ITS4.4/AS4.1

Seasonal climate predictions can forecast the climate variability up to several months ahead and support a wide range of societal activities. The coarse spatial resolution of seasonal forecasts needs to be refined to the regional/local scale for specific applications. Statistical downscaling aims at learning empirical links between the large-scale and local-scale climate, i.e., a mapping from a low-resolution gridded variable to a higher-resolution grid.

Statistical downscaling of gridded climate variables is a task closely related to that of super-resolution in computer vision, and unsurprisingly, several deep learning-based approaches have been explored by the climate community in recent years. In this study, we downscale the SEAS5 ECMWF seasonal forecast of temperature over the Iberian Peninsula using deep convolutional networks in supervised and generative adversarial training frameworks. Additionally, we apply the traditional analog method for statistical downscaling, which assumes that similar atmospheric configurations (e.g., the predictors) lead to similar meteorological outcomes in a K-Nearest Neighbors fashion. 

The deep learning-based algorithms are trained on the UERRA gridded temperature  dataset and several ERA5 reanalysis predictor variables. Finally, we evaluate the accuracy of our deep learning-based downscaling of SEAS5 temperature and compare it to the analog downscaling and a bicubic interpolation, as the simplest baseline method.

How to cite: Gómez-Gonzalez, C. A., Palma Garcia, L., Lledó, L., Marcos, R., Gonzalez-Reviriego, N., Carella, G., and Soret Miravet, A.: Deep learning-based downscaling of seasonal forecasts over the Iberian Peninsula, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12253, https://doi.org/10.5194/egusphere-egu21-12253, 2021.

In certain measurement endeavours spatial resolution of the data is restricted, while in others data have poor temporal resolution. Typical example of these scenarios come from geoscience where measurement stations are fixed and scattered sparsely in space which results in poor spatial resolution of acquired data. Thus, we ask if it is possible to use a portion of data as a proxy to estimate the rest of the data using different machine learning techniques. In this study, four supervised machine learning methods are trained on the wind data from the Adriatic Sea and used to reconstruct the missing data. The vector wind data components at 10m height are taken from ERA5 reanalysis model in range from 1981 to 2017 and sampled every 6 hours. Data taken from the northern part of the Adriatic Sea was used to estimate the wind at the southern part of Adriatic. The machine learning models utilized for this task were linear regression, K-nearest neighbours, decision trees and a neural network. As a measure of quality of reconstruction the difference between the true and estimated values of wind data in the southern part of Adriatic was used. The result shows that all four models reconstruct the data few hundred kilometres away with average amplitude error below 1m/s. Linear regression, K-nearest neighbours, decision trees and a neural network show average amplitude reconstruction error of 0.52, 0.91, 0.76 and 0.73, and standard deviation of 1.00, 1.42, 1.23 and 1.17, respectively. This work has been supported by Croatian Science Foundation under the project UIP-2019-04-1737.

How to cite: Kalinić, H., Bilokapić, Z., and Matić, F.: Oceanographic data reconstruction using machine learning techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2046, https://doi.org/10.5194/egusphere-egu21-2046, 2021.

In large eddy simulations (LES), the subgrid-scale effects are modeled by physics-based or data-driven methods. This work develops a convolutional neural network (CNN) to model the subgrid-scale effects of a two-dimensional turbulent flow. The model is able to capture both the inter-scale forward energy transfer and backscatter in both a priori and a posteriori analyses. The LES-CNN model outperforms the physics-based eddy-viscosity models and the previous proposed local artificial neural network (ANN) models in both short-term prediction and long-term statistics. Transfer learning is implemented to generalize the method for turbulence modeling at higher Reynolds numbers. Encoder-decoder network architecture is proposed to generalize the model to a higher computational grid resolution.

How to cite: Guan, Y., Chattopadhyay, A., Subel, A., and Hassanzadeh, P.: Stable and accurate a posteriori LES of 2D turbulence with convolutional neural networks: Backscatter analysis and generalization via transfer learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-402, https://doi.org/10.5194/egusphere-egu21-402, 2021.

The prediction of the onset date of the Indian Summer Monsoon (ISM) is crucial for effective agricultural planning and water resource management on the Indian subcontinent, with more than one billion inhabitants. Existing approaches focus on extended-range to subseasonal forecasts, i.e., provide skillful predictions of the ISM onset date at horizons of 10 to 60 days. Here we propose a method for ISM onset prediction and show that it has high forecast skill at longer, seasonal time scales. The method is based on recurrent neural networks and allows for ensemble forecasts to quantify uncertainties. Our approach outperforms state-of-the-art numerical weather prediction models at comparable or longer lead times. To our knowledge, there is no statistical forecasting approach at comparable, seasonal time scales. Our results suggest that predictability of the ISM onset emerges earlier than previously assumed.

How to cite: Mitsui, T. and Boers, N.: Seasonal prediction of Indian Summer Monsoon onset with machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1754, https://doi.org/10.5194/egusphere-egu21-1754, 2021.

