THE NEAR FUTURE WEATHER DATA FOR BUILDING ENERGY SIMULATION USING DYNAMICAL DOWNSCALING OF RESULTS FROM GLOBAL CLIMATE MODEL

Abstract Dynamical downscaling is the most widely used physics-based approach to obtaining fine-scale weather and climate information. However, traditional dynamical downscaling approaches are often degraded by biases in the large-scale forcing. To improve the confidence in future projection of regional climate, we used a novel bias-corrected global climate model (GCM) dataset to drive a regional climate model (RCM) over the period for 1980–2014. The dynamical downscaling simulations driven by the original GCM dataset (MPI-ESM1-2-HR model) (hereafter WRF_GCM), the bias-corrected GCM (hereafter WRF_GCMbc) are validated against that driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 dataset (hereafter WRF_ERA5), respectively. The results suggest that, compared with the WRF_GCM, the WRF_GCMbc shows a 50–90% reduction in RMSEs of the climatological mean of downscaled variables (e.g. temperature, precipitation, wind, relative humidity). Similarly, the WRF_GCMbc also shows improved performance in simulating the interannual variability of downscaled variables. The RMSEs of interannual variances of downscaled variables are reduced by 30–60%. An EOF analysis suggests that the WRF_GCMbc can successfully reproduce the dominant tri-pole mode in the interannual summer precipitation variations observed over eastern China as opposed to the mono-pole precipitation pattern simulated by the WRF_GCM. Such improvements are primarily caused by the correct simulation of the location of the western North Pacific subtropical high by the WRF_GCMbc due to the GCM bias correction.

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Building Energy Simulation with On-Site Weather Station

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.859.88 ◽

2016 ◽

Vol 859 ◽

pp. 88-92 ◽

Cited By ~ 1

Author(s):

Radu Manescu ◽

Ioan Valentin Sita ◽

Petru Dobra

Keyword(s):

Energy Consumption ◽

Energy Demand ◽

Building Energy ◽

Energy Simulation ◽

Weather Data ◽

Building Energy Simulation ◽

Existing Buildings ◽

Existing Building ◽

Simulation Tools ◽

Heating And Cooling

Energy consumption awareness and reducing consumption are popular topics. Building energy consumption counts for almost a third of the global energy consumption and most of that is used for building heating and cooling. Building energy simulation tools are currently gaining attention and are used for optimizing the design for new and existing buildings. For O&M phase in existing buildings, the multiannual average weather data used in the simulation tools is not suitable for evaluating the performance of the building. In this study an existing building was modeled in EnergyPlus. Real on-site weather data was used for the dynamic simulation for the heating energy demand with the aim of comparing the measured energy consumption with the simulated one. The aim is to develop an early fault detection tool for building management.

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Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-17-0087.1 ◽

2017 ◽

Vol 56 (12) ◽

pp. 3245-3262 ◽

Cited By ~ 10

Author(s):

A. Wootten ◽

A. Terando ◽

B. J. Reich ◽

R. P. Boyles ◽

F. Semazzi

Keyword(s):

Climate Models ◽

Climate Model ◽

Dynamical Downscaling ◽

Global Climate ◽

Global Climate Model ◽

Global Climate Models ◽

Model Experiments ◽

Complete Set ◽

Regional Adaptation ◽

Model Ensembles

AbstractIn recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

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Dynamical Downscaling Luaran Global Climate Model (GCM) Menggunakan Model REGCM3 untuk Proyeksi Curah Hujan di Kabupaten Indramayu

Agromet ◽

10.29244/j.agromet.28.1.9-16 ◽

2018 ◽

Vol 28 (1) ◽

pp. 9

Author(s):

Syamsu Dwi Jadmiko ◽

Akhmad Faqih

Keyword(s):

Regional Climate Model ◽

Spatial Resolution ◽

Climate Model ◽

Dynamical Downscaling ◽

High Spatial Resolution ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Equations Of Motion ◽

Daily Rainfall

Future rainfall projection can be predicted by using Global Climate Model (GCM). In spite of low resolution, we are not able specifically to describe a local or regional information. Therefore, we applied downscaling technique of GCM output using Regional Climate Model (RCM). In this case, Regional Climate Model version 3 (RegCM3) is used to accomplish this purpose. RegCM3 is regional climate model which atmospheric properties are calculated by solving equations of motion and thermodynamics. Thus, RegCM3 is also called as dynamic downscaling model. RegCM3 has reliable capability to evaluate local or regional climate in high spatial resolution up to 10 × 10 km. In this study, dynamically downscaling techniques was applied to produce high spatial resolution (20 × 20 km) from GCM EH5OM output which commonly has rough spatial resolution (1.875<sup>o</sup> × 1.875<sup>o</sup>). Simulation show that future rainfall in Indramayu is relatively decreased compared to the baseline condition. Decreased rainfall generally occurs during the dry season (July-June-August/JJA) in a range 10-20%. Study of extreme daily rainfall indicates that there is no significant increase or decrease value.

