Cell-scale atmospheric moisture flows dataset reconciled with ERA5 reanalysis

Abstract Water vapour flows in the atmosphere are fundamental to the hydrological cycle, linking evaporation sources to precipitation sinks. Recent atmospheric tracking models have provided valuable insights, allowing one to trace the sources of precipitation and determine where evaporated water fro...

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Main Authors: Elena De Petrillo, Luca Monaco, Marta Tuninetti, Arie Staal, Francesco Laio
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-04964-3
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author Elena De Petrillo
Luca Monaco
Marta Tuninetti
Arie Staal
Francesco Laio
author_facet Elena De Petrillo
Luca Monaco
Marta Tuninetti
Arie Staal
Francesco Laio
author_sort Elena De Petrillo
collection DOAJ
description Abstract Water vapour flows in the atmosphere are fundamental to the hydrological cycle, linking evaporation sources to precipitation sinks. Recent atmospheric tracking models have provided valuable insights, allowing one to trace the sources of precipitation and determine where evaporated water from specific regions will eventually precipitate. Despite improvements in model accuracy, there remain significant discrepancies between reconstructed and observed evaporation and precipitation data from reanalysis. To address these discrepancies and enhance the reliability of tracking models’ estimates, we propose a procedure based on Iterative Proportional Fitting (IPF). Using this approach, we reconcile atmospheric moisture flows reconstructed by the Lagrangian model UTrack with ERA5 reanalysis data. This ensures that the traced atmospheric water matches the total evaporation and the precipitation annually. The reconciled bilateral connections provide a new dataset (RECON) centred on the period 2008-2017 that facilitates the exploration of atmospheric vapour flows between evaporation and precipitation basins at the global scale with a spatial resolution of 0.5°. Further, the proposed framework applies to any cell-scale dataset of atmospheric moisture tracking.
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spelling doaj-art-3dae277147c940c682e7b7fcfaf8a1c62025-08-20T03:18:39ZengNature PortfolioScientific Data2052-44632025-04-0112111710.1038/s41597-025-04964-3Cell-scale atmospheric moisture flows dataset reconciled with ERA5 reanalysisElena De Petrillo0Luca Monaco1Marta Tuninetti2Arie Staal3Francesco Laio4Department of Environment, Land and Infrastructure Engineering, Politecnico di TorinoDepartment of Environment, Land and Infrastructure Engineering, Politecnico di TorinoDepartment of Environment, Land and Infrastructure Engineering, Politecnico di TorinoCopernicus Institute of Sustainable Development, Utrecht UniversityDepartment of Environment, Land and Infrastructure Engineering, Politecnico di TorinoAbstract Water vapour flows in the atmosphere are fundamental to the hydrological cycle, linking evaporation sources to precipitation sinks. Recent atmospheric tracking models have provided valuable insights, allowing one to trace the sources of precipitation and determine where evaporated water from specific regions will eventually precipitate. Despite improvements in model accuracy, there remain significant discrepancies between reconstructed and observed evaporation and precipitation data from reanalysis. To address these discrepancies and enhance the reliability of tracking models’ estimates, we propose a procedure based on Iterative Proportional Fitting (IPF). Using this approach, we reconcile atmospheric moisture flows reconstructed by the Lagrangian model UTrack with ERA5 reanalysis data. This ensures that the traced atmospheric water matches the total evaporation and the precipitation annually. The reconciled bilateral connections provide a new dataset (RECON) centred on the period 2008-2017 that facilitates the exploration of atmospheric vapour flows between evaporation and precipitation basins at the global scale with a spatial resolution of 0.5°. Further, the proposed framework applies to any cell-scale dataset of atmospheric moisture tracking.https://doi.org/10.1038/s41597-025-04964-3
spellingShingle Elena De Petrillo
Luca Monaco
Marta Tuninetti
Arie Staal
Francesco Laio
Cell-scale atmospheric moisture flows dataset reconciled with ERA5 reanalysis
Scientific Data
title Cell-scale atmospheric moisture flows dataset reconciled with ERA5 reanalysis
title_full Cell-scale atmospheric moisture flows dataset reconciled with ERA5 reanalysis
title_fullStr Cell-scale atmospheric moisture flows dataset reconciled with ERA5 reanalysis
title_full_unstemmed Cell-scale atmospheric moisture flows dataset reconciled with ERA5 reanalysis
title_short Cell-scale atmospheric moisture flows dataset reconciled with ERA5 reanalysis
title_sort cell scale atmospheric moisture flows dataset reconciled with era5 reanalysis
url https://doi.org/10.1038/s41597-025-04964-3
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AT martatuninetti cellscaleatmosphericmoistureflowsdatasetreconciledwithera5reanalysis
AT ariestaal cellscaleatmosphericmoistureflowsdatasetreconciledwithera5reanalysis
AT francescolaio cellscaleatmosphericmoistureflowsdatasetreconciledwithera5reanalysis