Identification

Title

Geostatistical model averaging for locally calibrated probabilistic quantitative precipitation forecasting

Abstract

Accurate weather benefit many key societal functions and activities, including agriculture, transportation, recreation, and basic human and infrastructural safety. Over the past two decades, ensembles of numerical weather prediction models have been developed, in which multiple estimates of the current state of the atmosphere are used to generate probabilistic forecasts for future weather events. However, ensemble systems are uncalibrated and biased, and thus need to be statistically postprocessed. Bayesian model averaging (BMA) is a preferred way of doing this. Particularly for quantitative precipitation, biases and calibration errors depend critically on local terrain features. We introduce a geostatistical approach to modeling locally varying BMA parameters, as opposed to the extant method that holds parameters constant across the forecast domain. Degeneracies caused by enduring dry periods are overcome by Bayesian regularization and Laplace approximations. The new approach, called geostatistical model averaging (GMA), was applied to 48-hour-ahead forecasts of daily precipitation accumulation over the North American Pacific Northwest, using the eight-member University of Washington Mesoscale Ensemble. GMA had better aggregate and local calibration than the extant technique, and was sharper on average.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7rn38m1

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

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Temporal reference

Temporal extent

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Dataset reference date

date type

publication

effective date

2011-12-01T00:00:00Z

Frequency of update

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Use constraints

This is a preprint of an article submitted for consideration in the Journal of the American Statistical Association 2012 copyright Taylor & Francis; Journal of the American Statistical Association is available online at: http://dx.doi.org/10.1198/jasa.2011.ap10433.

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