Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.     

Ground-Based In Situ Measurements of Near-Surface Aerosol Mass Concentration over Anantapur: Heterogeneity in Source Impacts

Surface measurements of aerosol physical properties were made at Anantapur(14.62°N,77.65 °E,331 m a.s.l),a semiarid rural site in India,during August 2008-July 2009.Measurements included the segregated sizes of aerosolsas as well as total mass concentration and size distributions of aerosols measured at low relative humidity(RH<75%) using a Quartz Crystal Microbalance(QCM) in the 25-0.05 μm aerodynamic diameter range.The hourly average total surface aerosol mass concentration in a day varied from 15 to 70 μg m-3,with a mean value of 34.02±9.05μgm-3 for the entire study period.A clear diurnal pattern appeared in coarse,accumulation and nucleation-mode particle concentrations,with two local maxima occurring in early morning and late evening hours.The concentration of coarse-mode particles was high during the summer season,with a maximum concentration of 11.81±0.98μgm-3 in the month of April,whereas accumulationmode concentration was observed to be high in the winter period contributed >68% to the total aerosol mass concentration.Accumulation aerosol mass fraction,A f(=Ma/Mt) was highest during winter(mean value of Af～0.80) and lowest(Af～0.64) during the monsoon season.The regression analysis shows that both R eff and R m are dependent on coarse-mode aerosols.The relationship between the simultaneous measurements of daily mean aerosol optical depth at 500 nm(AOD500) and PM 2.5 mass concentration([PM2.5]) shows that surface-level aerosol mass concentration increases with the increase in columnar aerosol optical depth over the observation period.     

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.