Sentence examples for estimation of rain from inspiring English sources

Prior to quantitative estimation of rain from MADRAS, rain affected observations are identified.

With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed.

The incorporation of the model of decay parameter provides a better estimation of rain attenuation than the existing SAM and ITU-R model.

Results show that hourly adjustment mean field bias adjustment outperform daily mean field bias adjustment with regards to estimation of rainfall totals and peak rain rates.

Such uncertainty strongly depends on the measured rainfall intensity (RI) with systematic underestimation of high RIs, leading to a biased estimation of extreme rain rates statistics.

In and around the study average rainfall of 10 years have been taken from the rain gauge station for the estimation of rainfall erosivity.

The CV error statistics indicate that merging of the two datasets improves the estimation of spatial rainfall, and more so where the rain gauge network is sparse.

The regional model for estimation of extreme rainfall characteristics in Denmark has been updated with data from the augmented rain database 1 January 1997 1 August 2005, corresponding to almost twice the amount of data used in the previous study (1 January 1979 1 January 1997).

By using the exclusive MCSs algorithm, reasonably good improvement in the accuracy of rain intensity estimation is observed.

By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation.

A case study of a comparison of rain intensity estimation by the exclusive-MCS regional algorithm and the global TRMM 2A12 rain product with a Doppler Weather Radar shows significant improvement in rain intensity estimation by the developed regional algorithm.