Method and system for dynamic generation of high-resolution climate projections

A system for dynamic generation climate projections, the system comprising:

• a storage device configured to store program instructions;
• and
• one or more processors operably connected to the storage device and configured to execute the program instructions to cause the system to:
• receive past climate data of a first spatial resolution, wherein the past climate data comprises a plurality of climatological variables;
• convert the past climate data of the first spatial resolution to past climate data of a second spatial resolution;
• allocate a first subset of the past climate data of the first spatial resolution and corresponding past climate data of the second spatial resolution to a training set of pairs of the past climate data of the first and second spatial resolutions;
• allocate a second subset of remaining past climate data of the first spatial resolution and corresponding past climate data of the second spatial resolution to a validation set of pairs of the past climate data of the first and second spatial resolutions;
• train a machine learning model with a deep learning algorithm with the training set of pairs of the past climate data of the first and second spatial resolutions to generate a trained model object that maps a relationship between the past climate data of the first spatial resolution and the past climate data of the second spatial resolution;
• validate the trained model with the validation set of pairs of the past climate data of the first and second spatial resolutions;
• and
• receive the climate projections of the second spatial resolution;
• and
• apply the trained model object to the climate projections of the second spatial resolution to generate the climate projections of the first spatial resolution.

A computer program product for dynamic generation climate projections, the computer program product comprising:

• a computer readable storage medium having program instructions embodied thereon to perform the steps of:
• receiving past climate data of a first spatial resolution comprising a plurality of climatological variables;
• converting the past climate data of the first spatial resolution to past climate data of a second spatial resolution;
• allocating a first subset of the past climate data of the first spatial resolution and corresponding past climate data of the second spatial resolution to a training set of pairs of the past climate data of the first and second spatial resolutions;
• allocating a second subset of remaining pairs of past climate data of the first spatial resolution and the corresponding past climate data of the second spatial resolution to a validation set;
• training a machine learning model with a deep learning algorithm with the training set of pairs of the past climate data of the first and second spatial resolutions to generate a trained model object that maps a relationship between the past climate data of the first spatial resolution and the past climate data of the second spatial resolution;
• validating the trained model object using the validation set of pairs of the past climate data of the first and second spatial resolutions;
• receiving climate projections of the second spatial resolution;
• and
• applying the trained model object to the climate projections of the second spatial resolution to generate climate projections of the first spatial resolution.