Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis

• Published in: Earth system science data Vol. 7; no. 2; pp. 157 - 171
• Main Authors: Vuichard, N, Papale, D
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 13-07-2015; Copernicus Publications; Copernicus
• Subjects: Analysis; Atmospheric models; Capacity; Carbon; Continental interfaces, environment; Covariance; Data centers; Datasets; Ecological monitoring; Ecosystem models; Ecosystems; Eddy covariance; Eddy currents; Environment models; GEOSCIENCES; Heat; Hydrology; Information centres; Mathematical models; Meteorological data; Methods; Modelling; Ocean, Atmosphere; Pangea; Performance evaluation; Policies; Precipitation; Respiration; Root-mean-square errors; Sciences of the Universe; Standard deviation; Temporal resolution; Time series; Weather; Wind speed
• ISSN: 1866-3516; 1866-3508; 1866-3516; 1866-3508
• DOI: 10.5194/essd-7-157-2015

Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations. The ERA-Interim reanalysis data de-biased at FLUXNET sites can be downloaded from the PANGAEA data centre (http://doi.pangaea.de/10.1594/PANGAEA.838234).