This paper presents three multivariate geostatistical algorithms for incorporating a digital elevation model into the spatial prediction of rainfall: simple kriging with varying local means; kriging with an external drift; and colocated cokriging. The techniques are illustrated using annual and monthly rainfall observations measured at 36 climatic stations in a 5000 km region of Portugal. Cross...

Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormously complex and subtle so that uncertainty in a prediction using all these parameters is enormous even for a ...

One of the key challenges of climate science today is the understanding and prediction of rainfall and other precipitation events [6]. It holds high values for both scientists and policymakers in terms of obtaining novel scientific insight into the processes of precipitation formation, as well as predictions and associated uncertainties which decide future policy decisions based on them. One of...

The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from sa...

rainfall is considered a highly valuable climatologic resource, particularly in arid regions. as one of the primaryinputs that drive watershed dynamics, rainfall has been shown to be crucial for accurate distributed hydrologicmodeling. precipitation is known only at certain locations; interpolation procedures are needed to predict this variablein other regions. in this study, the ordinary cokri...

Gridded estimated rainfall intensity values at very high spatial and temporal resolution levels are needed as main inputs for weather prediction models to obtain accurate precipitation forecasts, and to verify the performance of precipitation forecast models. These gridded rainfall fields are also the main driver for hydrological models that forecast flash floods, and they are essential for dis...

-------------------------------------------------------------------ABSTRACT---------------------------------------------------------------Prediction of rainfall for a region is of utmost importance for planning, design and management of irrigation and drainage systems. This can be achieved by different approaches such as deterministic, conceptual, stochastic and Artificial Neural Network (ANN)....

The method currently recommended by the ITU-R for the prediction of rain attenuation in terrestrial line-of sight links considers an equivalent path length over which the rain intensity is assumed to be constant. In other methods, the spatial and temporal variations of the rainfall rate along the path are characterized by an effective rainfall rate that is dependent on the actual path length. B...

Data Mining is study of how to determine underlying patterns in the data. Data mining techniques like machine learning, alongside the conventional methods are deployed. Different Data mining techniques like GRNN, MLP, NNARX, CART, RBF, ARIMA and so on are used for the prediction of Rainfall. In this paper, analysis of various algorithms of data mining is used for rainfall prediction model. It i...

APRIL 2003 AMERICAN METEOROLOGICAL SOCIETY | O f all the weather elements for which forecasts are provided to the public, rainfall is perhaps of the greatest interest. While most people simply want to know whether they will need an umbrella that day, there is growing demand from industry, agriculture, government, and many other sectors for more detailed rainfall predictions. Unfortunately, rain...