Identification

Title

Enhancement of land surface information and its impact on atmospheric modeling in the Heihe River Basin, Northwest China

Abstract

With increasing computational resources, atmospheric/environmental models continue to run at finer-grid spacing that can resolve land surface characteristics, such as topography, land use/land cover, and soil texture. This paper assesses the improvement in land surface information data sets and its impact on atmospheric modeling. The study focuses on the Heihe River Basin (HRB) in northwestern China. Fine-scale, remotely sensed, and in situ land surface data in HRB are derived and compared with the global data sets used in most mesoscale models. The incorporation of these fine-scale land surface data, compared to those currently used in MM5, yields substantially improved HRB land surface data sets. HRB local and regional data sets and the global land data set are used in a nonhydrostatic mesoscale model (MM5) to investigate the influences of land surface uncertainty on meteorological modeling in the lower atmosphere. Main results suggest the following: (1) enhanced land data sets have a stronger impact on atmospheric water vapor fields in the lower boundary layer than other meteorological fields. Soil texture data greatly impacts the local precipitation simulation and landuse data improves the air temperature simulation in the lower atmosphere; (2) generally, the average land surface temperature biases are reduced using the enhanced land surface information, but the low bias in zones with higher elevation and high bias in zones with lower elevation still persist; (3) the wet bias over rugged terrain and dry bias in the simulated water vapor in the flat plains are both reduced. Area mean bias of simulated accumulated monthly precipitation is greatly reduced using the enhanced soil data. Convective available potential energy was larger in the HRB mountain regions using the default land data, while it was decreased using the enhanced ones; (4) analysis of the correlation coefficient between simulation bias and the geographic features shows that there are some patterns in the simulation bias distribution. Generally, larger bias still exists in the foothills of the mountains.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7zw1n6q

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

2008-10-28T00:00:00Z

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