Investigation of the Spatial and Temporal Variability of the Precipitation and Temperature Lapse Rates in Greece and its Application in Evaluation and Calibration of Metanalysis Meteorological Data

Accurate meteorological forcing is a prerequisite for reliable hydrological modelling, particularly in regions with complex topography like Greece. Global reanalysis datasets offer continuous coverage but often fail to capture local orographic effects when downscaled using standard, constant lapse rates. This study investigates the spatial and temporal variability of precipitation and temperature gradients across Greece and evaluates their application in calibrating reanalysis data.

We utilized a hybrid dataset comprising long-term records from 140 meteorological stations and a dense network of 777 stations for the year 2023. To process this data, we developed a specialized Python-based algorithm to estimate lapse rates and the Coefficient of Determination ($R^2$) dynamically across the domain. The methodology utilizes a "moving-window" approach, where the window dimensions and moving step were first optimized by maximizing the determination coefficient ($R^2$) to ensure statistical robustness. Using these optimized parameters, we estimated the lapse rate and $R^2$ at each grid point of the study area. Subsequently, spatial interpolations were generated to create continuous maps of vertical gradients and their statistical reliability.

The resulting spatial patterns were analyzed in relation to the country’s distinct geomorphology, including the complex coastline, the orientation of major mountain ranges (Pindos), and the insular environments. The analysis revealed that while temperature lapse rates exhibit high spatial coherence and predictability, precipitation gradients are highly sensitive to local topographic features and continentality.

These empirically derived, spatially explicit lapse rates were applied to downscale and bias-correct AgERA5 temperature and precipitation fields for the DT-Agro Digital Twin. The proposed methodology significantly reduced biases in mountainous and coastal zones compared to standard interpolation methods, demonstrating that geomorphologically informed, dynamic gradient estimation is critical for effective model calibration in data-scarce, complex terrains.