S. K. Roy Bhowmik

Prediction of monsoon rainfall with a nested grid mesoscale limited area model

At the India Meteorological Department (IMD), New Delhi, a 12-level limited area model with 100 km horizontal resolution has been in use for weather forecasting. The present study uses this model together with a higher horizontal resolution (50 km) and vertical resolution (16-levels) model to examine the impact of increased resolution to simulate mesoscale features of rainfall during monsoon disturbances. The model was run for 22 days in the month of August 1997 and one week in September 1997 during three monsoon depressions and one cyclonic storm in the Bay of Bengal. The model results are compared with observations. The study shows that the model can capture mesoscale convective organization associated with monsoon depression.

Development of multimodel ensemble based district level medium range rainfall forecast system for Indian region

India Meteorological Department has implemented district level medium range rainfall forecast system applying multimodel ensemble technique, making use of model outputs of state-of-the-art global models from the five leading global NWP centres. The pre-assigned grid point weights on the basis of anomaly correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.25° ×0.25° utilizing two season datasets (1 June–30 September, 2007 and 2008) and the multimodel ensemble forecasts (day-1 to day-5 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district, taking the average value of all grid points falling in a particular district. In this paper, we describe the development strategy of the technique and performance skill of the system during summer monsoon 2009. The study demonstrates the potential of the system for improving rainfall forecasts at five days time scale over Indian region. Districtwise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most states of the country, particularly over the states where the monsoon systems are more dominant.

A Dynamical Statistical Model for Prediction of a Tropical Cyclone

A dynamical statistical method is applied for operational forecasting of the Bay of Bengal tropical cyclone “Nargis” of April–May 2008. The method consists of three forecast components, namely (a) analysis of Genesis Potential Parameter (GPP) and maximum potential intensity, (b) track prediction, and (c) 12 hourly intensity prediction for forecasts up to 72 hours. The results of the study showed that GPP could provide necessary predictive signal at early stages of development on the further intensification of the low pressure system into a tropical cyclone. The landfall forecast position errors by different operational numerical models (NWP) showed landfall position errors ranging from 10 km to 150 km and landfall time error ranges from 6 hours early to 6 hours delay. The dynamical statistical model is capable to provide 12 hourly nearly realistic intensity forecasts up to 60 hours of forecast.

Development of NWP-Based Cyclone Prediction System for Improving Cyclone Forecast Service in the Country

India Meteorological Department (IMD) operationally runs two regional model WRF and Hurricane WRF (HWRF) model for short-range prediction and Global model T574L64 for medium range prediction (7 days). As part of WMO programme to provide a guidance of tropical cyclone (TC) forecasts in near real-time for the ESCAP/WMO member countries based on the TIGGE Cyclone XML (CXML) data, IMD also implemented JMA supported software for real-time TC forecast over North Indian Ocean (NIO).

Growth of cyclone Viyaru and Phailin – a comparative study

The tropical cyclone Viyaru maintained a unique quasi-uniform intensity during its life span. Despite being in contact with sea surface for >120 hr travelling about 2150 km, the cyclonic storm (CS) intensity, once attained, did not intensify further, hitherto not exhibited by any other system over the Bay of Bengal. On the contrary, the cyclone Phailin over the Bay of Bengal intensified into very severe cyclonic storm (VSCS) within about 48 hr from its formation as depression. The system also experienced rapid intensification phase (intensity increased by 30 kts or more during subsequent 24 hours) during its life time and maximum intensity reached up to 115 kts. In this paper, a comparative study is carried out to explore the evolution of the various thermodynamical parameters and possible reasons for such converse features of the two cyclones. Analysis of thermodynamical parameters shows that the development of the lower tropospheric and upper tropospheric potential vorticity (PV) was low and quasi-static during the lifecycle of the cyclone Viyaru. For the cyclone Phailin, there was continuous development of the lower tropospheric and upper tropospheric PV, which attained a very high value during its lifecycle. Also there was poor and fluctuating diabatic heating in the middle and upper troposphere and cooling in the lower troposphere for Viyaru. On the contrary, the diabatic heating was positive from lower to upper troposphere with continuous development and increase up to 6∘C in the upper troposphere. The analyses of cross sections of diabatic heating, PV, and the 1000–500 hPa geopotential metre (gpm) thickness contours indicate that the cyclone Viyaru was vertically tilted (westward) and lacked axisymmetry in its structure and converse features (axisymmetric and vertical) that occurred for the cyclone Phailin. In addition, there was a penetration of dry air in the middle troposphere of Viyaru, whereas high moisture existed in the middle troposphere of Phailin. The vertical wind shear (5–10 ms−1) near the core of the storm region between 850 and 200 hPa was favourable for both the systems but was higher in the northern region of the cyclone Viyaru. The divergent development of these thermodynamic features conspired to produce converse characteristic of the two cyclones.

