Page under construction To be completed by JRC/ MDCC - Text below is copied from EFAS 4
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OS LISFLOOD model requires gridded meteorological maps of precipitation, average temperature, potential evaporation rate from free water surface
,and bare soil surface, and
evapo-transpirationevapotranspiration rate for reference crop surface.
For EFAS
v4v5.0, precipitation and temperature 6-hourly
5 km1 arcmin grids were produced by interpolating point data using
SPHEREMAP algorithm. SPHEREMAP (WillmottSPHEREMAP 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 inverseof the angular distance weighting scheme (Shepard, 1968).
It is based on a combined distance and angular weighting plus a correction using the gradient of the observationsMore specifically, 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
utilisedused to interpolate the value at the grid point. If less than four station
wereare 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
takeadequately account for the
possibleclustering of stations
into account. Clustered station are less weighted: clustered station have lower weight 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 evaporation and evapotranspiration
Evaporation daily gridswere 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
createdcreate 6-hourly grids.
For each day, evaporationEach daily evaporation and evapotranspiration rates stay the same for each of the four 6-hourly
gridgrids and they correspond to the rates in the daily grids (to meet OS LISFLOOD implementation requirements).
The data record spans from January 1990 to December 2017.
Figure 3.1 - EFAS v4.0 - Spatial distribution of available 6-hourly precipitation observations for the period 2010-2017
Figure 3.2 - EFAS v4.0 - Spatial distribution of available 6-hourly average temperature observations for the period 2010-2017
REFERENCE2021. The CEMS Meteorological Data Collection Centre (CEMS MDCC) collects, quality controls, and post-process in-situ observations of meteorological variables. Figure 1 (left) and Figure 2 show the spatial distribution of the in-situ stations that provided 6-houly measurements of precipitation and mean temperature in the time interval from January 1990 to December 2021. Data sets from research projects (e.g. CarpatClim, EURO4M-APGD), but also operational data sets like ERA-Interim were integrated into the CEMS MDCC database to increase the data coverage over highly complex terrain and in data sparse areas. The gridded data sets are called ‘virtual stations’ and their spatial distribution is shown in Figure 1 (right). The CEMS Meteorological Data Collection Centre Annual Reportsprovide 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, temperature (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)
Figure 1 - Location of the in-situ stations that provided 6-hourly precipitation measurements in the period 01/01/1990-31/12/2021 (left); gridded datasets used to increase data coverage over highly complex terrains and in data sparce areas in the period 01/01/1990-31/12/2021 (right). In both the figures, the green area represents the EFASv5 computational domain.
Figure 2 - Location of the in-situ stations that provided 6-hourly mean temperature measurements in the period 01/01/1990-31/12/2021; the green area represents the EFASv5 computational domain.
(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, 489-509
Krige, 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, 13-38Willmott, 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