Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: an overview of methods, issues, and perspectives, Wiley Interdiscip. Rev. Clim. Change, 9, e535,
https://doi.org/10.1002/wcc.535, 2018.
a,
b,
c
CIMO: WMO Guide to Meteorological Instruments and Methods of Observation, Tech. Rep., World Meteorological Organization, ISBN 978-92-63-10008-5, 2014. a
Crespi, A., Lussana, C., Brunetti, M., Dobler, A., Maugeri, M., and Tveito, O. E.: High-resolution monthly precipitation climatologies over Norway (1981–2010): Joining numerical model data sets and in situ observations, Int. J. Climatol., 39, 2057–2070,
https://doi.org/10.1002/joc.5933, 2019.
a
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. R. Meteorol. Soc., 131, 3385–3396,
https://doi.org/10.1256/qj.05.108, 2005.
a
de Vos, L. W., Droste, A. M., Zander, M. J., Overeem, A., Leijnse, H., Heusinkveld, B. G., Steeneveld, G. J., and Uijlenhoet, R.: Hydrometeorological Monitoring Using Opportunistic Sensing Networks in the Amsterdam Metropolitan Area, B. Am. Meteorol. Soc., 101, E167–E185,
https://doi.org/10.1175/BAMS-D-19-0091.1, 2020.
a
Dyrrdal, A. V., Lenkoski, A., Thorarinsdottir, T. L., and Stordal, F.: Bayesian hierarchical modeling of extreme hourly precipitation in Norway, Environmetrics, 26, 89–106,
https://doi.org/10.1002/env.2301, 2015.
a
Erdin, R.: Combining rain gauge and radar measurements of a heavy precipitation event over Switzerland: Comparison of geostatistical methods and investigation of important influencing factors, PhD thesis, Bundesamt für Meteorologie und Klimatologie, MeteoSchweiz, Zurich, Switzerland, 2009. a
Fletcher, S. J. and Zupanski, M.: A data assimilation method for log-normally distributed observational errors, Q. J. R. Meteorol. Soc., 132, 2505–2519,
https://doi.org/10.1256/qj.05.222, 2006.
a
Fortin, V., Roy, G., Stadnyk, T., Koenig, K., Gasset, N., and Mahidjiba, A.: Ten Years of Science Based on the Canadian Precipitation Analysis: A CaPA System Overview and Literature Review, Atmosphere-Ocean, 56, 178–196,
https://doi.org/10.1080/07055900.2018.1474728, 2018.
a
Frei, C.: Interpolation of temperature in a mountainous region using nonlinear profiles and non-Euclidean distances, Int. J. Climatol., 34, 1585–1605,
https://doi.org/10.1002/joc.3786, 2014.
a
Frei, C. and Isotta, F. A.: Ensemble spatial precipitation analysis from rain gauge data: Methodology and application in the European Alps, J. Geophys. Res.-Atmos., 124, 5757–5778,
https://doi.org/10.1029/2018JD030004, 2019.
a,
b
Frogner, I.-L., Singleton, A. T., Køltzow, M. Ø, and Andrae, U.: Convection-permitting ensembles: Challenges related to their design and use, Q. J. R. Meteorol. Soc., 145 (Suppl. 1), 90–106,
https://doi.org/10.1002/qj.3525, 2019.
a,
b,
c
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. R. Meteorol. Soc., 125, 723–757, 1999. a
Greybush, S. J., Kalnay, E., Miyoshi, T., Ide, K., and Hunt, B. R.: Balance and ensemble Kalman filter localization techniques, Mon. Weather Rev., 139, 511–522, 2011. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. R. Meteorol. Soc., 149, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
a
Hiebl, J. and Frei, C.: Daily precipitation grids for Austria since 1961–development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling, Theor. Appl. Climatol., 132, 327–345,
https://doi.org/10.1007/s00704-017-2093-x, 2018.
a
Hofstra, N., Haylock, M., New, M., Jones, P., and Frei, C.: Comparison of six methods for the interpolation of daily, European climate data, J. Geophys. Res.-Atmos., 113, D21110,
https://doi.org/10.1029/2008JD010100, 2008.
a
Huang, S., Eisner, S., Magnusson, J. O., Lussana, C., Yang, X., and Beldring, S.: Improvements of the spatially distributed hydrological modelling using the HBV model at 1
km resolution for Norway, J. Hydrol., 577, 123585,
https://doi.org/10.1016/j.jhydrol.2019.03.051, 2019.
a
Ide, K., Courtier, P., Ghil, M., and Lorenc, A.: Unified notation for data assimilation: operational, sequential and variational, Practice, 75, 181–189, 1997.
