Articles | Volume 15, issue 3
https://doi.org/10.5194/essd-15-1441-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-1441-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
31 Mar 2023
Data description paper |
| 31 Mar 2023
| 31 Mar 2023
EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Else van den Besselaar
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Gerard van der Schrier
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Jan Fokke Meirink
R&D Satellite Observations, Royal Netherlands Meteorological Institute, Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Emiel van der Plas
R&D Satellite Observations, Royal Netherlands Meteorological Institute, Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
Hidde Leijnse
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, Utrechtseweg 297, 3731 GA De Bilt, the Netherlands
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Short summary
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Cited articles
Al-Sakka, H., Boumahmoud, A.-A., Fradon, B., Frasier, S. J., and Tabary, P.: A
new fuzzy logic hydrometeor classification scheme applied to the French
X-, C-, and S-band polarimetric radars, J. Appl. Meteorol. Clim.,
52, 2328–2344, https://doi.org/10.1175/JAMC-D-12-0236.1, 2013. a
Allen, R. J. and DeGaetano, A. T.: Considerations for the use of
radar-derived precipitation estimates in determining return intervals for
extreme areal precipitation amounts, J. Hydrol., 315, 203–219,
https://doi.org/10.1016/j.jhydrol.2005.03.028, 2005. a
Barnes, S. L.: A technique for maximizing details in numerical weather map
analysis, J. Appl. Meteorol., 3, 396–409, 1964. a
Barton, Y., Sideris, I., Raupach, T., Gabella, M., Germann, U., and Martius,
O.: A multi-year assessment of sub-hourly gridded precipitation for
Switzerland based on a blended radar–Rain-gauge dataset, Int. J.
Climatol., 40, 5208–5222, https://doi.org/10.1002/joc.6514, 2020. a
Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., and Meirink, J. F.: The MSG-SEVIRI-based cloud property data record CLAAS-2, Earth Syst. Sci. Data, 9, 415–-434, https://doi.org/10.5194/essd-9-415-2017, 2017. a
Berenguer, M., Sempere-Torres, D., Corral, C., and Sánchez-Diezma, R.: A fuzzy
logic technique for identifying nonprecipitating echoes in radar scans, J.
Atmos. Ocean. Tech., 23, 1157–1180, https://doi.org/10.1175/JTECH1914.1, 2006. a
Berg, P., Christensen, O. B., Klehmet, K., Lenderink, G., Olsson, J., Teichmann, C., and Yang, W.: Summertime precipitation extremes in a EURO-CORDEX 0.11∘ ensemble at an hourly resolution, Nat. Hazards Earth Syst. Sci., 19, 957–971, https://doi.org/10.5194/nhess-19-957-2019, 2019. a
Bližňák, V., Kašpar, M., and Müller, M.: Radar-based summer
precipitation climatology of the Czech Republic, Int. J. Climatol., 38,
677–691, https://doi.org/10.1002/joc.5202, 2018. a
Carey, L. D., Rutledge, S. A., Ahijevych, D. A., and Keenan, T. D.: Correcting
propagation effects in C-band polarimetric radar observations of tropical
convection using differential propagation phase, J. Appl. Meteorol., 39,
1405–1433, https://doi.org/10.1175/1520-0450(2000)039<1405:CPEICB>2.0.CO;2, 2000. a
Cornes, R. C., Van der Schrier, G., Van den Besselaar, E. J. M., and Jones,
P. D.: An ensemble version of the E-OBS temperature and precipitation data
sets, J. Geophys. Res.-Atmos., 123, 9391–9409,
https://doi.org/10.1029/2017JD028200, 2018. a, b, c
Crisologo, I., Vulpiani, G., Abon, C. C., David, C. P. C., Bronstert, A., and
Heistermann, M.: Polarimetric rainfall retrieval from a C-band weather
radar in a tropical environment (the Philippines), Asia-Pac. J. Atmos.
