[1] In the present study, the feasibility of nowcasting convective activity is examined by using thermodynamic indices derived from the ground-based microwave radiometer (MWR) observations located at a tropical station, Gadanki (13.5 N, 79.2 E). There is a good comparison between thermodynamic parameters derived from MWR and colocated GPS radiosonde observations, indicating that MWR observations can be used to develop techniques for nowcasting severe convective activity. Using MWR observations, a nowcasting technique was developed with the data of 26 thunderstorm cases observed at Gadanki. The analysis showed that there are sharp changes in some thermodynamic indices, such as the K index, the humidity index, precipitable water content, the stability index, and equivalent potential temperature lapse rates, about 2-4 h before the occurrence of thunderstorm. A superepoch analysis was made to examine the composite temporal variations of the thermodynamic indices associated with the occurrence of thunderstorms. The superepoch analysis revealed that 2-4 h prior to the storm occurrence, appreciable variations in many parameters are observed, suggesting thermodynamic evolution of the boundary layer convective instability. It is further demonstrated that by monitoring these variations it is possible to predict the ensuing thunderstorm activity over the region at least 2 h in advance. The association between the temporal evolution of thermodynamic indices and convective activity has been tested for the independent case of nine thunderstorms. The present results suggest that ground-based MWR observations can be used effectively to predict the occurrence of thunderstorms at least 2 h in advance.Citation: Madhulatha, A., M. Rajeevan, M. Venkat Ratnam, J. Bhate, and C. V. Naidu (2013), Nowcasting severe convective activity over southeast India using ground-based microwave radiometer observations,

A prediction model based on the perfect prognosis method was developed to predict the probability of lightning and probable time of its occurrence over the southeast Indian region. In the perfect prognosis method, statistical relationships are established using past observed data. For real time applications, the predictors are derived from a numerical weather prediction model. In the present study, we have developed the statistical model based on Binary Logistic Regression technique. For developing the statistical model, 115 cases of lightning that occurred over the southeast Indian region during the period 2006-2009 were considered. The probability of lightning (yes or no) occurring during the 12-hour period 0900-2100 UTC over the region was considered as the predictand. The thermodynamic and dynamic variables derived from the NCEP Final Analysis were used as the predictors. A three-stage strategy based on Spearman Rank Correlation, Cumulative Probability Distribution and Principal Component Analysis was used to objectively select the model predictors from a pool of 61 potential predictors considered for the analysis. The final list of six predictors used in the model consists of the parameters representing atmospheric instability, total moisture content in the atmosphere, low level moisture convergence and lower tropospheric temperature advection. For the independent verifications, the probabilistic model was tested for 92 days during the months of May, June and August 2010. The six predictors were derived from the 24-h predictions using a high resolution Weather Research and Forecasting model initialized with 00 UTC conditions. During the independent period, the probabilistic model showed a probability of detection of 77% with a false alarm rate of 35%. The Brier Skill Score during the independent period was 0.233, suggesting that the prediction scheme is skillful in predicting the lightning probability over the southeast region with a reasonable accuracy.

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In this study, the effects of different nesting methods on simulating a flash‐flood‐producing severe convective event over Cheongju, South Korea on July 16, 2017 was examined. This event developed as part of a mesoscale convective system (MCS) accompanied by frontal forcing. Numerical experiments were conducted using the Weather Research and Forecasting model (WRF) employing one‐way concurrent (OWC), one‐way sequential (OWS), and two‐way (TW) nesting approaches with advanced physics options from Korean Integrated Model (KIM). Analysis of model simulations against Tropical Rainfall Measuring Mission (TRMM) and Automatic Weather Station (AWS) observations suggests that the TW nesting method performs better than both OW nesting methods in simulating rainfall. Large‐scale features, moisture, instability in the boundary layer, and the vertical distribution of meteorological parameters favorable for convection are better represented by TW nesting. Probability distribution Function (PDF) analysis from AWS/WRF reveals that the local‐scale distribution of surface meteorological parameters which affect storm intensity were well captured using TW. Further assessment of Equitable Threat score (ETS) also showed better precipitation forecast skill over different thresholds in TW. Vertical velocity in the innermost domain simulated using TW nesting is more consistent with ERA5 reanalysis. An additional MCS (11–13 July 2006) simulated using a similar numerical setup also benefited from TW nesting, increasing confidence in the initial findings. Along with frequent lateral boundary conditions, TW nesting allows multi‐scale interactions between parent and nested domains and improves the representation of both synoptic and local‐scale features, enhanced cloud hydrometeor, vertical velocity distributions, and subsequent rainfall.