| dc.description.abstract | Rainfall monitoring is a crucial aspect of climate modeling and water
resource management. The widespread availability of satellite
rainfall data, such as Integrated Multi-satellitE Retrievals for Global
Precipitation Measurement (IMERG), offers the potential to obtain
high-resolution global precipitation information. However, it is
essential to evaluate the validity of IMERG satellite rainfall data
against ground-based station measurements. The aim of this study is
to evaluate the validity of IMERG satellite rainfall data in
representing ground station rainfall data. In this study, IMERG
satellite rainfall data with a 10 km resolution is downscaled to a
resolution of 250 m using the Geographically Weighted Regression
(GWR) method. The GWR model incorporates the Normalized
Difference Vegetation Index (NDVI) data as an environmental
variable. Then, the downscaled data is calibrated with the ground
station rainfall data. The calibration ensures optimal alignment
between the downscaled data and the ground observations. The
calibrated rainfall data is then compared to ground station rainfall
data using various validation metrics, including R-squared for linear
regression, Bias, Root Mean Squared Error (RMSE), and Mean
Absolute Error (MAE). In general, the validation results indicate that
the calibrated IMERG provides more accurate results (R2=0,95;
RMSE=148,80 mm; MAE=124,91 mm; and Bias=0,05) compared to
the original IMERG and downscaled results. High correlation and
low prediction errors suggest that the calibrated IMERG is
sufficiently reliable and can serve as a good alternative rainfall data
to represent the actual rainfall occurring in the field in 2021. | en_US |
