Features:

FileName: of CTG examination
Date: of the examination
b: start instant
e: end instant
LBE: baseline value (medical expert)
LB: baseline value (SisPorto)
AC: accelerations (SisPorto)
FM: foetal movement (SisPorto)
UC: uterine contractions (SisPorto)
ASTV: percentage of time with abnormal short term variability (SisPorto)
mSTV: mean value of short term variability (SisPorto)
ALTV: percentage of time with abnormal long term variability (SisPorto)
mLTV: mean value of long term variability (SisPorto)
DL: light decelerations
DS: severe decelerations
DP: prolongued decelerations
DR: repetitive decelerations
Width: histogram width
Min: low freq. of the histogram
Max: high freq. of the histogram
Nmax: number of histogram peaks
Nzeros: number of histogram zeros
Mode: histogram mode
Mean: histogram mean
Median: histogram median
Variance: histogram variance
Tendency: histogram tendency: -1=left assymetric; 0=symmetric; 1=right assymetric
A: calm sleep
B: REM sleep
C: calm vigilance
D: active vigilance
SH: shift pattern (A or Susp with shifts)
AD: accelerative/decelerative pattern (stress situation)
DE: decelerative pattern (vagal stimulation)
LD: largely decelerative pattern
FS: flat-sinusoidal pattern (pathological state)
SUSP: suspect pattern
CLASS: Class code (1 to 10) for classes A to SUSP
NSP:- Normal=1; Suspect=2; Pathologic=3

This dataset contains daily histograms of wind speed at 100m ("WS100"), wind direction at 100 m ("WD100") and an atmospheric stability proxy ("STAB") derived from the ERA5 hourly data on single levels [1] accessed via the Copernicus Climate Change Climate Data Store [2]. The dataset covers six geographical regions (illustrated in regions.png) on a reduced 0.5 x 0.5 degrees regular grid and covers the period 1994 to 2023 (both years included). The dataset is packaged as a zip folder per region which contains a range of monthly zip folders following the convention of zarr ZipStores (more details here: https://zarr.readthedocs.io/en/stable/api/storage.html). Thus, the monthly zip folders are intended to be used in connection with the xarray python package (no unzipping of the monthly files needed).Wind speed and wind direction are derived from the U- and V-components. The stability metric makes use of a 5-class classification scheme [3] based on the Obukhov length whereby the required Obukhov length was computed using [4]. The following bins (left edges) have been used to create the histograms:Wind speed: [0, 40) m/s (bin width 1 m/s)Wind direction: [0,360) deg (bin width 15 deg)Stability: 5 discrete stability classes (1: very unstable, 2: unstable, 3: neutral, 4: stable, 5: very stable)Main Purpose: The dataset serves as minimum input data for the CLIMatological REPresentative PERiods (climrepper) python package (https://gitlab.windenergy.dtu.dk/climrepper/climrepper) in preparation for public release).References:[1] Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)[2] Copernicus Climate Change Service, Climate Data Store, (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)'[3] Holtslag, M. C., Bierbooms, W. A. A. M., & Bussel, G. J. W. van. (2014). Estimating atmospheric stability from observations and correcting wind shear models accordingly. In Journal of Physics: Conference Series (Vol. 555, p. 012052). IOP Publishing. https://doi.org/10.1088/1742-6596/555/1/012052[4] Copernicus Knowledge Base, ERA5: How to calculate Obukhov Length, URL: https://confluence.ecmwf.int/display/CKB/ERA5:+How+to+calculate+Obukhov+Length, last accessed: Nov 2024