Dynamical and statistical downscaling methods

  • How to do statistical downscaling?

    For Statistical downscaling, a statistical relationship is developed between the historic observed climate data and the output of the climate model for the same historical period.
    The relationship is used to develop the future climate data.
    Statistical downscaling can be combined with bias correction/adjustment..

  • What are the methods of downscaling climate?

    Downscaling tries to obtain observed small- scale (often station level) variables and using larger (GCM) scale variables, using either regional climate models, analogue methods (circulation typing), regression analysis, or neural network methods..

  • What are the methods of downscaling?

    Downscaling tries to obtain observed small- scale (often station level) variables and using larger (GCM) scale variables, using either regional climate models, analogue methods (circulation typing), regression analysis, or neural network methods..

  • What are the models for dynamical downscaling?

    For Dynamical downscaling a higher resolution climate model is used.
    These models are often called regional climate models (RCM).
    RCM use lower resolution climate models (in most cases GCMs) as boundary conditions and physical principles to reproduce local climate..

  • What is a challenge for using statistical downscaling techniques?

    However, downscaling of temporal sequences, extremes in daily precipitation, and handling of nonstationary precipitation in future conditions are considered common challenges for most statistical downscaling methods..

  • What is dynamic downscaling of climate data?

    Dynamical downscaling refers to the use of high-resolution regional simulations to dynamically extrapolate the effects of large-scale climate processes to regional or local scales of interest..

  • What is dynamical downscaling techniques?

    For Dynamical downscaling a higher resolution climate model is used.
    These models are often called regional climate models (RCM).
    RCM use lower resolution climate models (in most cases GCMs) as boundary conditions and physical principles to reproduce local climate..

  • What is the statistical downscaling technique?

    Statistical downscaling first derives statistical relationships between observed small-scale (often station level) variables and larger (GCM) scale variables, using either analogue methods (circulation typing), regression analysis, or neural network methods..

  • Downscaling tries to obtain observed small- scale (often station level) variables and using larger (GCM) scale variables, using either regional climate models, analogue methods (circulation typing), regression analysis, or neural network methods.
  • Dynamical downscaling refers to the use of high-resolution regional simulations to dynamically extrapolate the effects of large-scale climate processes to regional or local scales of interest.
Dynamical-statistical downscaling involves the use of an RCM to downscale GCM output before statistical equations are used to further downscale RCM output to a finer resolution.
Statistical downscaling and dynamical downscaling are two approaches to generate high-resolution regional climate models based on the large-scale information from either reanalysis data or global climate models. In this study, these two downscaling methods are used to simulate the surface climate of China and compared.

What are the different types of downscaling techniques?

To resolve this issue, different downscaling techniques have been developed.
There are two main approaches to downscaling climate model outputs:

  1. Statistical and Dynamical downscaling

For Dynamical downscaling a higher resolution climate model is used.
These models are often called regional climate models (RCM).
,

What are the strengths and weaknesses of statistical downscaling?

Each downscaling method has its strengths and weaknesses in the application.
Statistical downscaling is less computationally demanding and can be easily applied, but it requires long-term, high-quality surface observation to establish a robust statistical relationship between large-scale variables and local variables.

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What is dynamical downscaling?

Dynamical downscaling refers to the use of regional climate models (RCMs) driven by GCM output or reanalysis data to produce regionalized climate information [ Giorgi, 1990; Mass et al ., 2002; Wang et al ., 2004; Rockel, 2015 ].

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Why do statistical downscaling methods have a high differential performance?

Because of same statistical downscaling method in them, the differential performance is highly attributed to the quality of corresponding observation datasets that establish math-statistics mapping between simulation and observation.


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