Articles | Volume 28, issue 1
Nonlin. Processes Geophys., 28, 61–91, 2021
Nonlin. Processes Geophys., 28, 61–91, 2021

Research article 22 Jan 2021

Research article | 22 Jan 2021

Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation

Cristian Lussana et al.

Related authors

TITAN automatic spatial quality control of meteorological in-situ observations
Line Båserud, Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, Louise Oram, and Trygve Aspelien
Adv. Sci. Res., 17, 153–163,,, 2020
Short summary
seNorge_2018, daily precipitation, and temperature datasets over Norway
Cristian Lussana, Ole Einar Tveito, Andreas Dobler, and Ketil Tunheim
Earth Syst. Sci. Data, 11, 1531–1551,,, 2019
Short summary
seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day
Cristian Lussana, Tuomo Saloranta, Thomas Skaugen, Jan Magnusson, Ole Einar Tveito, and Jess Andersen
Earth Syst. Sci. Data, 10, 235–249,,, 2018
Short summary
A spatial bootstrap technique for parameter estimation of rainfall annual maxima distribution
F. Uboldi, A. N. Sulis, C. Lussana, M. Cislaghi, and M. Russo
Hydrol. Earth Syst. Sci., 18, 981–995,,, 2014
An empirical method for estimating probability density functions of gridded daily minimum and maximum temperature
C. Lussana
Adv. Sci. Res., 10, 59–64,,, 2013

Related subject area

Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
Applications of matrix factorization methods to climate data
Dylan Harries and Terence J. O'Kane
Nonlin. Processes Geophys., 27, 453–471,,, 2020
Short summary
Detecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysis
Jaqueline Lekscha and Reik V. Donner
Nonlin. Processes Geophys., 27, 261–275,,, 2020
Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Moritz N. Lang, Sebastian Lerch, Georg J. Mayr, Thorsten Simon, Reto Stauffer, and Achim Zeileis
Nonlin. Processes Geophys., 27, 23–34,,, 2020
Short summary

Cited articles

Agersten, S., Håvelsrud Andersen, A. S., Berger, A. C., Verpe Dyrrdal, A., Køltzow, M., and Tunheim, K.: Intense byger med store konsekvenser i Sogn og Fjordane 30 juli 2019, available at: (last access: 12 January 2021), 2019. a, b, c
Amezcua, J. and Leeuwen, P. J. V.: Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach, Tellus A, 66, 23493,, 2014. a
Båserud, L., Lussana, C., Nipen, T. N., Seierstad, I. A., Oram, L., and Aspelien, T.: TITAN automatic spatial quality control of meteorological in-situ observations, Adv. Sci. Res., 17, 153–163,, 2020. a
Bertino, L., Evensen, G., and Wackernagel, H.: Sequential Data Assimilation Techniques in Oceanography, Int. Stat. Rev., 71, 223–241,, 2003. a, b, c
Bocquet, M., Raanes, P. N., and Hannart, A.: Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation, Nonlin. Processes Geophys., 22, 645–662,, 2015. a, b
Short summary
An unprecedented amount of rainfall data is available nowadays, such as ensemble model output, weather radar estimates, and in situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is precipitation reconstruction through the combination of numerical model outputs with observations from multiple data sources.