Preprints
https://doi.org/10.5194/esd-2024-18
https://doi.org/10.5194/esd-2024-18
10 Jul 2024
 | 10 Jul 2024
Status: this preprint is currently under review for the journal ESD.

Simple physics-based adjustments reconcile the results of Eulerian and Lagrangian techniques for moisture tracking

Alfredo Crespo-Otero, Damián Insua-Costa, Emilio Hernández-García, Cristóbal López, and Gonzalo Míguez-Macho

Abstract. The increase in the number and quality of numerical moisture tracking tools has greatly improved our understanding of the hydrological cycle in recent years. However, the lack of observations has prevented a direct validation of these tools, and it is common to find large discrepancies among the results produced by them, especially between Eulerian and Lagrangian methodologies. Here, we evaluate two diagnostic tools for moisture tracking, WaterSip and UTrack, using simulations from the Lagrangian model FLEXPART. We assess their performance against the Weather Research and Forecasting (WRF) model with Eulerian Water Vapor Tracers (WRF-WVTs). Assuming WRF-WVTs results as a proxy for reality, we explore the discrepancies between the Eulerian and Lagrangian approaches for five precipitation events associated with atmospheric rivers and propose some physics-based adjustments to the Lagrangian tools. Our findings reveal that UTrack, constrained by evaporation and precipitable water data, has a slightly better agreement with WRF-WVTs than WaterSip, constrained by specific humidity data. As in previous studies, we find a negative bias in the contribution of remote sources, such as tropical ones, and an overestimation of local contributions. Quantitatively, the root-mean-square-error (RMSE) for contributions from selected source regions is 5.55 for WaterSip and 4.64 for UTrack, highlighting UTrack's narrowly superior performance. Implementing our simple and logical corrections leads to a significant improvement in both methodologies, effectively reducing the RMSE by over 50 % and bridging the gap between Eulerian and Lagrangian outcomes. Our results suggest that the major discrepancies between the different methodologies were not rooted in their inherently different nature, but in the obviation of basic physical considerations that may be easily straightened out.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Alfredo Crespo-Otero, Damián Insua-Costa, Emilio Hernández-García, Cristóbal López, and Gonzalo Míguez-Macho

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2024-18', Anonymous Referee #1, 15 Jul 2024
  • RC2: 'Reviewer comment on esd-2024-18', Harald Sodemann, 16 Aug 2024
Alfredo Crespo-Otero, Damián Insua-Costa, Emilio Hernández-García, Cristóbal López, and Gonzalo Míguez-Macho
Alfredo Crespo-Otero, Damián Insua-Costa, Emilio Hernández-García, Cristóbal López, and Gonzalo Míguez-Macho

Viewed

Total article views: 394 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
302 69 23 394 33 14 13
  • HTML: 302
  • PDF: 69
  • XML: 23
  • Total: 394
  • Supplement: 33
  • BibTeX: 14
  • EndNote: 13
Views and downloads (calculated since 10 Jul 2024)
Cumulative views and downloads (calculated since 10 Jul 2024)

Viewed (geographical distribution)

Total article views: 371 (including HTML, PDF, and XML) Thereof 371 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Nov 2024
Download
Short summary
We evaluated two Lagrangian moisture tracking tools, WaterSip and UTrack, and compared them against the WRF model with Water Vapor Tracers. Our results show that UTrack, which relies on evaporation and precipitable water data, has a slightly better agreement with WRF-WVTs than WaterSip, based on specific humidity data. Implementing simple physics-based changes substantially improved both methodologies, reducing discrepancies by about 50 % and narrowing the the disparities among all approaches.
Altmetrics