the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Dec 2022
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.
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CC1: 'Comment on egusphere-2022-1177', Ekaterina Kurzeneva, 16 Jan 2023
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.
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AC3: 'Reply on CC1', Tom Kimpson, 01 Jun 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1177/egusphere-2022-1177-AC3-supplement.pdf
-
AC3: 'Reply on CC1', Tom Kimpson, 01 Jun 2023
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RC1: 'Comment on egusphere-2022-1177', Anonymous Referee #1, 19 Jan 2023
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 the Supplement file.
-
AC1: 'Reply on RC1', Tom Kimpson, 01 Jun 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1177/egusphere-2022-1177-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Tom Kimpson, 01 Jun 2023
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RC2: 'Comment on egusphere-2022-1177', Anonymous Referee #2, 10 Feb 2023
Reviewer comments on
Deep Learning for Verification of Earth-System Parametrisation of
Water Bodies byReviewer comments on
Deep Learning for Verification of Earth-System Parametrisation of
Water Bodies by
Tom Kimpson, Margarita Choulga, Matthew Chantry, Gianpaolo Balsamo,
Souhail Boussetta, Peter Dueben and Tim PalmerThis study presents, evidently for the first time, VESPER, kind of
"statistical twin" to the IFS dynamical earth-system model. The idea
is to apply deep learning method to test the impact of land surface
description to predicted land surface (skin) temperature. For this,
selected transient and static near-surface fields from ERA5 reanalysis
are used as input to neural network regression model. For validation
and comparison, surface temperature from Aqua MODIS is used. The study
focuses on lake surface temperature, that is treated by Freshwater
Lake parametrizations in the IFS model when producing ERA5 reanalysis.The study presents new ideas, results, interesting
discussions. Especially I liked the nice combination of global view to
analysis of local details. Unfortunately the manuscript is written in
a very raw and messy way. It needs a major revision in order to focus
in essential and present the work in an organised way.I would suggest to improve and possibly extend the presentation of
basics of VESPER. My feeling is that your ideas have emerged in
connection to the development of the lake data base and then quickly
grown towards other areas of the surface physiography
description. Evidently, VESPER is a powerful tool for impact studies
beyond the lakes, as indeed discussed in the paper. This is why it is
important to present it systematically, with sufficient basic facts
included. In the introduction you might even shortly describe how it
evolved, and to make clear that later in this paper you will focus on
evaluation of the impacts of lake physiography description updates. To
keep the paper size reasonable, you might consider if all results and
examples now included are essential. One possibility could be to keep
them minimal for illustration of the power and possibilities of VESPER
only, and write a second part already entering the details of lake
applications?My expertise is not sufficient to judge the applied machine learning
method itself. This is why I will asume everything is fine with it and
instead make some questions and suggestions concerning data,
variables, application of the results. I have not systematically
checked for typos or language mistakes, will only comment unclear
formulations that prevent understanding.
Specific comments
-----------------I would suggest modification of the manuscript strucure to
something like this:1. Introduction
2. Constructing VESPER
  2.1.  Features and targets
  Table about the variables, their sources  (now in 3.1)Â
  feature transient/static source         updated
              surface description
(GlobCover2009 and GLDBv1 GSWE and GLDBv3 ...)
        ERA reanalysis
        MODIS  2.2   Data sources
  2.1.1  ERA5
 Â
  2.1.2  Aqua MODIS  2.3   Joining the data
  2.4   Constructing a regression model
  add a Table about realizations: Â
  V15 V15x V20 V20x
  what they mean, how differ, what were the updates done
  (now some explanations are in 3.1, some jump only among the results much later)3.  Results (or Evaluation of ...)
4. Â Discussion
5. Â ConclusionÂ
l.54:I find the definition "when we refer in this work to “lake
parametrisation" we mean both the model and the parameters"
problematic. In the IFS model, FLake is a model that is used as
parametrization scheme in grid cells where is some fraction of
lake. You show that in ERA5, a significant percentage of the 31-km
cells indeed contain some amount of lake. If there is no lake in
gridpoint, then FLake does not work there and vice versa. When FLake
is run coupled to atmosphere and surface inside the IFS model, it
needs in addition the lake depth. Can you confirm that in the IFS
model, every surface type (lake, sea, ice/glacier, forest etc) is tied
to a specific parametrization scheme? If yes, then you might perhaps
indeed bundle each scheme to the physiography description it needs.This was the case when ERA5 was prepared or is when the weather
forecast is run. Now, you possibly use the word model to denote the
statistical twin, VESPER, not the IFS model itself. (You might check
the consistency of your text in this sense). When building VESPER, you
use near-surface atmospheric fields from ERA5, and the same or updated
physiography fields that IFS model uses but you do not apply the
dynamical parametrizations like FLake here nor do you essentially
interact with the atmosphere.The variables are listed in your Table 2 (to which I suggested
modifications above). Could you please add in the VESPER description
explanation how these variables were chosen? In that context, you
might clarify the roles of VESPER v.s. IFS model. More generally, you
might discuss, after presenting the results, why you think that the
the physiography fields will have similar type of impact in the earth
system model as they have in VESPER, why you expect that you can
generalise the result.l. 123 ERA5
Please explain shortly how ERA5 skin temperature is obtained as this
is your central variable in this study.ÂWhen explaining ERA5 please mention if FLake was a part of the ECLand.
