Furthermore, the interannual variation of summer precipitation is attributable to the variation of heavy rainfall frequency over the HRV. The heavy rainfall frequency, in turn, is influenced by sea surface temperature anomalies (SSTAs) over the north Indian Ocean, equatorial western Pacific, and the tropical Atlantic. The tropical SSTAs modulate the HRV heavy rainfall events by influencing atmo...

One of the essential sectors of Myanmar economy is agriculture which is sensitive to climate variation. The most important climatic element which impacts on agriculture sector is rainfall. Thus rainfall prediction becomes an important issue in agriculture country. Multi variables polynomial regression (MPR) provides an effective way to describe complex nonlinear input output relationships so th...

The annual estimate of the availability of the amount of water for the agricultural sector has become a lifetime in places where rainfall is scarce, as is the case of northwestern Argentina. This work proposes to model and simulate monthly rainfall time series from one geographical location of Catamarca, Valle El Viejo Portezuelo. In this sense, the time series prediction is mathematical and co...

Long-term rainfall prediction is a challenging task especially in the modern world where we are facing the major environmental problem of global warming. In general, climate and rainfall are highly non-linear phenomena in nature exhibiting what is known as the "butterfly effect". While some regions of the world are noticing a systematic decrease in annual rainfall, others notice increases in fl...

The prediction of the small-scale spatial-temporal pattern of intense rainfall events is crucial for flood risk assessment in small catchments and urban areas. In the absence of a full deterministic modelling of small-scale rainfall, it is common practice to resort to the use of stochastic downscaling models to generate ensemble rainfall predictions to be used as inputs to rainfall-runoff model...

Rainfall prediction is an essential and challenging task in hydro-meteorology. Most of the existing weather dataset used for prediction consists of observatory record of several atmospheric parameters. Identifying the significant parameters from irrelevant and redundant parameter set for weather prediction is important because irrelevant parameters may decrease the prediction accuracy. The main...

Accurate rainfall time series prediction is one of the important tasks in hydrological study. A conventional time series model such as autoregressive moving average or an intelligent model such as artificial neural network have been used efficiently to perform this task. However, such models are difficult to interpret by human analysts because their prediction mechanism is in the parametric for...

Short-term prediction (nowcasting) of high-impact weather events can lead to significant improvement in warnings and advisories resulting in substantial savings of life and property. While nowcasting of precipitation has traditionally been done using radar reflectivity (Zh) data, research over the last decade indicates that using the specific differential phase (Kdp), defined as one-half the ra...

Dynamic numerical weather prediction models have been designed to deal with large-scale, highly predictable midlatitude atmospheric patterns. However, the capability of these models to simulate thermodynamically driven warm-season rainfall events, such as afternoon airmass thunderstorm formation in subtropical summers, is highly limited. Current methods of addressing this issue have included en...

The monsoon rainfall has very important affect on the agricultural production, livestock as well as human ecology. In this study, try to fit Artificial Neural Network (ANN) model to predict average monsoon rainfall. For the ANN models monthly average rainfall, sea surface temperature, wind speed, monsoon rainfall, temperature is used as inputs to predict the monsoon rainfall. The feed forward n...