Sci., 50, 43–55, https://doi.org/10.1007/s13143-014-0049-y, 2014. a
De Vos, L. W., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Quality control
for crowdsourced personal weather stations to enable operational rainfall
monitoring, Geophys. Res. Lett., 46, 8820–8829,
https://doi.org/10.1029/2019GL083731, 2019. a
Derrien, M. and Le Gléau, H.: MSG/SEVIRI cloud mask and type from SAFNWC,
Int. J. Remote Sens., 26, 4707–4732, 2005. a
Doviak, R. J. and Zrnić, D. S.: Doppler Radar and Weather Observations, second edn.,
Dover Publications, Inc., Mineola, New York, ISBN 0-486-45060-0, 1993. a
Durrans, S. R., Julian, L. T., and Yekta, M.: Estimation of depth-area
relationships using radar-rainfall data, J. Hydrol. Eng., 7, 356–367,
https://doi.org/10.1061/(ASCE)1084-0699(2002)7:5(356), 2002. a
Fabry, F.: Radar Meteorology: Principles and Practice, Cambridge University
Press, Cambridge, U.K., https://doi.org/10.1017/CBO9781107707405, 2015. a, b
Fabry, F., Meunier, V., Treserras, B. P., Cournoyer, A., and Nelson, B.: On the
climatological use of radar data mosaics: possibilities and challenges,
B. Am. Meteorol. Soc., 98, 2135–2148, https://doi.org/10.1175/BAMS-D-15-00256.1,
2017. a, b
Finkensieper, S., Meirink, J.-F., van Zadelhoff, G.-J., Hanschmann, T., Benas,
N., Stengel, M., Fuchs, P., Hollmann, R., and Werscheck, M.: CLAAS-2: CM SAF
CLoud property dAtAset using SEVIRI – Edition 2, Satellite Application
Facility on Climate Monitoring, https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002, 2016. a
Frech, M., Hagen, M., and Mammen, T.: Monitoring the absolute calibration of a
polarimetric weather radar, J. Atmos. Ocean. Tech., 34, 599–615,
https://doi.org/10.1175/JTECH-D-16-0076.1, 2017. a
Frederick, R. H., Myers, V. A., and Auciello, E. P.: Storm depth-area relations
from digitized radar returns, Water Resour. Res., 13, 675–679, 1977. a
Gabella, M. and Notarpietro, R.: Ground clutter characterization and
elimination in mountainous terrain, in: Proceedings of the 2nd European conference on radar meteorology ERAD and the COST 717 mid-term seminar, https://www.copernicus.org/erad/online/erad-305.pdf (last access: 23 March 2023), 2002. a, b
Garcia-Marti, I., Overeem, A., Noteboom, J. W., de Vos, L., de Haij, M., and
Whan, K.: From proof-of-concept to proof-of-value: approaching third-party
data to operational workflows of national meteorological services, Int. J.
Climatol., 43, 275–292, https://doi.org/10.1002/joc.7757, 2023. a
Goudenhoofdt, E. and Delobbe, L.: Evaluation of radar-gauge merging methods for quantitative precipitation estimates, Hydrol. Earth Syst. Sci., 13, 195–203, https://doi.org/10.5194/hess-13-195-2009, 2009. a
Goudenhoofdt, E. and Delobbe, L.: Generation and verification of rainfall
estimates from 10-yr volumetric weather radar measurements, J. Hydrometeorol.,
17, 1223–1242, https://doi.org/10.1175/JHM-D-15-0166.1, 2016. a, b, c
Gourley, J. J., Tabary, P., and Parent du Chatelet, J.: A fuzzy logic algorithm
for the separation of precipitating from nonprecipitating echoes using
polarimetric radar observations, J. Atmos. Ocean. Tech., 24,
1439–1451, https://doi.org/10.1175/JTECH2035.1, 2007. a, b
Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020. a
Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H.,
and Bárdossy, A.: Rainfall estimates from opportunistic sensors in
Germany across spatio-temporal scales, J. Hydrol.: Regional Studies, 37,
1448–1463, https://doi.org/10.1016/j.ejrh.2021.100883, 2021. a, b