We present a flexible non-linear framework of Gaussian Process Regression (GPR) for the reconstruction of past climate indexes such as the Atlantic Multidecadal Variability (AMV). These reconstructions are needed because the historical observation period is too short to provide a long-term perspective on climate variability. Climate indexes can be reconstructed from proxy data (e.g. tree rings) with the help of statistical models. Previous reconstructions of climate indexes mostly used some form of linear regression methods, which are known to underestimate the true amplitude of variability and perform poorly if noisy input data is used.

We implement the machine-learning method GPR for climate index reconstruction with the goal of preserving the amplitude of past climate variability. To test the framework in a controlled environment, we create pseudo-proxies from a coupled climate model simulation of the past 2000 years. In our test environment, the GPR strongly improves the reconstruction of the AMV with respect to a multi-linear Principal Component Regression. The amplitude of reconstructed variability is very close to the true variability even if non-climatic noise is added to the pseudo-proxies. In addition, the framework can directly take into account known proxy uncertainties and fit data-sets with a variable number of records in time. Thus, the GPR framework seems to be a highly suitable tool for robust and improved climate index reconstructions.

How to cite: Klockmann, M. and Zorita, E.: Gaussian Process Regression – A tool for improved climate index reconstructions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4457, https://doi.org/10.5194/egusphere-egu21-4457, 2021.

When predicting complex systems such as parts of the Earth system, one typically relies on differential equations which can often be incomplete, missing unknown influences or higher order effects. By augmenting the equations with artificial neural networks we can compensate these deficiencies. The resulting hybrid models are also known as universal differential equations. We show that this can be used to predict the dynamics of high-dimensional chaotic partial differential equations, such as the ones describing atmospheric dynamics, even when only short and incomplete training data are available. In a first step towards a hybrid atmospheric model, simplified, conceptual atmospheric models are used in synthetic examples where parts of the governing equations are replaced with artificial neural networks. The forecast horizon for these high dimensional systems is typically much larger than the training dataset, showcasing the large potential of the approach. 

How to cite: Gelbrecht, M., Boers, N., and Kurths, J.: Neural Partial Differential Equations for Simple Climate Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8262, https://doi.org/10.5194/egusphere-egu21-8262, 2021.

Climate model outputs are commonly corrected using statistical univariate bias correction methods. Most of the time, those 1d-corrections do not modify the ranks of the time series to be corrected. This implies that biases in the spatial or inter-variable dependences of the simulated variables are not adjusted. Hence, over the last few years, some multivariate bias correction (MBC) methods have been developed to account for inter-variable structures, inter-site ones, or both. As proof-of-concept, we propose to adapt  a computer vision technique used for Image-to-Image translation tasks (CycleGAN) for the adjustment of spatial dependence structures of climate model projections. The proposed algorithm, named MBC-CycleGAN, aims to transfer simulated maps (seen as images) with inappropriate spatial dependence structure from climate model outputs to more realistic images with spatial properties similar to the observed ones. For evaluation purposes, the method is applied to adjust maps of temperature and precipitation from climate simulations through two cross-validation approaches. The first one is designed to assess two different post-processing schemes (Perfect Prognosis and Model Output Statistics). The second one assesses the influence of non-stationary properties of climate simulations on the performance of MBC-CycleGAN to adjust spatial dependences. Results are compared against a popular univariate bias correction method, a "quantile-mapping" method, which ignores inter-site dependencies in the correction procedure, and two state-of-the-art multivariate bias correction algorithms aiming to adjust spatial correlation structure. In comparison with these alternatives, the MBC-CycleGAN algorithm reasonably corrects spatial correlations of climate simulations for both temperature and precipitation, encouraging further research on the improvement of this approach for multivariate bias correction of climate model projections.

How to cite: François, B., Thao, S., and Vrac, M.: Adjusting spatial dependence of climate model outputs with Cycle-Consistent Adversarial Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8357, https://doi.org/10.5194/egusphere-egu21-8357, 2021.

Deep learning-based models have been recently shown to be competitive with, or even outperform, state-of-the-art long range forecasting models, such as for projecting the El Niño-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale dependencies, such as teleconnections, that are particularly important for long range projections. Hence, we propose to explicitly model large-scale dependencies with Graph Neural Networks (GNN) to enhance explainability and improve the predictive skill of long lead time forecasts.

In preliminary experiments focusing on ENSO, our GNN model outperforms previous state-of-the-art machine learning based systems for forecasts up to 6 months ahead. The explicit modeling of information flow via edges makes our model more explainable, and it is indeed shown to learn a sensible graph structure from scratch that correlates with the ENSO anomaly pattern for a given number of lead months.

How to cite: Rühling Cachay, S., Erickson, E., Fender C. Bucker, A., Pokropek, E., Potosnak, W., Osei, S., and Lütjens, B.: Graph Deep Learning for Long Range Forecasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9141, https://doi.org/10.5194/egusphere-egu21-9141, 2021.

To fuse together output from ensembles of climate models with observations, we have developed a custom Bayesian neural network that produces more accurate and uncertainty aware projections.

