Description
Air temperature data retrieved from global atmospheric models may show a systematic bias with respect to measurements from weather stations. This is a common concern in local climate studies. The current study presents two methods based upon copulas and Conditional Probability (CP) to predict bias-corrected mean air temperature in a data-scarce environment: CP-I offers a single conditional probability as a predictor, CP-II is a pixel-wise version of CP-I and offers spatially varying predictors. The methods were applied on daily air temperature in the Qazvin Plain, Iran that were collected at 24 weather stations and 150 ECMWF ERA-interim grid cells from a single month (June) over 11 years. We compared the methods with the commonly applied conditional expectation and conditional median methods.
Earth sciences
Earth sciences
Date made available | 18 Mar 2019 |
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Publisher | DATA Archiving and Networked Services (DANS) |
Temporal coverage | Jun 2008 - Jun 2019 |
Date of data production | 2008 - 2019 |
Geographical coverage | Qazvin Plain, Iran |
Geospatial point | 36.299885, 50.018246Show on map |