BIAS CORRECTION METHOD FOR SOLAR RADIATION BASED ON QUANTILE MAPPING TO PROVIDE WEATHER DATA FOR BUILDING ENERGY SIMULATIONS

The implications of weather and climate extremes on the viticulture and winemaking sector can be particularly detrimental and acquire more relevance under a climate change context. A four-member ensemble of the Regional Climate Model-Global Climate Model chain simulations is used to evaluate the potential impacts of climate change on indices of extreme temperature and precipitation, as well as on agroclimatic indices of viticultural suitability in the Douro Wine Region, Portugal, under current and future climate conditions, following the RCP8.5 anthropogenic radiative forcing scenario. Historical (1989–2005) and future (2051–2080) periods are considered for this purpose. Although model outputs are bias-corrected to improve the accuracy of the results, owing to the sensitivity of the climatic indicators to the specific bias correction method, the performance of the linear and quantile mapping methods are compared. The results hint at the importance of choosing the most accurate method (quantile mapping), not only in replicating extremes events but also in reproducing the accumulated agroclimatic indices. Significant differences between the bias correction methods are indeed found for the number of extremely warm days (maximum temperature > 35 °C), number of warm spells, number of warm spell days, number of consecutive dry days, the Dryness Index, and growing season precipitation. The Huglin Index reveals lower sensitivity, thus being more robust to the choice of the method. Hence, an unsuitable bias correction method may hinder the accuracy of climate change projections in studies heavily relying on derived extreme indices and agroclimatic indicators, such as in viticulture. Regarding the climate change signal, significant warming and drying trends are projected throughout the target region, which is supported by previous studies, but also accompanied by an increase of intensity, frequency, and duration of extreme events, namely heatwaves and dry spells. These findings thereby corroborate the need to adopt timely and effective adaptation strategies by the regional winemaking sector to warrant its future sustainability and enhance climate resilience.

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A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield

Sensors ◽

10.3390/s17061413 ◽

2017 ◽

Vol 17 (6) ◽

pp. 1413 ◽

Cited By ~ 14

Author(s):

Justine Ringard ◽

Frederique Seyler ◽

Laurent Linguet

Keyword(s):

Bias Correction ◽

Correction Method ◽

Guiana Shield ◽

Quantile Mapping ◽

Bias Correction Method ◽

Mapping Bias

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Bias-Corrected CMIP5-Derived Single-Forcing Future Wind-Wave Climate Projections toward the End of the Twenty-First Century

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0297.1 ◽

2020 ◽

Vol 59 (9) ◽

pp. 1393-1414

Author(s):

Gil Lemos ◽

Alvaro Semedo ◽

Mikhail Dobrynin ◽

Melisa Menendez ◽

Pedro M. A. Miranda

Keyword(s):

Bias Correction ◽

Correction Method ◽

Wave Climate ◽

First Century ◽

Climate Projections ◽

Wave Direction ◽

Quantile Mapping ◽

Bias Correction Method ◽

Twenty First Century ◽

Projected Changes

AbstractA quantile-based bias-correction method is applied to a seven-member dynamic ensemble of global wave climate simulations with the aim of reducing the significant wave height HS, mean wave period Tm, and mean wave direction (MWD) biases, in comparison with the ERA5 reanalysis. The corresponding projected changes toward the end of the twenty-first century are assessed. Seven CMIP5 EC-EARTH runs (single forcing) were used to force seven wave model (WAM) realizations (single model), following the RCP8.5 scenario (single scenario). The biases for the 1979–2005 reference period (present climate) are corrected using the empirical Gumbel quantile mapping and empirical quantile mapping methods. The same bias-correction parameters are applied to the HS, Tm (and wave energy flux Pw), and MWD future climate projections for the 2081–2100 period. The bias-corrected projected changes show increases in the annual mean HS (14%), Tm (6.5%), and Pw (30%) in the Southern Hemisphere and decreases in the Northern Hemisphere (mainly in the North Atlantic Ocean) that are more pronounced during local winter. For the upper quantiles, the bias-corrected projected changes are more striking during local summer, up to 120%, for Pw. After bias correction, the magnitude of the HS, Tm, and Pw original projected changes has generally increased. These results, albeit consistent with recent studies, show the relevance of a quantile-based bias-correction method in the estimation of the future projected changes in swave climate that is able to deal with the misrepresentation of extreme phenomena, especially along the tropical and subtropical latitudes.

