The Role of Soil Moisture and Surface and Subsurface Water Flows on Predictability of Convection
J. Arnault () and
H. Kunstmann
Additional contact information
J. Arnault: Karlsruhe Institute of Technology (KIT), Institute for Meteorology and Climate Research (IMK-IFU)
H. Kunstmann: Karlsruhe Institute of Technology (KIT), Institute for Meteorology and Climate Research (IMK-IFU)
A chapter in High Performance Computing in Science and Engineering '19, 2021, pp 493-499 from Springer
Abstract:
Abstract Since June 2018 we have further assessed the mechanism through which a more sophisticated treatment of terrestrial hydrological processes in a numerical weather prediction model potentially improves the predictability of convection. In order to achieve this, we have implemented the so-called soil-vegetation-atmospheric water tagging procedure in WRF and WRF-Hydro (Skamarock and Klemp in J Comp Phys 227:3465–3485, 2008 [3]; Gochis et al. in TheWRF-Hydro model technical description and users guide, version 3.0, NCAR Technical Document :120, 2015 [2]). This tagging procedure is used to track a source of water through the terrestrial and atmospheric water compartments in the model. The tagging enhanced versions of WRF and WRF-Hydro are named WRF-tag and WRF-Hydro-tag. A publication detailing the implementation of WRF-tag and WRF-Hydro-tag with an application case-study has been recently published in Water Resources Research (Arnault et al. in Water Resour Res 55:6217–6243 (2019) [1]). In particular, WRF-tag and WRF-Hydro-tag are applied to the case of a precipitation event in the Upper Danube river basin. A comparison between WRF-tag and WRF-Hydro-tag results allows to deduce the role of lateral terrestrial water flow on land-atmospheric water pathways, including precipitation.
Date: 2021
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-66792-4_33
Ordering information: This item can be ordered from
http://www.springer.com/9783030667924
DOI: 10.1007/978-3-030-66792-4_33
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().