Joint bias correction of temperature and precipitation in climate model simulations
By C. Li, E. Sinha, D. Horton, N. Diffenbaugh, Anna M. Michalak. 2014
103 citations recorded by the source corpus.
Subfields: Ecology, Evolution, Behavior and Systematics, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/0592d42a1366ef7e2b3cee4cad508f849c0a6b26
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology, Evolution, Behavior and Systematics
In Global and Planetary Change
Citations in the corpus
- A new two-stage multivariate quantile mapping method for bias correcting climate model outputs (2019, methodology): “2009; Li 2014) for inducing desired correlations between precipitation amounts and durations or inducing spatial correlations for multi-site precipitation time series.”
- Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? (2015, unknown): “First, previous studies that have looked at the influence of bias correction on simulated precipitation changes over large spatial domains have focused on the mean rather than extremes (Maurer and Pierce 2014).”
- A multivariate conditional model for streamflow prediction and spatial precipitation refinement (2015, unknown): “, 2004; Liu et al., 2015]. Multiple linear regression and autoregressive moving average models are probably the most widely used data-driven methods for hydrological forecasting [McKerchar and Delleur, 1974, Noakes et al., 1985; Adamowski et al., 2012]. More recently, the use of machine learning has”
- The effect of univariate bias adjustment on multivariate hazard estimates (2019, methodology): “In recognition of these issues, several multivariate bias-adjustment methods have been suggested in the recent past (Piani and Haerter, 2012; Li et al., 2014; Vrac and Friederichs, 2015; Cannon, 2016; Mehrotra and Sharma, 2016; Vrac, 2018).”
- Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool (2018, methodology): “On the one hand, RCMs present biases in inter-variable correlation fields (Wilcke et al. 2013), thus requiring specific multi-variate bias-adjustment techniques (e.g., Li et al. 2014; Vrac and Friederichs 2015; Cannon 2016) able to reconstruct the intervariable dependences of the observations.”