Three novel copula-based bias correction methods for daily ECMWF air temperature data

Abstract

Abstract. Data retrieved from global weather forecast systems are typically biased with respect to measurements at local weather stations. This paper presents three copula-based methods for bias correction of daily air temperature data derived from the European Centre for Medium-range Weather Forecasts (ECMWF). The aim is to predict conditional copula quantiles at different unvisited locations, assuming spatial stationarity of the underlying random field. The three new methods are: bivariate copula quantile mapping (types I and II), and a quantile search. These are compared with commonly applied methods, using 10 data from an agricultural area in the Qazvin Plain in Iran containing five weather stations. Cross-validation is carried out to assess the performance. The study shows that the new methods are able to predict the conditional quantiles at unvisited locations, improve the higher order moments of marginal distributions, and take the spatial variabilities of the bias-corrected variable into account. It further illustrates how a choice of the bias correction method affects the bias-corrected variable and highlights both theoretical and practical issues of the methods. We conclude that the three new methods improve local 15 refinement of weather data, in particular if a low number of observations is available.

Copula-based interpolation methods for air temperature data using collocated covariates

“…The average bias for all stations equals 3.9 • C and 3.4 • C at d 6 and d 22 , respectively. We applied a bias correction method to obtain bias corrected values (Alidoost and Stein, 2016). A two-sample Kolmogorov-Smirnov test was performed of the null hypothesis that measurements and bias corrected values are drawn from the same distribution.…”

Bivariate bias correction of the regional climate model ensemble over the Adriatic region

“…Piani and Haerter (2012) used empirical copulas to bias correct temperature and precipitation simultaneously and concluded that after the correction, the relationship between the two variables was unchanged. Copulas were also used in a few studies considering bias correction of temperature and precipitation data, each variable was corrected separately but at different locations (e.g., Alidoost et al ., 2017; Lazoglou and Anagnostopoulou, 2019). In addition to bias correction, copulas in climatology are used to investigate trends in extreme precipitation and temperature, as in Mirakbari et al .…”

“…Copulas were also used in a few studies considering bias correction of temperature and precipitation data, each variable was corrected separately but at different locations (e.g., Alidoost et al, 2017;Lazoglou and Anagnostopoulou, 2019). In addition to bias correction, copulas in climatology are used to investigate trends in extreme precipitation and temperature, as in Mirakbari et al (2020).…”

Linking ECMWF 2 m Temperature Forecast Errors with Upper-Level Circulation Situation: A Case-Study for China

“…At present, the forecast centers in various countries and regions provide many products of model forecast. Among them, the European Centre for Medium-Range Weather Forecasts (ECMWF) model forecast products have become widely used by various regional operational forecast centers in China, including temperature [28][29][30][31] and precipitation [23,32,33], even tropical cyclone path and intensity forecasts and other products [34]. At the same time, some evaluation studies on regional forecasts in China show that the forecast accuracy of ECMWF model is higher than those of the other forecast centers.…”