Ensembles of physical models are typically used to increase the accuracy of projections and quantify projective uncertainties. However, few methods for combining ensemble output consider differing model performance or similarity between models. Current weighting strategies that do, typically assume model weights are invariant in time and space though this is rarely the case in models.

Our Bayesian neural network infers spatiotemporally varying model weights, bias and uncertainty to capture that some regions or seasons are better simulated in certain models. The Bayesian neural network learns how to optimally combine multiple models in order to replicate observations and can also be used to infill gaps in historic observations. In regions of sparse observations, it infers from both the surrounding data and similar physical conditions. Although we are using a typically black box technique, the attribution of model weights and bias maintains interpretability.

We demonstrate the utility of the Bayesian neural network by using it to combine multiple chemistry climate models to produce continuous historic predictions of the total ozone column (1980-2010) and projections of total ozone column for the 21st century, both with principled uncertainty estimates. Rigorous validation shows that our Bayesian neural network predictions outperform standard methods of assimilating models.

How to cite: Amos, M., Sengupta, U., Hosking, S., and Young, P.: Fusing model ensembles and observations together with Bayesian neural networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11905, https://doi.org/10.5194/egusphere-egu21-11905, 2021.

Historical ocean temperature measurements are important in studying climate change due to the high proportion of heat absorbed by the ocean. These measurements come from a variety of sources, including Expendable Bathythermographs (XBTs), which are an important source of such data. Their measurements need bias corrections which are dependent on the type of XBT used, but poor metadata collection practices mean the type is often missing, increasing the measurement uncertainty and thus the uncertainty of the downstream dataset. 

This talk will describe efforts to fill in missing instrument type metadata using machine learning techniques so better bias corrections can be applied and the uncertainty in ocean temperature datasets reduced. I will describe the challenge arising from the nature of the dataset in applying standard ML techniques to the problem. I will also describe how we have used this project to explore the benefits of different platforms for ML and what open reproducible science looks like for Machine Learning projects.

How to cite: Haddad, S., Killick, R., Palmer, M., and Webb, M.: Using Machine Learning to reduce uncertainty in historical ocean temperature measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1158, https://doi.org/10.5194/egusphere-egu21-1158, 2021.

Although the air-sea gas transfer velocity k is usually parameterized with wind speed, the so-called small-eddy model suggests a relationship between k and the ocean surface turbulence in the form of the dissipation rate of turbulent kinetic energy ε. However, available observations of ε from oceanographic cruises are spatially and temporally sparse. In this study, we use a Gaussian Process (GP) model to investigate the relationship between the observed profiles of ε and co-located atmospheric and oceanic fields from the ERA5 reanalysis. The model is then used to construct monthly maps of ε and to estimate the climatological air-sea gas transfer velocity from existing parametrizations. As an independent  validation,  the same model is also trained on EC-Earth3 outputs with the objective of reproducing the temporal and spatial patterns of turbulence kinetic energy as simulated by EC-Earth3. The ability to predict ε is instrumental to achieve better estimates of air-sea gas exchange that take into account multiple sources of upper ocean turbulence beyond wind stress.

How to cite: Carella, G., Esters, L., Galí Tàpias, M., Gomez Gonzalez, C., and Bernardello, R.: Estimating the air-sea gas transfer velocity from a statistical reconstruction of ocean turbulence observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10045, https://doi.org/10.5194/egusphere-egu21-10045, 2021.

A lot of effort has been put in the representation of surface ecosystem processes in global carbon cycle models, in particular through the grouping of organisms into Plankton Functional Types (PFTs) which have specific influences on the carbon cycle. In contrast, the transfer of ecosystem dynamics into carbon export to the deep ocean has received much less attention, so that changes in the representation of the PFTs do not necessarily translate into changes in sinking of particulate matter. Models constrain the air-sea CO₂ flux by drawing down carbon into the ocean interior. This export flux is five times as large as the CO₂ emitted to the atmosphere by human activities. When carbon is transported from the surface to intermediate and deep ocean, more CO₂ can be absorbed at the surface. Therefore, even small variability in sinking organic carbon fluxes can have a large impact on air-sea CO₂ fluxes, and on the amount of CO₂ emissions that remain in the atmosphere.

In this work we focus on the representation of organic matter sinking in global biogeochemical models, using the PlankTOM model in its latest version representing 12 PFTs. We develop and test a methodology that will enable the systematic use of new observations to constrain sinking processes in the model. The approach is based on a Neural Network (NN) and is applied to the PlankTOM model output to test its ability to reconstruction small and large particulate organic carbon with a limited number of observations. We test the information content of geographical variables (location, depth, time of year), physical conditions (temperature, mixing depth, nutrients), and ecosystem information (CHL a, PFTs). These predictors are used in the NN to test their influence on the model-generation of organic particles and the robustness of the results. We show preliminary results using the NN approach with real plankton and particle size distribution observations from the Underwater Vision Profiler (UVP) and plankton diversity data from Tara Oceans expeditions and discuss limitations.

