Global Meteorology

A dynamic model for solar radiation in the Tyrrhenian Basin of Central Mediterranean area

Alessia Naccarato — 2014-09-22

Studying the level of solar radiation is important for problems related to both environmental pollution and alternative energy development. In this work a space-time model for solar radiation in the Tyrrhenian basin is presented. Three main features of the model must be stressed because of their importance in modelling space-time variability of a phenomenon. The first and most important one is that relations between solar radiation in different sites are an outcome of the model’s estimation procedure. With this approach spatial weights are not bound to be symmetrical and proportional to distance between locations or to be constant over time. The second one is the presence of a simultaneous effect among locations as the solar radiation in one of them is a function of what simultaneously happens in all the other ones. The third main feature of the model is represented by constrained estimation on the basis of a priori knowledge about the phenomenon that allows to cope the problem of the increased number of parameters.

Towards precipitation enhancement through cloud seeding in Kenya

Joshua Ngaina — 2015-02-03

The study investigated potential of enhancing precipitation through cloud seeding during October-November-December (OND) season. Rainfall, cloud top temperature (CTT), aerosol optical depth (AOD) and wind data were used. Short-Cut Bartlett correlation, composite wind and time series analysis, and HYSPLIT backward trajectory analysis were used to achieve the objectives of study. Precipitation showed decreasing patterns with peaks between pentad 65 and 68. Delineated dry years (18) exceeded wet years (9). Low level winds were predominantly north-easterly during dry years characterized by continental trajectory. AOD values increased in all stations during dry year with aerosol load being higher in areas characterized by depressed rainfall. Pollutants suspended 1000 above mean sea level (AMSL) originated from Arabian and India subcontinent and pollutants suspended below 1000 AMSL were predominantly south easterly during wet years originated from Western Indian Ocean and characterized by maritime trajectory. Mean CTT during dry/wet years were positve over coastal areas while central, Rift-valley and Lake Victoria basin showed negative values, indicating presence of seedable conditions and thus potential cloud seeding to enhance rainfall and alleviate existing water stress.

Assessing the skill of precipitation forecasts on seasonal time scales over East Africa from a Climate Forecast System model

Emily Bosire — 2015-05-13

It is becoming increasingly important to be able to verify the skill of precipitation forecasts, especially with the advent of high-resolution numerical weather prediction models. This study focused on assessing the skill of climate forecast system (CFS) model in predicting rainfall on seasonal time scales over East Africa region for the period January 1981 to December 2009. The rainfall seasons considered were March to May (MAM) and October to December (OND). The data used in the study included the observed seasonal rainfall totals from January 1981 to December 2009 and CFS model forecast data for the same period. The model had 15 Runs. The measure of skill employed was the categorical skill scores and included Heidke skill scores, bias, probability of detection and false alarm ratio. The results from the categorical skill scores confirmed relatively higher skills during OND season as compared to MAM. When compared with individual Runs, the mean of all the 15 Runs depicted relatively higher accuracy during OND season. Some individual Runs – 1, 7, 9 and 10 – also performed better during OND season. During MAM season, the mean of all the 15 Runs showed relatively lower accuracy in predicting rainfall. Some individual Runs – 5, 10, 12 and 14 – performed better than the mean of all the 15 Runs. The prediction of seasonal rainfall over East Africa region using CFS model depends on the season considered. During MAM, the prediction of seasonal rainfall is better as Runs are fewer, which showed relatively higher averaged skills; on the other hand, during OND the prediction of seasonal rainfall is better when using the mean of all the 15 Runs.