Deep Learning for Verification of Earth-System Parametrisation of Water Bodies Article Swipe
Tom Kimpson
,
Margarita Choulga
,
Matthew Chantry
,
Gianpaolo Balsamo
,
Souhail Boussetta
,
Peter Dueben
,
T. N. Palmer
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2022-1177
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2022-1177
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.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-2022-1177
- https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1177/egusphere-2022-1177.pdf
- OA Status
- gold
- Cited By
- 8
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310911983
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4310911983Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/egusphere-2022-1177Digital Object Identifier
- Title
-
Deep Learning for Verification of Earth-System Parametrisation of Water BodiesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-09Full publication date if available
- Authors
-
Tom Kimpson, Margarita Choulga, Matthew Chantry, Gianpaolo Balsamo, Souhail Boussetta, Peter Dueben, T. N. PalmerList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-2022-1177Publisher landing page
- PDF URL
-
https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1177/egusphere-2022-1177.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1177/egusphere-2022-1177.pdfDirect OA link when available
- Concepts
-
Environmental science, Parametrization (atmospheric modeling), Numerical weather prediction, Latent heat, Meteorology, Surface water, Sensible heat, Atmosphere (unit), Climatology, Geology, Geography, Quantum mechanics, Environmental engineering, Physics, Radiative transferTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 7Per-year citation counts (last 5 years)
- References (count)
-
19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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