Identification

Title

Improving station-based ensemble surface meteorological analyses using numerical weather prediction: A case study of the Oroville Dam crisis precipitation event

Abstract

Surface meteorological analyses serve a wide range of research and applications, including forcing inputs for hydrological and ecological models, climate analysis, and resource and emergency management. Quantifying uncertainty in such analyses would extend their utility for probabilistic hydrologic prediction and climate risk applications. With this motivation, we enhance and evaluate an approach for generating ensemble analyses of precipitation and temperature through the fusion of station observations, terrain information, and numerical weather prediction simulations of surface climate fields. In particular, we expand a spatial regression in which static terrain attributes serve as predictors for spatially distributed 1/16 degrees daily surface precipitation and temperature by including forecast outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model as additional predictors. We demonstrate the approach for a case study domain of California, focusing on the meteorological conditions leading to the 2017 flood and spillway failure event at Lake Oroville. The approach extends the spatial regression capability of the Gridded Meteorological Ensemble Tool (GMET) and also adds cross validation to the uncertainty estimation component, enabling the use of predictive rather than calibration uncertainty. In evaluation against out-of-sample station observations, the HRRR-based predictors alone are found to be skillful for the study setting, leading to overall improvements in the enhanced GMET meteorological analyses. The methodology and associated tool represent a promising method for generating meteorological surface analyses for both research-oriented and operational applications, as well as a general strategy for merging in situ and gridded observations.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7xp78pb

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

2022-07-01T00:00:00Z

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