About the ERA grid cells, please make clear that reduced Gaussian grid
cells are approximately of the same size all over the globe.l. 133 MODIS
Please explain how MODIS surface (skin) temperature is derived. What
is the role of the surface emissivity, what are the related data and
assumptions when converting the outgoing LW radiation to Ts? To see
the surface, optical wavelenght instrument needs to clear the clouds,
perhaps you could shortly mention how this is done for MODIS.l. 146 and Figure 3
What do you mean with "a 4 km resolution on a regular
latitude-longitude grid"? Either 4 km or regular lat/lon, not both?
Could you please explain if you indeed need the intermediate lat/lon
grid between the raw satellite pixels (of about equal size 1 km all
over the globe?) and ERA5 grid cells? Wouldn't it be possible to pick
boxes of e.g. 4 x 4 km size along the satellite track, with ca 16
pixels and use the box average, central coordinate and time when
collecting data into needed ERA5 cells? Is it possible that the
problems shown in Figure 3 - a lot of data at polar latitudes, lack of
data around Equator - are partly due to the artificial transformations
to lat/lon grid and back? The satellite does not care about the
coordinate convergence when flying over the rotating earth, simply
looks down over some view angle. In this sense, the Figure 3. caption
might need reformulation, too.l. 184
Please move to this section the V15 etc model realizations, a new
table and descriptions.Figure 4 and related text
Please define the prediction error (VESPER or ERA5 against MODIS,
which measure?) Please check the consistency in other Figures and
Tables.I think Figure4 is important. Please discuss why VESPER overperforms
ERA5. This means also discussion of the grid-scale skin temperature
Ts. Aspects here:- If you train VESPER Ts towards MODIS, does this mean that the
 improved result is closer to reality or only to MODIS, including
 also its problems over Himalayas etc?- You showed a map of MODIS uncertainties, there are also specific
 studies like
 (https://www.tandfonline.com/doi/full/10.3402/tellusa.v66.21534
 Kheyrollah Pour et al., 2014) evaluating MODIS against in-situ lake
 surface water temperature. So how close MODIS is to reality? Any
 comments here in this respect? There are comments about uncertainty
 in the general discussion, though.- If VESPER would indeed overperform ERA5 Ts, then why not to use it
 for forecasting instead the dynamical model? What could be the use
 of Ts beoynd the earth system model where it is an internal variable
 related to surface energy balance, interaction atmosphere via
 radiation and atmospheric turbulence etc?- What about comparing outgoing LW radiation in clear-sky pixels by
 models to the corresponding MODIS variable? This would treat the
 surface emissivity in different way than the derived Tskin?
Section 3 ResultsPlease move the general VESPER realization materials with Table
earlier into a basic description, leave here the presentation of
lake-related results.Please explain what happens in the physiography fields when e.g. Lake
Aral disappears - what comes instead of it? You discuss this in
diffent cases but perhaps a general explanation would be good in the
beginning.(Somewhere later in discussions:) Would VESPER realizations and ECLand
parametrizations, including FLake and snow schemes etc. within IFS
model, react to the changes in similar way? This is a question of
applicability of VESPER results to the full earth system model
development.In general, the Section of results is interesting and detailed, but
perhaps consider condensing it and/or making another part of the paper
out of it. There might be more things to study related snow and ice on
lakes and surroundings etc. that deserve attention later.
Section 4 DiscussionAbove in the comments I have presented some possible questions to be
discussed here.Â
Citation: https://doi.org/10.5194/egusphere-2022-1177-RC2 -
AC2: 'Reply on RC2', Tom Kimpson, 01 Jun 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1177/egusphere-2022-1177-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Tom Kimpson, 01 Jun 2023
Interactive discussion
Status: closed
-
CC1: 'Comment on egusphere-2022-1177', Ekaterina Kurzeneva, 16 Jan 2023
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.
