A purposely built deep learning algorithm for the Verification of
Earth-System ParametERisation (VESPER) is used to assess recent upgrades of the
global physiographic datasets underpinning the quality of the Integrated
Forecasting System (IFS) of the European Centre for Medium-Range Weather
Forecasts (ECMWF), which is used both in numerical weather prediction and
climate reanalyses. A neural network regression model is trained to learn the
mapping between the surface physiographic dataset plus the meteorology from
ERA5, and the MODIS satellite skin temperature observations. Once trained, this
tool is applied to rapidly assess the quality of upgrades of the land-surface
scheme. Upgrades which improve the prediction accuracy of the machine learning
tool indicate a reduction of the errors in the surface fields used as input to
the surface parametrisation schemes. Conversely, incorrect specifications of
the surface fields decrease the accuracy with which VESPER can make
predictions. We apply VESPER to assess the accuracy of recent upgrades of the
permanent lake and glaciers covers as well as planned upgrades to represent
seasonally varying water bodies (i.e. ephemeral lakes). We show that for
grid-cells where the lake fields have been updated, the prediction accuracy in
the land surface temperature (i.e mean absolute error difference between
updated and original physiographic datasets) improves by 0.37 K on average,
whilst for the subset of points where the lakes have been exchanged for bare
ground (or vice versa) the improvement is 0.83 K. We also show that updates to
the glacier cover improve the prediction accuracy by 0.22 K. We highlight how
neural networks such as VESPER can assist the research and development of
surface parameterizations and their input physiography to better represent
Earth's surface couples processes in weather and climate models.Comment: 26 pages, 16 figures. Submitted to Hydrology and Earth System
Sciences (HESS