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Performance verification of typical and design weather year by thermal load calculation targeting office building

Building Services Engineering Research and Technology ◽

10.1177/0143624418754377 ◽

2018 ◽

Vol 39 (2) ◽

pp. 147-160

Author(s):

Yusuke Arima ◽

Ryozo Ooka ◽

Hideki Kikumoto

Keyword(s):

Energy Demand ◽

Building Energy ◽

Cumulative Distribution ◽

Weather Data ◽

Quantile Mapping ◽

Data Set ◽

Peak Energy ◽

Wide Range ◽

New Type ◽

Energy Simulations

We proposed a new type of weather year data for building energy simulations named the typical and design weather year, which can be used for estimating both average and peak energy demand for one year of building energy simulation. The typical and design weather year is generated using a quantile mapping method. In this paper, we made the typical and design weather year for three cities, Tokyo, Sapporo, and Kagoshima, representing a wide range of climatic conditions in Japan, and evaluated its performance by conducting building energy simulations targeting prototypical office buildings. We found that the typical and design weather year was more than twice as accurate in estimating average energy demand as the existing typical weather year data. Typical and design weather year can also estimate peak energy demand with high accuracy. Practical application: The cumulative distribution functions of a target weather data set, on which quantile mapping is performed, are modified to consist entirely of parent multi-year weather data. Therefore, typical and design weather years based on quantile mapping are expected to be useful as versatile weather year data representing the various weather characteristics of multi-year conditions. In this study, we found that the typical and design weather year can estimate both average and peak energy demands in building energy simulations. New type of weather year data named the typical and design weather year can be used as both typical and design weather data.

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Impact of Bias-Correction Methods in Assessing the Potential Flood Frequency Change in the Bago River

Journal of Disaster Research ◽

10.20965/jdr.2020.p0288 ◽

2020 ◽

Vol 15 (3) ◽

pp. 288-299

Author(s):

Ralph Allen E. Acierto ◽

Akiyuki Kawasaki ◽

Win Win Zin ◽

◽

Keyword(s):

Climate Change ◽

Frequency Analysis ◽

Bias Correction ◽

Correction Method ◽

Flood Frequency ◽

Climate Projections ◽

Frequency Change ◽

Annual Maximum ◽

Quantile Mapping ◽

Bias Correction Method

The increasing flood risks in the Bago River due to rapid urbanization and climate change have great implications on the local development and quality of life in the basin. Therefore, the current flood hazard and potential future changes in flooding due to climate change must be assessed. This study investigates the potential flood frequency change in the Bago River and its sensitivity to the bias-correction method used in climate projections from the downscaled Global Climate Model (GCM) output. A pseudo-global warming method using MIROC5 RCP 8.5 was employed to produce 12-km 30-y historical and future climate projections. Empirical quantile mapping (EQM), gamma quantile mapping (GQM), and the multiplicative scaling method (SCM) were used for bias-correcting the rainfall input of the water-energy budget distributed hydrological model (WEB-DHM). The impacts of bias-correction methods used in reproducing the annual maximum series in the frequency analysis are sensitive to the trend of potential future changes in flood discharge frequency estimation. All methods exhibited decreases in the flood peak discharge for 50-yr and 100-yr flood predictions, which may primarily be due to the MIROC5 GCM used. However, the variation in the magnitude of the change is wide. This demonstrates the uncertainty of the frequency analysis for flood magnitude due to the employed bias-correction method. This uncertainty has significant implications on risk quantification conducted using downscaled climate projections. The effect of the uncertainty of the bias-correction method on the annual maximum rainfall time series should be communicated properly when conducting risk and hazard assessment studies.

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DEVELOPMENT OF BIAS CORRECTION METHOD FOR TROPICAL CYCLONES IN GLOBAL CLIMATE MODEL AND FUTURE CHANGE PROJECTION OF STORM SURGE USING AMR MODEL

Journal of Japan Society of Civil Engineers Ser B2 (Coastal Engineering) ◽

10.2208/kaigan.74.i_607 ◽

2018 ◽

Vol 74 (2) ◽

pp. I_607-I_612 ◽

Cited By ~ 1

Author(s):

Yosuke YAMAMOTO ◽

Nobuthito MORI ◽

Marc KJERLAND

Keyword(s):

Tropical Cyclones ◽

Storm Surge ◽

Bias Correction ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Correction Method ◽

Future Change ◽

Bias Correction Method

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Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Energies ◽

10.3390/en14040802 ◽

2021 ◽

Vol 14 (4) ◽

pp. 802

Author(s):

Kristian Skeie ◽

Arild Gustavsen

Keyword(s):

Solar Radiation ◽

Web Service ◽

Meteorological Data ◽

Surface Wind ◽

Building Energy ◽

Energy Performance ◽

Geospatial Data ◽

Weather Data ◽

Building Site ◽

Incident Solar Radiation

In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

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