-
AC3: 'Reply on CC1', Tom Kimpson, 01 Jun 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1177/egusphere-2022-1177-AC3-supplement.pdf
-
AC3: 'Reply on CC1', Tom Kimpson, 01 Jun 2023
-
RC1: 'Comment on egusphere-2022-1177', Anonymous Referee #1, 19 Jan 2023
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 the Supplement file.
-
AC1: 'Reply on RC1', Tom Kimpson, 01 Jun 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1177/egusphere-2022-1177-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Tom Kimpson, 01 Jun 2023
-
RC2: 'Comment on egusphere-2022-1177', Anonymous Referee #2, 10 Feb 2023
Reviewer comments on
Deep Learning for Verification of Earth-System Parametrisation of
Water Bodies byReviewer comments on
Deep Learning for Verification of Earth-System Parametrisation of
Water Bodies by
Tom Kimpson, Margarita Choulga, Matthew Chantry, Gianpaolo Balsamo,
Souhail Boussetta, Peter Dueben and Tim PalmerThis study presents, evidently for the first time, VESPER, kind of
"statistical twin" to the IFS dynamical earth-system model. The idea
is to apply deep learning method to test the impact of land surface
description to predicted land surface (skin) temperature. For this,
selected transient and static near-surface fields from ERA5 reanalysis
are used as input to neural network regression model. For validation
and comparison, surface temperature from Aqua MODIS is used. The study
focuses on lake surface temperature, that is treated by Freshwater
Lake parametrizations in the IFS model when producing ERA5 reanalysis.The study presents new ideas, results, interesting
discussions. Especially I liked the nice combination of global view to
analysis of local details. Unfortunately the manuscript is written in
a very raw and messy way. It needs a major revision in order to focus
in essential and present the work in an organised way.I would suggest to improve and possibly extend the presentation of
basics of VESPER. My feeling is that your ideas have emerged in
connection to the development of the lake data base and then quickly
grown towards other areas of the surface physiography
description. Evidently, VESPER is a powerful tool for impact studies
beyond the lakes, as indeed discussed in the paper. This is why it is
important to present it systematically, with sufficient basic facts
included. In the introduction you might even shortly describe how it
evolved, and to make clear that later in this paper you will focus on
evaluation of the impacts of lake physiography description updates. To
keep the paper size reasonable, you might consider if all results and
examples now included are essential. One possibility could be to keep
them minimal for illustration of the power and possibilities of VESPER
only, and write a second part already entering the details of lake
applications?My expertise is not sufficient to judge the applied machine learning
method itself. This is why I will asume everything is fine with it and
instead make some questions and suggestions concerning data,
variables, application of the results. I have not systematically
checked for typos or language mistakes, will only comment unclear
formulations that prevent understanding.
Specific comments
-----------------I would suggest modification of the manuscript strucure to
something like this:1. Introduction
2. Constructing VESPER
  2.1.  Features and targets
  Table about the variables, their sources  (now in 3.1)Â
  feature transient/static source         updated
              surface description
(GlobCover2009 and GLDBv1 GSWE and GLDBv3 ...)
        ERA reanalysis
        MODIS  2.2   Data sources
  2.1.1  ERA5
 Â
  2.1.2  Aqua MODIS  2.3   Joining the data
  2.4   Constructing a regression model
  add a Table about realizations: Â
  V15 V15x V20 V20x
  what they mean, how differ, what were the updates done
  (now some explanations are in 3.1, some jump only among the results much later)3.  Results (or Evaluation of ...)
4. Â Discussion
5. Â ConclusionÂ
l.54:I find the definition "when we refer in this work to “lake
parametrisation" we mean both the model and the parameters"
problematic. In the IFS model, FLake is a model that is used as
parametrization scheme in grid cells where is some fraction of
lake. You show that in ERA5, a significant percentage of the 31-km
cells indeed contain some amount of lake. If there is no lake in
gridpoint, then FLake does not work there and vice versa. When FLake
is run coupled to atmosphere and surface inside the IFS model, it
needs in addition the lake depth. Can you confirm that in the IFS
model, every surface type (lake, sea, ice/glacier, forest etc) is tied
to a specific parametrization scheme? If yes, then you might perhaps
indeed bundle each scheme to the physiography description it needs.This was the case when ERA5 was prepared or is when the weather
forecast is run. Now, you possibly use the word model to denote the
statistical twin, VESPER, not the IFS model itself. (You might check
the consistency of your text in this sense). When building VESPER, you
use near-surface atmospheric fields from ERA5, and the same or updated
physiography fields that IFS model uses but you do not apply the
dynamical parametrizations like FLake here nor do you essentially
interact with the atmosphere.The variables are listed in your Table 2 (to which I suggested
modifications above). Could you please add in the VESPER description
explanation how these variables were chosen? In that context, you
might clarify the roles of VESPER v.s. IFS model. More generally, you
might discuss, after presenting the results, why you think that the
the physiography fields will have similar type of impact in the earth
system model as they have in VESPER, why you expect that you can
generalise the result.l. 123 ERA5
Please explain shortly how ERA5 skin temperature is obtained as this
is your central variable in this study.ÂWhen explaining ERA5 please mention if FLake was a part of the ECLand.
