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An improved physical and analytical three-layer model of gravitational vortex columns (GVCs) for solar chimney power plants is developed in this paper. In essence, this model represents a further improvement and upgrade of the three-layer model proposed in Ninić et al. (2009). The improvements of the three-layer model deal with internal friction, variable vertical velocity (by height) in the central GVC spiraling upward flow, and variable angular momentum of the downdraft shell. A numerical solution of the improved model is given as a characteristic case and is compared to the elementary GVC model. The results show that the introduced improvements are important parameters for further analysis of gravitational vortex columns. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the productivity of research concerning the Adriatic Sea, with a focus on oceanography and meteorology. Productivity and impact were measured by analysing articles and citations from the Thomson Reuters Web of Science database, spanning the period 1994-2008. The most productive country was Italy but the highest number of citations was achieved by articles from Germany (all Adriatic publications) and Spain ("Oceanography" and "Meteorology and atmospheric sciences", only). By contrast, the second-most productive country, Croatia, had the lowest citation rate. Collaborations between Adriatic researchers were driven not only by the geographical position of a country (e.g., Italy vs. Croatia), but also by investment rates in Adriatic research (e.g., Italy vs. USA and Croatia vs. USA). Such collaborations substantially improved the impact of the research, especially from transitional countries such as Croatia. [ABSTRACT FROM AUTHOR]

India Meteorological Department (IMD) has been using a Limited Area Model (LAM) on operational basis for the forecast up to 48 hours with the first guess fields for objective analysis and lateral boundary conditions from the global spectral model (T-80) run of the National Center for Medium Range Weather Forecasting (NCMRWF), New Delhi. In this paper the model code has been modified and made more flexible, delinking it from the NCMRWF (T-80). This has allowed the use of initial and boundary conditions directly from the NCEP Global Forecast System (GFS) products available at the resolution of 1° × 1° lat./long. The main interest in this study is to improve the analysis and forecast in the short range time scale (up to 72 hours) by improving the model (LAM) resolution and using better Initial and boundary conditions from the NCEP GFS instead of the NCMRWF T-80 model. Simulation experiments are performed on wide variety of synoptic situations which occur very often over the Indian sub-continent. The performance evaluation in terms of qualitative comparison between the model simulated outputs against actual observations and the outputs of the operational model indicates that the modified version of the model is capable to provide a improved numerical guidance on the occurrence of heavy rainfall in the 48-72 hours forecast scale. [ABSTRACT FROM AUTHOR]

Accurate prediction of monsoon heavy rainfall events in the equatorial region has always been a challenge to weather forecasters. In this paper, forecast of a severe precipitation event that occurred over the eastern central coast of Peninsular Malaysia was attempted using the state-of-the-art Florida State University (FSU) Global and Regional Spectral Models. The sensitivity of parameterized convection in these models on precipitation forecast skill is studied using two different parameterization schemes for cumulus convection (the Relaxed Arakawa-Schubert scheme and the modified Kuo scheme). Low precipitation threshold of rainfall less than 2 mm day-1 was successfully predicted by both versions of the FSU model. However, the convection schemes lacked skill in predicting the correct placement of the area and amount for the high precipitation threshold greater than 40 mm day-1. Further evaluation of the predictive skills showed that the Relaxed Arakawa-Schubert scheme was a consistently better predictor of rainfall due to its low bias and lower root mean square errors (RMSEs) compared to the modified Kuo parameterization scheme. [ABSTRACT FROM AUTHOR]

In this paper an Artificial Neural Network (NN) approach has been applied to improve the quality of the INSAT derived sub-division quantitative precipitation estimates (IMD-QPE) over the Indian region for the summer monsoon season. Data for the years 2001, 2003 and 2004 have been used as the training sample. The method is tested with independent sample data for the year 2005. For the subdivisions over the domains of high orographic and monsoon low pressure system, where very rainfall occasionally occurs, different network architectures are applied to minimize the IMD-QPE errors. An inter-comparison between NNQPE (NN model output IMD-QPE), IMD-QPE and actual rainfall indicates that the pattern of NNQPE is closer to the observed rainfall distribution. The weekly mean absolute error of IMD-QPE with respect to observed rainfall, which ranges between 10-99 mm, becomes 4-70 mm in case of NNQPE. The performance statistics shows that the proposed NN model is able to produce better IMD-QPE with higher skill score and correlation co-efficient with respect to observation in most of the sub-divisions. The method is found to be promising for operational application. [ABSTRACT FROM AUTHOR]