A tropical cyclone (TC) Vayu developed over the Arabian Sea during June, 2019. It followed a northward track from southeast Arabian Sea to northeast Arabian Sea close to Gujarat coast during 10–12 June 2019 as a very severe cyclonic storm. It skirted south Gujarat coast by recurving west-northwestwards during 13th–14th June and again made a northeastward recurvature on 16th June towards Gujarat coast. However, it weakened over Sea on 17th. There was large divergence among various models in predicting the track of TC Vayu leading to over warning for Gujarat state and also delay in dewarning leading to evacuation of people from coastal region. Hence, a study has thus been taken up to analyze the performance of various numerical weather prediction (NWP) models in forecasting the track of TC Vayu so as to find out the reason for above limitation of NWP models. Results suggest that there is a need to relook into the existing multi-model ensemble (MME) technique which outperforms individual models in track forecasting. There is also a need to improve the individual deterministic model guidance so as to suitably represent the interaction between mid-latitude westerlies with the TC and steering anticyclone by improving the initial and boundary conditions through augmented direct and remotely sensed observations over the Arabian Sea and their assimilation in NWP models.

$\bf{Highlights}$

$\bullet$ The multiple interactions among the wind fields of TC Vayu, middle latitude westerlies and anticyclones over central India & Arabian Peninsula led to the unique track of Vayu with two recurvatures in its life cycle.

$\bullet$ The prediction of time and point of recurvature in the track of TCs is still a challenge for the NWP models and hence the operational forecast, as models could not represent the interaction of mid-latitude westerlies with the TC and steering anticyclone over either side of the TC.

$\bullet$ Comparing the average track forecast errors of different models and multi-model ensemble (MME) for the recurving TCs during 2009–2019, the MME shows minimum average track forecast error. However, the consistency in MME based track forecast decreases with increase in lead period.

$\bullet$ There is a need to look into the existing MME and improve it by re-defining the best constituent members and improving the performance of individual models through augmentation of direct & remotely sensed observations, data assimilation and the physical processes in the model.

The major objective of any national weather forecasting services is to provide weather forecast and warnings and other meteorological related information to the public and government for the safety of life and property and economic activities. The heavy rainfall causes huge loss to the public in form of flood and landslide in varying severity mainly during monsoon season (June–September). Hence its accurate prediction is essential and the accuracy of prediction needs to be verified quantitatively to evaluate its strength and weakness. The National Weather Forecasting Centre (NWFC) of India Meteorological Department (IMD) issues heavy rainfall (HR) warnings for the safety of life and property of the public. In this study, verification of operational heavy rainfall (HR) warning issued by NWFC of IMD for 36 sub-divisions of India is carried out. The verification scores presented in the study are for 24 hrs (D1), 48 hrs (D2) and 72 hrs (D3) lead period average warning skills during 2014–2018 and year-wise trend of the HR warnings for the period 2002–2018. In general, it is observed that there are significant improvements in skill scores in recent years. The improvement in D3 is at higher rate as compared to D1 scores. The improvement in the recent years is mainly due to improvement in model resolution and data assimilation in the Numerical Prediction (NWP) Models runs by Ministry of Earth Sciences (MoES), Government of India and their interpretation and utilization by the forecasters for objective consensus forecast using an objective decision support system and synoptic value addition.

$\bf{Highlights}$

$\bullet$ There is significant improvement in heavy rainfall warning skill of India Meteorological Department during monsoon season in recent two years (2017 and 2018) as compared to 2002–2016.

$\bullet$ The skill scores namely, Probability of Detection (PoD), Critical Success Index (CSI) and Heidke Skill Score (HSI) has improved by 48%, 46% and 33%, respectively, as compared to mean of scores between 2002–2016 for Day 1 (D1) warning.

$\bullet$ In Day 3 (D3) warning, there is an improvement by 69%, 54% and 54% in PoD, CSI and HSS respectively during 2017–2018 as compared to mean of 2013–2015. The improvement in D3 warning is at higher rate as compared to D1 warning.

$\bullet$ In general, the skill scores are higher over the regions with higher frequency of heavy rainfall and lower over less prone regions of heavy rainfall.

