Application of the Bias Correction and Spatial Downscaling Algorithm on the Temperature Extremes From CMIP5 Multimodel Ensembles in China

Abstract High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Short-range (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of high-resolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods.

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Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850–2100)

10.5194/essd-2021-418 ◽

2021 ◽

Author(s):

Xikun Wei ◽

Guojie Wang ◽

Donghan Feng ◽

Zheng Duan ◽

Daniel Fiifi Tawia Hagan ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Spatial Resolution ◽

Bias Correction ◽

High Spatial Resolution ◽

Super Resolution ◽

Future Climate Change ◽

Observation Data ◽

Near Surface ◽

Spatial Downscaling

Abstract. Future global temperature change would have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore the future climate change. However, ESMs still exist great uncertainty and often run at a coarse spatial resolution (The majority of ESMs at about 2 degree). Accurate temperature data at high spatial resolution are needed to improve our understanding of the temperature variation and for many applications. We innovatively apply the deep-learning(DL) method from the Super resolution (SR) in the computer vision to merge 31 ESMs data and the proposed method can perform data merge, bias-correction and spatial-downscaling simultaneously. The SR algorithms are designed to enhance image quality and outperform much better than the traditional methods. The CRU TS (Climate Research Unit gridded Time Series) is considered as reference data in the model training process. In order to find a suitable DL method for our work, we choose five SR methodologies made by different structures. Those models are compared based on multiple evaluation metrics (Mean square error(MSE), mean absolute error(MAE) and Pearson correlation coefficient(R)) and the optimal model is selected and used to merge the monthly historical data during 1850–1900 and monthly future scenarios data (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) during 2015–2100 at the high spatial resolution of 0.5 degree. Results showed that the merged data have considerably improved performance than any of the individual ESM data and the ensemble mean (EM) of all ESM data in terms of both spatial and temporal aspects. The MAE displays a great improvement and the spatial distribution of the MAE become larger and larger along the latitudes in north hemisphere, presenting like a ‘tertiary class echelon’ condition. The merged product also presents excellent performance when the observation data is smooth with few fluctuations in time series. Additionally, this work proves that the DL model can be transferred to deal with the data merge, bias-correction and spatial-downscaling successfully when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632 (Wei et al., 2021).

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Return Levels of Temperature Extremes in Southern Pakistan

10.5194/esd-2016-72 ◽

2017 ◽

Cited By ~ 1

Author(s):

Maida Zahid ◽

Richard Blender ◽

Valerio Lucarini ◽

Maria Caterina Bramati

Keyword(s):

Bias Correction ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Temperature Extremes ◽

Extreme Temperatures ◽

Return Levels ◽

Rural And Urban ◽

The World ◽

Peak Over Threshold

Abstract. Southern Pakistan (Sindh) is one of the hottest regions in the world and is highly vulnerable to temperature extremes. In order to improve rural and urban planning, information about the recurrence of temperature extremes is required. In this work, return levels of the daily maximum temperature Tmax are estimated, as well as the daily maximum wet-bulb temperature TWmax extremes. The method used is the Peak Over Threshold (POT) and it represents a novelty among the approaches previously used for similar studies in this region. Two main datasets are analyzed: temperatures observed in nine meteorological stations in southern Pakistan from 1980 to 2013, and the ERA Interim data for the nearest corresponding locations. The analysis provides the 2, 5, 10, 25, 50 and 100-year Return Levels (RLs) of temperature extremes. The 90 % quantile is found to be a suitable threshold for all stations. We find that the RLs of the observed Tmax are above 50 °C in northern stations, and above 45 °C in the southern stations. The RLs of the observed TWmax exceed 35 °C in the region, which is considered as a limit of survivability. The RLs estimated from the ERA Interim data are lower by 3 °C to 5 °C than the RLs assessed for the nine meteorological stations. A simple bias correction applied to ERA Interim data improves the RLs remarkably, yet discrepancies are still present. The results have potential implications for the risk assessment of extreme temperatures in Sindh.

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Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3309-2012 ◽

2012 ◽

Vol 16 (9) ◽

pp. 3309-3314 ◽

Cited By ~ 179

Author(s):

B. Thrasher ◽

E. P. Maurer ◽

C. McKellar ◽

P. B. Duffy

Keyword(s):

Bias Correction ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Strong Relationship ◽

Technical Note ◽

Daily Temperature ◽

Daily Maximum ◽

Temperature Extremes ◽

Quantile Mapping

Abstract. When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

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Projected changes of temperature extremes over nine major basins in China based on the CMIP5 multimodel ensembles

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-018-1569-2 ◽

2018 ◽

Vol 33 (1) ◽

pp. 321-339 ◽

Cited By ~ 5

Author(s):

Kai Xu ◽

Chuanhao Wu ◽

Bill X. Hu

Keyword(s):

Temperature Extremes ◽

Multimodel Ensembles ◽

Projected Changes

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Kriging Approach to Quantile Delta Mapping (QDM) for Spatial Downscaling of Climate Change Scenario

10.5194/egusphere-egu2020-20829 ◽

2020 ◽

Author(s):

Yong-Tak Kim ◽

Carlos H R Lima ◽

Hyun-Han Kwon

Keyword(s):

Climate Change ◽

Spatial Variability ◽

Bias Correction ◽

Climate Change Scenario ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Change Scenario ◽

Proposed Model ◽

Spatial Downscaling

Rainfall simulation by climate model is generally provided at coarse grids and bias correction is routinely needed for the hydrological applications. This study aims to explore an alternative approach to downscale daily rainfall simulated by the regional climate model (RCM) at any desired grid resolution along with bias correction using a Kriging model, which better represents spatial dependencies of distribution parameters across the watershed. The Kringing model also aims to reproduce the spatial variability observed in the ground rainfall gauge. The proposed model is validated through the entire weather stations in South Korea and climate change scenarios simulated by the five different RCMs informed by two GCMs. The results confirmed that the proposed spatial downscaling model could reproduce the observed rainfall statistics and spatial variability of rainfall. The proposed model further applied to the climate change scenario. A discussion of the potential uses of the mode is offered.KEYWORDS: Climate Change Scenario, Global Climate Models, Regional Climate Models, Statistical Downscaling, Spatial-Temporal Bias&#160;AcknowledgementThis work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-01215)

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Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-5515-2012 ◽

2012 ◽

Vol 9 (4) ◽

pp. 5515-5529 ◽

Cited By ~ 1

Author(s):

B. L. Thrasher ◽

E. P. Maurer ◽

C. McKellar ◽

P. B. Duffy

Keyword(s):

Bias Correction ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Strong Relationship ◽

Technical Note ◽

Daily Temperature ◽

Daily Maximum ◽

Temperature Extremes ◽

Quantile Mapping

Abstract. When applying a quantile-mapping based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

Download Full-text