a,
b
Isotta, F. A., Begert, M., and Frei, C.: Long-Term Consistent Monthly Temperature and Precipitation Grid Data Sets for Switzerland Over the Past 150 Years, J. Geophys. Res.-Atmos., 124, 3783–3799,
https://doi.org/10.1029/2018JD029910, 2019.
a,
b
Jazwinski, A. H.: Stochastic processes and filtering theory, Courier Dover Publications, Mineola, New York, USA, 2007.
a,
b
Jermey, P. and Renshaw, R.: Precipitation representation over a two-year period in regional reanalysis, Q. J. R. Meteorol. Soc., 142, 1300–1310,
https://doi.org/10.1002/qj.2733, 2016.
a,
b
Kalnay, E.: Atmospheric modeling, data assimilation and predictability, Cambridge University Press, Cambridge, UK, 2003. a
Kotlarski, S., Szabó, P., Herrera, S., Räty, O., Keuler, K., Soares, P. M., Cardoso, R. M., Bosshard, T., Pagé, C., Boberg, F., Gutiérrez, J. M., Isotta, F. A., Jaczewski, A., Kreienkamp, F., Liniger, M. A., Lussana, C., and Pianko-Kluczyńska, K.: Observational uncertainty and regional climate model evaluation: A pan-European perspective, Int. J. Climatol., 39, 3730–3749,
https://doi.org/10.1002/joc.5249, 2017.
a
Kuusela, M. and Stein, M. L.: Locally stationary spatio-temporal interpolation of Argo profiling float data, Proc. Math. Phys. Eng. Sci., 474, 20180400,
https://doi.org/10.1098/rspa.2018.0400, 2018.
a,
b
Lanzante, J. R.: Resistant, robust and non-parametric techniques for the analysis of climate data: theory an
d examples, including applications to historical radiosonde station data, Int. J. Climatol., 16, 1197–1226, 1996. a
Lespinas, F., Fortin, V., Roy, G., Rasmussen, P., and Stadnyk, T.: Performance Evaluation of the Canadian Precipitation Analysis (CaPA), J. Hydrometeorol., 16, 2045–2064,
https://doi.org/10.1175/JHM-D-14-0191.1, 2015.
a,
b
Lien, G.-Y., Kalnay, E., and Miyoshi, T.: Effective assimilation of global precipitation: simulation experiments, Tellus A, 65, 19915,
https://doi.org/10.3402/tellusa.v65i0.19915, 2013.
a,
b,
c
Lönnberg, P. and Hollingsworth, A.: The statistical structure of short-range forecast errors as determined from radiosonde data Part II: The covariance of height and wind errors, Tellus A, 38A, 137–161,
https://doi.org/10.1111/j.1600-0870.1986.tb00461.x, 1986.
a
Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S.: Our skill in modeling mountain rain and snow is bypassing the skill of our observational networks, B. Am. Meteorol. Soc., 100, 2473–2490,
https://doi.org/10.1175/BAMS-D-19-0001.1, 2019.
a,
b
Lussana, C., Salvati, M. R., Pellegrini, U., and Uboldi, F.: Efficient high-resolution 3-D interpolation of meteorological variables for operational use, Adv. Sci. Res., 3, 105–112,
https://doi.org/10.5194/asr-3-105-2009, 2009.
a
Lussana, C., Saloranta, T., Skaugen, T., Magnusson, J., Tveito, O. E., and Andersen, J.: seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day, Earth Syst. Sci. Data, 10, 235–249,
https://doi.org/10.5194/essd-10-235-2018, 2018.
a
Lussana, C., Seierstad, I. A., Nipen, T. N., and Cantarello, L.: Spatial interpolation of two-metre temperature over Norway based on the combination of numerical weather prediction ensembles and in situ observations, Q. J. R. Meteorol. Soc., 145, 3626–3643,
https://doi.org/10.1002/qj.3646, 2019.
a
Lussana, C., Tveito, O. E., Dobler, A., and Tunheim, K.: seNorge_2018, daily precipitation, and temperature datasets over Norway, Earth Syst. Sci. Data, 11, 1531–1551,
https://doi.org/10.5194/essd-11-1531-2019, 2019.
a
Magnusson, J., Eisner, S., Huang, S., Lussana, C., Mazzotti, G., Essery, R., Saloranta, T., and Beldring, S.: Influence of Spatial Resolution on Snow Cover Dynamics for a Coastal and Mountainous Region at High Latitudes (Norway), Water Resour. Res., 55, 5612–5630,
https://doi.org/10.1029/2019WR024925, 2019.
