A framework for high‐resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions

Abstract

The knowledge of historical French weather has recently been improved through the development of the Spatially COherent Probabilistic Extended (SCOPE) climate reconstructions. This high‐resolution ensemble daily reconstruction dataset of precipitation and temperature covers the period 1871–2012 and is derived through a statistical downscaling of the Twentieth Century Reanalysis. Historical surface observations – even though rather scarce and sparse – do exist from at least the beginning of the period considered, and this information does not currently feed SCOPE Climate. We propose to construct a new high‐resolution surface reanalysis over France – called FYRE Climate (French hYdrometeorological REanalysis Climate) – by assimilating daily temperature and precipitation observations into SCOPE Climate through an offline Ensemble Kalman Filter. The goal of the study is to test methodological choices for developing the future FYRE Climate surface reanalysis. The data assimilation scheme is evaluated over the 2009–2012 period with an increasing assimilated observation density reproducing historical densities. A consistent set of independent stations is retained for validation in terms of continuous ranked probability score, daily bias and daily correlation. Results highlight the importance of the localization as well as the Gaussian anamorphosis for precipitation. They show that: (a) the reanalysis has a lower uncertainty than the initial SCOPE Climate reconstructions, (b) its reliability is similar, and (c) the data assimilation allows reaching the performance of the reference Safran surface reanalysis over France, even for a density of observations as low as the one of 1950. These results thus pave the way for a full 140‐year high‐resolution precipitation and temperature surface reanalysis over France.

Moving beyond the catchment scale: Value and opportunities in large‐scale hydrology to understand our changing world

“…This is reflected by the aforementioned difficulty of separating human influence from natural drivers of hydrological drought (Van Loon et al, , ). For such reasons, endeavours such as large‐scale hydrological data rescue (e.g., Le Gros et al, ), reconstructing long‐term and large‐scale high‐resolution climate datasets (Devers, Vidal, Lauvernet, Graff, & Vannier, ) and corresponding near‐natural hydrological datasets (e.g., Hanel et al, ; Moravec, Markonis, Rakovec, Kumar, & Hanel, ) are central in understanding the large temporal and spatial variations of hydrology. Compatibility between, or merging of, national‐scale datasets (e.g., Caillouet, Vidal, Sauquet, Graff, & Soubeyroux, ; Keller et al, ) would be a further advance, as would improved quality assessment of large repositories such as the Global Runoff Data Centre under the auspices of the World Meteorological Organisation.…”

Spatio-temporal and cross-scale interactions in hydroclimate variability: a case-study in France

“…Owing to the recent addition of long term, high spatial resolution hydroclimate data sets (e.g. Fyre reconstructions, Devers et al (2020Devers et al ( , 2021) it is now possible to apply the clustering and cross-scale analyses to better characterize the effects that long term hydroclimate variability (e.g. multi-decadal) has on smaller time scales.…”

Intercomparaison des évènements d’étiage extrême en France depuis 1871

“…Caillouet et al (2016) ont ainsi généré une reconstruction probabiliste des champs de précipitation, de température et d'évapotranspiration à une résolution spatiale de 8 km et au pas de temps journalier sur la France, grâce à une méthode de descente d'échelle par analogie à partir de la réanalyse 20CR. Ce jeu de données, SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) (Caillouet, 2019), a ensuite servi d'ébauche (ou information a priori) pour l'assimilation des observations historiques de Météo-France à l'aide d'un filtre de Kalman d'ensemble (EnKF, Evensen, 2003), permettant de créer la réanalyse de surface ensembliste FYRE Climate (French hYdrometeorological REanalysis of Climate) sur la période 1871-2012 (Devers et al, 2020(Devers et al, , 2021. Une modélisation hydrologique continue ainsi que l'assimilation d'observations historiques de débit à l'aide d'un filtre de Kalman d'ensemble ont ensuite permis de créer FYRE Hydro, une réanalyse fournissant des débits journaliers, disponible sur 661 bassins faiblement anthropisés (Devers, 2019).…”