Le sauvetage de données climatologiques anciennes à Météo-France : De la conservation des documents à la mise à disposition des données

Jourdain, Sylvie; Roucaute, Emeline; Dandin, Philippe; Javelle, Jean-Pierre; Donet, Isabelle; Ménassère, Sylvia; Cenac, Nadine

doi:10.4267/2042/56598

Cited by 5 publications

(5 citation statements)

References 7 publications

(9 reference statements)

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“…Data rescue efforts are ongoing (Jourdain et al, 2015) but the present state of databases prevents us performing centurylong analyses of climate variability and extremes over large regions in a both spatially and temporally homogeneous way. In some other countries with an observation network that started earlier with a higher density like the UK, long-term gridded datasets of daily rainfall have been derived based on the interpolation between stations from 1890 onwards (Keller et al, 2015). Similarly, daily gridded estimates of potential evapotranspiration have recently been derived from the network of mean monthly temperature observations in the UK from 1891 onwards (Tanguy et al, 2018).…”

Section: Introduction To Scope Climatementioning

confidence: 99%

SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France

Caillouet

Vidal

Sauquet

et al. 2019

Earth Syst. Sci. Data

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Abstract. SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year daily high-resolution reconstructions of precipitation, temperature, and Penman–Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate provides an ensemble of 25 spatially coherent gridded multivariate time series. It is derived from the statistical downscaling of the Twentieth Century Reanalysis (20CR) by the SCOPE method, which is based on the analogue approach. SCOPE Climate performs well in comparison to both dependent and independent data for precipitation and temperature. The ensemble aspect corresponds to the uncertainty related to the SCOPE method. SCOPE Climate is the first century-long gridded high-resolution homogeneous dataset available over France and thus has paved the way for improving knowledge on specific past meteorological events or for improving knowledge on climate variability, since the end of the 19th century. This dataset has also been designed as a forcing dataset for long-term hydrological applications and studies of the hydrological consequences of climate variability over France. SCOPE Climate is freely available for any non-commercial use and can be downloaded as NetCDF files from https://doi.org/10.5281/zenodo.1299760 for precipitation, https://doi.org/10.5281/zenodo.1299712 for temperature, and https://doi.org/10.5281/zenodo.1251843 for reference evapotranspiration.

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Section: Introduction To Scope Climatementioning

confidence: 99%

SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France

Caillouet

Vidal

Sauquet

et al. 2019

Earth Syst. Sci. Data

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“…14-5). Jourdain et al (2015) provide a discussion of the early instrumental records available in France. I do not include series from Paris because they were used by Luterbacher et al (2004) and Pauling et al (2006) in their reconstructions.…”

Section: Independent Variablesmentioning

confidence: 99%

Environmental shocks, religious struggle, and resilience: a contribution to the economic history of Ancien Régime France

Chambru

2022

European Review of Economic History

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The potential link between climate variability, conflict, and migration is increasingly viewed as a security issue by policy makers. Climate variability and extremes raise critical challenges to agriculture and food production all over the world, and lead to diminished coping capacity, loss of livelihoods, as well as migration flows. The essays in this dissertation raise the question of resilience to shocks in preindustrial economies using the cases of France and Savoy during the Ancien Régime. More specifically, it documents the role of institutions and migration in reducing the vulnerability to climate shocks and hence violence. It further studies both the effect of seasonal migration as a resilience strategy against adverse economic conditions, and the socio-economic consequences of large and unexpected episode of migration.

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“…Climatologists have always been interested in extreme weather, variability, and recurring patterns which may help long‐term climatic prediction (Mascart, ). The rescue and reuse of historical weather data includes using high‐resolution subdaily observations to better understand extreme, short‐lived and high impact events such as storm and flooding (e.g., Ashcroft et al ., ; Bosilovitch et al ., ; Dupigny‐Giroux et al ., ; Jourdain et al ., ; Kaspar et al ., ; Tan et al ., ).…”

Section: Introductionmentioning

confidence: 99%

“…Several weather-related citizen science projects have been successful in the data rescue transcription of historical records, such as DataRescue@Home (Kaspar et al, 2015) and Old Weather (Brohan, 2016). Recent advances in computing power and memory have allowed more data to be stored digitally, leading to an explosive growth in weather data rescue projects in the last few decades (Allan et al, 2011;Brunet and Jones, 2011;Jourdain et al, 2015;Kaspar et al, 2015). Historical weather data rescue is a relatively young field compared to other citizen science areas (Silvertown, 2009) and literature documenting projects is still scanty outside technical presentations and recommendations (e.g., Ryan et al, 2018, Thorne et al, 2017Kaspar et al, 2015, Bosilovitch et al, 2013Allan et al, 2011).…”

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confidence: 99%

From books to bytes: A new data rescue tool

Slonosky¹,

Sieber

Burr

et al. 2019

Geoscience Data Journal

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Historical data provides observational information crucial to our understanding of the evolution of geophysical processes. However, there is a gap between predigital age observations, which are typically handwritten, and data that is discoverable and analysable. The data rescue protocols here address this gap, covering the information lifecycle from handwritten register pages to transcription‐ready content, describing the historical data, the database design for the data rescue, and the development of an application design to transcribe the meteorological information directly from an image file to the database. The preparatory steps necessary to organize, curate, image, and structure the meteorological information, prior to transcribing the historical data, are outlined here in an integrated methodology. The initial organization, the development of an image file nomenclature to link the rescued data to the original source, and the description of a metadata schema to optimize the transcription application are all vital to the process of ensuring traceability and transparency in the data rescue process. Taken together, these steps describe best practices guidelines for similar projects. Although we designed the methodology and application to be used in any data rescue context, our particular concern was to accommodate the needs of citizen scientists. We thus focused on making our application easily maintained, flexible, direct to database, clear, and simple to use. Open Practices This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://citsci.geog.mcgill.ca. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.

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Le sauvetage de données climatologiques anciennes à Météo-France : De la conservation des documents à la mise à disposition des données

Cited by 5 publications

References 7 publications

SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France

SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France

Environmental shocks, religious struggle, and resilience: a contribution to the economic history of Ancien Régime France

From books to bytes: A new data rescue tool

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