Sentence examples for rain data from inspiring English sources

Rain data for the Cochin station were not be available for the parallel period.

Typically, water quality variables are sampled fortnightly, whereas the rain data is sampled daily.

Most of the obtained decision trees to predict overflows from rain data had accuracies ranging from 70%to83%3%.

The monthly rain data of 26 years (1980 2005) obtained at three stations (we had no rain data for the Cochin station) were used to analyze rainfall variations with CAPE data.

Rain data, produced by a stochastic rain generator, emulates the average volumes expected for a given place.

No linear trend is discerned in the rain data, such as at the Delhi and Kolkata stations.

At monthly time scale, good linear relationships between TRMM rainfall and rain gauges rainfall data are received with the determination coefficients (R2) varying between 0.81 and 0.89 for the individual stations and 0.88 for areal average rainfall data, respectively.

The nonparametric ANN approach approximates the best nonlinear function between multispectral information about pixel derived from MSG data and TMI data on one hand and rain information from PR data on the other hand to detect rainfall.

But, the results demonstrate the potential of combining TRMM rainfall data with rain gauge data to drive distributed hydrological model for rainfall-runoff stimulation in data-sparse area.

It is of significant importance to do a comparative analysis job among TRMM rainfall products and other rainfall data such as rain gauge data, satellite products (Jiang et al. 2012; Kim et al. 2013) and reanalysis data (Artan et al. 2007; Li et al. 2013) to figure out their predictive capability in distributed hydrological model.

Owing to the lack of 1-min rain-rate data in South Africa and the availability of 5-min and hourly rainfall data, we have used rain-rate conversion models and the refined Moupfouma model to convert the available data into 1-min rain-rate statistics.