How to cite: Denvil-Sommer, A., Le Quéré, C., Buitenhuis, E., Guidi, L., and Irisson, J.-O.: Using new observations and Machine Learning to improve organic sinking processes in the PlankTOM global ocean biogeochemical model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11356, https://doi.org/10.5194/egusphere-egu21-11356, 2021.

Spatio-temporal interpolation applications are important in the context of ocean surface modeling. Current state-of-the-art techniques typically rely either on optimal interpolation or on model-based approaches which explicitly exploit a dynamical model. While the optimal interpolation suffers from smoothing issues making it unreliable in retrieving fine-scale variability, the selection and parametrization of a dynamical model, when considering model-based data assimilation strategies, remains a complex issue since several trade-offs between the model's complexity and its applicability in sea surface data assimilation need to be carefully addressed. For these reasons, deriving new data assimilation architectures that can perfectly exploit the observations and the current advances in signal processing, modeling and artificial intelligence is crucial.

In this work, we explore new advances in data-driven data assimilation to exploit the classical Kalman filter in the interpolation of spatio-temporal fields. The proposed algorithm is written in an end-to-end differentiable setting in order to allow for the learning of the linear dynamical model from a data assimilation cost. Furthermore, the linear model is formulated on a space of observables, rather than the space of observations, which allows for perfect replication of non-linear dynamics when considering periodic and quasi-periodic limit sets and providing a decent (short-term) forecast of chaotic ones. One of the main advantages of the proposed architecture is its simplicity since it utilises a linear representation coupled with a Kalman filter. Interestingly, our experiments show that exploiting such a linear representation leads to better data assimilation when compared to non-linear filtering techniques, on numerous applications, including the sea level anomaly reconstruction from satellite remote sensing observations.

How to cite: Ouala, S., Fablet, R., Pascual, A. P., Chapron, B., Collard, F., and Gaultier, L.: Reconstructing Sea Surface Dynamics Using a Linear Koopman Kalman Filter, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15128, https://doi.org/10.5194/egusphere-egu21-15128, 2021.

Earth Observation (EO) satellites are drawing considerable attention in areas of water resource management, given their potential to provide unprecedented information on the condition of aquatic ecosystems. Despite ocean colours long history; water quality parameter retrievals from shallow and inland waters remains a complex undertaking. Consistent, cross-mission retrievals of the primary optical parameters using state-of-the-art algorithms are limited by the added optical complexity of these waters. Less work has acknowledged their non- or weakly optical parameter counterparts. These can be more informative than their vivid counterparts, their potential covariance would be regionally specific. Here, we introduce a multi-input, multi-output Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in shallow and inland water bodies. The model is trained and validated using a sizeable historical database in excess of 1,000,000 samples across 38 optical and non-optical parameters, spanning 20 years across 500 surface waters in Scotland. The single network learns to predict concurrently Chlorophyll-a, Colour, Turbidity, pH, Calcium, Total Phosphorous, Total Organic Carbon, Temperature, Dissolved Oxygen and Suspended Solids from real Landsat 7, Landsat 8, and Sentinel 2 spectra. The MDN is found to fully preserve the covariances of the optical and non-optical parameters, while known one-to-many mappings within the non-optical parameters are retained. Initial performance evaluations suggest significant improvements in Chl-a retrievals from existing state-of-the-art algorithms. MDNs characteristically provide a means of quantifying the noise variance around a prediction for a given input, now pertaining to real data under a wide range of atmospheric conditions. We find this to be informative for example in detecting outlier pixels such as clouds, and may similarly be used to guide or inform future work in academic or industrial contexts. 

How to cite: Harding, J.: Unified, high resolution water quality retrievals from Earth Observation satellites, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16181, https://doi.org/10.5194/egusphere-egu21-16181, 2021.

Through the availability of multi-year ground based ozone observations on a global scale, substantial geospatial meta data, and high performance computing capacities, it is now possible to use machine learning for a global data-driven ozone assessment. In this presentation, we will show a novel, completely data-driven approach to map tropospheric ozone globally.

Our goal is to interpolate ozone metrics and aggregated statistics from the database of the Tropospheric Ozone Assessment Report (TOAR) onto a global 0.1° x 0.1° resolution grid.  It is challenging to interpolate ozone, a toxic greenhouse gas because its formation depends on many interconnected environmental factors on small scales. We conduct the interpolation with various machine learning methods trained on aggregated hourly ozone data from five years at more than 5500 locations worldwide. We use several geospatial datasets as training inputs to provide proxy input for environmental factors controlling ozone formation, such as precursor emissions and climate. The resulting maps contain different ozone metrics, i.e. statistical aggregations which are widely used to assess air pollution impacts on health, vegetation, and climate.

The key aspects of this contribution are twofold: First, we apply explainable machine learning methods to the data-driven ozone assessment. Second, we discuss dominant uncertainties relevant to the ozone mapping and quantify their impact whenever possible. Our methods include a thorough a-priori uncertainty estimation of the various data and methods, assessment of scientific consistency, finding critical model parameters, using ensemble methods, and performing error modeling.

