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 control in typology

In this paper, we propose two new statistical controls for genealogical and areal bias in typological samples. Our test case being the effect of VO-order effect on affix position (prefixation vs. suffixation), we show how statistical modeling including a phylogenetic regression term (phylogenetic control) and a two-dimensional Gaussian Process (areal control) can be used to capture genealogical and areal effects in a large but unbalanced sample. We find that, once these biases are controlled for, VO-order has no effect on affix position. Another important finding, which is in line with previous studies, is that areal effects are as important as genealogical effects, emphasizing the importance of areal or contact control in typological studies built on language samples. On the other hand, we also show that strict probability sampling is not required with the statistical controls that we propose, as long as the sample is a variety sample large enough to cover different areas and families. This has the crucial practical consequence that it allows us to include as much of the available information as possible, without the need to artificially restrict the sample and potentially lose otherwise available information.

Evaluating quantile-based bias adjustment methods for climate change scenarios

Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

Comparing Occupant Self-Assessed Behaviour to Actual Metered Consumption

A post-­‐occupancy evaluation (POE) is a comprehensive building performance review that includes occupant surveys and provides feedback on the overall success of a building design in addressing end-­‐user requirements. In doing so, POE often identifies disparities between expected and actual energy usage patterns. Part of determining the source of these disparities is the evaluation of tenant responses. Since these are heavily dependent on the users’ ability to accurately recall their usage patterns, their potential inaccuracy may misinform building retrofits and future projects. This study seeks to compare occupant self-­‐assessed behaviour to actual metered consumption. A recently retrofit multi-­‐unit residential building (MURB) and Tower Renewal pilot project was selected for the evaluation, and access to the electricity consumption of the pilot was obtained from building management. The project has 146 units, each approximately 20.5m A post-­‐retrofit survey has been carried out, which amongst other factors attempted to collect information on small appliances and electronics and their use. 48 valid samples were obtained. The monthly electricity consumption of each unit has been calculated based on the tenant responses, and these values have been compared to actual consumption values from the electronic meters. The average estimated consumption was found to be 45% more than the average metered consumption, with 46% of the survey-­‐based estimates exceeding their respective metered readings by more than 50%. As many as 86% of tenants whose consumption estimate exceeded 50% of the metered value incurred time overestimation, while 23% incurred statistical bias. It was also found that all tenants who incurred statistical bias also incurred time overestimation. While individual estimates tend to disagree with metered data, large-­‐sample assessments may still be possible. Mode-­‐based assessments help to limit sources of discrepancy by eliminating tenant responses that occur infrequently, thus creating sample cases that resemble the contents of a ‘typical unit’. However, great care must be taken to avoid introducing further bias. To this end, more rigorous statistical analysis is required. It is recommended that future surveys avoid overestimation by arranging time-­‐related questions in a manner that allows quick revision, tightening the ranges for usage questions to minimize assumptions made, and including relevant custom-­‐made questions that either clarify questions for the tenants or minimize ambiguity in the results.

Comparing Occupant Self-Assessed Behaviour to Actual Metered Consumption

A post-­‐occupancy evaluation (POE) is a comprehensive building performance review that includes occupant surveys and provides feedback on the overall success of a building design in addressing end-­‐user requirements. In doing so, POE often identifies disparities between expected and actual energy usage patterns. Part of determining the source of these disparities is the evaluation of tenant responses. Since these are heavily dependent on the users’ ability to accurately recall their usage patterns, their potential inaccuracy may misinform building retrofits and future projects. This study seeks to compare occupant self-­‐assessed behaviour to actual metered consumption. A recently retrofit multi-­‐unit residential building (MURB) and Tower Renewal pilot project was selected for the evaluation, and access to the electricity consumption of the pilot was obtained from building management. The project has 146 units, each approximately 20.5m A post-­‐retrofit survey has been carried out, which amongst other factors attempted to collect information on small appliances and electronics and their use. 48 valid samples were obtained. The monthly electricity consumption of each unit has been calculated based on the tenant responses, and these values have been compared to actual consumption values from the electronic meters. The average estimated consumption was found to be 45% more than the average metered consumption, with 46% of the survey-­‐based estimates exceeding their respective metered readings by more than 50%. As many as 86% of tenants whose consumption estimate exceeded 50% of the metered value incurred time overestimation, while 23% incurred statistical bias. It was also found that all tenants who incurred statistical bias also incurred time overestimation. While individual estimates tend to disagree with metered data, large-­‐sample assessments may still be possible. Mode-­‐based assessments help to limit sources of discrepancy by eliminating tenant responses that occur infrequently, thus creating sample cases that resemble the contents of a ‘typical unit’. However, great care must be taken to avoid introducing further bias. To this end, more rigorous statistical analysis is required. It is recommended that future surveys avoid overestimation by arranging time-­‐related questions in a manner that allows quick revision, tightening the ranges for usage questions to minimize assumptions made, and including relevant custom-­‐made questions that either clarify questions for the tenants or minimize ambiguity in the results.

Hair mineralogram analysis for health assessment: Statistical bias from gender and aesthetic treatments

The hair mineralogram is a complementary multielement analysis that provides information to aid in the diagnosis of a patient’s health status; however, aesthetic treatments can affect the analysis results. This research aimed to identify standard patterns among mineralogram results and some variables, such as gender and the use of aesthetical treatments that can point out differences and causes of variation in elemental concentrations in hair. For this purpose, 151 hair samples were obtained from volunteers and analyzed by inductively coupled plasma mass spectrometry (ICP-MS). This work is pilot research, part of a project to encourage girls to the STEM area, called “Girls in Science”, with financial support from the Brazilian Government. Mineralogram results were compared through statistical analysis. The results of natural hair indicate significant differences (p<0.05) between genders in the concentrations of Ca, Mg, Sr, and Mo, being higher in women. This behavior was related to the remodeling of minerals in bones, which is different between men and women. The metal concentration in natural hair from women was also compared among different skin colors and no significant differences were observed. Hair treatment, in contrast, has affected significantly the concentrations of many elements. Concentrations increased in hair submitted to dyeing only or with straightening, when compared to natural hair, especially for Ca, Mg, Sr, Ba, and Ni. These results confirm the recommendation of physicians to let the hair grow free of aesthetic treatments for at least 3 months before performing the mineralogram.