Identification

Title

The effects of imputing missing data on ensemble temperature forecasts

Abstract

A major issue for developing post-processing methods for NWP forecasting systems is the need to obtain complete training datasets. Without a complete dataset, it can become difficult, if not impossible, to train and verify statistical post-processing techniques, including ensemble consensus forecasting schemes. In addition, when ensemble forecast data are missing, the real-time use of the consensus forecast weighting scheme becomes difficult and the quality of uncertainty information derived from the ensemble is reduced. To ameliorate these problems, an analysis of the treatment of missing data in ensemble model temperature forecasts is performed to determine which method of replacing the missing data produces the lowest Mean Absolute Error (MAE) of consensus forecasts while preserving the ensemble calibration. This study explores several methods of replacing missing data, including ones based on persistence, a Fourier fit to capture seasonal variability, ensemble member mean substitution, three day mean deviation, and an Artificial Neural Network (ANN). The analysis is performed on 48-hour temperature forecasts for ten locations in the Pacific Northwest. The methods are evaluated according to their effect on the forecast performance of two ensemble post-processing forecasting methods, specifically an equal-weight consensus forecast and a ten day performance-weighted window. The methods are also assessed using rank histograms to determine if they preserve the calibration of the ensembles. For both postprocessing techniques all imputation methods, with the exception of the ensemble mean substitution, produce mean absolute errors not significantly different from the cases when all ensemble members are available. However, the three day mean deviation and ANN have rank histograms similar to that for the baseline of the non-imputed cases (i.e. the ensembles are appropriately calibrated) for all locations, while persistence, ensemble mean, and Fourier substitution do not consistently produce appropriately calibrated ensembles. The three day mean deviation has the advantage of being computationally efficient in a real-time forecasting environment.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7b56m2k

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

2011-02-01T00:00:00Z

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