Convective downdraft motions and related outflow wind considered as an eventual source of potential damage which can be more severe in the aviation sector. A great variety of atmospheric environments can produce these downdraft motions. These events are not easily detectable using conventional weather radar or wind shear alert systems, while Doppler radars are useful for identifying these Downbursts. In order to identify the situations that can cause these downdraft events different diagnostic tools are designed. Recently launched Indian satellite INSAT-3D, with atmospheric sounder and imager on board, is capable of identifying regions of downburst occurrence and can help in monitoring them in real time. Some Downburst events reported over different parts of India, during January-April period is investigated using Microburst Wind Speed Potential Index (MWPI) and thermodynamic characteristics derived from the NCMRWF GFS (NGFS) model. An attempt is made to make a short range prediction of these events using MWPI computed from NGFS model forecasts. The results are validated with in-situ observations and also by employing INSAT-3D data and it is shown that the method has a reasonable success. All the investigated downdraft events are associated with the hybrid Microburst environment.
During the month of June 2015, the South Asian (or Southwest) monsoon advanced steadily from the southern to the northwestern states of India. The progression of the monsoon had an apparent effect on the relative strength of convective storm downbursts that occurred during June and July 2015. A convective downburst prediction algorithm, involving the Microburst Windspeed Potential Index (MWPI) and a satellite-derived three-band microburst risk product, and applied with meteorological geostationary satellite (KALPANA-1 VHRR and METEOSAT-7) and MODIS Aqua data, was evaluated and found to effectively indicate relative downburst intensity in both pre-monsoon and monsoon environments over various regions of India. The MWPI product, derived from T574L64 Global Forecast System (NGFS) model data, is being generated in real-time by National Center for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, India. The validation process entailed direct comparison of measured downburst-related wind gusts at airports and India Meteorological Department (IMD) observatories to adjacent MWPI values calculated from GFS and India NGFS model datasets. Favorable results include a statistically significant positive correlation between MWPI values and proximate measured downburst wind gusts with a confidence level near 100%. Case studies demonstrate the influence of the South Asian monsoon on convective storm environments and the response of the downburst prediction algorithm.
Performance of an EnVar hybrid data assimilation system based on 3D Var NGFS (NCMRWF Global Forecast System) of T574 configuration and Ensemble Kalman Filter is investigated. The experiment is conducted during the Indian monsoon season (June-September) 2015 and compared against operational GSI 3D Var system. Two way coupled dual resolution hybrid system with 80 member ensemble of T254L64 configuration are used and forecasts are done for 10days. In hybrid experiment 75% weight is given to ensemble covariance and 25% for static covariance. The forecast skill of experiments over different spatial domains is compared against observations and respective analysis. The hybrid experiment produced significant improvement in forecasts compared to 3D Var in all fields except lower level temperature over tropical regions. Improvement is also seen in the prediction of extreme rainfall events. The prediction of monsoon onset and track of cyclone Ashobaa with hybrid and 3D var system is discussed.
The accurate estimation of water vapour with high spatial and temporal resolution is needed for operational weather forecasts and weather and climate research. Moisture representation in numerical weather prediction models is inadequate for forecasting meso-scale precipitation events and accurate information of middle and upper tropospheric moisture determines strength, effectiveness and longevity deep convective processes. Global Navigation Satellite System (GNSS) provides a way for measuring atmospheric humidity continuously at a low operational cost by co-locating GPS receiver with meteorological sensor. Impact of assimilation of GPS-IPW observations from NOAA-NWS and EUMETNET network on NCMRWF GFS forecast is investigated during May-June period in 2014. Impact of assimilation of GPS-IPW observations are not only confined to the regions of dense GPS-IPW network, but can be seen in other regions also. Ingestion of IPW observations impacted prediction of rainfall over the Indian monsoon region even though very few IPW stations located in the region. Impact of assimilation is not uniform on temperature, wind and humidity and different over different region. GPS-IPW observations can impact forecast of individual rainfall events at large and major impact on rainfall forecast is seen in the regions of large integrated precipitable water in the model. In India MoES has already setup many GPS-IPW stations and also some more are in the pipeline . The quality of these present observations from MoES and plans for the future GPS-IPW stations are discussed.
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