Identification

Title

Evaluation and process-oriented diagnosis of the GEFSv12 reforecasts

Abstract

Three levels of process-oriented model diagnostics are applied to evaluate the Global Ensemble Forecast System version 12 (GEFSv12) reforecasts. The level-1 diagnostics are focused on model systematic errors, which reveals that precipitation onset over tropical oceans occurs too early in terms of column water vapor accumulation. Since precipita-tion acts to deplete water vapor, this results in prevailing negative biases of precipitable water in the tropics. It is also asso-ciated with overtransport of moisture into the mid-and upper troposphere, leading to a dry bias in the lower troposphere and a wet bias in the mid-upper troposphere. The level-2 diagnostics evaluate some major predictability sources on the extended-range time scale: the Madden-Julian oscillation (MJO) and North American weather regimes. It is found that the GEFSv12 can skillfully forecast the MJO up to 16 days ahead in terms of the Real-time Multivariate MJO indices (bivariate correlation $ 0.6) and can reasonably represent the MJO propagation across the Maritime Continent. The weak-ened and less coherent MJO signals with increasing forecast lead times may be attributed to humidity biases over the Indo-Pacific warm pool region. It is also found that the weather regimes can be skillfully predicted up to 12 days ahead with persistence comparable to the observation. In the level-3 diagnostics, we examined some high-impact weather systems. The GEFSv12 shows reduced mean biases in tropical cyclone genesis distribution and improved performance in capturing tropical cyclone interannual variability, and midlatitude blocking climatology in the GEFSv12 also shows a better agree-ment with the observations than in the GEFSv10. SIGNIFICANCE STATEMENT: The latest U.S. operational weather prediction model -Global Ensemble Forecast System version 12 -is evaluated using a suite of physics-based diagnostic metrics from a climatic perspective. The foci of our study consist of three levels: 1) systematic biases in physical processes, 2) tropical and extratropical extended -range predictability sources, and 3) high-impact weather systems like hurricanes and blockings. Such process-oriented diagnostics help us link the model performance to the deficiencies of physics parameterization and thus provide useful information on future model improvement.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7tm7g3k

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

2023-06-15T00:00:00Z

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