Our work aims to increase the reliability and integrity of the derived ozone maps through the provision of scientific robustness to a data-centric machine learning task. This study hence represents a blueprint for how to formulate an environmental machine learning task scientifically, gather the necessary data, and develop a data-driven workflow that focuses on optimizing transparency and applicability of its product to maximize its scientific knowledge return.

How to cite: Betancourt, C., Stadtler, S., Stomberg, T., Edrich, A.-K., Patnala, A., Roscher, R., Kowalski, J., and Schultz, M. G.: Global fine resolution mapping of ozone metrics through explainable machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7596, https://doi.org/10.5194/egusphere-egu21-7596, 2021.

The accurate prediction of precipitation, in particular of extremes, remains a challenge for numerical weather prediction (NWP) models. A large source of error are subgrid-scale parameterizations of processes that play a crucial role in the complex, multi-scale dynamics of precipitation, but are not explicitly resolved in the model formulation. Recent progress in purely data-driven deep learning for regional precipitation nowcasting [1] and global medium-range forecasting [2] tasks has shown competitive results to traditional NWP models.
Here we follow a hybrid approach, in which explicitly resolved atmospheric variables are forecast in time by a general circulation model (GCM) ensemble and then mapped to precipitation using a deep convolutional autoencoder. A frequency-based weighting of the loss function is introduced to improve the learning with regard to extreme values.
Our method is validated against a state-of-the-art GCM ensemble using three-hourly high resolution data. The results show an improved representation of extreme precipitation frequencies, as well as comparable error and correlation statistics.
   

[1] C.K. Sønderby et al. "MetNet: A Neural Weather Model for Precipitation Forecasting." arXiv preprint arXiv:2003.12140 (2020). 
[2] S. Rasp and N. Thuerey "Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution." arXiv preprint arXiv:2008.08626 (2020).

How to cite: Hess, P. and Boers, N.: Inferring precipitation from atmospheric general circulation model variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-678, https://doi.org/10.5194/egusphere-egu21-678, 2021.

Wind energy is widely used in renewable energy systems but the randomness and the intermittence of the wind make its accurate prediction difficult. This study develops an advanced and reliable model for multi-step wind variability prediction using long short-term memory (LSTM) network based on deep learning neural network (DLNN). A 20 Hz Ultrasonic anemometer was positioned in northern France (LOG site) to measure the random wind variability for the duration of thirty-four days. Real-time turbulence kinetic energy is computed from the measured wind velocity components, and multi-resolution features of wind velocity and turbulent kinetic energy are used as input for the prediction model. These multi-resolution features of wind variability are extracted using one-dimensional discrete wavelet transformation. The proposed DLNN is framed to implement multi-step prediction ranging from 10 min to 48 h. For velocity prediction, the root mean square error, mean absolute error and mean absolute percentage error are 0.047 m/s, 0.19 m/s, and 11.3% respectively. These error values indicate a good reliability of the proposed DLNN for predicting wind variability. We found that the present model performs well for mid-long-term (6-24h) wind velocity prediction. The model is also good for the long-term (24-48h) turbulence kinetic energy prediction.

How to cite: Roy, S.: Multi-step wind variability prediction based on deep learning neural network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1487, https://doi.org/10.5194/egusphere-egu21-1487, 2021.

Precise and reliable information on the tropospheric temperature and water vapour profiles plays a key role in weather and climate studies. Among the sensors supporting the atmosphere's observation, one can distinguish the Global Navigation Satellite System Radio Occultation (RO) technique, which provides accurate and high-quality meteorological profiles of temperature, pressure and water vapour. However, external knowledge about temperature is essential to estimate other physical atmospheric parameters. Hence, to overcome the constraint of the need of a priori temperature profile for each RO event, I trained and evaluated 4 different machine learning models comprising Artificial Neural Network (ANN) and Random Forest regression algorithms, where no auxiliary meteorological data is needed. To develop the models, I employed almost 7000 RO profiles between October 2019 and June 2020 over the part of the western North Pacific in Taiwan's vicinity (110-130° E; 10-30° N). Input vectors consisted of bending angle or refractivity profiles from the Formosa Satellite‐7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 mission together with the month, hour, and latitude of the RO event. Whilst temperature, pressure and water vapour profiles derived from the modern ERA5 reanalysis and interpolated to the RO location served as the models' targets. Evaluation on the testing data set revealed a good agreement between all model outputs and ERA5 targets. Slightly better statistics were noted for ANN and refractivity inputs, however, these differences can be considered as negligible. Root mean square error (RMSE) did not exceed 2 K for the temperature, 1.5 hPa for pressure, and reached slightly more than 2.5 hPa for water vapour below 2 km altitude. Additional validation with 56 colocated radiosonde observations and operational one-dimensional variational product confirms these findings with vertically averaged RMSE of around 1.3 K, 1.0 hPa and 0.5 hPa for the temperature, pressure and water vapour, respectively.

How to cite: Lasota, E.: New machine learning approaches for tropospheric profiling based on COSMIC-2 data over Taiwan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2577, https://doi.org/10.5194/egusphere-egu21-2577, 2021.

