Raghavendra Ashrit

Simulation of a Himalayan cloudburst event

Intense rainfall often leads to floods and landslides in the Himalayan region even with rainfall amounts that are considered comparatively moderate over the plains; for example, ‘cloudbursts’, which are devastating convective phenomena producing sudden high-intensity rainfall (∼10 cm per hour) over a small area. Early prediction and warning of such severe local weather systems is crucial to mitigate societal impact arising from the accompanying flash floods. We examine a cloudburst event in the Himalayan region at Shillagarh village in the early hours of 16 July 2003. The storm lasted for less than half an hour, followed by flash floods that affected hundreds of people. We examine the fidelity of MM5 configured with multiple-nested domains (81, 27, 9 and 3 km grid-resolution) for predicting a cloudburst event with attention to horizontal resolution and the cloud microphysics parameterization. The MM5 model predicts the rainfall amount 24 hours in advance. However, the location of the cloudburst is displaced by tens of kilometers

Verification of Medium Range Probabilistic Rainfall Forecasts Over India

Forecasting rainfall in the tropics is a challenging task further hampered by the uncertainty in the numerical weather prediction models. Ensemble prediction systems (EPSs) provide an efficient way of handling the inherent uncertainty of these models. Verification of forecasts obtained from an EPS is a necessity, to build confidence in using these forecasts. This study deals with the verification of the probabilistic rainfall forecast obtained from the National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Forecast system (NGEFS) for three monsoon seasons, i.e., JJAS 2012, 2013 and 2014. Verification is done based on the Brier Score (BS) and its components (reliability, resolution and uncertainty), Brier Skill Score (BSS), reliability diagram, relative operating characteristic (ROC) curve and area under the ROC (AROC) curve. Three observation data sets are used (namely, NMSG, CPC-RFE2.0 and TRMM) for verification of forecasts and the statistics are compared. BS values for verification of NGEFS forecasts using NMSG data are the lowest, indicating that the forecasts have a better match with these observations as compared to both TRMM and CPC-RFE2.0. This is further strengthened by lower reliability, higher resolution and BSS values for verification against this data set. The ROC curve shows that lower rainfall amounts have a higher hit rate, which implies that the model has better skill in predicting these rainfall amounts. The reliability plots show that the events with lower probabilities were under forecasted and those with higher probabilities were over forecasted. From the current study it can be concluded that even though NGEFS is a coarse resolution EPS, the probabilistic forecast has good skill. This in turn leads to an increased confidence in issuing operational probabilistic forecasts based on NGEFS.

Improved Track and Intensity Predictions Using Cyclone Bogusing and Regional Assimilation

Tropical cyclones (TC) originate and intensify over the oceans where data coverage is sparse. This leads to inaccurate representation of location and intensity of tropical cyclones in the initial condition (IC) of the NWP models; one of the reasons for large errors in the forecast track and intensity. These errors are reduced by use of bogus vortex in the initial condition (Trinh and Krishnamurti, 1992; Kurihara et al., 1993; Leslie and Holland, 1995). Kurihara et al. (1993) proposed a scheme to improve the representation of a TC in the IC of a high-resolution hurricane model. Satellite data coverage over the ocean along with high resolution data assimilation tools (3DVAR or 4DVAR) also provides an opportunity to improve the IC. This study concentrates on the impact of bogus vortex and regional assimilation in WRF model on track predictions of some of the 2010 TCs of North Indian Ocean.

