From the forecast weather log, the most accurately forecasted elements were visibility, humidity and atmospheric pressure (Wunderground.com, 2016). Air pressure is the weight pressing down on the earth and the ocean, and this depends on the amount of air at a given point where the atmospheric pressure is measured. This is the main reason forecasting was accurate as compared to temperature. Additionally, air pressure is related to a density which also correlates with air temperature plus the height above the sea level. Atmospheric pressure varies with other weather elements and is the most important factor that influences weather pattern of a place. Visibility and humidity were also accurately forecasted because most factors that affect these elements are temperatures and precipitation in an area.
Least Accurately Forecast Elements
The least accurately predicted an element from the forecast weather log is air temperature. When the temperature is measured and get approximate numbers at some trusted weather location, many dont know the exact distribution of the parameters. In this case, the inaccuracies add up, and the chaotic systems are also unforgiving to these errors in temperature recording. For example, a weather station along a paved way would absorb more heat and record more temperatures from the sun than a weather station located close to a forest during the day. Additionally, there exist an increasing air temperature, which turns the wind direction and affects the humidity and the cloud formation patterns in the weather stations. The cloud formation patterns weather scattered, few or clear, affect significantly temperature distribution in an area, and it becomes almost impossible to forecast temperatures on a short and long scale (Wagner & Adler, 2004).
Different weather elements are slowly extended by the use of denser sensor grids, detailed satellites, and accurate maps. Additionally, the contemporary technology has incorporated the involvement of mathematical and computation models to allow for faster and more accurate weather forecast in different sites and meteorological stations. And since the atmosphere is a dynamic and chaotic system, to change the situation of inaccuracy in the temperature and other weather elements, two solutions for the future nature of the atmosphere, as discussed by the Numerical Weather Prediction (NWP) model exist. These solutions state that even the smallest differences in the initial field in the two model simulation should result in errors that should often grow exponentially. Secondly, since there exist a practical matter in temperature recording, it is impossible to measure the state with absolute accuracy. Therefore, there should be a nonlinear error that eventually destroys the importance of any forecast.
With the Numerical Weather Prediction (NWP) model, error in temperature and other weather elements usually occurs within a time limit of 7 days as long as a 10-12 prediction for large-scale weather patterns and 2-3 prediction for the smaller scale weather predictions. In the same scenario, the rate of nonlinear error growth is very sensitive in relation to the quality and density of the observation team inputs which are always available to be assimilated and the expected analysis of a given day and time for a particular model cycle. The atmospheric conditions at the time and the quality of the model are associated with the accuracy of the temperatures and other weather elements recorded (Wunderground.com, 2016).
Finally, yet important, these models have biases and are constructed with imperfect physics theories and represents partial knowledge of the entire working process that is not explicitly corrected by a particular model of convection for the global NWP model like the GFS. This does not conserve the energy or the momentum and many other shortcomings in the inaccuracies noted.
Trends or Correlations Between Weather Elements
The global warming is a threat has continued to activate investigations in the trends in the various meteorological parameters that exist at different temporal scales. It is important to note that even if there is global warming, it does not occur uniformly everywhere across the world and at any time. In Virginia Beach, VA it is observed that the weather in the late winter season and springs have changed from the previous years which were warmer, whereas no significant tendency can be noted from the previous seasons. For the last 30 years, the monthly average air temperature has risen by 4.50C in December and the snow cover duration reduced by three days in January. The mean air temperature did not rise during the last century in Virginia Beach, VA but the summer period, and thermal growing increased. This was attributed to the earlier start of the climatic season in the spring followed by a change in the autumn season to a later date.
The trends in cloud cover also improved as compared to other earlier seasons. The results in the weather log illustrate that low clouds increased in January by between 1.4 and 3.5 tenths depending on the stations. The correlation shows that the amounts of low clouds in December and January have been influenced by some large scale factors which are also related to the changes in the atmospheric circulation.
Fronts Experienced in the Area
There were days when wild weather ruled the area. Next to the beach, dry cold fronts moved the region. During the days, the significant changes observed increased in the mid and high level of cloudiness that leaned toward warmer guidance. Frontal passages dominated the nights and were dry as the middle and upper levels which forced ascending of residing to the northern part of Virginia Beach, VA.
During the same time, temperatures remain well above the normal, but the problem was the warm looks. Trimmed high temperatures were experienced in most locations coupled with low-level anticyclone which began a bit late for ideal warming. The model also showed high-level cloud during the front which streams from west to east within the zonal flow aloft.
The cool fronts also covered some areas with warm effects combined with a weak perturbation at mid-levels that warranted rain showers. Temperatures during the early January were cooler before the aforementioned cold front and were expected to continue to 10 degrees above the normal. Additionally, highs in the 40s and 50s are expected in the area with cloudy shy with higher chances of rain showers.
Characteristics of the Front
Temperature contrast influenced the thickness of fronts in an inversely proportional manner whereby there was a sudden change in temperature from the front. Additionally, change in pressure was also experienced which was reflected in the bending of isobars towards the low-pressure region. The isobars were smooth, and the isobars lied in the low-pressure troughs. The fronts also experienced a wind shift since the wind movement was the function of the pressure gradient and the Coriolis force. The southwest wind air masses gave way to the northwest wind of the polar air mass across the fronts in the area. Most of the frontal activities were associated with cloudiness and precipitation as a result of the ascent of the warm air which acted as a coolant and condensed hence causing rainfall. The intensity of rainfall during the period recorded in the weather log depended on the slope of the ascent and the amount of water vapor that was present in the air at that given time.
Over the years, Virginia Beach has had a warm, humid temperate climate with some aspects of hot summers and dry seasons. The overall area is covered with 58% of seas and oceans, 3 % lakes and about 16% of cropland. Winters are always mild, and snows are light. The average annual rainfall is 15.30C with the wettest seasons in spring and summer even though rain is constant all year through. The highest temperature recorded was 410C in 2010 and the lowest -190C in January 1985 recorded at the Norfolk International Airport. During the 30 day period, there were some deviation and anomalies from the historical weather in that time in different weather elements recorded in the weather log (Weather Underground, 2016)
Temperature: The hottest day of the first month of 2016 was January 10, recording a temperature of 320C. The average air temperature for the day was 260C, which exceeds the highest temperature recorded for the day by ten degrees. Still on the temperature anomalies, the longest spell in the weather log was from December 28 to January 3, consisting of 5 consecutive days which had warmer temperatures than the average high temperatures. December ha...
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