Integrated Agrometeorological Advisory Services in India

There is a considerable scope for decreasing the vulnerability of agriculture to increasing weather and climatic variability through weather forecast based agro-advisories. India Meteorological Department (IMD), Ministry of Earth Sciences (MoES), is operating an integrated Agro-Meteorological Advisory Service (AAS) at district level, in India, which represents a small step towards agriculture management in rhythm with weather and climate variability leading to weather proofing for farm production. Under AAS, needs of farming community was defined through ascertaining information requirement of diverse groups of end-users. It emerged, that prime need of the farmer is location specific weather forecast in quantitative terms. Hence, the same was developed and made operational in June, 2008. Thereafter, mechanism was developed to integrated weather forecast and climatic information along with agro-meteorological information to prepare district level agro-advisories outlining the farm management actions to harness favorable weather and mitigate impacts of adverse weather. A system has also been developed to communicate and disseminate the agro-meteorological advisories to strengthen the information out reach. The institutional dissemination channels such as farmer association, non-governmental organizations (NGOs), input suppliers, progressive farmers are also employed.

Operational Tropical Cyclone Forecasts Models at IMD and Their Performance

There has been a massive up-gradation of weather forecasting capabilities in India under the modernization programme of the Government of India, which covers various components such as atmospheric observation network; strengthening of computing facilities; data integration and product generation; and dissemination of information to an optimum level. It has improved forecasting capabilities for high impact weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. IMD now has a network of automatic weather stations, Doppler Weather Radars (DWR), state-of-the-art upper air systems etc. These observations are now being used to run numerical prediction models on High Performance Computing Systems (HPCS). Global Forecast System (GFS T574/L64) was made operational at IMD New Delhi, incorporating Global Statistical Interpolation (GSI) scheme as the global data assimilation for the forecast up to seven days. Mesoscale forecast system WRF (ARW) with 3DVAR data assimilation is being operated daily twice, at 27 km, 9 km and 3 km horizontal resolutions for the forecast up to three days using initial and boundary conditions from the IMD GFS T574. At ten other regional centres, very high resolution mesoscale models (WRF at 3 km resolution) are made operational with the installation of High End Server. Doppler weather and mesoscale dynamical model-based Nowcast system was made operational for the national Capital of Delhi. Polar WRF is implemented to provide day-to-day short range (48 hours) weather forecast for the Maitri region over Antarctica. District level quantitative five days weather forecasts based on Multi-Model Ensemble (MME) system are being generated to support Agro-Meteorological Advisory Service of India. All these NWP products are routinely made available on the IMD web site www.imd.gov.in.

Performance of Global Forecast System for the Prediction of Intensity and Track of Very Severe Cyclonic Storm ‘Phailin’ over North Indian Ocean

Tropical cyclone (TC) formation involves interaction of a variety of processes, both on the synoptic scale as well as the mesoscale. Gray (1979) identified several large-scale conditions as necessary for tropical cyclogenesis, including preexisting low-level relative vorticity and high mid-tropospheric humidity. TCs are one of the most dangerous natural calamities throughout the globe. The Bay of Bengal TC disaster is the costliest and deadliest natural hazard in the Indian sub-continent. It has a significant socio-economic impact on the countries bordering the Bay of Bengal, especially India, Bangladesh and Myanmar. Every year, they cause considerable loss of life and do immense damage to property. India and Bangladesh have a coastline of more than 8,000 km, which is prone to very severe cyclone formations in the Arabian Sea and Bay of Bengal. Therefore, reasonably accurate prediction of these storms has great importance to avoid the loss of valuable lives.

Simulation of Heavy Rainfall Event over Gujarat During September 2013

Heavy rainfall events are known to occur over the Indian subcontinent during southwest monsoon season under the influence of off-shore troughs, off-shore vortices, depressions over the Bay of Bengal and Arabian Sea and Mid-Tropospheric Cyclones (MTC). Rainfall amounts of 100–300 mm in a day at and around the weather systems along the west coast of India and other parts of the country are common during southwest monsoon season. These rainfall events are caused by organized Mesoscale Convective Systems (MCSs) embedded in large scale synoptic systems (Benson and Rao 1987; Sikka and Gadgil 1980). Extreme rainfall events result in landslides, flash floods and damage to crops that have major impacts on the society, economy and environment. Although prediction of such extreme weather events is still fraught with uncertainties, a proper assessment of likely future trends would help in setting up infrastructure for disaster preparedness.

An Analysis of Environmental Dynamical Control of Tropical Cyclone Intensity over the Bay of Bengal during 1981-2010

The intensity of tropical cyclones (TCs) is quite sensitive to SST, which mainly determines the surface energy transfer from the ocean to the TC. However, strong surface wind stress under a TC can generate strong turbulence in the upper ocean and deepening the ocean mixed layer. This causes significant decreases in SST due to entrainment of cooler water from the thermocline into the mixed layer.