About the ERA grid cells, please make clear that reduced Gaussian grid
cells are approximately of the same size all over the globe.l. 133 MODIS
Please explain how MODIS surface (skin) temperature is derived. What
is the role of the surface emissivity, what are the related data and
assumptions when converting the outgoing LW radiation to Ts? To see
the surface, optical wavelenght instrument needs to clear the clouds,
perhaps you could shortly mention how this is done for MODIS.l. 146 and Figure 3
What do you mean with "a 4 km resolution on a regular
latitude-longitude grid"? Either 4 km or regular lat/lon, not both?
Could you please explain if you indeed need the intermediate lat/lon
grid between the raw satellite pixels (of about equal size 1 km all
over the globe?) and ERA5 grid cells? Wouldn't it be possible to pick
boxes of e.g. 4 x 4 km size along the satellite track, with ca 16
pixels and use the box average, central coordinate and time when
collecting data into needed ERA5 cells? Is it possible that the
problems shown in Figure 3 - a lot of data at polar latitudes, lack of
data around Equator - are partly due to the artificial transformations
to lat/lon grid and back? The satellite does not care about the
coordinate convergence when flying over the rotating earth, simply
looks down over some view angle. In this sense, the Figure 3. caption
might need reformulation, too.l. 184
Please move to this section the V15 etc model realizations, a new
table and descriptions.Figure 4 and related text
Please define the prediction error (VESPER or ERA5 against MODIS,
which measure?) Please check the consistency in other Figures and
Tables.I think Figure4 is important. Please discuss why VESPER overperforms
ERA5. This means also discussion of the grid-scale skin temperature
Ts. Aspects here:- If you train VESPER Ts towards MODIS, does this mean that the
 improved result is closer to reality or only to MODIS, including
 also its problems over Himalayas etc?- You showed a map of MODIS uncertainties, there are also specific
 studies like
 (https://www.tandfonline.com/doi/full/10.3402/tellusa.v66.21534
 Kheyrollah Pour et al., 2014) evaluating MODIS against in-situ lake
 surface water temperature. So how close MODIS is to reality? Any
 comments here in this respect? There are comments about uncertainty
 in the general discussion, though.- If VESPER would indeed overperform ERA5 Ts, then why not to use it
 for forecasting instead the dynamical model? What could be the use
 of Ts beoynd the earth system model where it is an internal variable
 related to surface energy balance, interaction atmosphere via
 radiation and atmospheric turbulence etc?- What about comparing outgoing LW radiation in clear-sky pixels by
 models to the corresponding MODIS variable? This would treat the
 surface emissivity in different way than the derived Tskin?
Section 3 ResultsPlease move the general VESPER realization materials with Table
earlier into a basic description, leave here the presentation of
lake-related results.Please explain what happens in the physiography fields when e.g. Lake
Aral disappears - what comes instead of it? You discuss this in
diffent cases but perhaps a general explanation would be good in the
beginning.(Somewhere later in discussions:) Would VESPER realizations and ECLand
parametrizations, including FLake and snow schemes etc. within IFS
model, react to the changes in similar way? This is a question of
applicability of VESPER results to the full earth system model
development.In general, the Section of results is interesting and detailed, but
perhaps consider condensing it and/or making another part of the paper
out of it. There might be more things to study related snow and ice on
lakes and surroundings etc. that deserve attention later.
Section 4 DiscussionAbove in the comments I have presented some possible questions to be
discussed here.Â
Citation: https://doi.org/10.5194/egusphere-2022-1177-RC2 -
AC2: 'Reply on RC2', Tom Kimpson, 01 Jun 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1177/egusphere-2022-1177-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Tom Kimpson, 01 Jun 2023
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Cited
Tom Kimpson
Margarita Choulga
Matthew Chantry
Gianpaolo Balsamo
Souhail Boussetta
Peter Dueben
Tim Palmer
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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