Nivar

Nivar

Forecasting short-term precipitation using artificial intelligence on the southern coast of the Caspian Sea

Document Type : Original Article

Authors
Donya Sadeghnezhad 1
Gholamali Kamali 2
Ebrahim Fattahi 3
Zahra Ghassabi 4
Majid Vazifedoust 5
1 Doctoral student of Meteorology, Islamic Azad University, Science and Research Unit, Tehran, Iran
2 Associate Professor of Climatology, Islamic Azad University, Science and Research Unit, Tehran, Iran
3 Associate Professor of Climatology, Research Institute of Meteorology and Atmospheric Sciences, Tehran, Iran
4 Assistant Professor of Meteorology, Research Institute of Meteorology and Atmospheric Sciences, Tehran, Iran
5 Assistant Professor, Department of Water Engineering, University of Guilan
10.30467/nivar.2025.521906.1335
Abstract
Rainfall is considered one of the most important input data for hydrological systems, and studying and measuring it is often necessary for runoff, groundwater, flooding, and sediment studies. Traditionally, rainfall measurement is conducted using rain gauges. Rain gauges are the most reliable source of rainfall observations and are used in most studies as a reference for comparing and validating satellite data. However, they have limited and irregular spatial coverage. In recent decades, advancements in remote sensing technologies for rainfall have led to a significant reduction in the chronic lack of spatial information, making vast datasets on precipitation available with unprecedented resolution in both space and time. Today, various meteorological satellites such as TRMM, ERA5, CHIRPS, and GPM are actively monitoring the Earth's atmospheric conditions, cloud cover, and humidity, with a high spatial resolution of around kilometers. Precipitation maps from these datasets have been continuously and consistently available on a global scale, but the resulting output images have a low spatial resolution of 25×25 kilometers. This spatial resolution is not highly applicable in small basins and irrigation and drainage networks. In this study, in order to develop an algorithm to increase the spatial resolution of satellite-based precipitation products, an algorithm was extracted using downscaling methods SVM ,RF, KNN,GBR with the help of the relationship between the physical characteristics of clouds and precipitation. Between September 2018 and December 2018 and September 2019 to December 2019 on the GEE platform, precipitation data were downscaled to 1 kilometer using cloud physical properties and data downscaling methods. The downscaled data RF has a higher coefficient of determination and better agreement with ground station data.
Keywords
Physical characteristics of the cloud
Short-term forecast
Machine learning model
precipitation
Subjects
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Nivar
Volume 49, Special Issue (S1) - Serial Number 1
Special Issue (S1)
November 2025
Pages 41-59

  • Receive Date 07 May 2025
  • Revise Date 07 December 2025
  • Accept Date 20 December 2025
  • Publish Date 23 September 2025