensuring that the observers accurate reports which aided a more substantive analysis.
All the cloud cover data for the 30 day period are subsequently normalized for the purpose of producing the same average irradiance in the whole Houston area. The daily occurrence and observations findings of the cloud cover are consistent with those of the Weather Channel, except on day 11, 16 and 22. On these aforementioned days, the skies were reported to be clear, although the WC had forecasted it to be” dense cloud”. Understandably, there are other microclimatic variations with the WC log. However, there is a higher level of comparison for the key features between the observed data and the WC data. Nonetheless, the all inclusive cloud cover portrays a marked cloudy singularity for the forecast period.
Accordingly, it can be rightly reported that the Weather Channel data systematically report cloud cover more accurately. This trend is consistent throughout the 30 year period, of course except a few variations, which can be understandable. The inconsistent observed in some days may be due to the special “augmented” status of some regions or stations of the county (Stokes, and Schwartz, 1994). In such stations, the ceilometer cloud observations may be supplemented by humans who because of various factors may be hindered by specific limitations such as winter, hills, and planes among others. In this respect, it is therefore recommended that future accuracy analysis data should either be retrieved from other weather forecasters or from satellite observations. This will provide a more accurate complimentary and verification data.
In this analysis, the verification for the visibility forecast by the Weather Channel was conducted using visibility observation for 12 and 24 hour lead times. The lowest value observed was employed in scoring against the lowest forecasted value. Visibility was placed into categories of clear which covered between 5000-9999, and poor within the range of 0-1000m as well as fair within the range of 2000-4000m. The entries were then made into a 3 level table for the 12 hour lead time. The scatter plot was utilized in evaluating accuracy at 0hr and 24 hours with determinable visibility both in the observations and in the forecast. Different skill score were then assed from the contingency table. The analysis established that skill and accuracy scores for visibility forecasts are high for the first 6 hours compared to the 24th hour lead time. This generally indicates that the accuracy of the visibility forecasts and skill drops significantly after a six hour time-span.
This paper has conducted an analysis of the accuracy of forecasts of weather elements in the Houston County, USA. The county is notorious for a weather which is highly variable and hence; challenging to the day-to-day weather forecasting. From this analysis, Day 1 of minimum temperatures are as accurate as day 15 of minimum temperatures during the month. Although there is reduced skill and accuracy in some of the days, such as day 13, day 20 and 25, the average error in this forecast is below that of regular or actual whether occurrence. This generally indicates a higher level of accuracy for Houston weather forecast as well as positive skill. Similarly, worded forecasts for rainfall, cloud cover, temperature, humidity, air pressure, wind, precipitation, and visibility for the 30 day period possess positive skill and high effectiveness. Accordingly, it can be rightly reported that the Weather Channel data systematically report cloud cover more accurately. This trend is consistent throughout the 30 year period, of course except a few variations, which can be understandable.
Apparently, the Weather Channel had a low average absolute error for both high and overall temperature forecast for the month of February. Furthermore, the organization had a high percentage of overall, low, and high temperature forecast within three degrees. Overall, the Weather Channel can be statistically tied at a 99% confidence interval for the weather elements analyzed.
The only major inaccuracy of the Weather Channel which is also apparently consisted pertains to the higher and lower probabilities of raining. In most cases, the agency forecasts a higher probability of raining than what there is in actual sense. According to (), this phenomena is popularly known as the wet bias, and it involves a forecaster erring towards predicting more rain than it is actually possible.
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