M Das Gupta
Articles written in Journal of Earth System Science
Volume 112 Issue 2 June 2003 pp 223-232
A daily rainfall dataset and the corresponding rainfall maps have been produced by objective analysis of rainfall data. The satellite estimate of rainfall and the raingauge values are merged to form the final analysis. Associated with epochs of monsoon these rainfall maps are able to show the rainfall activities over India and the Bay of Bengal region during the BOBMEX period. The intra-seasonal variations of rainfall during BOBMEX are also seen using these data. This dataset over the oceanic region compares well with other available popular datasets like GPCP and CMAP. Over land this dataset brings out the features of monsoon in more detail due to the availability of more local raingauge stations.
Volume 116 Issue 4 August 2007 pp 275-304
Obtaining an accurate initial state is recognized as one of the biggest challenges in accurate model prediction of convective events. This work is the first attempt in utilizing the India Meteorological Department (IMD) Doppler radar data in a numerical model for the prediction of mesoscale convective complexes around Chennai and Kolkata. Three strong convective events both over Chennai and Kolkata have been considered for the present study. The simulation experiments have been carried out using fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) mesoscale model (MM5) version 3.5.6. The variational data assimilation approach is one of the most promising tools available for directly assimilating the mesoscale observations in order to improve the initial state. The horizontal wind derived from the DWR has been used alongwith other conventional and non-conventional data in the assimilation system. The preliminary results from the three dimensional variational (3DVAR) experiments are encouraging. The simulated rainfall has also been compared with that derived from the Tropical Rainfall Measuring Mission (TRMM) satellite. The encouraging result from this study can be the basis for further investigation of the direct assimilation of radar reflectivity data in 3DVAR system. The present study indicates that Doppler radar data assimilation improves the initial field and enhances the Quantitative Precipitation Forecasting (QPF) skill.
Volume 117 Issue 5 October 2008 pp 603-620
Performance of four mesoscale models namely,the MM5,ETA,RSM and WRF,run at NCMRWF for short range weather forecasting has been examined during monsoon-2006.Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind,temperature,speciﬁc humidity,geopotential height,rainfall,systematic errors,root mean square errors and speciﬁc events like the monsoon depressions.
It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none ’.Perhaps an ensemble approach would be the best.However, if we must make a ﬁnal verdict,it can be stated that in general,(i)the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and,the MM5 is able to produce best All India rainfall forecasts in day-3,but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India,(ii)the MM5 is able to produce least RMSE of wind and geopotential ﬁelds at most of the time,and (iii)the RSM is able to produce least errors in the day-1 forecasts of the tracks,while the ETA model produces least errors in the day-3 forecasts.
Volume 122 Issue 6 December 2013 pp 1571-1582
Sea surface vector winds from scatterometers onboard satellites play an important role to make accurate Numerical Weather Prediction (NWP) model analysis over the data sparse oceanic region. Sea surface winds from Oceansat-2 scatterometer (OSCAT) over the Indian Ocean were validated against the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) buoy winds to establish the accuracy of OSCAT winds. The comparison of OSCAT winds against RAMA buoy winds for a period of one year (2011) shows that the wind speeds and directions derived from OSCAT agree with RAMA buoy winds. The monthly mean wind speeds from both OSCAT and RAMA buoy show maximum value during the monsoon period as expected. In the complete annual cycle (2011), the monthly mean root mean square differences in the wind speed and wind direction were less than ∼2.5 ms−1 and ∼20°, respectively. The better match between the OSCAT and RAMA buoy wind is observed during Indian summer monsoon (June–September). During monsoon 2011, the root mean square differences in wind speed and wind direction were less than 1.9 ms−1 and 11°, respectively. Collocation of scatterometer winds against equatorial and off-equatorial buoys clearly brought out the monsoon circulation features. Collocation of Advanced Scatterometer (ASCAT) winds on-board European Space Agency (ESA) MeTop satellite with respect to RAMA buoy winds during monsoon 2011 also showed that the OSCAT wind statistics are comparable with that of ASCAT over the Indian Ocean, and indicates that the accuracy of both the scatterometers over the Indian Ocean are essentially the same.
Volume 123 Issue 2 March 2014 pp 247-258
Medium range weather forecasts are being generated in real time using Global Data Assimilation Forecasting System (GDAFS) at NCMRWF since 1994. The system has been continuously upgraded in terms of data usage, assimilation and forecasting system. Recently this system was upgraded to a horizontal resolution of T574 (about 22 km) with 64 levels in vertical. The assimilation scheme of this upgraded system is based on the latest Grid Statistical Interpolation (GSI) scheme and it has the provision to use most of available meteorological and oceanographic satellite datasets besides conventional meteorological observations. The new system has an improved procedure for relocating tropical cyclone to its observed position with the correct intensity. All these modifications have resulted in improvement of skill of medium range forecasts by about 1 day.