Uncovering the shortcomings of a weather typing method

Abstract

Abstract. In recent years many methods for statistical downscaling of the precipitation climate model outputs have been developed. Statistical downscaling is performed under general and method-specific (structural) assumptions but those are rarely evaluated simultaneously. This paper illustrates the verification and evaluation of the downscaling assumptions for a weather typing method. Using the observations and outputs of a global climate model ensemble, the skill of the method is evaluated for precipitation downscaling in central Belgium during the winter season (December to February). Shortcomings of the studied method have been uncovered and are identified as biases and a time-variant predictor–predictand relationship. The predictor–predictand relationship is found to be informative for historical observations but becomes inaccurate for the projected climate model output. The latter inaccuracy is explained by the increased importance of the thermodynamic processes in the precipitation changes. The results therefore question the applicability of the weather typing method for the case study location. Besides the shortcomings, the results also demonstrate the added value of the Clausius–Clapeyron relationship for precipitation amount scaling. The verification and evaluation of the downscaling assumptions are a tool to design a statistical downscaling ensemble tailored to end-user needs.

Convection‐permitting modeling with regional climate models: Latest developments and next steps

“…Various methods have been proposed to downscale the RCM outputs to higher spatio-temporal resolution, but most approaches lack a physical ground to preserve the interdependence between the variables (Maraun et al, 2017). Most downscaling methods also consider a constant bias or transfer function over time (Fu et al, 2018), that may not be representative of the changes in large/small scale atmospheric interactions (Van Uytven et al, 2020). In addition, recent studies suggest that a direct scaling between daily and hourly precipitation intensities may not be valid under climate change (Ganguli & Coulibaly, 2017;Innocenti et al, 2019;Martel et al, 2020), requiring more advanced methods able to represent subdaily rainfall fields (Benoit et al, 2018).…”

The effects of weather classification on regression-based downscaling of daily temperature extrema in the United States

“…Weather classification shares similarities with the analog method. With the weather classification method the examined period is partitioned into the specified weather types and a separate statistical downscaling technique (such as regression) is applied to establish the relationship between predictor and predictand under each weather classification (Cortesi 2014;Van Uytven et al 2020). The technique used for this study was multiple linear regression (MLR), which is a simple and computationally inexpensive method for downscaling.…”