activity
20162022
most citedA Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts

201 citations · 201 across the 1 of their papers we have counts for

collaborators

5 papers

physics.ao-ph2022★ 201 cited

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts

Lucy Harris, Andrew T. T. McRae, Matthew Chantry +2

Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is t…

physics.ao-ph2020

Using reduced-precision arithmetic in the adjoint model of MITgcm

Andrew T. T. McRae, Tim N. Palmer

In recent years, it has been convincingly shown that weather forecasting models can be run in single-precision arithmetic. Several models or components thereof have been tested wit…

math.NA2017

The scaling and skewness of optimally transported meshes on the sphere

Chris J. Budd, Andrew T. T. McRae, Colin J. Cotter

In the context of numerical solution of PDEs, dynamic mesh redistribution methods (r-adaptive methods) are an important procedure for increasing the resolution in regions of intere…

math.NA2016

Optimal-transport-based mesh adaptivity on the plane and sphere using finite elements

Andrew T. T. McRae, Colin J. Cotter, Chris J. Budd

In moving mesh methods, the underlying mesh is dynamically adapted without changing the connectivity of the mesh. We specifically consider the generation of meshes which are adapte…

cs.MS2016

A structure-exploiting numbering algorithm for finite elements on extruded meshes, and its performance evaluation in Firedrake

Gheorghe-Teodor Bercea, Andrew T. T. McRae, David A. Ham +5

We present a generic algorithm for numbering and then efficiently iterating over the data values attached to an extruded mesh. An extruded mesh is formed by replicating an existing…