The K-nearest neighbors (K-NN) is an analogous approach. This method has its origin as a non-parametric statistical pattern recognition procedure to distinguish between different patterns according to a selection criterion. Through this method, researchers can generate future data. In other words, the KNN is a technique that conditionally resamples the values from the observed record based on the conditional relationship specied. The KNN is most simple approach.

The most promising non-parametric technique for generating weather data is the K-nearest neighbor (K-NN) resampling approach. The K-NN method is based on recognizing a similar pattern of target le within the historical observed weather data which could be used as reduction of the target year (Young, 1994; Yates, 2003; Eum et al., 2010). The target year is the initial seed of data which, together with the historical data, are required as

input les for running the model. This method relies on the assumption that the actual weather data observed during the target year could be a replication of weather recorded in the past. The k-NN technique does not use any predened mathematical functions to estimate a target variable.

Actually, the algorithm of this method typically involves selecting a specied number of days similar in characteristics to the day of interest. One of these days is randomly resampled to represent the weather of the next day in the simulation period. The nearest neighbor approach involves simultaneous sampling of the weather variables, such as precipitation and temperature. The sampling is carried out from the observed data, with replacement.

The K-NN method is widely used in agriculture (Bannayan and Hoogenboom, 2009), forestry (Lopez et al., 2001) and hydrology (Clark et al., 2004; Yates et al., 2003).

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Unidatas Network Common Data Form (NetCDF) was developed and is maintained at Unidata, part of the University Corporation for Atmospheric Research (UCAR) Community Programs (UCP). Unidata is funded primarily by the National Science Foundation.

NetCDF is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. These data are quite large, which is another characteristic of real data. For a better understanding of NetCDF file, pay attention the following concepts:

If a user would need to call and access meteorological data on a daily, monthly, and annual scale with different attributes for all regions of the world (with three and four dimensions), he/she cannot easily worked with them by other format files (Excel, text, etc.), due to these format of data have different attributes so they have large capacity and they have been called metadata. In this case he/she faces with a lot of trouble.

Consequently, there is a need for a specific data format so that the data can be easily process. Therefore, NetCDF format file are so essential to maintenance of metadata. This format of data files are quite large and many users need to access small subsets of large datasets -in various formats- for representing scientific data. These datasets may differ in size, number of dimensions and may represent data for different regions.

One NetCDF file may contain several datasets and each value in this datasets can be located in space (relative to each-based coordinates and) time. Also, it has a standard for data access and for representing scientific data.

The libraries of NetCDF files have been consist of C, FORTRAN, C++, Java, and other languages. Programming interfaces are also available for Python, IDL, MATLAB, R, C++, Ruby, and Perl. NetCDF files should have the file name extension ‘.nc’. NetCDF is run on Linux, Windows, and various other platforms.