Preprints
https://doi.org/10.5194/hess-2022-379
https://doi.org/10.5194/hess-2022-379
 
14 Dec 2022
14 Dec 2022
Status: this preprint is currently under review for the journal HESS.

Improving predictions of land-atmosphere interactions based on a hybrid data assimilation and machine learning method

Xinlei He1, Yanping Li2, Shaomin Liu1, Tongren Xu1, Fei Chen3, Zhenhua Li2, Zhe Zhang2, Rui Liu4, Lisheng Song5, Ziwei Xu1, Zhixing Peng1, and Chen Zheng6 Xinlei He et al.
  • 1State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
  • 2School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
  • 3National Center for Atmospheric Research, Boulder, CO, USA
  • 4Institute of Urban Study, School of Environmental and Geographical Sciences (SEGS), Shanghai Normal University, Shanghai, China
  • 5School of Geography and Tourism, Anhui Normal University, Wuhu, China
  • 6Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Abstract. The energy and moisture exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe land surface states and fluxes. The performance of the hybrid method on the regional climate was evaluated in the Heihe River Basin (HRB), the second largest endorheic river basin in Northwest China. The findings indicate that the DA-ML method improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The WRF simulations overestimated (underestimated) the air temperature (specific humidity) in the vegetated areas of the HRB. In contrast, the estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations, especially in the midstream oasis. The DA-ML framework enhanced oasis-desert interactions by improving the soil and vegetation characteristics. The wetting and cooling effects and wind shield effects of the oasis were enhanced by the DA-ML. The wetting and cooling effect of the oasis can transfer water vapor to the surrounding desert, which benefits the oasis-desert ecosystem. The results show that the wetting and cooling effects only negligibly changed the local precipitation in the midstream oasis. However, upstream of the HRB, the integration of LAI and SM will induce water vapor intensification and promote precipitation, particularly on windward slopes.

Xinlei He et al.

Status: open (until 08 Feb 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-379', Anonymous Referee #1, 27 Dec 2022 reply
  • RC2: 'Comment on hess-2022-379', Anonymous Referee #2, 08 Jan 2023 reply
  • RC3: 'Comment on hess-2022-379', Anonymous Referee #3, 08 Jan 2023 reply
  • RC4: 'Comment on hess-2022-379', Anonymous Referee #4, 08 Jan 2023 reply

Xinlei He et al.

Xinlei He et al.

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Short summary
This study highlights the role of integrating vegetation and multi-source soil moisture observations in regional climate models via a hybrid data assimilation and machine learning method. In particular, we show that this approach can improve land surface fluxes, near-surface air conditions, and land-atmosphere interactions in the arid and semi-arid vegetated regions.