Mitra, AK; Sharma, AK; Bajpai, I; Kundu, PK (2012). An atmospheric instability derived with MODIS profile using real-time direct broadcast data over the Indian region. NATURAL HAZARDS, 63(2), 1007-1023.
In this study, assessing the atmospheric instability, a new index, named here as MODIS (Moderate Resolution Imaging Spectroradiometer) profile index (MPI), has been statistically computed using temperature and moisture profile data from the real-time direct broadcast receiving systems installed at three places of India Meteorological Department. The training dataset has been prepared using MODIS temperature and moisture profile from the Aqua and Terra satellites over the Indian region for clear and convective weather conditions during the period of March to June 2011. The MPI values are produced at 5 x 5 km pixel resolution when at least 6 out of 9 FOVs from MODIS granules are found cloud free. If more than 3 FOVs are cloudy, the MPI has not been computed. The formulation of MPI and its comparison have been examined with well-established traditionally used K index, Lifted Index and total totals index derived from radiosonde profiles of temperature, pressure and humidity. It has been observed that in most of the cases, MPI has well correlated with those derived from ground truth observations. Therefore, spatially interpolated MPI can be utilized as an indicator for regional and location-specific forecast over the areas where radiosonde data are not available. The results also indicated that MPI can be used as a sensitive measure in very early stages of instability developments such as thunderstorm and rainfall because no other single stability index can provide a distinct threshold value for these events. Therefore, a single MPI value at a certain threshold can be treated as a stability index instead of other available indices. It is also being proposed that the inclusion of MPI as a stability parameter in physical or numerical modeling can improve accurate local severe storm predictions as a useful predictor and can also be used as diagnostic tools. The MPI can make a useful simulation using entire temperature and moisture profile data for the assessment of instability significantly to severe weather forecasting since other instability indices are often derived from a fixed pressure level quantity of vertical profile parameters.