P. C. Joshi

Impact of bogus vortex for track and intensity prediction of tropical cyclone

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.

Assimilation of INSAT data in the simulation of the recent tropical Cyclone Aila

The atmospheric motion vectors (AMVs) from the operational geostationary Indian National Satellite Kalpana-1 are now regularly available at the Space Applications Centre, Indian Space Research Organization (ISRO). ISRO also provides a large number of near real-time surface observations, such as winds, temperature, relative humidity, pressure, etc., from automatic weather stations (AWS) at various locations in India under the Prediction of Regional Weather with Observational Meso-Network and Atmospheric Modeling (PRWONAM) project. A series of experimental forecasts are attempted here to evaluate the impact of AMVs derived from Kalpana-1 and AWS surface observations for the track and intensity prediction of the recent Bay of Bengal Cyclone Aila using the Advanced Research Weather Research Forecast model (ARW-WRF). The insertion of AMVs using Cressman objective analysis techniques has had some positive, though not significant, impact in the initial position errors and track forecasts when compared with the corresponding control experiments. However, no significant improvement is noticed in the simulations of cyclone intensities, that is, minimum sea-level pressure and maximum surface winds forecasts when satellite winds are used for assimilation. Moreover, the model performance is also evaluated by repeating the same sets of experiments using AMV, AWS surface observations and upper-air radiosonde data together for assimilation. The simulation of initial position errors, track and intensity forecasts from all experiments are comparable. Though these results are preliminary with respect to the Kalpana-1 AMV, the present study can provide some insight for WRF model users over the Indian Ocean region.

Artificial neural network approach for estimation of surface specific humidity and air temperature using multifrequency Scanning Microwave Radiometer

Microwave sensor MSMR (Multifrequency Scanning Microwave Radiometer) data onboard Oceansat-1 was used for retrieval of monthly averages of near surface specific humidity (Qa) and air temperature (Ta) 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 (Qa andTa) parameters. Input data consist of nearly 28 months (June 1999 – September 2001) of monthly averages of MSMR observed brightness temperature and surface marine observations ofQa andTa 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.

Estimation of surface latent heat fluxes from IRS-P4/MSMR satellite data

The brightness temperatures of the Microwave sensor MSMR (Multichannel Scanning Microwave Radiometer) launched in May 1999 onboard Indian Oceansat-1 IRS-P4 are used to develop a direct retrieval method for latent heat flux by multivariate regression technique. The MSMR measures the microwave radiances at 8 channels at frequencies of 6.6, 10.7, 18 and 21 GHz at both vertical and horizontal polarizations. It is found that the surface LHF (Latent Heat Flux) is sensitive to all the channels. The coefficients were derived using the National Centre for Environmental Prediction (NCEP) reanalysis data of three months: July, September, November of 1999. The NCEP daily analyzed latent heat fluxes and brightness temperatures observed by MSMR were used to derive the coefficients. Validity of the derived coefficients was checked within situ observations over the Indian Ocean and with NCEP analyzed LHF for global points. The LHF derived directly from the MSMR brightness temperature (Tb) yielded an accuracy of 35 watt/m2. LHF was also computed by applying bulk formula using the geophysical parameters extracted from MSMR. In this case the errors were higher apparently due to the errors involved in derivation of the geophysical parameters.

Characteristics of low frequency oscillations of the atmosphere-ocean coupling

The low frequency oscillation of latent heat flux over the tropical oceans has been studied. The NCEP reanalyzed fields of wind and humidity alongwith Reynolds SST are used to compute the instantaneous as well as monthly mean surface latent heat fluxes (LHF) for the year 1999. The procedure of LHF computation is based on bulk method. Spectral analysis shows that significant energy is contained in Madden Julian Oscillation band in the winds, SST, moisture and in the latent heat flux. The global distribution of wind, humidity, SST and LHF oscillation on the time scale of 30–50 days are analyzed. Maximum amplitude of oscillation on this time scale in all the above mentioned parameters were found over the Indian Ocean. The fluctuation of surface wind speed and moisture controls the latent heat flux on this time scale. The fluctuation of SST on this time scale does not seem to be important over most of the oceans.

Early prediction of onset of south west monsoon from ERS-1 scatterometer winds

Detailed analysis of the surface winds over the Indian Ocean derived from ERS-1 scatterometer data during the years 1993 and 1994 has been used to understand and unambiguously identify the onset phase of south-west monsoon. Five day (pentad) averaged wind vectors for the period April to June during both years have been examined to study the exact reversal of wind direction as well as the increase in wind speed over the Arabian Sea in relation to the onset of monsoon over the Indian west coast (Kerala). The related upper level humidity available from other satellites has also been analysed.The results of our analysis clearly show a consistent dramatic reversal in wind direction over the western Arabian Sea three weeks in advance of the onset of monsoon. The wind speed shows a large increase coinciding with the onset of monsoon. These findings together show the dominant role of sea surface winds in establishing the monsoon circulation. The study confirms that the cross equatorial current phenomenon becomes more important after the onset of monsoon.

Impact of downscaling on the simulation of seasonal monsoon rainfall over the Indian region using a global and mesoscale model.

A global model (T80L18; Triangular Truncation at wave number 80 with 18 vertical layers) and a mesoscale model MM5 (nested at 90 and 30 km resolutions) are integrated for 5 monsoon years 1998-2002. The impact of dynami- cal downscaling from global to mesoscale in the simulations of Indian summer monsoon rainfall is studied. Comparisons between the global and the mesoscale models show that, though the global model has an edge over the mesoscale model in simulating the all-India mean rainfall closer to the observation, the T80L18 model lacks in simulating the spatial variations in rainfall. The effect of downscaling is better represented in the rainfall variations produced by MM5 both quantitatively and qualitatively over the foothills of the Himalayas and along Nepal to North-eastern India. It is also seen that the mesoscale model is able to represent the dispersion (standard deviation) present in the observed rainfall over India. In the five monsoon seasons, RMSE of mean rainfall (monthly and seasonal) of T80L18 forecasts are mostly lower than that of MM5 forecasts. However, synoptic features like the Somali Jet and Tibetan anticyclone are better rep- resented by MM5. This model has also simulated the regions of convection better than the T80L18 model. However, the MM5 simulations produced an anomalous circulation over the Saudi Arabian region (15-20 0 N and 45-50 0 E) in many cases. The mesoscale model simulates better wind fields than the global model in general. Over peninsular India T80L18 model showed higher temperature gradient but, over Central India this model has better temperature field as compared to MM5. Over southern and north-eastern India, the temperature field of T80L18 and MM5 are very similar.

Proper depiction of monsoon depression through IRS-P4 MSMR

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.

A review of stochastic dynamic method of weather prediction

The stochastic dynamic method of weather prediction (SDP) has been suggested recently for better understanding of the numerical weather prediction. The SDP is described using a simple one-dimensional advection equation. The salient features of the method, its scope and limitations, are discussed.