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A Hybrid Downscaling Approach for Future Temperature and Precipitation Change

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-20-0013.1 ◽

2020 ◽

Vol 59 (11) ◽

pp. 1793-1807 ◽

Cited By ~ 1

Author(s):

Helene Birkelund Erlandsen ◽

Kajsa M. Parding ◽

Rasmus Benestad ◽

Abdelkader Mezghani ◽

Marie Pontoppidan

Keyword(s):

Regional Climate Model ◽

Statistical Models ◽

Large Scale ◽

Climate Model ◽

Dynamical Downscaling ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Small Scale ◽

Regional Climate Model Output

AbstractWe used empirical–statistical downscaling in a pseudoreality context, in which both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model, and the small-scale conditions were taken from dynamical downscaling of the same global model with a convection-permitting regional climate model covering southern Norway. This hybrid downscaling approach, a “perfect model”–type experiment, provided 120 years of data under the CMIP5 high-emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical–statistical model configurations and predictor choices and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the interannual correlation and long-term trends in seasonal 2-m temperature T2m, wet-day frequency fw, and wet-day mean precipitation μ. We found that different 30-yr calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and it provides a robust set of empirically downscaled GCM runs.

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Bias in downscaled rainfall characteristics

10.5194/hess-2019-139 ◽

2019 ◽

Author(s):

Nicholas J. Potter ◽

Francis H. S. Chiew ◽

Stephen P. Charles ◽

Guobin Fu ◽

Hongxing Zheng ◽

...

Keyword(s):

Water Resource ◽

Bias Correction ◽

Climate Model ◽

Dynamical Downscaling ◽

Transition Probabilities ◽

Global Climate ◽

Global Climate Model ◽

Hydrological Modelling ◽

Quantile Mapping ◽

Rainfall Characteristics

Abstract. Dynamical downscaling of future projections of global climate model outputs can potentially provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand for regional water resource planning processes. By explicitly modelling climate processes within and across global climate model gridcells for a region, dynamical downscaling can provide higher resolution hydroclimate projections, as well as independent (from historical timeseries) and physically plausible future rainfall timeseries for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile-quantile mapping to rainfall dynamically downscaled by NARCliM in the State of Victoria, Australia and examine the effect of this on: (i) biases both before and after bias correction in different rainfall metrics; (ii) change signals in metrics in comparison to the bias; and (iii) the effect of bias correction on wet-wet and dry-dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile-quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. This demonstrates that any quantile mapping bias correction methods are unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias correct dynamical downscaling rainfall outputs.

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Procedures for Calibrating Hourly Simulation Models to Measured Building Energy and Environmental Data

Journal of Solar Energy Engineering ◽

10.1115/1.2888069 ◽

1998 ◽

Vol 120 (3) ◽

pp. 193-204 ◽

Cited By ~ 45

Author(s):

J. S. Haberl ◽

T. E. Bou-Saada

Keyword(s):

Goodness Of Fit ◽

Building Energy ◽

Building Envelope ◽

Energy Simulation ◽

Environmental Data ◽

Simulation Program ◽

Weather Data ◽

Mean Bias Error ◽

Building Energy Simulation ◽

Calibration Methods

This paper discusses procedures for creating calibrated building energy simulation programs. It begins with reviews of the calibration techniques that have been reported in the previous literature and presents new hourly calibration methods including a temperature bin analysis to improve hourly x−y scatter plots, a 24-hour weather-daytype bin analysis to allow for the evaluation of hourly temperature and schedule dependent comparisons, and a 52-week bin analysis to facilitate the evaluation of long-term trends. In addition, architectural rendering is reviewed as a means of verifying the dimensions of the building envelope and external shading placement as seen by the simulation program. Several statistical methods are also presented that provide goodness-of-fit indicators, including percent difference calculations, mean bias error (MBE), and the coefficient of variation of the root mean squared error (CV(RMSE)). The procedures are applied to a case study building located in Washington, D. C. where nine months of hourly whole-building electricity data and sitespecific weather data were measured and used with the DOE-2.1D building energy simulation program to test the new techniques. Simulations that used the new calibration procedures were able to produce an hourly MBE of –0.7% and a CV(RMSE) of 23.1% which compare favorably with the most accurate hourly neural network models (Kreider and Haberl, 1994a, b).

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