$\bullet$ These improvements in the forecast warning skill may be attributed to availability and use of latest forecasting models with high resolution and better data assimilation. Apart from the above, the structured monitoring of the monsoon circulations parameters, interpretation of NWP models guidance through Forecast Demonstration Project (FDP), objective consensus through decision support system and subjective consensus amongst the forecasters through video conference contributed significantly improved HR warning in recent years.

In the recent past, there were extensive floods in the Western Himalayan region (WHR) due to continuous long spells of heavy rainfall for 3–4 days that caused a huge loss in life and property over the region. WHR is a data sparse region, with limited meteorological stations having a continuous long spell of daily precipitation data. In the present study, spatial and temporal variability of seasonal as well as annual precipitation, precipitation days and maximum accumulated daily, 2 days, 3 days, 4 days and 5 days precipitation over WHR is considered by using daily precipitation data of 18 meteorological stations of the region. Out of 18 meteorological stations, five stations have continuous data from 1901 to 1980 and remaining 13 stations data is considered for their common period from 1981 to 2014. Accordingly, the analysis is carried out in two parts, first for 1901–1980 (for 5 stations) and second for 1981–2014 (for all the 18 stations). The analysis suggests high variability in the spatial and temporal distribution of seasonal as well as maximum accumulated daily to 5 days precipitation over WHR. In general, increasing trends in maximum accumulated precipitation in lower altitude stations and decreasing trends in higher altitude stations are observed in monsoon season and vice-versa in the winter season during the period 1981–2014. The increase in maximum accumulated daily to 5 days precipitation is up to 9.7 mm per decade during 1901–1980 and is up to 45.5 mm per decade during 1981–2014 in monsoon season in lower altitude stations. Thus, the increase in maximum accumulated precipitation during monsoon season becomes manifold during 1981–2014 as compared to the period 1901–1980.

Two extremely severe cyclonic storms (ESCSs) Phailin and Hudhud developed over the Bay of Bengal (BoB) during October 2013 and 2014 and crossed the east coast of India near Gopalpur (Odisha) and Visakhapatnam (Andhra Pradesh) at 1700 UTC of 12th October 2013 and 0700 UTC of 12th October 2014, respectively, causing immense loss of property. Considering the devastating effect associated with the typical characteristics of the two tropical cyclones (TCs) and their occurrence during same period of the post-monsoon season, a study has been undertaken to compare the vital parameters including location, movement, intensity, size, etc., of these TCs. The results of this study can be utilized for better understanding and prediction of structural characteristics of TCs over the north Indian Ocean (NIO) and hence the associated adverse weather like heavy rain, gale wind and storm surge. The higher intensity,higher rate of intensification, longer duration in very severe cyclonic storm (VSCS) or higher stage, lower rate of decay after landfall and larger size were the typical characteristics in the case of TC Phailin leading to its higher damage potential in terms of accumulated cyclone energy (ACE) and hence higher loss interms of power dissipation index (PDI) as compared to TC Hudhud.

$\bf{Highlights}$

$\bullet$ The damage potential and losses were higher in case of tropical cyclone (TC) Phailin due to higher intensity, rapid intensification, longer duration in very severe cyclonic storm (VSCS)/extremely severe cyclonic storm (ESCS) stage, lower rate of decay after landfall and larger size as compared to TC Hudhud.

$\bullet$ The introduction of Doppler Weather Radars in recent years and improved modelling capabilities blended with subjective value addition through synoptic guidance enabled IMD to accurately monitor the track, intensity and landfall characteristics of these TCs.

$\bullet$ Though, the track forecast difficulty was higher in case of Hudhud as compared to Phailin, the errors were less in Hudhud and its forecast was more skillful due to improvements in numerical weather prediction (NWP) modelling in 2014. However, the intensity forecast difficulty was higher in case of Phailin as compared to Hudhud due to rapid intensification which could not be predicted by the dynamical and statistical models of India Meteorological Department (IMD).

$\bullet$ There is scope to improve NWP models and hence the operational intensity forecast especially rapid intensification forecasting.

