Saji Mohandas

Improvements in medium range weather forecasting system of India

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.

Simulation of Tropical Cyclones over Bay of Bengal with NCMRWF Regional Unified Model

This study delineates the relative performance of the 12-km resolution NCMRWF regional Unified Model (NCUM-R) over the operational global NCUM (NCUM-G) model. Forecasts of four Bay of Bengal (BoB) landfalling tropical cyclones (TCs) using several different initial conditions (ICs) are used to compare the performance of two models. The position and intensity errors of the TCs are estimated with respect to the India Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) best-track datasets and an inter-comparison study is also carried out between IMD and JTWC. The overall results suggest that the NCUM-R simulates the position and intensity of TCs more accurately compared to the NCUM-G. A majority of the TC tracks in the NCUM-G diverge more from the IMD track when compared to NCUM-R simulated tracks. It is also clearly noticed that both the models are more skillful in track prediction when initialized at intensity stages greater than “cyclone” category. However, the mean position errors at different forecast hours and landfall errors of TCs are reduced by approximately 31 and 47% in the NCUM-R simulations compared to NCUM-G simulations, respectively. The mean gain in skill of the NCUM-R in cross track (CT) and along track (AT) error is around 29 and 24% over NCUM-G, respectively. The intensity errors are less in the NCUM-R simulations. The mean rainfall skill scores are considerably improved in the NCUM-R simulations in day-1 and day-2 as compared to the NCUM-G simulations. It is noticed that the mean position errors of the TCs are approximately 8% lower when compared against the JTWC tracks than the IMD tracks. However, the intensity errors are higher against the JTWC than that of IMD most likely due to the averaging period of the wind speed.

Behaviour of predicted convective clouds and precipitation in the high resolution Unified Model over the Indian summer monsoon region

National Centre for Medium Range Weather Forecasting (NCMRWF) high resolution regional convective scale Unified Model (NCUM-R) with latest tropical science settings is used to evaluate vertical structure of cloud and precipitation over two prominent monsoon regions: Western Ghats (WG) and Monsoon Core Zone (MCZ). Model radar reflectivity generated using Cloud Feedback Model Inter-comparison Project (CFMIP) observation simulator package (COSP) along with CloudSat profiling radar reflectivity is sampled for an active synoptic situation based on a new method using Budykos index of turbulence (BT). Regime classification based on BT-precipitation relationship is more predominant during the active monsoon period when convective scale models resolution increases from 4 km to 1.5 km. Model predicted precipitation and vertical distribution of hydrometeors are found to be generally in agreement with Global Precipitation Measurement (GPM) products and BT based CloudSat observation respectively. Frequency of occurrence of radar reflectivity from model implies that the low level clouds below freezing level is underestimated compared to the observations over both regions. In addition, high level clouds in the model predictions are much lesser over WG than MCZ.

Evaluation of NCMRWF unified model vertical cloud structure with CloudSat over the Indian summer monsoon region

Representation of rainfall distribution and monsoon circulation in the high resolution versions of NCMRWF Unified model (NCUM-REG) for the short-range forecasting of extreme rainfall event is vastly dependent on the key factors such as vertical cloud distribution, convection and convection/cloud relationship in the model. Hence it is highly relevant to evaluate the vertical structure of cloud and precipitation of the model over the monsoon environment. In this regard, we utilized the synergy of the capabilities of CloudSat data for long observational period, by conditioning it for the synoptic situation of the model simulation period. Simulations were run at 4-km grid length with the convective parameterization effectively switched off and on. Since the sample of CloudSat overpasses through the monsoon domain is small, the aforementioned methodology may qualitatively evaluate the vertical cloud structure for the model simulation period. It is envisaged that the present study will open up the possibility of further improvement in the high resolution version of NCUM in the tropics for the Indian summer monsoon associated rainfall events.

Validation of two gridded soil moisture products over India with in-situ observations

Surface level soil moisture from two gridded datasets over India are evaluated in this study. The first one is the UK Met Office (UKMO) soil moisture analysis produced by a land data assimilation system based on Extended Kalman Filter method (EKF), which make use of satellite observation of Advanced Scatterometer (ASCAT) soil wetness index as well as the screen level meteorological observations. Second dataset is a satellite soil moisture product, produced by National Remote Sensing Centre (NRSC) using passive microwave Advanced Microwave Scanning Radiometer 2 measurements. In-situ observations of soil moisture from India Meteorological Department (IMD) are used for the validation of the gridded soil moisture products. The difference between these datasets over India is minimum in the non-monsoon months and over agricultural regions. It is seen that the NRSC data is slightly drier (0.05%) and UKMO soil moisture analysis is relatively wet during southwest monsoon season. Standard AMSR-2 satellite soil moisture product is used to compare the NRSC and UKMO products. The standard AMSR-2 and UKMO values are closer in monsoon season and AMSR-2 soil moisture is higher than UKMO in all seasons. NRSC and AMSR-2 showed a correlation of 0.83 (significant at 0.01 level). The probability distribution of IMD soil moisture observation peaks at 0.25 m3/m3, NRSC at 0.15 m3/m3, AMSR-2 at 0.25 m3/m3 and UKMO at 0.35 m3/m3 during June–September period. Validation results show UKMO analysis 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 soil moisture during monsoon season over India in NRSC data suggests the necessity of incorporating the actual vegetation for a better soil moisture retrieval using passive microwave sensors. Both products have good agreement over bare soil, shrubs and grassland compared to needle leaf tree, broad leaf tree and urban land cover types.