README for dataset: Climatological adjustment factors for operational radar rainfall bias reduction in the Netherlands

##############################
# Contact information
##############################
Authors: Imhoff, R. & C. Brauer & K.J. van Heeringen & H. Leijnse & A. Overeem & A. Weerts & R. Uijlenhoet
Affiliations: 
Hydrology and Quantitative Water Management Group, Wageningen University & Research, Wageningen, The Netherlands
Operational Water Management & Early Warning, Department of Inland Water Systems, Deltares, Delft, The Netherlands
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, De Bilt, The Netherlands
Department of Water Management, Delft University of Technology, Delft, The Netherlands

Corresponding author: Imhoff, R. (Ruben)
ruben.imhoff@wur.nl


##############################
# General dataset information
##############################
This dataset contains gridded adjustment factors for correction of the Quantitative Precipitation Estimations (QPE) of the two operational C-band weather radars operated by the Royal Netherlands Meteorological Institute (KNMI). The factors are available for every yearday (temporal resolution of one day) and are based on ten years (2009 - 2018) of radar data. 
For the derivation of the factor, both the operational radar QPE (https://doi.org/10.4121/uuid:05a7abc4-8f74-43f4-b8b1-7ed7f5629a01) and a reference rainfall dataset of KNMI (https://dataplatform.knmi.nl/catalog/datasets/index.html?x-dataset=rad_nl25_rac_mfbs_em_5min&amp&x-dataset-version=2.0) are used. The reference is not operationally available, but becomes available with a one to two month delay and was therefore available for this climatological factor derivation.
The derivation method was as follows per grid cell in the radar domain (Imhoff et al., 2021):
1. For every day in the period 2009--2018, an accumulation took place of all 5-min rainfall sums (of both the unadjusted radar QPE and the reference) within a moving window of 15 days prior to and 15 days after the day of interest. The purpose of the moving window was to smooth the day-to-day variability of the rainfall in the 10-year datasets.
2. For every yearday, the accumulations (per day) from the previous step were averaged over the ten years. This means that the final day sum for e.g. 1 January consisted of the average day sum of all accumulations for 1 January in the ten years.
3. Gridded climatological adjustment factors (Fclim) were calculated per yearday as: Fclim(i,j) = RA(i,j) / RU(i,j). In this equation, RA(i,j) is the reference rainfall sum for the ten years and RU(i,j) the operationally available unadjusted radar QPE sum, based on the previous two steps, at grid cell (i, j).

For more details about the method, see Imhoff et al. (2021). For more information about the reference dataset, which consists of the radar QPE spatially adjusted with observations from 31 automatic and 325 manual rain gauges, see Overeem et al. (2009a,b).
 

##############################
# File type
##############################
netCDF-3 (.nc)


##############################
# Data specific information
##############################
Variable name: factor
Units of measurement: - (dimensionless). Note that the data is saved as 1 / factor, so the operational radar QPE should be multiplied with 1 / factor in order to obtain the bias adjusted QPE. 
Data type: Float

Spatial resolution: 1 km
Temporal resolution: 1 day
Temporal coverage: 2017-12-30 until 2019-01-01. The data is climatological and thus are the year days for 2018 the same for any other year.

Number of rows: 765
Number of columns: 700
No data value: -9999.0

Projection (PROJ4): "+proj=stere +lat_0=90 +lon_0=0.0 +lat_ts=60.0 +a=6378.137 +b=6356.752 +x_0=0 +y_0=0" 
False northing: 3650.0 (km)
Extent in lat-lon: 55.974, 0.0 [ULC]; 55.389, 10.856 [URC]; 49.362, 0.0 [LLC]; 48.895, 9.009 [LRC]


##############################
# License
##############################
CC-BY


##############################
# References
##############################
Holleman, I. (2007). Bias adjustment and long-term verification of radar-based precipitation estimates. Meteorological Applications, 14, 195--203, doi: 10.1002/met.22.
Imhoff, R.O., Brauer, C.C., Heeringen van, K-J., Leijnse, H., Overeem, A., Weerts, A. & Uijlenhoet, R. (2021). A climatological benchmark for operational radar rainfall bias reduction. Submitted to Hydrology and Earth Systems Sciences. 
Overeem, A., Holleman, I., & Buishand, A. (2009a). Derivation of a 10-year radar based climatology of rainfall. Journal of Applied Meteorology and Climatology, 48, 1448--1463. doi: 10.1175/2009JAMC1954.1.
Overeem, A., Buishand, T.A. & Holleman, I. (2009b). Extreme rainfall analysis and estimation of depth-duration-frequency curves using weather radar. Water Resources Research, 45, W10424, doi:10.1029/2009WR007869.