Tropical Cyclone Ockhi was an intense cyclone, with a peculiar and long track, in the Arabian Sea in 2017. It caused severe damage to coastal infrastructure and death of 282 people. Indian National Centre for Ocean Information Services (INCOIS) issued the Joint INCOIS-IMD (India Meteorological Department) bulletins on the Ocean State Forecasts (OSF) and alerts/warnings during Ockhi. Validation of the OSF from INCOIS using buoys reveals that the forecasts were in good agreement with the observations [average correlation 0.9, RMSE ${\le}$0.8 m (for larger waves), and scatter index ${\le}$25%]. Climatological analysis of Genesis Potential Index (GPI) suggests that the southeast Arabian Sea, where the TC-Ockhi was intensified, had all the favourable conditions for intensification during November/December. Moreover, it was found that four days before the genesis of Ockhi, the environmental vorticity and relative humidity were more favourable for the cyclogenesis compared to vertical wind shear and potential intensity. The intensification rate was rapid as experienced by earlier cyclones in this region. Also, the cyclone track closely matched the background tropospheric winds. The present study suggests that the forecasters should look into the background dynamic and thermodynamic conditions extensively in addition to multi-model guidance to better predict the genesis, intensity and track of the cyclones.

$\bf{Higlights}$

$\bullet$ In the Arabian Sea, during the TC-Ockhi, the forecasts of wave parameters from the model forced with bias-corrected ECMWF winds resulted in very good agreement with observations.

$\bullet$ Climatologically, TC-Ockhi region has large potential for the genesis and intensification of TC due to an enhanced low-level cyclonic vorticity and the reduction in vertical wind shear.

$\bullet$ During the TC-Ockhi period, low-level vorticity and mid-tropospheric relative humidity were the dominant contributing factors, which lead to an enhanced GPI in the Arabian Sea.

$\bullet$ TC-Ockhi also had rapid intensification in a similar fashion the earlier cyclones in this region behaved.

$\bullet$ There is no abnormality also in the TC-Ockhi track, as the TC-Ockhi track matches well with the background tropospheric flow.

Consensus-based algorithm for estimating the current intensity (CI) of tropical cyclones (TCs) combining infrared and microwave-based intensity estimates is used by the Cooperative Institute for Meteorological Satellite Studies (CIMSS) to generate a satellite-based consensus (SATCON) on TC intensity. In the present study, an attempt has been made to evaluate the performance of the SATCON method for estimation of TC intensity over the North Indian Ocean during 2016–2020 (5 years). For this purpose, the SATCON based algorithm has been evaluated for 22 TCs over the NIO, including 12 TCs over the Bay of Bengal (BOB) and 10 TCs over the Arabian Sea (AS), and the estimates have been compared with besttrack estimates of the Regional Specialized Meteorological Centre (RSMC), New Delhi. Both the maximum sustained surface winds (V$_{max}$) and estimated central pressure (ECP) have been compared for the different ‘T’ numbers and different categories of the storm during the year as a whole and during the premonsoon (March–May) and post-monsoon (October–December) seasons. The SATCON algorithm performance is found to be reasonably good in estimating the intensity of stronger TCs (T ${\ge}$ 4.0). It overestimates the intensity of weaker TCs (T ${\le}$ 3.5) and also for very strong TCs (T6.0 or more). Its performance is found to be better in the post-monsoon season over the BOB and the AS.

With increasing number of cyclonic disturbances (CDs) over the North Indian Ocean, the prediction of pre-genesis, along with the accurate forecast of the track and intensity, is the need of the hour. The present study evaluates the skill of prediction of genesis as well as the veriBcation of CDs (total of 16 cases are considered) that occurred during the year 2020–2021, using Global Ensemble Forecast System (GEFS) T1534. Different initial conditions with longer lead hours are analysed for genesis, track, intensity and landfall errors. The model has the ability to predict genesis location with error of about ${\sim}$200 km at day 2 lead. VeriBcation of all CDs indicates that the landfall in terms of time and position is well predicted by the model. In general, the model shows the northward bias in genesis location error for the initial 12 hours and after that southwestward bias. From depression till dissipation, the track error for ensemble mean is less than that of the control run. The track error is less than 150 km for ensemble mean till day 3 and is about 220 km on day 4. The model shows northward bias in predicting the track of CDs. For predicting the landfall locations, southwestward bias is shown by model. The probabilistic ensemble skill scores are also evaluated for GEFS T1534, which shows reasonable skill.