Coupling a New Version of the Common Land Model (CoLM) to the Global/Regional Assimilation and Prediction System (GRAPES): Implementation, Experiment, and Preliminary Evaluation

Zhenyi Yuan and Nan Wei
Additional contact information
Zhenyi Yuan: Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
Nan Wei: Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China

Land, 2022, vol. 11, issue 6, 1-25

Abstract: Land surface processes can significantly influence weather and climate. The Common Land Model version 2005 (CoLM2005) has been coupled to the Global Forecast System of the Global/Regional Assimilation and Prediction System (GRAPES_GFS), which is independently developed by the China Meteorological Administration. Since a new version of CoLM has been developed (CoLM2014) with updated soil basic data and parts of hydrological processes, we coupled CoLM2014 with GRAPES_GFS to investigate whether the land surface model can help to improve the prediction skill of the weather forecast model. The forecast results were evaluated against global validation datasets at different forecasting lengths and over various regions. The results demonstrate that GRAPES_GFS coupled with CoLM2005 and CoLM2014 can both well reproduce the spatial patterns and magnitude of atmospheric variables, and the effective predictable lengths of time are up to 3 days on the global scale and even up to 6 days on regional scales. Moreover, the GRAPES_GFS coupled with CoLM2014 outperforms the original one in predicting atmospheric variables. In addition, GRAPES_GFS coupled with both versions of CoLM reproduce acceptably accurate spatial distribution and magnitude of land variables. GRAPES_GFS coupled with CoLM2014 significantly improves the forecast of land surface state variables compared to the one coupled with CoLM2005, and the improvement signal is more notable than that in atmospheric variables. Overall, this study shows that CoLM is suitable for coupling with GRAPES_GFS, and the improvement of the land surface model in a weather forecast model can significantly improve the prediction skill of both atmospheric and land variables.

Keywords: common land model; GRAPES model; land–atmosphere coupling; model evaluation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/11/6/770/pdf (application/pdf)
https://www.mdpi.com/2073-445X/11/6/770/ (text/html)

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:gam:jlands:v:11:y:2022:i:6:p:770-:d:823055

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().