The development of a shape factor instability index to guide severe weather forecasts for aviation safety
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| 3. Results and discussion Figure 1 shows the profiles of EPT for both weather categories. Since a negative gradient is indicative of thermal instability, both weather categories demonstrate some pre-convective activity. However, the more negative gradient is shown for the cases when severe weather activity occurred. The data show the marked drop near 5 km, which would accounted for the more negative value of SF since it represents an integrated effect over the entire profile. Figure 2 shows the profiles of the mixing ratio for water vapour. As expected, the data show the presence of significantly higher concentrations of water vapour below a height of 6 km. These results indicate that at the times of the recorded data, the water vapour had not yet precipitated out of the atmosphere. The values of SF as Figure 1. Vertical gradient of EPT (K km) versus geopotential altitude (km) for rain-free weather (plus signs) and a highly convective event with severe weather activity (dots). described above represent mathematically the line integral of the gradient of the EPT along the curve itself. Therefore, preliminary results suggest that SF can be used as a benchmark indicator of local weather conditions. The typical temperature profiles for both weather conditions considered in this study are shown in Figure Notice that the temperature values in the case for severe weather are slightly higher at several altitude locations in the profile. This is due to the release of sensible heat associated with the condensation of water vapour. Three examples showing the profiles for the moisture level depths (MLD) for both weather conditions are displayed in Figure 4. The MLD is the difference between the temperature and the dew point temperature. The dew point temperature is the temperature of the air parcel fora specific air pressure at which condensation commences. The smaller the MLD the closer the air parcel is to becoming saturated, and the closer the atmosphere is to being unstable, initiating the conditions for weather with precipitation. Other important factors contributing to Figure 2. Water vapour mixing ratio (g g) versus geopotential altitude (km) for rain-free weather (plus signs) and a highly convective event with severe weather activity (dots). Figure 3. Typical tropospheric temperature (K) versus geopotential altitude (km) for rain-free weather (plus signs) and a highly convective event with severe weather activity (dots). Copyright 2008 Royal Meteorological Society Meteorol. Appl. 15: 465–473 (2008) DOI: 10.1002/met I. WALKER ET AL. Figure 4. (a)–(c)Three typical profiles of moisture level depth (K) versus geopotential altitude (km) for rain-free weather conditions (plus signs) and highly convective events with severe weather (dots). instability include the lapse rate and the vertical structure of moisture. One notable and consistent feature shown in Figure is that the lowest value of MLD for the case of severe weather occurs at an altitude near 5 km, which is the same altitude associated with the prominent negative value of the gradient of the equivalent potential temperature as shown in Figure 1. Another interesting characteristic of the data is the appearance of two distinguishable peaks of the MLD for the case of no rain. The reason for this phenomenon is unclear to the authors. Calculations of SF used temperature profiles from weather reporting stations in the Mid-Atlantic and southeast Atlantic coast of the United States. The weather and time were determined by obtaining information from the NWS. In order to define the training set (a set of data to determine SF values critical for the occurrence of severe weather) for this methodology, values of SF were computed only for conditions that were either completely rain-free (neither fog nor light rain) and for cases when there was severe weather. The sample consisted of datasets, each with an average of 20 profiles and the SF was computed based on the averages of the first and second derivatives of EPT using Equation (The data for both weather conditions were accumulated and plotted in the form of histograms. These plots are shown in Figures 5 and 6. The data demonstrated a marked difference between the data corresponding to no precipitation and the data that was for severe weather conditions. For the case of clear weather, there was a peak centred on an SF of. For the heavy precipitation data the peak occurred at and another less prominent peak at. A threshold value distinguishing the two opposite weather conditions was at an SF of. These results suggest that SF can be used as predictor of severe weather with thunderstorms. Based on the information displayed in Figures 5 and, the thresholds of SF for clear weather and severe weather were for values that were greater than or equal 1 2 3 4 5 6 7 8 9 -40 -20 0 20 Download 152.18 Kb. Share with your friends:
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