Hazenberg, P., Torfs, P. J. J. F., Leijnse, H., Delrieu, G., and Uijlenhoet,
R.: Identification and uncertainty estimation of vertical reflectivity
profiles using a Lagrangian approach to support quantitative precipitation
measurements by weather radar, J. Geophys. Res.-Atmospheres, 118,
10243–10261, https://doi.org/10.1002/jgrd.50726, 2013. a, b
Heistermann, M., Jacobi, S., and Pfaff, T.: Technical Note: An open source library for processing weather radar data (wradlib), Hydrol. Earth Syst. Sci., 17, 863–871, https://doi.org/10.5194/hess-17-863-2013, 2013. a, b
Hitschfeld, W. and Bordan, J.: Errors inherent in the radar measurement of
rainfall at attenuating wavelengths, J. Meteorol., 11, 58–67,
https://doi.org/10.1175/1520-0469(1954)011<0058:EIITRM>2.0.CO;2, 1954. a
Holleman, I.: Bias adjustment and long-term verification of radar-based
precipitation estimates, Meteor. Appl., 14, 195–203, https://doi.org/10.1002/met.22,
2007. a
Huuskonen, A., Saltikoff, E., and Holleman, I.: The operational weather radar
network in Europe, B. Am. Meteorol. Soc., 95, 897–907,
https://doi.org/10.1175/BAMS-D-12-00216.1, 2014. a
Jacobi, S. and Heistermann, M.: Benchmarking attenuation correction procedures
for six years of single-polarized C-band weather radar observations in
South-West Germany, Geomat. Nat. Haz. Risk, 7, 1785–1799,
https://doi.org/10.1080/19475705.2016.1155080, 2016. a, b
Kitchen, M. and Blackall, R. M.: Representativeness errors in comparisons
between radar and gauge measurements of rainfall, J. Hydrol., 134, 13–33,
1992. a
Klein Tank, A. M. G., Wijngaard, J. B., Können, G. P., et al.: Daily dataset of 20th-century surface air
temperature and precipitation series for the European Climate
Assessment, Int. J. Climatol., 22, 1441–1453, https://doi.org/10.1002/joc.773,
2002. a
Klok, E. J. and Klein Tank, A. M. G.: Updated and extended European dataset of daily
climate observation, Int. J. Climatol., 29, 1182–1191,
https://doi.org/10.1002/joc.1779, 2008. a
KNMI: Precipitation – 5 minute precipitation accumulations from climatological
gauge-adjusted radar dataset for The Netherlands (1 km, extended mask) in
KNMI HDF5 format, KNMI Data Platform [data set], https://dataplatform.knmi.nl/dataset/rad-nl25-rac-mfbs-em-5min-2-0, last access: 27 March 2023. a
Krause, J. M.: A simple algorithm to discriminate between meteorological and
nonmeteorological radar echoes, J. Atmos. Ocean. Tech., 33, 1875–1885,
https://doi.org/10.1175/JTECH-D-15-0239.1, 2016. a
Leijnse, H., Uijlenhoet, R., and Stricker, J. N. M.: Rainfall measurement using
radio links from cellular communication networks, Water Resour. Res., 43,
W03201, https://doi.org/10.1029/2006WR005631, 2007. a
Lengfeld, K., Winterrath, T., Junghänel, T., Hafer, M., and Becker, A.:
Characteristic spatial extent of hourly and daily precipitation events in
Germany derived from 16 years of radar data, Meteorol. Z., 28, 363–378,
https://doi.org/10.1127/metz/2019/0964, 2019. a
Lengfeld, K., Walawender, E., Winterrath, T., and Becker, A.: CatRaRE: A
Catalogue of radar-based heavy rainfall events in Germany derived from 20
years of data, Meteorol. Z., 30, 469–487, https://doi.org/10.1127/metz/2021/1088, 2021. a
Lochbihler, K., Lenderink, G., and Siebesma, A. P.: The spatial extent of
rainfall events and its relation to precipitation scaling, Geophys. Res.
Lett., 44, 8629–8636, https://doi.org/10.1002/2017GL074857, 2017. a
Marra, F. and Morin, E.: Use of radar QPE for the derivation of
Intensity–Duration–Frequency curves in a range of climatic regimes, J.
