A new paradigm for short-range forecasting of severe weather over the Indian region

Abstract

While destruction associated with floods during the monsoon season and cyclones receives wide attention, the extreme weather in the form of hail, lightning and high winds have also caused widespread devastation over India on a small spatial scale in recent years, especially during the period of March to June. India Meteorological Department (IMD) organized a special forecast improvement campaign during the period March to June of 2017–2019 when the weather forecasts at all offices of IMD were targeted towards an accurate forecast of the extreme form of thunderstorms and their associated impact in short range to nowcasting timescale and their dissemination. The purpose of this study is to quantify the improvement in operational thunderstorm forecast accuracy, in short range (24 h Severe Weather Guidance at subdivisional level) and nowcast scale (nowcasts for individual stations valid for 3 h and issued every three hours) during March to June of 2017 to 2019 and compare the same with the accuracy of previous years. As a result of these efforts, there has been a significant jump in forecast accuracy in the 24-h thunderstorm forecast as well as 3-h nowcast guidance for thunderstorms across the country. Probability of Detection (POD) scores for India as a whole for the 24-h thunderstorm forecast has doubled, while the false alarms (FAR) have remained at the same level as before the start of the forecast campaign. The results indicate that since a thunderstorm is a disastrous weather event, the forecasters generally tend towards spatial over-forecasting. However, this is not uniform across the months. There is systematic lower accuracy in the season transition months of March (winter to summer) and June (dry summer to wet summer). While POD decreases in both March and June, FAR decrease throughout the season. The significant evolution of atmospheric parameters (moisture in particular) as the season changes, favours the maturation of thunderstorms to cumulonimbus stage as the season progresses, and the problem of over forecasting in March becomes a problem of under forecasting of thunderstorms in June. Another reason for false alarms is the unconscious linkage of the thunderstorm with the pattern of rainfall occurrence. However, since all rain-giving clouds over India do not necessarily mature to the cumulonimbus stage, and vice versa, the two are not always related. This is particularly true for the more arid regions of the country, especially in March, where false alarms are higher. The poor density of reporting observatories compared to the mesoscale nature of the events may also increase false alarms, especially over the small maritime islands and the arid regions of the mainland. The accuracy of the All India 3 hourly station level nowcast also improved systematically since 2017. Despite these constraints, the improvements at all scales were possible due to (a) augmentation of observation network by the rapid expansion of Doppler radars network throughout the Indian mainland as well as the installation of a ground-based lightning detection network, (b) numerical modeling products introduced in 2019 to provide short-range forecasts for all aspects of convection; both of which are incorporated into the forecast framework through Standard Operating Procedures (SOP) to standardize the forecast procedure throughout the Indian region. A more objective forecast strategy, using data generated from a denser network of DWRs and crowdsourcing methods as well as more accurate mesoscale models will go a long way to further improve the thunderstorm forecasts.