عنوان مقاله [English]
Prediction of thunderstorm is one of the most difficult issues in weather forecasting. Deep convective clouds develop at small spatial and temporal dimensions about 1-10 km and 1-12 h. Various Thermodynamic parameters have been discovered over the past 40 years, according to data obtained by radiosounds. The capability of these indices in forecasting instability varies over time and location. The performance of instability indices obtained from radiosound to predict thunderstorm have been examined. by Schultz (1989), Lee and Passner (1993), Huntrieser et al. (1996). Haklander and Van Delden (2003) have also studies the accuracy of 32 instability indices in the Netherlands. They provided estimations for the optimal thresholds and relative forecast skills of all these thunderstorm predictors employing skill score parameters such as True Skill Statistic (TSS) and Heidke Skill Score (HSS). When comparing forecast skills in a dichotomous forecasting scheme, the lowest 100 hPa Lifted Index scores the best, although other versions of the Lifted Index have relatively good performance (Haklander and Van Delden, 2003, 273). Kunz (2007) studied the preconvective environment on days with ordinary, widespread, and severe thunderstorms in Southwest Germany. Various thermodynamic and kinetic parameters calculated from radiosoundings at 12UTC were verified against subsequent thunderstorm observations derived from SYNOP station data, radar data, and damage reports of a building insurance company. For the ordinary decision whether a thunderstorm day was expected or not, the best results were obtained by the original Lifted Index, the Showalter Index, and the Modified K- Index (Kunz, 2007, 327). Rasooli et al. (2007) studied the changes in the temporal and spatial distribution of thunderstorms in the Northwest of Iran and concluded that the likelihood of thunderstorms precipitation is higher in spring and summer. Therefore, we have selected the spring and summer seasons for this study. Due to the low number and sparse spatial distribution of radiosound network and the high cost of lunching, a prediction based only on radiosound network will suffer from data deficiency. Considering the fact that the Terra and Aqua satellites can cover a very broad area by passing over a region, using MODIS sensor data can improve lack of upper level station observations. MODIS Profiles have been used and verified in some studies. Chryoulakis et al. (2003) used 3 instability indices extracted from MODIS and radiosound for assessing atmospheric instability and have shown that the three satellite derived instability indices are well correlated with those derived from radiosound. Halimi et al. (2011) studied the verification of MODIS temperature and dew point temperature profiles versus radiosonde’s temperature profiles at Mehrabad station. In that study, the MODIS temperature profiles showed acceptable conformity with the radiosound’s temperature profiles. The whole Bias of 1.95 and RMSE 2.41°K for above 780 mbar level were obtained. Jafari (2012) compared 3 instability indices TT, L and K obtained from MODIS device with radiosound at Tabriz station. It was observed that the TT, L and K indices show good correlation coefficients of 0.50, 0.58 and 0.63 in spring and 0.77, 0.75 and 0.72 in summer, respectively.
The aim of this study is to evaluate the performance of instability indices derived from vertical profiles of MODIS in predicting instability at Urmia Station.
The instability indices of TT, L and K obtained from satellite have been compared with the 3-hour-daily synoptic reports in Urmia station. The World Meteorological Organization has defined some codes to determine the current and past weather conditions, marked by double digits (00-99). In this study, the codes 13, 14, 15, 17, 18, 19, 25, 26, 27, 29 and 80-99 related to thunderstorm activities are used. If any of these numbers is recorded at the station, the day is considered a thunderstorm day. Detecting the number of thunderstorm days in spring and summer 2008 from synoptic station reports, we selected the month May and July as the representatives of spring and summer. The Terra and Aqua satellite images were extracted from LAADS website during these two months. The MODIS atmospheric profile product (MOD-07) consists of several parameters: total ozone burden, atmospheric stability, temperature and moisture profiles, and atmospheric water vapor. All of these parameters are produced day and night for level 2 at 5*5 km pixel resolution when at least 9 FOVs are cloud free. With writing a program in IDL environment the desired pixel values have been extracted from the images. By comparing the instability indices derived from MODIS images and 3-hourly synoptic reporting, the validity of these results in forecasting the probability of occurrence of thunderstorms and weather instability was assessed.
Results and discussion
In this study up to 83 images are studied for month May, in which 14 images are related to the days that phenomenon of shower and storm was reported. For month July we examined 98 images in which 12 images were related to the stormy days. Examining the table of contingency the highest HSS rating is for LI with the score of 0.30. After LI, TT with 0.24 and K with 0.21 were in the next rating. The results of this study for the heist HSS are consistent with results of Haklander and Van delden (2003) and Kunz, (2007). In this way, the potential of thunderstorm based on instability indices are more depended on hidden instability like L and then on potential instability and at the end conditional instability. It can be inferred that using MODIS instability indices can be a good replacement of radisound observations.
In conclusion we have studied the potential of MODIS atmospheric profiles in predicting instability of the north west of Iran. By comparing the contingency tables for both spring and summer we have concluded that the better results and higher HSS scores goes for L (indicates hidden instability) instability index and then TT and K (indicating potential instability and conditional instability) indices.