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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/npg-2020-20
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2020-20
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 19 Jun 2020

Submitted as: research article | 19 Jun 2020

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This preprint is currently under review for the journal NPG.

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

Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo Cristian Lussana et al.
  • Norwegian Meteorological Institute, Oslo, Norway

Abstract. Hourly precipitation over a region is often simultaneously simulated by numerical models and observed by multiple data sources. An accurate precipitation representation based on all available information is a valuable result for numerous applications and a critical aspect of climate. Inverse problem theory offers an ideal framework for the combination of observations with a numerical model background. In particular, we have considered a modified ensemble optimal interpolation scheme, that takes into account deficiencies of the background. An additional source of uncertainty for the ensemble background has been included. A data transformation based on Gaussian anamorphosis has been used to optimally exploit the potential of the spatial analysis, given that precipitation is approximated with a gamma distribution and the spatial analysis requires normally distributed variables. For each point, the spatial analysis returns the shape and rate parameters of its gamma distribution. The Ensemble-based Statistical Interpolation scheme with Gaussian AnamorPhosis (EnSI-GAP) is implemented in a way that the covariance matrices are locally stationary and the background error covariance matrix undergoes a localization process. Concepts and methods that are usually found in data assimilation are here applied to spatial analysis, where they have been adapted in an original way to represent precipitation at finer spatial scales than those resolved by the background, at least where the observational network is dense enough. The EnSI-GAP setup requires the specification of a restricted number of parameters and specifically the explicit values of the error variances are not needed, since they are inferred from the available data. The examples of applications presented provide a better understanding of the characteristics of EnSI-GAP. The data sources considered are those typically used at national meteorological services, such as local area models, weather radars and in-situ observations. For this last data source, measurements from both traditional and opportunistic sensors have been considered.

Cristian Lussana et al.

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Cristian Lussana et al.

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Latest update: 10 Jul 2020
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Short summary
An unprecedented amount of rainfall data is nowadays available, 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 the precipitation reconstruction through the combination of numerical model output with observations from multiple data sources.
An unprecedented amount of rainfall data is nowadays available, such as ensemble model output,...
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