Download and Install RCMES
If you have not installed RCMES, click hereto do so now using either the Virtual Machine or Easy-OCW.
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How do you use statistical downscaling?
The relationship is used to develop the future climate data.
Statistical downscaling can be combined with bias correction/adjustment.
A tool to do this has been developed by Wageningen University and can be downloaded here. linear regression is a simple widely used method for bias correction.
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Method 1: Statistical Downscaling Using Delta Addition
The difference between present and future simulations are added to the present observation - See Plot 1 below.
First, the mean difference between present simulation (green) and future simulation (red) is calculated.
And the calculated difference is added to the present observation (blue) to make a downscaled future prediction.
In this method, only .
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Method 3: Statistical Downscaling Using Quantile Mapping
In this method, biases are calculated for each percentile in the cumulative distribution function from present simulation (blue).
Then the calculated biases are added to the future simulation to correct the biases of each percentile.
Edit the MPI_tas_JJA.yaml to change the downscaling method to 3 (quantile mapping).
Compile and run the statistical .
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What are the different types of meteorological downscaling methods?
Wilby and Wigley placed meteorological downscaling techniques into four categories:
- regression methods
- weather pattern-based approaches
- stochastic weather generators
- which are all statistical downscaling methods
- limited-area modeling (which corresponds to dynamical downscaling methods )
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What is the difference between BC and other downscaling methods?
BC applies a bias correction to the selected statistics, whereas the other three downscaling methods return a modified precipitation time series.
BC utilises a direct downscaling strategy by applying the relative change factors directly to the dry-spell-related research indicators.
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Which statistical downscaling methods are used in gridded observations over China?
In this study, four widely used statistical downscaling methods, BCSD, BCCI, BCCAQ and CDF-t, are evaluated via a comparison with gridded observations over China for the present climate (1961–2005), as applied to seven GCMs from CMIP5.
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Why Do We Need to Downscale GCM outputs?
Global climate models (GCMs) cannot simulate climate at the local to regional scale. - delta method - bias correction - spatial disaggregation (BCSD) - quantile mapping - asynchronous regression approach
Procedure to infer high-resolution information from low-resolution variables
Downscaling is any procedure to infer high-resolution information from low-resolution variables.
This technique is based on dynamical or statistical approaches commonly used in several disciplines, especially meteorology, climatology and remote sensing.
The term downscaling usually refers to an increase in spatial resolution, but it is often also used for temporal resolution.
This is not to be confused with image downscaling which is a process of reducing an image from a higher resolution to a lower resolution.
