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Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models.

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Title: Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models.
Authors: Khaniani, Ali Sam1 (AUTHOR) ali.sam@nit.ac.ir, Motieyan, Hamid1 (AUTHOR) h.motieyan@nit.ac.ir, Mohammadi, Atefeh2 (AUTHOR) mohamadi.atefeh@yahoo.com
Source: Journal of Atmospheric & Solar-Terrestrial Physics. Mar2021, Vol. 214, pN.PAG-N.PAG. 1p.
Subjects: Artificial neural networks, Precipitable water, Water vapor, Rainfall, Standard deviations, Global modeling systems
Geographic Terms: Tehran (Iran)
Abstract: In recent years, it has been found that the Precipitable Water Vapor (PWV) time series derived from ground-based GPS measurements can be used in to forecast precipitation in different regions. However, it is inevitable to consider the impact of several meteorological parameters such as temperature, pressure, relative humidity, water vapor pressure, total cloud cover and day of year (doy) besides PWV on rainfall prediction. In order to predict the precipitation at Tehran station, two types of Artificial Neural Network (ANN), including Multi-Layer Perceptron (MLP) and Nonlinear Auto-Regressive with Exogenous Inputs (NARX) were employed based on mentioned parameters. At first, these neural networks were trained under various circumstances (i.e. with and without PWV) with the help of collocated meteorological and GPS data from years 2007–2010 and then the networks were utilized to forecast different intensities of precipitation over 2011. The results showed that deletion of PWV values from input data will reduce the precision of MLP predictions for the range of rainfalls less than 6 mm. For the range of precipitation above 3 mm, the use of PWV has a positive impact on the output of the NARX model. In addition, the effect of the length of training data on the performance of the proposed models was investigated in terms of Mean Bias Error (MBE), Root Mean Square Error (RMSE) and False Alarm Ratio (FAR) statistics. The best results in the study region were achieved from 4years trained MLP and 2years trained NARX models. Comparing the outputs of the MLP and NARX models with the Global Forecasting System (GFS) 6h forecasts as a standard meteorological forecast showed that the efficiency of the NARX model is higher than the MLP and GFS, especially in moderate and strong rainfall classes. Also, seasonal comparison of these errors showed that both models underestimate rainfall values higher than 3 mm. In almost all seasons, the underestimation of the NARX model was less than MLP. With all pros and cons, the NARX model showed greater performance than MLP for both non-rainfall and rainfall events. • The MLP and NARX networks were proposed for rainfall forecasting in Tehran. • By removing PWV values from the inputs the precision of MLP is reduced for the rainfall ranges less than 6 mm/h. • For the range of precipitation above 3 mm/h, the use of PWV has a positive impact on the output of the NARX model. • The best results were achieved from 4years trained MLP and 2years trained NARX model. • For almost all seasons, the underestimation of the NARX model was less than MLP. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In recent years, it has been found that the Precipitable Water Vapor (PWV) time series derived from ground-based GPS measurements can be used in to forecast precipitation in different regions. However, it is inevitable to consider the impact of several meteorological parameters such as temperature, pressure, relative humidity, water vapor pressure, total cloud cover and day of year (doy) besides PWV on rainfall prediction. In order to predict the precipitation at Tehran station, two types of Artificial Neural Network (ANN), including Multi-Layer Perceptron (MLP) and Nonlinear Auto-Regressive with Exogenous Inputs (NARX) were employed based on mentioned parameters. At first, these neural networks were trained under various circumstances (i.e. with and without PWV) with the help of collocated meteorological and GPS data from years 2007–2010 and then the networks were utilized to forecast different intensities of precipitation over 2011. The results showed that deletion of PWV values from input data will reduce the precision of MLP predictions for the range of rainfalls less than 6 mm. For the range of precipitation above 3 mm, the use of PWV has a positive impact on the output of the NARX model. In addition, the effect of the length of training data on the performance of the proposed models was investigated in terms of Mean Bias Error (MBE), Root Mean Square Error (RMSE) and False Alarm Ratio (FAR) statistics. The best results in the study region were achieved from 4years trained MLP and 2years trained NARX models. Comparing the outputs of the MLP and NARX models with the Global Forecasting System (GFS) 6h forecasts as a standard meteorological forecast showed that the efficiency of the NARX model is higher than the MLP and GFS, especially in moderate and strong rainfall classes. Also, seasonal comparison of these errors showed that both models underestimate rainfall values higher than 3 mm. In almost all seasons, the underestimation of the NARX model was less than MLP. With all pros and cons, the NARX model showed greater performance than MLP for both non-rainfall and rainfall events. • The MLP and NARX networks were proposed for rainfall forecasting in Tehran. • By removing PWV values from the inputs the precision of MLP is reduced for the rainfall ranges less than 6 mm/h. • For the range of precipitation above 3 mm/h, the use of PWV has a positive impact on the output of the NARX model. • The best results were achieved from 4years trained MLP and 2years trained NARX model. • For almost all seasons, the underestimation of the NARX model was less than MLP. [ABSTRACT FROM AUTHOR]
ISSN:13646826
DOI:10.1016/j.jastp.2020.105533