Bias Correction of Zero-Inflated RCM Precipitation Fields: A Copula-Based Scheme for Both Mean and Extreme Conditions

Abstract

Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide.

Assessment of impacts of potential climate change on meteorological drought characteristics at regional scales

“…This was done to remove the influence of days with small values of rainfall that results in a skewed distribution of RCM precipitation. The presence of this drizzle effect and zero‐inflated precipitation is likely to affect drought characterization and have been addressed in the literature (Maraun et al ., 2017; Maity et al ., 2019). In DBC, a threshold value of daily rainfall ( P th , m ) was chosen for each month m to make the number of rainy days in model baseline precipitation data match with the observed data.…”

“…This technique was found to perform better than monthly bias correction (MBC) by Ojha et al (2013). In addition, bias correction at a monthly scale cannot fix the discrepant wet day frequency in the model data (RCM data may contain too many wet days with light precipitation and too many zero rain days; Maity et al, 2019). Therefore, we adopted a method combining daily bias correction (DBC) and monthly NBC.…”

Validation of Surface Temperature Derived From MERRA‐2 Reanalysis Against IMD Gridded Data Set Over India

“…The systematic overestimation or underestimation of climatic variables by any climate model is known as bias and may rely on climatological and geographical factors and also on the choice of the climate model (Hagemann et al, ; Maity et al, ; Mao et al, ; Maraun et al, ). Fan and Van den Dool ()) in their study found the biases between reanalysis product against observed, that is, the Global Historical Climatology Network version 2 + Climate Anomaly Monitoring System, which vary according to season and with global domain.…”

Vine Copula Ensemble Downscaling for Precipitation Projection Over the Loess Plateau Based on High‐Resolution Multi‐RCM Outputs

“…Due to their capacity in constructing joint dependence structure among different variables and the flexibility to select multiple functions, copula-related methods were developed for use in hydrological and climate studies (Fan et al, 2015;Kong et al, 2015;Nelsen, 2006;Samaniego et al, 2010;Serinaldi, 2009;Wei & Liu, 2018). More recently, methods based on bivariate copulas (or Bi-Copula methods) were introduced to the field of bias correction in climate projections (Alidoost et al, 2019;Maity et al, 2019;Mao et al, 2015;Vogl et al, 2012;Zhou et al, 2018). For instance, Mao et al (2015) and Zhou et al (2018) have applied Bi-Copula method to correct precipitation projections from WRF (Weather Research and Forecasting Model) and temperature projections from PRECIS (Providing Regional Climates for Impacts Studies), respectively.…”

“…Postdownscaling methods (such as Qmap and Bi-Copula) face a number of general challenges. (i) They are based on individual RCM outputs, and reflect only one-to-one relationships between simulations (from each RCM output) and observations (e.g., PRECIS in Zhou et al [2018], and WRF in Mao et al [2015], and Maity et al [2019]). (ii) Different RCMs perform unevenly when simulating regional climate, leading to great deviations especially in the case of precipitation in areas with complex terrains.…”