Hydrol., 531, 427–440, https://doi.org/10.1016/j.jhydrol.2015.08.064, 2015. a
McGraw, D., Nikolopoulos, E. I., Marra, F., and Anagnostou, E. N.:
Precipitation frequency analyses based on radar estimates: An evaluation over
the contiguous United States, J. Hydrol., 573, 299–310,
https://doi.org/10.1016/j.jhydrol.2019.03.032, 2019. a
Messer, H. A., Zinevich, A., and Alpert, P.: Environmental monitoring by
wireless communication networks, Science, 312, 713,
https://doi.org/10.1126/science.1120034, 2006. a
Met Office: Cartopy: a cartographic python library with a Matplotlib
interface, https://scitools.org.uk/cartopy (last access: 23 March 2023), 2022. a
Michelson, D. B., Lewandowski, R., Szewczykowski, M., Beekhuis, H., Haase, G.,
Mammen, T., Faure, D., Simpson, M., Leijnse, H., and Johnson, D.:
EUMETNET OPERA weather radar information model for implementation
with the HDF5 file format Version 2.3, EUMETNET OPERA,
https://www.eumetnet.eu/wp-content/uploads/2019/01/ODIM_H5_v23.pdf (last access: 23 March 2023),
2019. a, b, c
Mühlbauer, K., Heistermann, M., and Goudenhoofdt, E.: wradlib/wradlib:
wradlib release v1.9.0 (1.9.0), Zenodo [code], https://doi.org/10.5281/zenodo.4290240, 2020. a, b
Nelson, B. R., Prat, O. P., Seo, D.-J., and Habib, E.: Assessment and
implications of NCEP Stage IV quantitative precipitation estimates for
product intercomparisons, Weather Forecast., 31, 371–394,
https://doi.org/10.1175/WAF-D-14-00112.1, 2016. a
NWC SAF: Algorithm Theoretical Basis Document for ”Cloud Products”
(CMa-PGE01 v3.2, CT-PGE02 v2.2 & CTTH-PGE03 v2.2), Tech. Rep.
SAF/NWC/CDOP2/MFL/SCI/ATBD/01, Issue 3, Rev. 2.1, EUMETSAT Satellite
Application Facility on Nowcasting and Short range Forecasting, https://www.nwcsaf.org/AemetWebContents/ScientificDocumentation/Documentation/MSG/SAF-NWC-CDOP2-MFL-SCI-ATBD-01_v3.2.1.pdf (last access: 27 March 2023), 2013. a
OPERA: OPERA – Eumetnet,
https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/ (last access: 23 March 2023),
2022. a
Overeem, A.: EURADCLIM-tools (v.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7473816, 2022. a
Overeem, A., Buishand, T. A., and Holleman, I.: Extreme rainfall analysis and
estimation of depth-duration-frequency curves using weather radar, Water
Resour. Res., 45, W10424, https://doi.org/10.1029/2009WR007869,
2009a. a
Overeem, A., Holleman, I., and Buishand, A.: Derivation of a 10-year
radar-based climatology of rainfall, J. Appl. Meteorol. Clim., 48,
1448–1463, https://doi.org/10.1175/2009JAMC1954.1, 2009b. a, b
Overeem, A., Buishand, T. A., Holleman, I., and Uijlenhoet, R.: Extreme value
modeling of areal rainfall from weather radar, Water Resour. Res., 46,
W09514, https://doi.org/10.1029/2009WR008517, 2010. a
Overeem, A., Leijnse, H., and Uijlenhoet, R.: Two and a half years of
country‐wide rainfall maps using radio links from commercial cellular
telecommunication networks, Water Resour. Res., 52, 8039–8065,
https://doi.org/10.1002/2016WR019412, 2016. a
Overeem, A., Uijlenhoet, R., and Leijnse, H.: Full-year evaluation of
non-meteorological echo removal with dual-polarization fuzzy logic for two
C-band radars in a temperate climate, J. Atmos. Ocean. Tech., 37,
1643–1660, https://doi.org/10.1175/JTECH-D-19-0149.1, 2020. a, b
Overeem, A., de Vries, H., Al Sakka, H., Uijlenhoet, R., and Leijnse, H.:
Rainfall-induced attenuation correction for two operational dual-polarization
C-band radars in the Netherlands, J. Atmos. Ocean. Tech., 38,
1125–1142, https://doi.org/10.1175/JTECH-D-20-0113.1, 2021. a, b
Overeem, A., van den Besselaar, E., van der Schrier, G., Meirink, J., van der
Plas, E., and Leijnse, H.: EURADCLIM: The European climatological
gauge-adjusted radar precipitation dataset (1-h accumulations), KNMI Data Platform [data set],
https://doi.org/10.21944/7ypj-wn68, 2022a. a, b
Overeem, A., van den Besselaar, E., van der Schrier, G., Meirink, J., van der
Plas, E., and Leijnse, H.: EURADCLIM: The European climatological
