What is statistical downscaling?

Description. Downscaling is a method for obtaining high-resolution climate or climate change information. from relatively coarse-resolution global climate models (GCMs). Typically, GCMs have a resolution of 150-300 km by 150-300 km.

What is downscaling and why is it useful in projecting climate change?

Downscaled regional climate models (RCMs) provide grist for climate change adaptation planning at the local and regional level. “Downscaling” climate models are an attempt to bridge the gap between global and local effects by layering local-level data over larger-scale climate models.

What is the difference between upscaling and downscaling?

Upscaling has no performance impact. It is just taking the image and stretching it to fit fullscreen. Downscaling runs at the higher res so has the same amount of resources used regardless of monitor res but just squeezes the image to show on a smaller res which has the same effect as SSAA.

What is bias correction method?

Bias correction is the process of scaling climate model outputs to account for their systematic errors, in order to improve their fitting to observations. Several bias correction methods exist [8]. Linear scaling corrects projections based on monthly errors [9].

How do you correct bias correction on climate data?

To do a good bias correction, it is important to have a good dataset of observations. If correcting extreme precipitation, then long-term data sets are needed. The simplest approach is the Delta change method, which is often used in climate impact research.

What is bias correction in statistics?

Bias correction is the science of scaling climate model values to reflect the statistical properties such as mean, variance or wet-day probabilities of observed climate (Teutschbein and Seibert 2012; Maraun 2016).

How do you calculate bias correction?

To correct future data (2070-2099) and calculate future average rainfall, we need relative bias correction factors: divide the observation output by the GCM output. To correc the future data, multiply the non-bias corrected GCM output with the relative bias correction factor, and calculate the average for each GCM.

How do you correct sampling bias?

How to avoid or correct sampling bias

1. Define a target population and a sampling frame (the list of individuals that the sample will be drawn from).
2. Make online surveys as short and accessible as possible.
3. Follow up on non-responders.
4. Avoid convenience sampling.

How do you reduce statistical bias?

Here are three ways to avoid sampling bias:

1. Use Simple Random Sampling. Probably the most effective method researchers use to prevent sampling bias is through simple random sampling where samples are selected strictly by chance.
2. Use Stratified Random Sampling.
3. Avoid Asking the Wrong Questions.

Can statistical downscaling be used for climate models?

Statistical Downscaling is relatively easy to produce. There are assumptions of stationarity between the large and small scale dynamics when using statistical downscaling. Impact-relevant variables not simulated by climate models can be downscaled using statistical downscaling.

Why do we need to downscale GCMs?

1. Delta Addition 2. Delta Correction 3. Quantile Mapping 4. Asynchronous Linear Regression Why do we need to downscale GCM outputs? Global climate models (GCMs) cannot simulate climate at the local to regional scale. RCMES utilizes the following statistical downscaling methods used in previous studies (e.g. Stoner et al. 2013 ).

What is the mean bias of the downscaled future simulation?

So the downscaled future simulation (yellow) has zero mean bias to the present observation [right diagram]. (Plot 2) (downscaled future) = (future simulation) + (mean bias of present simulation from present observation)

How do I Run statistical downscaling in Visual Studio?

In your terminal, navigate to the Statistical Downscaling directory. Use the following command to list the files in the directory. You should be able to see the run_statistical_downscaling.py and MPI_tas_JJA.yaml. Open the MPI_tas_JJA.yaml using the following command: