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.

Microwave sensor MSMR (Multifrequency Scanning Microwave Radiometer) data onboard Oceansat-1 was used for retrieval of monthly averages of near surface specific humidity (Q_a) and air temperature (T_a) by means of Artificial Neural Network (ANN). The MSMR measures the microwave radiances in 8 channels at frequencies of 6.6, 10.7, 18 and 21 GHz for both vertical and horizontal polarizations.

The artificial neural networks (ANN) technique is employed to find the transfer function relating the input MSMR observed brightness temperatures and output (Q_a andT_a) parameters. Input data consist of nearly 28 months (June 1999 – September 2001) of monthly averages of MSMR observed brightness temperature and surface marine observations ofQ_a andT_a from Comprehensive Ocean-Atmosphere Data Set (COADS).

The performance of the algorithm is assessed with independent surface marine observations. The results indicate that the combination of MSMR observed brightness temperatures as input parameters provides reasonable estimates of monthly averaged surface parameters. The global root mean square (rms) differences are 1.0‡C and 1.1 g kg⁻¹ for air temperature and surface specific humidity respectively.

In this paper, daily variations of satellite-derived geophysical parameters such as integrated water vapour (IWV), cloud liquid water content (CLW), sea surface temperature (SST) and sea surface wind speed (SSW) have been studied for a case of monsoon depression that formed over the Bay of Bengal during 19th-24th August 2000. For this purpose, IRS P4 MSMR satellite data have been utilized over the domain equator — 25‡N and 40‡-100‡E. An integrated approach of satellite data obtained from IRS-P4, METEOSAT-5 and INSAT was made for getting a signal for the development of monsoon depression over the Indian region. Variations in deep convective activity obtained through visible, infrared and OLR data at 06 UTC was thoroughly analyzed for the complete life cycle of monsoon depression. Geophysical parameters obtained through IRS-P4 satellite data were compared with vorticity, convergence and divergence at 850 and 200 hPa levels generated through cloud motion vectors (CMVs) and water vapour wind vectors (WVWVs) obtained from METEOSAT-5 satellite. This comparison was made for finding proper consistency of geophysical parameters with dynamical aspects of major convective activity of the depression.

From the results of this study it is revealed that there was strengthening of sea surface winds to the south of low-pressure area prior to the formation of depression. This indicated the possibility of increase in cyclonic vorticity in the lower troposphere. Hence, wind field at 850 hPa with satellite input of CMVs in objective analysis of wind field using optimum interpolation (OI) scheme was computed. Maximum cyclonic vorticity field at 850 hPa was obtained in the region of depression just one day before its formation. Similarly, with the same procedure maximum anticyclonic vorticity was observed at 200 hPa with WVWVs input. Consistent convergence and divergence at 850 and 200 hPa was noticed with respect to these vorticities. In association with these developments, we could get lowest values of OLR (120 W/m² ) associated with major convective activity that was consistent with the maximum values of integrated water vapour (6-8gm/cm²) and cloud liquid water content (50-60 mg/cm² ) persisting particularly in the southwest sector of the monsoon depression.

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.

Since the beginning of the summer monsoon 2009, experimental mesoscale weather forecasts in real time are being generated using WRF model by the Meteorology and Oceanography Group at the Space Applications Centre (ISRO)and are disseminated through MOSDAC (www.mosdac.gov.in) to various users. To begin with, the 12 h, 24 h and 48 h forecasts for the western India region are made available. A study is undertaken to comprehensively assess the cloudiness prediction performance of WRF model. The evaluations have been made over the three months period during monsoon 2009. INSAT cloud imagery data has been used as a reference for these evaluations. The verification strategy includes computation of various skill scores. It is seen that probability of detection (POD)of cloud is 84% and the false alarm rate (FAR) is around 18%. It is hoped that this assessment will provide information on the use of these forecasts in various applications.

The three dimensional variational data assimilation scheme (3D-Var) is employed in the recently developed Weather Research and Forecasting (WRF) model. Assimilation experiments have been conducted to assess the impact of Indian Space Research Organisation’s (ISRO) Automatic Weather Stations (AWS) surface observations (temperature and moisture) on the short range forecast over the Indian region. In this study, two experiments, CNT (without AWS observations) and EXP (with AWS observations) were made for 24-h forecast starting daily at 0000 UTC during July 2008. The impact of assimilation of AWS surface observations were assessed in comparison to the CNT experiment. The spatial distribution of the improvement parameter for temperature, relative humidity and wind speed from one month assimilation experiments demonstrated that for 24-h forecast, AWS observations provide valuable information. Assimilation of AWS observed temperature and relative humidity improved the analysis as well as 24-h forecast. The rainfall prediction has been improved due to the assimilation of AWS data, with the largest improvement seen over the Western Ghat and eastern India.

A new stability index based on atmospheric refractivity at ∼500 hPa level and surface measurements of temperature, pressure and humidity is formulated. The new index named here as refractivity based lifted index (RLI) is designed to give similar results as traditionally used lifted index derived from radiosonde profiles of temperature, pressure and humidity. The formulation of the stability index and its comparison with the traditional temperature profile based lifted index (LI) is discussed. The index is tested on COSMIC radio occultation derived refractivity profiles over Indian region. The forecast potential of the new index for rainfall on 2° × 2° latitude–longitude spatial scale with lead time of 3–24 hours indicate that the refractivity based lifted index works better than the traditional temperature based lifted index for the Indian monsoon region. Decreasing values of RLI tend to give increasing rainfall probabilities.

Altimeter data have been assimilated in an ocean general circulation model using the water property conserving scheme. Two runs of the model have been conducted for the year 2004. In one of the runs, altimeter data have been assimilated sequentially, while in another run, assimilation has been suppressed. Assimilation has been restricted to the tropical Indian Ocean. An assessment of the strength of the scheme has been carried out by comparing the sea surface temperature (SST), simulated in the two runs, with in situ derived as well as remotely sensed observations of the same quantity. It has been found that the assimilation exhibits a significant positive impact on the simulation of SST. The subsurface effect of the assimilation could be judged by comparing the model simulated depth of the 20°C isotherm (hereafter referred to as D20), as a proxy of the thermocline depth, with the same quantity estimated from ARGO observations. In this case also, the impact is noteworthy. Effect on the dynamics has been judged by comparison of simulated surface current with observed current at a moored buoy location, and finally the impact on model sea level forecast in a free run after assimilation has been quantified in a representative example.

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.