gauge-adjusted radar precipitation dataset (24-h accumulations), KNMI Data Platform [data set],
https://doi.org/10.21944/1a54-gg96, 2022b. a, b
Project team ECA&D and Royal Netherlands Meteorological Institute KNMI:
European Climate Assessment & Dataset (ECA&D), Algorithm
Theoretical Basis Document (ATBD), Version 11,
https://knmi-ecad-assets-prd.s3.amazonaws.com/documents/atbd.pdf (last access: 23 March 2023), 2021. a
Rauber, R. M. and Nesbitt, S. L.: Radar Meteorology: A First Course, John Wiley
& Sons, Hoboken, NJ, USA, https://doi.org/10.1002/9781118432662, 2018. a
Ryzhkov, A. V. and Zrnic, D. S.: Radar Polarimetry for Weather Observations,
Springer Nature Switzerland AG, Cham, Switzerland,
https://doi.org/10.1007/978-3-030-05093-1, 2019. a
Saltikoff, E., Cho, J. Y. N., Tristant, P., Huuskonen, A., Allmon, L., Cook,
R., Becker, E., and Joe, P.: The threat to weather radars by wireless
technology, B. Am. Meteorol. Soc., 97, 1159–1167,
https://doi.org/10.1175/BAMS-D-15-00048.1, 2016. a
Saltikoff, E., Friedrich, K., Soderholm, J., Lengfeld, K., Nelson, B., Becker,
A., Hollmann, R., Urban, B., Heistermann, M., and Tassone, C.: An overview of
using weather radar for climatological studies: successes, challenges, and
potential, B. Am. Meteorol. Soc., 100, 1739–1752,
https://doi.org/10.1175/BAMS-D-18-0166.1, 2019a. a
Saltikoff, E., Haase, G., Delobbe, L., Gaussiat, N., Martet, M., Idziorek, D.,
Leijnse, H., Novák, P., Lukach, M., and Stephan, K.: OPERA the radar
project, Atmosphere, 10, 320, https://doi.org/10.3390/atmos10060320, 2019b. a, b
Skofronick‐Jackson, G., Kirschbaum, D., Petersen, W., Huffman, G., Kidd, C., Stocker, E., and Kakar, R.: The
Global Precipitation Measurement (GPM) mission's scientific
achievements and societal contributions: reviewing four years of advanced
rain and snow observations, Q. J. Roy. Meteorol. Soc., 144, 27–48,
https://doi.org/10.1002/qj.3313, 2018. a
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A
review of global precipitation data sets: data sources, estimation, and
intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574,
2018. a
Tabary, P., Vulpiani, G., Gourley, J. J., Illingworth, A. J., Thompson, R. J.,
and Bousquet, O.: Unusually high differential attenuation at C Band:
results from a two-year analysis of the French Trappes polarimetric radar
data, J. Appl. Meteorol. Clim., 48, 2037–2053,
https://doi.org/10.1175/2009JAMC2039.1, 2009. a
Testud, J., Le Bouar, E., Obligis, E., and Ali-Mehenni, M.: The rain profiling
algorithm applied to polarimetric weather radar, J. Atmos. Oceanic
Technol., 17, 332–356,
https://doi.org/10.1175/1520-0426(2000)017<0332:TRPAAT>2.0.CO;2, 2000. a
Van de Beek, C. Z., Leijnse, H., Torfs, P. J. J. F., and Uijlenhoet, R.:
Seasonal semi-variance of Dutch rainfall at hourly to daily scales, Adv.
Water Resour., 45, 76–85, https://doi.org/10.1016/j.advwatres.2012.03.023, 2012. a, b
Van der Plas, E., Schmeits, M., Hooijman, N., and Kok, K.: A comparative
verification of high-resolution precipitation forecasts using model output
statistics, Mon. Weather Rev., 145, 4037–4054, https://doi.org/10.1175/MWR-D-16-0256.1,
2017. a
Vulpiani, G., Montopoli, M., Passeri, L. D., Gioia, A. G., Giordano, P., and
Marzano, F. S.: On the use of dual-polarized C-band radar for operational
rainfall retrieval in mountainous areas, J. Appl. Meteorol. Clim., 51,
405–425, https://doi.org/10.1175/JAMC-D-10-05024.1, 2012.
a
Winterrath, T., Brendel, C., Hafer, M., Junghänel, T., Klameth, A.,
Lengfeld, K., Walawender, E., Weigl, E., and Becker, A.: RADKLIM
Version 2017.002: Reprocessed gauge-adjusted radar data, one-hour
precipitation sums (RW), DWD [data set], https://doi.org/10.5676/DWD/RADKLIM_RW_V2017.002, 2018. a, b
Wradlib: wradlib.clutter.histo_cut, wradlib,
https://docs.wradlib.org/en/stable/generated/wradlib.clutter.histo_cut.html (last access: 23 March 2023),
2022. a
Short summary
EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather...
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