meteorological stations usually contain some missing data for different reasons.there are several traditional methods for completing data, among them bivariate and multivariate linear and non-linear correlation analysis, double mass curve, ratio and difference methods, moving average and probability density functions are commonly used. in this paper a blended model comprising the bivariate expo...

introduction precipitation is one of the most important meteorological variables in comparison with other climatic parameters. it varies extremely over time and space. the occurrence of this climate phenomenon requires specific circumstances in environment. its accurate measurement is important to a wide range of decision makers including hydrologists, agriculturalists, industrialists and etc. ...

climate change disturbs the distribution of precipitation patterns and affects water resources. a lot of evidences imply that the increased atmospheric concentration of greenhouse gasses in turn increases the precipitation changes around the world. thus, since iran is located in an arid zone of the earth, identifying the effects of co2 concentrations on iran precipitation rate is highly importa...

The detection of outliers from spatio-temporal data is an important task due to the increasing amount of spatio-temporal data available, and the need to understand and interpret it. Due to the limitations of previous data mining techniques, new techniques to detect spatio-temporal outliers need to be developed. In this paper, we propose a spatio-temporal outlier detection algorithm called Outst...

A new method is required for change-point testing of precipitation data that is capable of applying valid precipitation models. First, stochastic precipation models are researched and classified. Typically, the occurrence of rain is modeled using a two-state, first-order Markov chain, and the intensity of rain is modeled using a two-parameter gamma distribution. Using the likelihood ratio test ...

 Analysis of the characteristics of precipitation fields;  Sampling of precipitation fields at different regular and irregular steps of grid and different time intervals;

A critical issue in biophysical modeling projects is to develop a set of reusable libraries to support the development of future applications. This is true for weather modeling as well. Rain is a software component providing a collection of stochastic approaches to generate precipitation data on daily and subdaily time steps. Synthetic data generated on a daily time step consists of precipitati...

A novel methodology is introduced for quantifying the added value of remotely sensed soil moisture products for global land surface modeling applications. The approach is based on the assimilation of soil moisture retrievals into a simple surface water balance model driven by satellite-based precipitation products. Filter increments (i.e., discrete additions or subtractions of water suggested b...