MeteoSerbia1km: the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period
- 1. University of Belgrade, Faculty of Civil Engineering
Description
MeteoSerbia1km is the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea level pressure, and total precipitation. Besides daily summaries, it contains monthly and annual summaries, daily, monthly, and annual long term means (LTM). Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology based on Random Forest and using nearest observations and distances to them as spatial covariates, together with environmental covariates.
Complete script in R and datasets used for modelling, tuning, validation, and prediction of daily meteorological variables are available here.
If you discover a bug, artifact or inconsistency in the MeteoSerbia1km maps, or if you have a question please use this channel.
File naming convention of .zip files and containing MeteoSerbia1km files:
- Daily summaries per year: day_yyyy_proj.zip
- var_day_yyyymmdd_proj.tif
- Monthly summaries: mon_proj.zip
- var_mon_yyyymm_proj.tif
- Annual summaries: ann_proj.zip
- var_ann_yyyy_proj.tif
- Daily, monthly and annual LTM: ltm_proj.zip
- daily LTM: var_ltm_day_mmdd_proj.tif
- monthly LTM: var_ltm_mon_mm_proj.tif
- annual LTM: var_ltm_ann_proj.tif
where:
- var is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp
- proj is a dataset projection - wgs84 or utm34
Units of the dataset values are
- temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)
- SLP - tenths of a mbar
- PRCP - tenths of a mm
All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.
Notes
Files
ann_utm34.zip
Files
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Additional details
Funding
References
- Sekulić, A., Kilibarda, M., Heuvelink, G. B., Nikolić, M. & Bajat, B. Random Forest Spatial Interpolation.Remote. Sens. 12, 1687, https://doi.org/10.3390/rs12101687 (2020).