 |
| Titel |
A statistical downscaling method for daily air temperature in data-sparse, glaciated mountain environments |
| VerfasserIn |
M. Hofer, B. Marzeion, T. Mölg |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1991-959X
|
| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 3 ; Nr. 8, no. 3 (2015-03-12), S.579-593 |
| Datensatznummer |
250116177
|
| Publikation (Nr.) |
 copernicus.org/gmd-8-579-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| This study presents a statistical downscaling (SD) method for high-altitude,
glaciated mountain ranges. The SD method uses an a priori selection
strategy of the predictor (i.e., predictor selection without data analysis).
In the SD model validation, emphasis is put on appropriately considering the
pitfalls of short observational data records that are typical of high
mountains. An application example is shown, with daily mean air temperature
from several sites (all in the Cordillera Blanca, Peru) as target variables,
and reanalysis data as predictors. Results reveal strong seasonal variations
of the predictors' performance, with the maximum skill evident for the wet
(and transitional) season months January to May (and September), and the
lowest skill for the dry season months June and July. The minimum number of
observations (here, daily means) required per calendar month to obtain
statistically significant skill ranges from 40 to 140. With increasing data
availability, the SD model skill tends to increase. Applied to a choice of
different atmospheric reanalysis predictor variables, the presented skill
assessment identifies only air temperature and geopotential height as
significant predictors for local-scale air temperature. Accounting for
natural periodicity in the data is vital in the SD procedure to avoid
spuriously high performances of certain predictors, as demonstrated here for
near-surface air temperature. The presented SD procedure can be applied to
high-resolution, Gaussian target variables in various climatic and
geo-environmental settings, without the requirement of subjective
optimization. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|