Identification

Title

Improving regional climate simulations based on a hybrid data assimilation and machine learning method

Abstract

The energy and water vapor 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 regional climate and land-atmosphere interactions. 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 results show that the estimated sensible (H) and latent heat (LE) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop - OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis-desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis-desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7348qc3

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2023-04-17T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact