A statistical bias correction technique for global climate model predicted near-surface temperature in India using the generalized regression neural network

Global climate models (GCMs) developed by the numerical simulation of physical processes in the atmosphere, ocean, and land are useful tools for climate prediction studies. However, these models involve parameterizations and assumptions for the simulation of complex phenomena, which lead to random and structural errors called biases. So, the GCM outputs need to be bias-corrected with respect to observed data before applying these model outputs for future climate prediction. This study develops a statistical bias correction approach using a four-layer feedforward radial basis neural network – a generalized regression neural network (GRNN) to reduce the biases of the near-surface temperature data in the Indian mainland. The input to the network is the CNRM-CM5 model output gridded data of near-surface temperature for the period 1951–2005, and the target to the model used for bias correcting the input data is the gridded near-surface temperature developed by the Indian Meteorological Department for the same period. Results show that the trained GRNN model can improve the inherent biases of the GCM modelled output with significant accuracy, and a good correlation is seen between the test statistics of observed and bias-corrected data for both the training and testing period. The trained GRNN model developed is then used for bias correction of CNRM-CM5 modelled projected near-surface temperature for 2006–2100 corresponding to the RCP4.5 and RCP8.5 emission scenarios. It is observed that the model can adapt well to the nature of unseen future temperature data and correct the biases of future data, assuming quasi-stationarity of future temperature data for both emission scenarios. The model captures the seasonal variation in near-surface temperature over the Indian mainland, having diverse topography appreciably, and this is evident from the bias-corrected output.

Statistical bias correction on the climate model for el nino index prediction

El Nino can harm many sectors in Indonesia by reducing precipitation levels in some areas. The occurrence of El Nino can be estimated by observing the sea surface temperature in Nino 3.4 region. Therefore, an accurate model on sea surface temperature prediction in Nino 3.4 region is needed to optimize the estimation of the occurrence of El Nino, such as ECMWF. However, the prediction model released by ECMWF still consists of some systematic errors or biases. This research aims to correct these biases using statistical bias correction techniques and evaluate the prediction model before and after correction. The statistical bias correction uses linear scaling, variance scaling, and distribution mapping techniques. The results show that statistical bias correction can reduce the prediction model bias. Also, the distribution mapping and variance scaling are more accurate than the linear scaling technique. Distribution mapping has better RMSE in December-March, and variance scaling has better RMSE in April-June also in October and November. However, in July-September, prediction from ECMWF has better RMSE. The application of statistical bias correction techniques has the highest refinement in January-March at the first lead time and in April at the fifth until the seventh lead time.

Statistical Bias Correction for Predictions of Indian Ocean Dipole Index with Quantile Mapping Approach

The Indian Ocean Dipole (IOD) is a phenomenon of ocean-atmosphere interaction that affects climate conditions in Indonesia. The IOD index shows the difference between the western and eastern Indian Ocean sea surface temperature. The impact of the IOD can increase the risk of forest fires, floods and crop failure. Thus, an IOD index prediction model is needed to anticipate the impact of the IOD. One of prediction models of sea surface temperature is the ECMWF prediction model. However, this prediction model has systematic errors that can be corrected using a quantile mapping approach. This method corrects the systematic error of the ECMWF model by connecting the distribution between the ECMWF model and OISST in a transfer function, such as different of quantile and polynomial function. Based on the results, the linear function has the highest chance to improve the accuracy of the model. Moreover, the result shows that statistical bias correction is a good method to improve the accuracy of the ECMWF model especially in Januari-April and September-December.

Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand

This study aims to investigate different statistical bias correction techniques to improve the output of a regional climate model (RCM) of daily rainfall for the upper Ping River Basin in Northern Thailand. Three subsamples are used for each bias correction method, which are (1) using full calibrated 30-year-period data, (2) seasonal subsampling, and (3) monthly subsampling. The bias correction techniques are classified into three groups, which are (1) distribution-derived transformation, (2) parametric transformation, and (3) nonparametric transformation. Eleven bias correction techniques with three different subsamples are used to derive transfer function parameters to adjust model bias error. Generally, appropriate bias correction methods with optimal subsampling are locally dependent and need to be defined specifically for a study area. The study results show that monthly subsampling would be well established by capturing the monthly mean variation after correcting the model's daily rainfall. The results also give the best-fitted parameter set of the different subsamples. However, applying the full calibrated data and the seasonal subsamples cannot substantially improve internal variability. Thus, the effect of internal climate variability of the study region is greater than the choice of bias correction methods. Of the bias correction approaches, nonparametric transformation performed best in correcting daily rainfall bias error in this study area as evaluated by statistics and frequency distributions. Therefore, using a combination of methods between the nonparametric transformation and monthly subsampling offered the best accuracy and robustness. However, the nonparametric transformation was quite sensitive to the calibration time period.