In coupled global circulation models, chemical interaction between atmospheric trace gases is modelled through dedicated atmospheric chemistry submodels. As these components tend to be computationally expensive, one is often faced with the situation to either run the models with chemistry in relatively coarse resolution, or to ignore atmospheric chemistry altogether. Here an alternative approach is presented in order to overcome the high computational costs while attaining comparable quality of results. A fully connected neural network is used to make predictions of chemical tendencies. As input data of the neural network serve chemical mixing ratios, temperature, pressure, the ozone column and the solar zenith angle, all resulting from the global numerical atmosphere-chemistry model EMAC. The time period considered is 3 month, divided in time steps of consecutive 11 hours. In total, 181 time steps are analysed, from which the first 128 are used as training data, the following 26 as validation data and the last 27 are kept for final testing. The EMAC model produces results of 110 chemicals at a horizontal grid of 160x320 and 90 vertical levels. In our preliminary approach, only 6 of these chemicals - which correspond to the chemicals describing the Chapman mechanism and the nitrogen oxides - are predicted and the analysis area is restricted to the stratosphere. Further, chemicals that are zero at 95% or more of the data points have been deleted from the input data. The results of the neural network represent the spatial patterns of the climate model data very well and are in the same order of magnitude. Spatial correlations depend on the chemical and the vertical level, but are in general >0.95 at levels where the considered variable is present. However, errors are increasing during the validation period, which is probably due to trends in the analysed data. This work presents a proof of concept that neural networks are able to predict atmospheric chemistry tendencies. Left for future work is a detailed hyperparameter tuning in order to optimize the model and the extension to longer time periods to overcome modelling problems due to seasonal trends in the data. 

How to cite: Albrecht, F., Stiehler, F., Sinnhuber, B.-M., Versick, S., and Weigel, T.: AI for Fast Atmospheric Chemistry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4570, https://doi.org/10.5194/egusphere-egu21-4570, 2021.

Tropical cyclone (TC) is among the most destructive weather phenomena on the earth, whose structure and intensity are strongly modulated by TC boundary layer. Mesoscale model used for TC research and prediction must rely on boundary layer parameterization due to low spacial resolution. These boundary layer schemes are mostly developed on field experiments under moderate wind speed. They often underestimate the influence of shear-driven rolls and turbulences. When applied under extreme condition like TC boundary layer, significant bias will be unavoidable. In this study, a novel machine learning model—one dimensional convolutional neural network (1D-CNN)—is proposed to tackle the TC boundary layer parameterization dilemma. The 1D-CNN saves about half of the learnable parameters and accomplishes a steady improvement compared to fully-connected neural network. TC large eddy simulation outputs are used as training data of 1D-CNN, which shows strong skewness in calculated turbulent fluxes. The data skewness problem is alleviated in order to reduce 1D-CNN model bias. It is shown in an offline TC boundary layer test that our proposed model, the 1D-CNN, performs significantly better than popular schemes now utilized in TC simulations. Model performance across different scales is essential to final application. It is found that the high resolution data contains the information of low resolution data but not vise versa. The model performance on the extreme data is key to final performance on the whole dataset. Training the model on the highest resolution non-extreme data plus extreme data of different resolutions can secure the robust performance across different scales.

How to cite: Wang, L.-Y. and Tan, Z.-M.: Machine Learning Parameterization of Mature Tropical Cyclone Boundary Layer, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9333, https://doi.org/10.5194/egusphere-egu21-9333, 2021.

In this study, we address point-forecasting using a deep learning LSTM-approach for renewable energy systems with focus on the short- to medium-range. Hourly resolution (medium-range) as well as 10-minute resolution (nowcasting) are the anticipated forecasting frequency. The forecasting approach is applied to: (i) wind speed at 10 meters height (observation sites), (ii) wind speed at hub-height of wind turbines, and (iii) solar power forecasts for selected solar power plants.

As input to the proposed method numerical weather prediction (NWP) data, gridded observations (analysis and/or reanalysis), and point data are used. The data of studied test-cases is extracted from the Austrian TAWES system (Teilautomatische Wetterstationen, meteorological observations in 10-minute intervals),  SCADA data of wind farms, solar power output of a solar power plant, INCA's (Integrated Nowcasting through Comprehensive Analysis) gridded observation fields, reanalysis fields from Merra2 and Era5-land, as well as, NWP data from the ECMWF IFS (European Center for Medium-Range Weather Forecast’s Integrated Forecasting System). These data-sources embrace very different temporal and spatial semantics, thus, careful pre-processing was carried out. Four daily runs over the course of one year for 12 synoptic sites + 38 wind turbines + 1 solar power plant test locations are conducted.

