Abstract
Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.
Highlights
Whereas climatic parameters such as temperature and pressure are nowadays well documented by in situ and remotely sensed platforms, and well predicted by numerical weather prediction models, precipitation, as one of the key variables in hydrometeorological applications, is still lacking sufficient monitoring precision, since it is characterized by high variability in space and time
According to Sharifi et al [22], who concluded that artificial neural networks (ANN) can predict precipitation with cloud optical and microphysical properties data input, this study focuses on optimizing the ANN by comparing different optimization algorithms
The four parameters used in our models as input to predict the precipitation (COT, cloud effective radius (CER), cloud water path (CWP), temperature) were extracted individually and the errors resulting from the elimination of each parameter were obtained
Summary
Whereas climatic parameters such as temperature and pressure are nowadays well documented by in situ and remotely sensed platforms, and well predicted by numerical weather prediction models, precipitation, as one of the key variables in hydrometeorological applications, is still lacking sufficient monitoring precision, since it is characterized by high variability in space and time. As precipitation varies greatly in space and time, gridded precipitation data in high spatiotemporal resolution is a considerable requirement as input for hydrometeorological and water resources management applications. This type of data is highly important for timely action (e.g., the initiation of landslide and mudslide movement) and decision making, such as evacuating an area with high potential for flooding, or to secure food and water supply [1]. High spatiotemporal resolution datasets are usually available only on a country level or cover a specific geographical region These type of datasets might be obtained by interpolation of in situ observations and reanalysis products, or derived through remote sensing observations [2]
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