Deep Learning for Verification of Earth-System Parametrisation of Water Bodies
Abstract. About 2/3 of all densely populated areas (i.e. at least 300 inhabitants per km2) around the globe are situated within a 9 km radius of a permanent waterbody (i.e. inland water or sea/ocean coast), since inland water sustains the vast majority of human activities. Water bodies exchange mass and energy with the atmosphere and need to be accurately simulated in numerical weather prediction and climate modelling as they strongly influence the lower boundary conditions such as skin temperatures, turbulent latent and sensible heat fluxes and moisture availability near the surface. All the non-ocean water (resolved and sub-grid lakes and coastal waters) are represented in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) model, by the Fresh-water Lake (FLake) parametrisation, which treats ~1/3 of the land. It is a continuous enterprise to update the surface parametrization schemes and their input fields to better represent small-scale processes. It is, however, difficult to quickly determine both the accuracy of an updated parametrisation, and the added value gained for the purposes of numerical modelling. The aim of our work is to quickly and automatically assess the benefits of an updated lake parametrisation making use of a neural network regression model trained to simulate satellite observed surface skin temperatures. We deploy this tool to determine the accuracy of recent upgrades to the FLake parametrisation, namely the improved permanent lake cover and the capacity 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 improves by 0.45 K on average, whilst for the subset of points where the lakes have been exchanged for bare ground (or vice versa) the improvement is 1.12 K. We also show that updates to the glacier cover improve further the prediction accuracy by 0.14 K. The inclusion of seasonal water is shown to be particularly effective for grid points which are highly time variable, generally improving the simulation accuracy by ~1 K. The neural network regression model has proven to be useful and easily adaptable to assess unforeseen impacts of ancillary datasets, also detecting inappropriate changes of high vegetation to bare ground, which would lead to decreased the skin temperature simulation accuracy by 0.49 K, proving to be a valuable support to model development.
Tom Kimpson et al.
Status: final response (author comments only)
CC1: 'Comment on egusphere-2022-1177', Ekaterina Kurzeneva, 16 Jan 2023
- AC3: 'Reply on CC1', Tom Kimpson, 01 Jun 2023
RC1: 'Comment on egusphere-2022-1177', Anonymous Referee #1, 19 Jan 2023
- AC1: 'Reply on RC1', Tom Kimpson, 01 Jun 2023
RC2: 'Comment on egusphere-2022-1177', Anonymous Referee #2, 10 Feb 2023
- AC2: 'Reply on RC2', Tom Kimpson, 01 Jun 2023
Tom Kimpson et al.
Tom Kimpson et al.
Viewed (geographical distribution)
In the paper, authors suggest to apply a neural network regression model to study a potential impact of external parameters of a physical model on its simulation results. They test how the neural network regression model VESPER can help to assess the potential impact the updated lake cover (lake fraction), lake depth and newly introduced lake salinity flag fields, on the simulations of the skin temperature (Land+Lake Surface Temperature, LST) by the NWP model IFS. They use VESPER to correct the LST results of IFS, applying several additional predictors, including updated and newly introduced lake data. For that, they use MODIS observations of LST as a ground truth. They show that a network regression model can help to see potential benefits and highlight potential problems. This is a very important and interesting finding! It is still doubtful from the manuscript, however, that this approach allows to calculate the accuracy of the possible physical model updates: many quantitative estimates in the paper are questionable.
There are several important essential comments and some editorial comments. Since there are many of them, I would suggest major revision of the paper. For the detailed comments, see Supplement file.