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OS LISFLOOD model requires gridded meteorological maps of precipitation, average temperature, potential evaporation rate from free water surface, bare soil surface and evapo-transpiration for reference crop surface.
For EFAS v5.0, precipitation and temperature 6-hourly 1 arcmin grids were produced by interpolating point data using SPHEREMAP algorithm. SPHEREMAP (Willmott SPHEREMAP interpolation scheme. SPHEREMAP (Becker et al., 2013) is a spherical adaptation (Willmott CJ et al., 1985) is the adaptation to spherical coordinates of Shepard's inverse of the angular distance weighting scheme (Shepard, 1968) . It and it is based on a combined distance and angular weighting plus a correction using the gradient of the observations. Therefore, SPHEREMAP interpolation scheme accounts for (1) the distances of the stations to the grid point (for limited number of nearest stations); (2) the directional distribution of stations in relation to the grid point (in order to avoid an overweighting of clustered stations), and (3) the gradients of the data field in the grid point environment. Several radius are defined within SPHEREMAP for the calculation of the weights. First of all, an initial search radius for stations around the grid point is defined based on the number of available stations in the data set and the size of the gridding area. Based on the initial radius, a radius for switching the calculation rule of the distance weights is defined. Finally, a small radius "epsilon" around the grid point is defined. Between four and up to ten stations are utilised to interpolate the value at the grid point. If less than four station were found within the initial search radius, then this radius is increased step wise until at least 4 stations are detected. The angular weights are used to take the possible clustering of stations into account. Clustered station are less weighted than solitary stations. Distance and angular weights are combined and adjusted by the gradient to get the final applied interpolation weights. Thiemig et al. (Creation of EFAS grids#DescriptionofSPHEREMAP).2022) provides a detailed assessment of the use of SPHEREMAP interpolation scheme to generate EFAS meteorological gridded dataset.
Gridded daily values of reference evaporation and evapotranspiration Evaporation daily grids were derived using Penman-Monteith equation and the Open Source LISVAP Evaporation Pre-Processor for the OS LISFLOOD model (Burek et al. 2013), based on . The input data were gridded minimum and maximum daily temperature, wind speed, solar radiation and vapour pressure. Daily grids were then dis-aggregated to created 6-hourly grids. For each day, evaporation rates stay the same for each 6-hourly grid and they correspond to the rates in the daily grids.
The data record spans from January 1990 to December 2021.
The CEMS Meteorological Data Collection Centre collects, quality controls, and post-process in-situ observations of meteorological variables. The CEMS Meteorological Data Collection Centre Annual Reports provide a comprehensive overview of the database (with information for each meteorological variable) and of the quality control and post-processing protocols: by the end of 2021, 30 data providers contributed to CEMS EFAS meteorological data collection database with more than 44,000 stations providing historical data and 23,000 stations providing real-time data (Rehfeldt et al, 2022)(1).
The high-resolution, (sub-)daily, gridded meteorological data set of total precipitation, temperatures (minimum and maximum), wind speed, solar radiation and water vapour pressure is available for download from EMO-1 (European Meteorological Observations) gridded (sub-)daily dataset (Gomes et al., 2020). Improvements to the dataset are timely registered and documented by version updates. EFAS v5 calibration made use of EMO-1 v.2.0.0.(2)
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Figure Figure 3. 1 - EFAS v5.0 - Spatial distribution of available 6-hourly precipitation observations for the period 2010-20172021
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Figure 3. 2 - EFAS v4v5.0 - Spatial distribution of available 6-hourly average temperature observations for the period 2010-2017REFERENCE2021
(1) For general applications, readers are recommended to follow the progress of the data collection by referring to the most recent Annual Report: for instance, Lemke et al. (2023) reported that 35 data providers contributed to the CEMS EFAS Meteorological database by the end of 2022.
(2) For further use of the dataset, readers are recommended to refer to the most recent version in order to benefit of all the latest improvements.
REFERENCES
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., & Ziese, M. (2013). A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present. Earth System Science Data, 5(1), 71–99. https://doi.org/10.5194/essd-5-71-2013
Gomes, Goncalo; Thiemig, Vera; Skøien, Jon Olav; Ziese, Markus; Rauthe-Schöch, Armin; Rustemeier, Elke; Rehfeldt, Kira; Walawender, Jakub; Kolbe, Christine; Pichon, Damien; Schweim, Christoph; Salamon, Peter (2020): EMO: A high-resolution multi-variable gridded meteorological data set for Europe. European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 PID: http://data.europa.eu/89h/0bd84be4-cec8-4180-97a6-8b3adaac4d26
Lemke, C., Schirmeister, Z., Walawender, J., Ziese, M., Pichon, D., Radke-Fretz, M., Schweim, C., Gomes, G., Grimaldi, S. and Salamon, P., CEMS Meteorological Data Collection Centre – Annual report 2022, European Commission, 2023, JRC134489
Rehfeldt, K., Schirmeister, Z., Pichon, D., Rauthe-Schöch, A., Schweim, C., Walawender, J., Ziese M., Gomes, G., Thiemig, V. and Salamon, P., The CEMS Meteorological Data
Collection Centre – Annual report 2021, European Commission, 2022, JRC129076
Thiemig, V., Gomes, G. N., Skøien, J. O., Ziese, M., Rauthe-Schöch, A., Rustemeier, E., Rehfeldt, K., Walawender, J. P., Kolbe, C., Pichon, D., Schweim, C., & Salamon, P. (2022). EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe. Earth System Science Data, 14(7), 3249–3272. https://doi.org/10.5194/essd-14-3249-2022
Yamamoto, J.; An Alternative Measure of the Reliability of Ordinary Kriging Estimates; Mathematical Geology, 2000, 32, 489-509Krige, D.;Two-dimensional weighted moving average trend surfaces for ore valuation; Proceedings of the Symposium on Mathematical Statistics and Computer Applications in Ore Valuation, 1966, 13-38
Willmott, C.; Rowe, C. & Philpot, W.; Small-scale climate maps: A sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring; The American Carthographer, 1985, 12, 5-16