CUHK Research Data Repository (Chinese University of Hong Kong)
Not a member yet
132 research outputs found
Sort by
Effect of a family-based multimedia intervention on the uptake of faecal immunohistochemical test among South Asian older adults: A cluster-randomised controlled trial
No full textData for the assessment of the feasibility, acceptability and effectiveness of a family-based multimedia intervention to enhance faecal immunohistochemical test utilisation among South Asian older adults in Hong Kon
Stringent containment measures without complete city lockdown to achieve low incidence and mortality across two waves of COVID-19 in Hong Kong
No full textThis data archive contains the data that supplement the manuscript in BMJ Global Health titled “Stringent containment measures without complete city lockdown to achieve low incidence and mortality across two waves of COVID-19 in Hong Kong”. The data consist of the characteristics of first 1038 confirmed COVID-19 cases across two epidemic waves in Hong Kong
Genome of Macrotyloma geocarpum
No full textGenome assembly and gene annotation of Macrotyloma geocarpu
1. Quality Control
No full textFigures of QC. Remarks: respective figures of QC (errors, GC content and filter) were compressed into .tar file
Policy Brief RECEPD No. 2023 – 002
No full textPupil teacher ratio as a measure of school quality of basic education in 11 cities in Greater Bay Area, 2015-202
DeepMD model of the air-water interface
No full textThis contains a model for DeePMD kit and the related training data for training that model. This repo also contains the input script for initiating the training
Uncovering the Historical Sketches of the Fuxi-Style Qin in the Rulan Chao Pian Collection
No full textImage and video collections created in the project "Uncovering the Historical Sketches of the Fuxi-Style Qin in the Rulan Chao Pian Collection"
3. Full texts of papers that are considered as potential meta-analysis papers
No full textThis file contains the full texts that are considered as potential meta-analysis paper
Bias-corrected CMIP6 Data
No full textThe bias-corrected CMIP6 global dataset for dynamical downscaling of the Earth’s historical and future climate (1979–2100) is now available. This dataset provides high-quality large-scale forcing for dynamical downscaling simulations and will improve the reliability of future projections of the regional climate and environment.
The traditional dynamical downscaling introduces biases that come from the boundary. To correct the biases, a series of steps are carried out. The method of correction decomposes the General Circulation Model (GCM) and reanalysis data into long-term trends and anomalies.
The long-term trends are computed using the multimodel ensemble (MME) mean derived from 18 CMIP6 models over historical and future time periods. In order to preserve the internal climate variability, one of the CMIP6 models is used to compute the anomalies. The high-resolution version of the MPI-M Earth system model (MPI-ESM1-2-HR), which is configured with a horizontal grid spacing of 100km in the atmosphere and 40km in the ocean, is used to produce the weather and interannual variability of the six-hourly large-scale forcing data. The data contain the upper air temperature, zonal wind, meridional wind, relative humidity, geopotential height, surface pressure, sea-level pressure, and sea surface temperature. The reanalysis dataset from 1979 to 2014 is retrieved from ECMWF ERA5. Both datasets were re-gridded to a horizontal grid spacing of (1.25° x 1.25°) using bilinear interpolation.
GCM variance bias corrections
The two datasets (GCM and ERA) can then be broken down into a long-term non-linear trend and an interannual perturbation term for each six-hour period and day of the year. The non-linear trend was computed using the ensemble empirical model decomposition (EEMD) method, excluding the long-term non-linear trend in the perturbation term. Since GCM may contain bias, that is measured by the ratio of the GCM variance to the reanalysis variance, in the amplitude of the interannual variations. In order to correct the variance bias, a scaling factor, which is the ratio of the standard deviation of the detrended reanalysis data to that of the detrended GCM data over the historical time period, can be multiplied to the perturbation term by assuming the variance bias remains the same from the historical period to a future period. Since the standard deviations are computed using the detrended data, the variance of the interannual and interdecadal variations are adjusted so that the non-linear trend remained unchanged. It is noted that the anomaly of the detrended HCM data at each six-hourly interval/day of the year is computed by subtracting the climatological mean of the detrended data from the detrended GCM data, before correcting the variance biases. The standard deviation of each six-hour interval and day of the year was calculated across 36 years from 1979 to 2014. The original standard deviation with all 36 years of data is first computed. Then the standard deviation is recalculated after removing the years with anomalies greater than three times of the original standard deviation. This helps remove the effects of the extreme events on the data, for instance, the unrealistic ratios of the standard deviation.
GCM mean bias correction
After that, the long-term non-linear trend derived from the single GCM is replaced by that derived from the MME, so the GCM data is then the sum of the long-term non-linear trend derived from MME with the EEMD over the historical-future time period and the GCM perturbation term multiplied by the scaling factor. The mean bias of the long-term trend of the GCM data relative to that of the reanalysis dataset over the historical period is removed to correct the GCM mean bias. Therefore, the bias-corrected six-hourly GCM data over the future period have a base climate provided by the reanalysis dataset over the historical period, with the change in future climate relative to the historical climatology generated by the MME and the future bias-corrected weather and climate variability derived from a single GCM.
It is noted that the long-term non-linear trends are assumed to remain the same for each six-hourly/daily value of variables within the same month to save the computing time when using EEMD method for computing the non-linear trend. Thus, the climatological mean of the detrended data is not zero because the GCM outputs and the long-term non-linear GCM trend are derived from six-hourly data and monthly data respectively.</p