INSAT visible and infrared imageries of three cyclones in the Bay of Bengal during the period 1984–1987 were analysed with a view to improve the cyclone track prediction in this region. It was observed that the rotation in the major structural cloud features (as seen from the cloud-top temperature maps) associated with these cyclones in the Bay of Bengal is followed with a change in direction of their movement. This method is seen to be particularly effective when the cyclone is severe and when the major cloud features persist for a reasonably longer time. In the present study, only the direction of movement is forecast assuming a uniform speed of the cyclone.

The satellite-derived moisture fields during different phases of two normal and poor monsoon years have been studied. Spectral analysis was performed in different zones of the monsoon region to study the nature and modes of intraseasonal fluctuations of lower layer moisture fields.

Seasonal mean fields of water vapour at low and middle layers show a dry anomaly over the Arabian subcontinent and a wet anomaly over the Bay of Bengal during good monsoon years, while the anomalies show an opposite trend during the poor monsoon years. The zonal and meridional propagation of low-frequency oscillations of moisture fields has also been examined. The southward movement of low-frequency oscillations seems to be suppressed in good monsoon years as compared to the poor monsoon years, whereas the northward movement of the same shows no particular difference. Fluctuations in the 30–50 day range are found shifted to longer time-period side in the poor monsoon years.

The aim of this paper is to study the feasibility of deriving vertical wind profiles from current satellite observations. With this aim, we carried out complex empirical orthogonal function (CEOF) analysis of a large number of radiosonde observations of wind profiles over the Indian Ocean during the monsoon months. It has been found that the first two CEOFs explain 67% of the total variance in wind fields. While the first principal component is well correlated with the winds at 850 mb (r = 0:80), the second one is highly correlated with winds at 200 mb (r = 0:89). This analysis formed the basis of a retrieval algorithm which ensures the retrieval of vertical profiles of winds using satellite tracked cloud motion vector winds. Under the assumption that accurate measurements of wind are available at the above mentioned levels, the r.m.s. error of retrieval of each component of wind is estimated to range between 2 ms^-1 and 6 ms^-1 at different levels, which is much less than the natural variance of winds at these levels. For a better visualization of retrieval, we have provided retrieved and true wind profiles side by side for four typical synoptic conditions during the monsoon season.

The initialization scheme designed to improve the representation of a tropical cyclone in the initial condition is tested during Orissa super cyclone (1999) over Bay of Bengal using the fifth-generation Pennsylvania State University — National Center for Atmospheric Research (Penn State — NCAR) Mesoscale Model (MM5). A series of numerical experiments are conducted to generate initial vortices by assimilating the bogus wind information into MM5. Wind speed and location of the tropical cyclone obtained from best track data are used to define maximum wind speed, and centre of the storm respectively, in the initial vortex. The initialization scheme produced an initial vortex that was well adapted to the forecast model and was much more realistic in size and intensity than the storm structure obtained from the NCEP analysis. Using this scheme, the 24-h, 48-h, and 72-h forecast errors for this case was 63, 58, and 46 km, respectively, compared with 120, 335, and 550 km for the non-vortex initialized case starting from the NCEP global analysis. When bogus vortices are introduced into initial conditions, the significant improvements in the storm intensity predictions are also seen.

The impact of the vortex size on the structure of the initial vortex is also evaluated. We found that when the radius of maximum wind (RMW) of the specified vortex is smaller than that of which can be resolved by the model, the specified vortex is not well adapted by the model. In contrast, when the vortex is sufficiently large for it to be resolved on horizontal grid, but not so large to be unrealistic, more accurate storm structure is obtained.

The summer monsoon season of the year 2006 was highlighted by an unprecedented number of monsoon lows over the central and the western parts of India,particularly giving widespread rainfall over Gujarat and Rajasthan.Ahmedabad had received 540.2 mm of rainfall in the month of August 2006 against the climatological mean of 219.8 mm.The two spells of very heavy rainfall of 108.4 mm and 97.7 mm were recorded on 8 and 12 August 2006 respectively.Due to meteorological complexities involved in replicating the rainfall occurrences over a region,the Weather Research and Forecast (WRF –ARW version)modeling system with two different cumulus schemes in a nested con figuration is chosen for simulating these events.The spatial distributions of large-scale circulation and moisture fields have been simulated reasonably well in this model,though there are some spatial biases in the simulated rainfall pattern.The rainfall amount over Ahmedabad has been underestimated by both the cumulus parameterization schemes.The quantitative validation of the simulated rainfall is done by calculating the categorical skill scores like frequency bias,threat scores (TS)and equitable threat scores (ETS).In this case the KF scheme has outperformed the GD scheme for the low precipitation threshold.

