Statistical post-processing of ensemble forecasts of the height of new snow

Nousu, Jari-Pekka; Lafaysse, Matthieu; Vernay, Matthieu; Bellier, Joseph; Evin, Guillaume; Joly, Bruno

doi:10.5194/npg-26-339-2019

Cited by 8 publications

(2 citation statements)

References 68 publications

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“…Indeed, under dispersion is a common issue in the NWP (e.g. Bellier et al, 2017) and snow cover modelling communities (Lafaysse et al, 2017;Nousu et al, 2019). Then, the ensemble modelling chain does not account for two important processes affecting the observations at the stations: the variability of the meteorological conditions inside SAFRAN massifs, and the snow redistribution by wind (Vionnet et al, 2018;Mott et al, 2018).…”

Section: 2)mentioning

confidence: 99%

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

Cluzet

Lafaysse

Deschamps-Berger

et al. 2021

Preprint

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Abstract. The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterisations, and unresolved terrain features. In-situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large scale modelling errors by means of data assimilation. In this work, we assimilate HS observations from an in-situ network of 295 stations covering the French Alps, Pyrenees and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialised snow cover modelling framework, we investigate whether such observations can be used to correct neighbouring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modelling, based on a Particle Filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localisation strategies to assimilate snow observations. These approaches are evaluated in a Leave-One-Out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that intermediate localisation radius of 35–50 km yield a slightly lower root mean square error (RMSE), and a better Spread-Skill than the strategy assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modelling errors are the largest, e.g. the Haute-Ariège, Andorra and the Extreme Southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses, and therefore snow simulations. In-situ HS observations thus shows an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas.

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Section: 2)mentioning

confidence: 99%

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

Cluzet

Lafaysse

Deschamps-Berger

et al. 2021

Preprint

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“…Finally, optimal use of the forecast errors in earlier projections can be used to reduce subsequent errors in the same forecast trajectory. This promising technique, named RAFT (rapid adjustment of forecast trajectories), is presented by Schuhen (2020).…”

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Preface: Advances in post-processing and blending of deterministic and ensemble forecasts

Hemri

Lerch

Taillardat³

et al. 2020

Nonlin. Processes Geophys.

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Abstract. The special issue on advances in post-processing and blending of deterministic and ensemble forecasts is the outcome of several successful successive sessions organized at the General Assembly of the European Geosciences Union. Statistical post-processing and blending of forecasts are currently topics of important attention and development in many countries to produce optimal forecasts. Ten contributions have been received, covering key aspects of current concerns on statistical post-processing, namely the restoration of inter-variable dependences, the impact of model changes on the statistical relationships and how to cope with it, the operational implementation at forecasting centers, the development of appropriate metrics for forecast verification, and finally two specific applications to snow forecasts and seasonal forecasts of the North Atlantic Oscillation.

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Ensemble forecasting system for the management of the Senegal River discharge: application upstream the Manantali dam

et al. 2020

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Providing useful inflow forecasts of the Manantali dam is critical for zonal consumption and agricultural water supply, power production, flood and drought control and management (Shin et al., Meteorol Appl 27:e1827, 2019). Probabilistic approaches through ensemble forecasting systems are often used to provide more rational and useful hydrological information. This paper aims at implementing an ensemble forecasting system at the Senegal River upper the Manantali dam. Rainfall ensemble is obtained through harmonic analysis and an ARIMA stochastic process. Cyclical errors that are within rainfall cyclical behavior from the stochastic modeling are settled and processed using multivariate statistic tools to dress a rainfall ensemble forecast. The rainfall ensemble is used as input to run the HBV-light to product streamflow ensemble forecasts. A number of 61 forecasted rainfall time series are then used to run already calibrated hydrological model to produce hydrological ensemble forecasts called raw ensemble. In addition, the affine kernel dressing method is applied to the raw ensemble to obtain another ensemble. Both ensembles are evaluated using on the one hand deterministic verifications such the linear correlation, the mean error, the mean absolute error and the root-mean-squared error, and on the other hand, probabilistic scores (Brier score, rank probability score and continuous rank probability score) and diagrams (attribute diagram and relative operating characteristics curve). Results are satisfactory as at deterministic than probabilistic scale, particularly considering reliability, resolution and skill of the systems. For both ensembles, correlation between the averages of the members and corresponding observations is about 0.871. In addition, the dressing method globally improved the performances of ensemble forecasting system. Thus, both schemes system can help decision maker of the Manantali dam in water resources management.

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Statistical post-processing of ensemble forecasts of the height of new snow

Cited by 8 publications

References 68 publications

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

Preface: Advances in post-processing and blending of deterministic and ensemble forecasts

Ensemble forecasting system for the management of the Senegal River discharge: application upstream the Manantali dam

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