the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Aug 2024
Skillful neural network predictions of Saharan dust
Abstract. Suspended in the atmosphere are millions of tonnes of mineral dust which inter- acts with weather and climate. Accurate representation of mineral dust in weather models is vital, yet remains challenging. Large scale weather models use high power supercomputers and take hours to complete the forecast. Such computational burden allows them to only include monthly climatological means of mineral dust as input states inhibiting their forecasting accuracy. Here, we introduce DustNet a simple, accurate and super fast forecasting model for 24-hours ahead predictions of aerosol optical depth (AOD). DustNet trains in less than 8 minutes and creates predictions in 2.1 seconds on a desktop computer. Created by DustNet predictions outperform the state-of-the-art physics-based model on coarse 1° x 1° resolution at 95 % of grid locations when compared to ground truth satellite data. Our results show DustNet’s potential for fast, accurate AOD fore- casting which could transform our understanding of dust’s impacts on weather patterns.
Status: open (until 25 Oct 2024)
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RC1: 'Comment on egusphere-2024-2259', Anonymous Referee #1, 26 Sep 2024
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The authors present a neural network model that predicts MODIS AOD retrievals over the Sahara based on ERA5 meteorology. The topic is relevant and my overall impression is that the model performs well in its task and the study is complemented with well documented code and data. However, there are several aspects that should be discussed or clarified before publication.
- Contrary to what the title and model name suggest (and to related studies, e.g. [1][2]), the model does not use any dust-specific data or model architectures, apart from the choice of a domain strongly affected by Saharan dust. Furthermore, the study area is not only affected by desert dust, but also by biomass burning aerosol. Therefore, the model presented here is more of an AOD model demonstrated over the Sahara. I suggest to adjust the title and the introduction of the manuscript accordingly.
- The model descriptions (pages 14 and 15) are not detailed enough, especially for the alternative models (Conv2D and U-Net). The exact model architectures and hyperparameters are not clear and providing schematics of the Conv2D and U-Net models would be very helpful in comparing the three models. All details are available in the code and data repository, but the model architectures should be clear from the manuscript itself.
- Some model design choices appear questionable. Zero padding of the input essentially introduces artificial, unrealistic input values at the boundaries, so that the approach is no longer translationally invariant, limiting the benefits of convolutional models. In addition, the 2 x 2 kernels collect data asymmetrically around each grid cell. For example, in the case of the Conv2D model, the AOD prediction for each grid cell appears to consider input from only one quadrant of its surroundings. Please discuss whether these are problems or why they are not. Not problematic, but apparently not necessary, is the imputation to the AOD observations. Instead, missing values could be excluded from the loss calculation.
- The limitation to midday predictions is important and should be mentioned in the abstract, as there does not seem to be an easy solution within this framework to go beyond this, e.g. to provide data with shorter time steps or daily averages. This, combined with the fact that the model does not produce dust-specific data such as dust concentrations or emissions, limits the value of the model compared to the expectations raised by the present title and abstract.
Minor comments
Page 3, Fig. 1, caption: The term "dimensionality" seems inappropriate here, as it is used in the context of extending and shrinking of existing dimensions
Page 3, bottom: Here only the climatological mean is mentioned as the baseline model, but clearly the persistence forecast mentioned on page 15 is both a simpler and better baseline model and should therefore be the reference.
Page 7, Fig. 3: Since all rows show AOD, I suggest using the same colour gradients
Page 8, 2nd paragraph: Please expand DOY
Page 9, Fig. 4, caption: "The background image, ..." refers to the image actually shown in Fig. 11/S7
Page 10, 1st paragraph: Surface soil moisture is another factor controlling dust emissions
Page 11, "Timestamps": The meaning of "multiplied the file" is somewhat clear, but perhaps "expanded the tensor" or something like that would be more technically correct
Page 12, "ERA5 regridding": "vertical resolution" should read "horizontal resolution"
Page 13, Eqs. (2), (3): Please define t as day of year
Page 13, "Training, validation, test split": You may add that splitting with consecutive time steps also avoids autocorrelation between the three subsets
Page 13, last paragraph: Given that you assessed performance based on MSE, it is not surprising that optimising with MSE-Loss performs best, making the test of different loss functions not very meaningful.
Page 15, "U-NET": "pool size 1 x 1" is confusing. What dimension are you downsampling, does this layer have any effect?
Page 15 and Page 3, Fig. 1:, "... while increasing the amount trainable parameters.": Do you mean increasing the number of channels/filters?
Page 16, Table 2: The persistence forecast is the more relevant baseline model and should be used in the table
Page 28, Fig. 6: It is difficult to identify the different distributions, it would be easier without colouring the areas under the curves
Page 34, Fig 13: Why is the RMSE between the constant climatological mean and the varying observations constant throughout the year?
[1] Klingmüller, K. and Lelieveld, J.: Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2, Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, 2023.[2] Kanngießer, F., & Fiedler, S. (2024): “Seeing” beneath the clouds—Machine-learning-based reconstruction of North African dust plumes. AGU Advances, 5, e2023AV001042. https://doi.org/10.1029/2023AV001042
Citation: https://doi.org/10.5194/egusphere-2024-2259-RC1
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