High resolution observational daily gridded dataset for Greece: The CLIMADAT-hub project
- 1National Observatory of Athens, Institute for Environmental Research and Sustainable Development, Greece
- 2Department of Physics, National and Kapodistrian University of Athens, Greece
- 3Laboratory of Climatology and Atmospheric Environment, Section of Geography and Climatology, Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, Greece
- 4Hellenic National Meteorological Service, Athens, Greece
- 5School of Chemical and Environmental Engineering, Technical University of Crete, 73100, Greece
CLIMADAT-hub is a two-year project within the framework of H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – NextGenerationEU. The project aims at bridging the gap between the available climatic information and the information required for assessing climate risks at the local scale by creating high resolution observational gridded datasets, as well as statistically downscaled seasonal forecasts and climate change projections for Greece.
Regarding the observational gridded datasets, the primary goal is to construct a state-of-the art, 1km high resolution gridded dataset for daily temperature, precipitation, relative humidity and wind speed for the period 1981-2021. Starting with temperature and precipitation, long term daily values have been collected from various meteorological networks and databases for a large number of locations in Greece. These raw daily data underwent quality control and homogenization. To obtain the daily gridded values for air temperatures (maximum, minimum, mean) and precipitation, at a spatial resolution of 1km the following methods have been examined: i) combination of classical geo statistical methods such as Thin Plate Splines and Kriging as used in the early versions of E-OBS and Iberia01, ii) Regression-Kriging, a spatial prediction technique commonly used in geostatistics that combines a regression of the dependent variable (e.g., temperature) on auxiliary/predictive variables (e.g., elevation, distance from shoreline) with kriging of the regression residuals (similar to EOBSv17 and afterwards), iii) ensemble machine learning, an approach to modeling, where instead of using a single best learner, multiple strong learners are used and, consequently, are combined into a single union, iv) hybrid method, where the available observations are blended with the Weather Research and Forecasting (WRF) model to produce the high resolution observed gridded datasets through gridding and bias adjustment techniques.
We report the results from evaluating all the created gridded datasets for temperature and precipitation against withheld station data to determine the best performing approach for each variable. Future work will extend these methodologies to include the remaining variables.
How to cite: Varotsos, K. V., Karali, A., Kitsara, G., Lemesios, G., Patlakas, P., Hatzaki, M., Tenentes, V., Katavoutas, G., Sarantopoulos, A., Koutroulis, A. G., Grillakis, M. G., Psiloglou, B., and Giannakopoulos, C.: High resolution observational daily gridded dataset for Greece: The CLIMADAT-hub project, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-643, https://doi.org/10.5194/ems2024-643, 2024.
