Impact of CAMEX-4 Data Sets for Hurricane Forecasts using a Global Model

This study explores the impact on hurricane data assimilation and forecasts from the use of dropsondes and remote-sensed moisture profiles from the airborne Lidar Atmospheric Sensing Experiment (LASE) system. We show that the use of these additional data sets, above those from the conventional world weather watch, has a positive impact on hurricane predictions. The forecast tracks and intensity from the experiments show a marked improvement compared to the control experiment where such data sets were excluded. A study of the moisture budget in these hurricanes showed enhanced evaporation and precipitation over the storm area. This resulted in these data sets making a large impact on the estimate of mass convergence and moisture fluxes, which were much smaller in the control runs. Overall this study points to the importance of high vertical resolution humidity data sets for improved model results. We note that the forecast impact from the moisture profiling data sets for some of the storms is even larger than the impact from the use of dropwindsonde based winds

Assimilation of IRS-P4 (MSMR) meteorological data in the NCMRWF global data assimilation system

Oceansat-1 was successfully launched by India in 1999, with two payloads, namely Multi-frequency Scanning Microwave Radiometer (MSMR) and Ocean Color Monitor (OCM) to study the biological and physical parameters of the ocean. The MSMR sensor is configured as an eight-channel radiometer using four frequencies with dual polarization. The MSMR data at 75 km resolution from the Oceansat-I have been assimilated in the National Centre for Medium Range Weather Forecasting (NCMRWF) data assimilation forecast system. The operational analysis and forecast system at NCMRWF is based on a T80L18 global spectral model and Spectral Statistical Interpolation (SSI) scheme for data analysis. The impact of the MSMR data is seen globally, however it is significant over the oceanic region where conventional data are rare. The dry-nature of the control analyses have been removed by utilizing the MSMR data. Therefore, the total precipitable water data from MSMR has been identified as a very crucial parameter in this study. The impact of surface wind speed from MSMR is to increase easterlies over the tropical Indian Ocean. Shifting of the positions of westerly troughs and ridges in the south Indian Ocean has contributed to reduction of temperature to around 30‡S

Impact of MSMR data on NCMRWF global data assimilation system

It is very essential to make use of non-conventional remotely sensed data mainly from various satellites for numerical weather prediction. IRS series of Indian satellites have been found very useful for various types of studies. Recently IRS-P4 was successfully launched and its Multi-frequency Scanning Microwave Radiometer (MSMR) sensor is giving near surface wind speed and total precipitable water content over oceanic region. An attempt has been made to assimilate directly these two geophysical parameters derived at 150 km resolution, along with other global meteorological data, in the National Center for Medium Range Weather Forecasting (NCMRWF), New Delhi Global Data Assimilation System. (GDAS). The paper describes the development work done in utilizing the surface wind speed and the total precipitable water content data in the NCMRWF operational GDAS. The basic algorithms for assimilating the MSMR data directly in the global analysis scheme have been described. The analyzed fields produced after running the six hourly GDAS cycle have been examined and various aspects of the impact of this data set on the global analysis especially for Indian oceanic region have been evaluated. Other aspects like, penalty contributions, root mean square (rms) errors of various types of data both with respect to the analysis and the background field, etc. have been examined. The impact of additional IRS-P4 data on assimilation and model simulation is found to be positive and beneficial