Till now low-level winds were retrieved using Kalpana-1 infrared (IR) images only. In this paper, an attempt has been made to retrieve low-level cloud motion vectors using Kalpana-1 visible (VIS) images at every half an hour. The VIS channel provides better detection of low level clouds, which remain obscure in thermal IR images due to poor thermal contrast. The tracers are taken to be 15 × 15 pixel templates and hence each wind corresponds to about 120km × 120km at sub-satellite point. Multiplet based wind retrieval technique is followed for VIS wind derivation. However, for height assignment of VIS winds, collocated IR image is used. Due to better contrast between cloud and ocean surface, the low level atmospheric flow is captured better as compared to IR winds. The validation of the derived VIS winds is done with Global Forecast System (GFS) model winds and Oceansat-II scatterometer (OSCAT) winds.

Using JTWC (Joint Typhoon Warning Center) best track analysis data for the Indian Ocean cyclones, we developed an empirical equation for prediction of maximum surface wind speed of tropical cyclones during first 6–12 hours of landfall along the coastline of Indian subcontinent. A non-linear data fitting approach, the Genetic Algorithm, has been used to develop the above empirical equation using data for 74 tropical cyclones that made landfall on the coasts of India, Bangladesh and Myanmar during the period 1978–2011. For an out of sample validation test, the mean absolute error of the prediction was found to be 5.2 kt, and a correlation of 0.97. Our analysis indicates that time-integration of land area intercepted by cyclones during the landfall is a better predictor of post-landfall intensity compared to post-landfall time span. This approach also helps to tackle the complexity of coastline geometry of Indian subcontinent area.

In the present study, the assessment of the Community Atmosphere Model (CAM) developed at National Centre for Atmospheric Research (NCAR) for seasonal forecasting of Indian Summer Monsoon (ISM) with different persistent SST is reported. Towards achieving the objective, 30-year model climatology has been generated using observed SST. Upon successful simulation of climatological features of ISM, the model is tested for the simulation of ISM 2011 in forecast mode. Experiments have been conducted in three different time-phases, viz., April, May and June; using different sets of initial conditions (ICs) and the persistent SSTs of the previous months of the time-phases.

The spatial as well as temporal distribution of model simulated rainfall suggest a below normal monsoon condition throughout the season in all the experiments. However, the rainfall anomaly shows some positive signature over north-east part of India in the month of June and August whereas the central Indian landmass had positive anomaly during August and September. The monthly accumulated All-India rainfall (AIR) over land for June to September 2011 are predicted to be 101% (17.6 cm), 86% (24.3 cm), 83% (21.0 cm) and 95% (15.5 cm) of normal AIR, respectively. This makes the seasonal accumulated AIR 78.4 cm which is 11% below the normal rainfall of 87.6 cm. The model prediction for the months of June and July is comparable with the observation; however, the simulation would not be able to capture the high rainfall during August and September. The intention behind this work is to assess the shortcomings in the CAM model prediction, which can later be improved for future monsoon forecast experiments.

We are proposing a statistical technique to analyze the best fit of the histogram of infrared brightness temperature of convective cloud pixels. For this we have utilized the infrared brightness temperatures (IRTB) of Kalpana-1 (8 km resolution) and globally merged infrared brightness temperatures of Climate Prediction Centre NCEP/NWS (4 km resolution, merged from all the available geostationary satellites GOES-8/10, METEOSAT-7/5 and GMS), for both deep convective and non-deep convective (shallow cloud) cases. It is observed that Johnson SB function is the best continuous distribution function in explaining the histogram of infrared brightness temperatures of the convective clouds. The best fit is confirmed by Kolmogorov–Smirnov statistic. Johnson SB’s distribution of histogram of infrared brightness temperatures clearly discriminates the cloud pixels of deep convective and non-deep convective cases. It also captures the asymmetric nature in histogram of infrared brightness temperatures. We also observed that Johnson SB distribution of infrared brightness temperatures for deep convective systems is different in each of the pre-monsoon, monsoon and post-monsoon seasons. And Johnson SB parameters are observed to be best in discriminating the Johnson SB distribution of infrared brightness temperatures of deep convective systems for each season. Due to these properties of Johnson SB function, it can be utilized in the modelling of the histogram of infrared brightness temperature of deep convective and non-deep convective systems. It focuses a new perspective on the infrared brightness temperature that will be helpful in cloud detection, classification and modelling.