The advantage of an LSTM architecture is that it includes recurrent steps in the ANN and, thus, is useful especially for time-series, such as meteorological observations or NWP forecasts. So far, comparatively few attempts have been made to integrate time-series with different semantics of a sensor network and physical models in one LSTM. We tackle this issue by conserving the time-steps of the delayed NWP along with their difference to recently observed time-series and, additionally, separate them into forecasting-intervals (e.g., of 3 to 12 subsequent forecasting hours being shortest in nowcasting). This enables us to employ a sequence-to-sequence LSTM based artificial neural network (ANN). The benefit of a sequence-to-sequence setup is to match an input- and output time-series in each sample, thereby, learning complex temporal relationships. To fully use the advantage of the diverse data a tailored pre- and post-processing of these heterogenous data sources in the renewable energy applications is needed.

The ANN’s results yield, in general, high forecast-skills, indicating a successful learning based on the used training data. Different combinations of inputs and processing-steps were investigated. It is shown that combining various data sources and implement an adequate pre- and post-processing yields the most promising results in the case studies (e.g.: a heuristic to estimate produced power based on the meteorological parameters and prediction of the offset to NWPs tailored to the studied location). Results are compared to traditional forecast methods and statistical methods such as a random forest and multiple-linear-regression.

How to cite: Papazek, P. and Schicker, I.: A deep learning LSTM forecasting approach for renewable energy systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9910, https://doi.org/10.5194/egusphere-egu21-9910, 2021.

Aerosols sourced from combustion such as black carbon (BC) are important short-lived climate forcers whose direct radiative forcing and atmospheric lifetime depend on their morphology. These aerosols are typically fractal aggregates consisting of ~20-80 nm spheres. This complex morphology makes modeling their optical properties difficult, contributing to uncertainty in both their direct and indirect climate effects. Accurate and fast calculations of BC optical properties are needed for remote sensing inversions and for radiative forcing calculations in atmospheric models, but current methods to accurately calculate the optical properties of these aerosols such as the multi-sphere T-matrix method or generalized multiple-particle Mie Theory are computationally expensive and must be compiled in extensive data-bases off-line and then used as a look-up table. Recent advances in machine learning approaches have applied the graph convolutional neural network (GCN) to various physical science applications, demonstrating skill in generalizing beyond initial training data by exploiting and learning internal properties and interactions inherent to the larger system. Here we demonstrate for the first time that a GCN trained to predict the optical properties of numerically-generated BC fractal aggregates can accurately generalize to arbitrarily shaped aerosol particles, even over much larger aggregates than in the training dataset, providing a fast and accurate method to calculate aerosol optical properties in atmospheric models and for observational retrievals. This approach could be integrated into atmospheric models or remote sensing inversions to more realistically predict the physical properties of arbitrarily-shaped aerosol and cloud particles. In addition, GCN’s can be used to gain physical intuition on the relationship between large-scale properties (here of the radiative properties of aerosols) and small-scale interactions (here of the spheres’ positions and their interactions).

How to cite: Lamb, K. D. and Gentine, P.: Predicting the Optical Properties of Arbitrarily Shaped Black Carbon Aerosols with Graph Neural Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10035, https://doi.org/10.5194/egusphere-egu21-10035, 2021.

Traditional weather forecasting approaches utilize numerous numerical simulations and empirical models to produce a gridded estimate of rainfall, the cells of which often span multiple regions and struggle to capture extreme events. The approach presented here combines the power of modern meteorological forecasts from the ECMWF C3S seasonal forecasts service with convolutional neural networks (CNNs) to improve the forecasting of total monthly regional rainfall in the UK. The CNN is trained using mean sea-level pressure and 2m air temperature forecasts from the ECMWF C3S service using three lead-times: one month, three months and six months. The training is supervised using the equivalent true rainfall data provided by the CEH-GEAR (Centre for Ecology and Hydrology, gridded estimates of areal rainfall). The resulting predictions are then compared with the total monthly regional rainfall values calculated from the precipitation forecasts provided by the ECMWF C3S service. The results of this comparison show the new CNN model out-performs the ECMWF model  across all three leadtimes. This performance is calculated using the root-mean square error between the predicted rainfall values for each region and the true values calculated from the CEH-GEAR dataset. The largest gap is found at a one month leadtime where the CNN model scores a root-mean square error (RMSE) 13% lower than the ECMWF model (RMSEs: 46.5 and 53.4 respectively), the smallest gap is found at a six month leadtime where the CNN scores an RMSE only 2.2% lower than the ECMWF model (RMSEs: 48.5 and 49.6 respectively). However, these differences are exacerbated at the extremes with the CNN producing errors 26% lower than the ECMWF model at a one-month leadtime, 19% lower at a three-month leadtime and 3% at a six-month leadtime. These results are then extended to show how the CNN made the predictions and by comparing the attribution patterns of North West and South East England we are able to show a reliance on both the mean sea-level pressure to the west of the UK and the 2m air temperature to the south west of the UK and over the European continent.

How to cite: Barnes, A., McCullen, N., and Kjeldsen, T. R.: Improving Regional Rainfall Forecasts using Convolutional-Neural Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11105, https://doi.org/10.5194/egusphere-egu21-11105, 2021.

