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.

There have been very few mesoscale modelling studies of the Indian monsoon, with focus on the verification and intercomparison of the operational real time forecasts. With the exception of Das et al (2008), most of the studies in the literature are either the case studies of tropical cyclones and thunderstorms or the sensitivity studies involving physical parameterization or climate simulation studies. Almost all the studies are based on either National Center for Environmental Prediction (NCEP), USA, final analysis fields (NCEP FNL) or the reanalysis data used as initial and lateral boundary conditions for driving the mesoscale model.

Here we present a mesoscale model forecast verification and intercomparison study over India involving three mesoscale models: (i) the Weather Research and Forecast (WRF) model developed at the National Center for Atmospheric Research (NCAR), USA, (ii) the MM5 model developed by NCAR, and (iii) the Eta model of the NCEP, USA. The analysis is carried out for the monsoon season, June to September 2008. This study is unique since it is based entirely on the real time global model forecasts of the National Centre for Medium Range Weather Forecasting (NCMRWF) T254 global analysis and forecast system. Based on the evaluation and intercomparison of the mesoscale model forecasts, we recommend the best model for operational real-time forecasts over the Indian region.

Although the forecast mean 850 hPa circulation shows realistic monsoon flow and the monsoon trough, the systematic errors over the Arabian Sea indicate an easterly bias to the north (of mean flow) and westerly bias to the south (of mean flow). This suggests that the forecasts feature a southward shift in the monsoon current. The systematic error in the 850 hPa temperature indicates that largely the WRF model forecasts feature warm bias and the MM5 model forecasts feature cold bias. Features common to all the three models include warm bias over northwest India and cold bias over southeast peninsula. The 850 hPa specific humidity forecast errors clearly show that the Eta model features dry bias mostly over the sea, while MM5 features moist bias over large part of domain. The RMSE computed at different levels clearly establish that WRF model forecasts feature least errors in the predicted free atmospheric fields. Detailed rainfall forecast verification further establishes that the WRF model forecast rainfall skill remains more or less same in day-2 and day-3 as in day-1, while the forecast skill in the MM5 and Eta models, deteriorates in day-2 and day-3 forecasts.

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.

Surface level soil moisture from two gridded datasets over India are evaluated in this study. The firstone is the UK Met Office (UKMO) soil moisture analysis produced by a land data assimilation systembased on Extended Kalman Filter method (EKF), which make use of satellite observation of AdvancedScatterometer (ASCAT) soil wetness index as well as the screen level meteorological observations. Seconddataset is a satellite soil moisture product, produced by National Remote Sensing Centre (NRSC) usingpassive microwave Advanced Microwave Scanning Radiometer 2 measurements. In-situ observations ofsoil moisture from India Meteorological Department (IMD) are used for the validation of the gridded soilmoisture products. The difference between these datasets over India is minimum in the non-monsoonmonths and over agricultural regions. It is seen that the NRSC data is slightly drier (0.05%) and UKMOsoil moisture analysis is relatively wet during southwest monsoon season. Standard AMSR-2 satellitesoil moisture product is used to compare the NRSC and UKMO products. The standard AMSR-2 andUKMO values are closer in monsoon season and AMSR-2 soil moisture is higher than UKMO in allseasons. NRSC and AMSR-2 showed a correlation of 0.83 (significant at 0.01 level). The probabilitydistribution of IMD soil moisture observation peaks at 0.25 m^3/m^3, NRSC at 0.15 m^3/m^3, AMSR-2 at0.25 m3/m3 and UKMO at 0.35 m^3/m^3 during June–September period. Validation results show UKMOanalysis has better correlation with in-situ observations compared to the NRSC and AMSR-2 datasets.The seasonal variation in soil moisture is better represented in UKMO analysis. Underestimation of soilmoisture during monsoon season over India in NRSC data suggests the necessity of incorporating theactual vegetation for a better soil moisture retrieval using passive microwave sensors. Both productshave good agreement over bare soil, shrubs and grassland compared to needle leaf tree, broad leaf treeand urban land cover types.

The current study reports for the first time an application of orography from the Cartosat-1 satellite digital elevation model (DEM) generated at a source resolution of 30 m in a convection-permitting numerical weather prediction model. The effects of improvements in the representation of orography have been examined in the high-resolution regional National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model predictions for a heavy rainfall event over the city of Chennai. A time-lagged ensemble method is employed to account for the uncertainties associated with the initial conditions, which can better forecast extreme weather events than single forecasts. The simulations reveal that the predictions based on Cartosat-1 DEM capture the local details of the rainfall distribution better than the National Aeronautics and Space Administration shuttle radar topography mission DEM-based predictions, and better represent the orographic and thermal uplifting. The spatio-temporal patterns of the simulated rainfall over Chennai are superior in Cartosat-1 DEM-based simulations mainly due to the enhanced wind convergence and moisture transport. The present study reveals the role of mountains in the enhancement of heavy rainfall events over coastal cities and highlights the potential use of high-resolution orography in the improvement of the operational weather forecasting skill of the NCMRWF Unified Model.