a
Mahfouf, J.-F., Brasnett, B., and Gagnon, S.: A Canadian precipitation analysis (CaPA) project: description and preliminary results, Atmos.-Ocean, 45, 1–17, 2007.
a,
b
MET Norway: Frost API, available at:
https://frost.met.no/ (last access: 20 January 2021), 2019. a
Müller, M., Homleid, M., Ivarsson, K.-I., Køltzow, M. A. Ø., Lindskog, M., Midtbø, K. H., Andrae, U., Aspelien, T., Berggren, L., Bjørge, D., Dahlgren, P., Kristiansen, J., Randriamampianina, R., Ridal, M., and Vignes, O.: AROME-MetCoOp: a nordic convective-scale operational weather prediction model, Weather Forecast., 32, 609–627,
https://doi.org/10.1175/WAF-D-16-0099.1, 2017.
a,
b,
c
Nipen, T. N., Seierstad, I. A., Lussana, C., Kristiansen, J., and Hov, Ø.: Adopting Citizen Observations in Operational Weather Prediction, B. Am. Meteorol. Soc., 101, E43–E57,
https://doi.org/10.1175/BAMS-D-18-0237.1, 2020.
a,
b
Norwegian Meteorological Institute: MEPS Archive, MET Norway Thredds Service, available at:
https://thredds.met.no/thredds/catalog/meps25epsarchive/catalog.html, last access: 12 January 2021a. a
Norwegian Meteorological Institute: Radar accr archive (Norway), MET Norway Thredds Service, available at:
https://thredds.met.no/thredds/catalog/remotesensingradaraccr/catalog.html, last access: 12 January 2021b. a
Pollock, M., Dutton, M., Quinn, P., O'connell, P., Wilkinson, M., and Colli, M.: Accurate rainfall measurement: The Neglected Achilles Heel of hydro-meteorology, in: WMO technical conference on meteorological and environmental instruments and methods of observation, St. Petersburg, Russia, 7–9 July 2014, pp. 7–9, 2014. a
Raanes, P. N., Carrassi, A., and Bertino, L.: Extending the Square Root Method to Account for Additive Forecast Noise in Ensemble Methods, Mon. Weather Rev., 143, 3857–3873,
https://doi.org/10.1175/MWR-D-14-00375.1, 2015.
a,
b
Sakov, P. and Bertino, L.: Relation between two common localisation methods for the EnKF, Comput. Geosci., 15, 225–237, 2011.
a,
b
Savage, L. J.: The foundations of statistics, Courier Corporation, New York, USA, 1972. a
Soci, C., Bazile, E., Besson, F., and Landelius, T.: High-resolution precipitation re-analysis system for climatological purposes, Tellus A, 68, 29879,
https://doi.org/10.3402/tellusa.v68.29879, 2016.
a,
b,
c
Tarantola, A.: Inverse Problem Theory and methods for model parameter estimation, edited by: Society for Industrial and applied mathematics (SIAM), Philadelphia, USA, ISBN 978-0-89871-572-9, eISBN 978-0-89871-792-1,
https://doi.org/10.1137/1.9780898717921, 2005.
a,
b
Tian, Y., Huffman, G. J., Adler, R. F., Tang, L., Sapiano, M., Maggioni, V., and Wu, H.: Modeling errors in daily precipitation measurements: Additive or multiplicative?, Geophys. Res. Lett., 40, 2060–2065, 2013. a
Uboldi, F., Lussana, C., and Salvati, M.: Three-dimensional spatial interpolation of surface meteorological observations from high-resolution local networks, Meteorol. Appl., 15, 331–345, 2008.
a,
b,
c
Vicente-Serrano, S. M., Van der Schrier, G., Begueria, S., Azorin-Molina, C., and Lopez-Moreno, J.-I.: Contribution of precipitation and reference evapotranspiration to drought indices under different climates, J. Hydrol., 526, 42–54,
https://doi.org/10.1016/j.jhydrol.2014.11.025, 2015.
a
Wilks, D. S.: Statistical methods in the atmospheric sciences (Fourth Edition), Elsevier, Amsterdam, the Netherlands,
https://doi.org/10.1016/B978-0-12-815823-4.00009-2, 2019.
a,
b,
c,
d,
e,
f
Wolff, M. A., Isaksen, K., Petersen-Øverleir, A., Ødemark, K., Reitan, T., and Brækkan, R.: Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study, Hydrol. Earth Syst. Sci., 19, 951–967,
https://doi.org/10.5194/hess-19-951-2015, 2015.
a
Zahumensky, I.: World Guidelines on Quality Control Procedures for Data from Automatic Weather Stations, World Meteorological Organization, Geneve, Switzerland, 2004. a