Performance of NCMRWF Model TC Track Forecasts During 2013

There are two tropical cyclone (TC) seasons over the North Indian Ocean (NIO), (including the Bay of Bengal (BOB) and the Arabian Sea (AS)), i.e. during the pre-monsoon months (April–early June) and the post-monsoon months (October–December) (Mohanty et al., Mar Geod 33:294–314, 2010). Further the Indian subcontinent happens to be one of the world’s highly vulnerable areas since the coastal population density is very high leading to an extensive damage to life and property. Therefore, forecasting of TC track and landfall location is critical for early warnings and mitigation of disaster. Track forecast errors over the NIO though improved significantly in recent years (Mohapatra et al., J Earth Syst Sci 122:589–601, 2013, J Earth Syst Sci 124:861–874. doi: 10.1007/s12040-015-0581-x, 2015) are still high relative to those over the Atlantic and Pacific Oceans. With advancements in computational power, development of better NWP models (both global and regional), the forecasting capability of meteorologists have greatly increased. Several meteorological centers like NCEP, UKMet office, ECMWF, JMA, JTWC etc give a real time forecast of TC tracks from their global NWP models (deterministic as well as Ensemble Prediction Systems (EPS)) (Hamill et al. Mon Weather Rev 139:3243–3247, 2011; Froude et al. Mon Weather Rev 135:2545–2567, 2007; Buckingham et al. Weather Forecast 25:1736–1754, 2010; Heming et al. Meteorol Appl 2:171–184, 1995; Heming and Radford Mon Weather Rev 126:1323–1331, 1998). TC track prediction from an ensemble forecasting system besides providing a track from each ensemble member also provides the strike probability (Weber Mon Weather Rev 133:1840–1852, 2005). For the TCs of NIO, Mohapatra et al. (J Earth Syst Sci 122:589–601, 2013, J Earth Syst Sci 124:861–874. doi: 10.1007/s12040-015-0581-x, 2015) provided a detailed verification of the official forecast tracks and its improvements in the recent past. This study provides a detailed verification of the NCMRWF NWP model forecasts of 2013 TC cases. Some of the earlier studies (Ashrit et al. Improved track and intensity predictions using TC bogusing and regional assimilation. In: Mohanty UC, Mohapatra M, Singh OP, Bandyopadhyay BK, Rathore LS (eds) Monitoring and prediction of TCs in the Indian ocean and climate change, Springer, Dordrecht, p 246–254, 2014; Chourasia et al. Mausam 64:135–148, 2013 and Mohandas and Ashrit Nat hazard 73:213–235, 2014) focused on the NCMRWF model TC forecasts and the impact of bogusing, assimilation and cumulus parameterisation etc. The present study is focused on the real time operational forecasts provided to India Meteorological Department (IMD). During May–December 2013, there were five TCs observed in the Bay of Bengal namely: Viyaru (May10–17), Phailin (October 4–14), Helen (November 19–23), Lehar (November 19–28) and Madi (December 6–13). This report summarises the performance of the real time prediction of these TC tracks by the NCMRWF Global Forecast Systems.

Spatial Verification of Rainfall Forecasts During Tropical Cyclone ‘Phailin’

During October 2013 Bay of Bengal (BOB) tropical cyclone (TC) ‘Phailin’ hit east coast of India. This was the most intense cyclone that made landfall over India after the Odisha Super Cyclone (29 October 1999). This TC originated from a remnant cyclonic circulation from the South China Sea. It intensified into a cyclonic storm on the 9 October 2013 and moved northwestwards. It further intensified into a very severe cyclonic storm on 10 October 2013 over east central BOB. It crossed Odisha coast near Gopalpur around 2230 h IST of 12 October 2013 with a sustained maximum surface wind speed of 200–210 kmph gusting to 220 kmph. Some of its unique features included the rapid intensification of the system from 10 October to 11 October 2013 resulting in an increase of wind speed from 83 to 215 kmph. Also, at the time of landfall on 12 October, maximum sustained surface wind speed in association with the cyclone was about 215 kmph and estimated central pressure was 940 hPa with pressure drop of 66 hPa at the center compared to surroundings (RSMC, New Delhi, 2014).

Forecasting of monsoon heavy rains: challenges in NWP

Last decade has seen a tremendous improvement in the forecasting skill of numerical weather prediction (NWP) models. This is attributed to increased sophistication in NWP models, which resolve complex physical processes, advanced data assimilation, increased grid resolution and satellite observations. However, prediction of heavy rains is still a challenge since the models exhibit large error in amounts as well as spatial and temporal distribution. Two state-of-art NWP models have been investigated over the Indian monsoon region to assess their ability in predicting the heavy rainfall events. The unified model operational at National Center for Medium Range Weather Forecasting (NCUM) and the unified model operational at the Australian Bureau of Meteorology (Australian Community Climate and Earth-System Simulator — Global (ACCESS-G)) are used in this study. The recent (JJAS 2015) Indian monsoon season witnessed 6 depressions and 2 cyclonic storms which resulted in heavy rains and flooding. The CRA method of verification allows the decomposition of forecast errors in terms of error in the rainfall volume, pattern and location. The case by case study using CRA technique shows that contribution to the rainfall errors come from pattern and displacement is large while contribution due to error in predicted rainfall volume is least.