Because the atmosphere is inherently chaotic, probabilistic weather forecasts are crucial to provide reliable information. In this work, we present an extension to the WeatherBench, a benchmark dataset for medium-range, data-driven weather prediction, which was originally designed for deterministic forecasts. We add a set of commonly used probabilistic verification metrics: the spread-skill ratio, the continuous ranked probability score (CRPS) and rank histograms. Further, we compute baseline scores from the operational IFS ensemble forecast. 

Then, we compare three different methods of creating probabilistic neural network forecasts: first, using Monte-Carlo dropout during inference with a range of dropout rates; second, parametric forecasts, which optimize for the CRPS; and third, categorical forecasts, in which the probability of occurrence for specific bins is predicted. We show that plain Monto-Carlo dropout does not provide enough spread. The parametric and categorical networks, on the other hand, provide reliable forecasts, with the categorical method being more versatile.

How to cite: Garg, S., Rasp, S., and Thuerey, N.: WeatherBench Probability: Medium-range weather forecasts with probabilistic machine learning methods., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11448, https://doi.org/10.5194/egusphere-egu21-11448, 2021.

Machine learning techniques like deep learning gained enormous momentum in recent years. This was mainly caused by the success story of the main drivers like image and speech recognition, video prediction and autonomous driving, to name just a few.
Air pollutant forecasting models are an example, where earth system scientists start picking up deep learning models to enhance the forecast quality of time series. Almost all previous air pollution forecasts with machine learning rely solely on analysing temporal features in the observed time series of the target compound(s) and additional variables describing precursor concentrations and meteorological conditions. These studies, therefore, neglect the "chemical history" of air masses, i.e. the fact that air pollutant concentrations at a given observation site are a result of emission and sink processes, mixing and chemical transformations along the transport pathways of air.
This study develops a concept of how such factors can be represented in the recently published deep learning model IntelliO3. The concept is demonstrated with numerical model data from the WRF-Chem model because the gridded model data provides an internally consistent dataset with complete spatial coverage and no missing values.
Furthermore, using model data allows for attributing changes of the forecasting performance to specific conceptual aspects. For example, we use the 8 wind sectors (N, NE, E, SE, etc.) and circles with predefined radii around our target locations to aggregate meteorological and chemical data from the intersections. Afterwards, we feed this aggregated data into a deep neural network while using the ozone concentration of the central point's next timesteps as targets. By analysing the change of forecast quality when moving from 4-dimensional (x, y, z, t) to 3-dimensional (x, y, t or r, φ, t) sectors and thinning out the underlying model data, we can deliver first estimates of expected performance gains or losses when applying our concept to station based surface observations in future studies.

How to cite: Kleinert, F., Leufen, L. H., Lupascu, A., Butler, T., and Schultz, M. G.: Representing chemical history for ozone time-series predictions - a method development study for deep learning models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12146, https://doi.org/10.5194/egusphere-egu21-12146, 2021.

Artificial intelligence (AI) methods currently experience rapid development and are also used more and more frequently in environmental and Earth system sciences. To date however, this is often done in the context of isolated rather than systematic solutions. In particular, for researchers there is often a discrepancy between the requirements of a solid and technically sound environmental data analysis and the availability of modern AI methods such as deep learning. Their systematic use is not yet established in environmental and Earth system sciences.

The recently started KI:STE project bridges this gap with a dedicated strategy that combines both, the development of AI applications and a strong training and network concept, thereby covering  different relevant aspects of environmental and Earth system research. It creates the technical prerequisites to make high-performance AI applications on environmental data portable for future users and to establish environmental AI as a key technology. 

Specifically, within KI:STE an AI-platform is envisioned which unifies machine learning (ML) workflows designed to study five core Earth system topics: cloud variability, hydrology, earth surface processes, vegetation health and air quality. All of them are strongly coupled and will profit from ML, e.g. to extend locally available information into global maps, or the track the interplay of spatio-temporal variability on different scales along process cascades. Besides being already connected across disciplines in the classical sense, KI:STE aims to furthermore bridge between these different topics by jointly addressing cutting edge ML research questions beyond pure algorithmic approaches. In particular, we will put emphasize on an explainable AI approach, which itself is a yet to be explored highly relevant topic within the Earth system sciences. It has the potential to connect the interdisciplinary work on yet another level.

KI:STE will also launch an e-learning platform in order to support the usage of the AI-platform as well as to communicate the knowledge to adequately use ML techniques within the different Earth system science domains.

How to cite: Stadtler, S., Kowalski, J., Abel, M., Roscher, R., Crewell, S., Gräler, B., Kollet, S., and Schultz, M.: KI:STE Project − AI Strategy for Earth System Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-211, https://doi.org/10.5194/egusphere-egu21-211, 2021.

Aerosol-cloud interactions remain the largest uncertainty in assessments of anthropogenic climate forcing, while the complexity of these interactions require methods that enable abstractions and simplifications that allow their improved treatment in climate models. Marine boundary layer clouds are an important component of the climate system as their large albedo and spatial coverage strongly affect the planetary radiative balance. High resolution simulations of clouds provide an unprecedented understanding of the structure and behavior of these clouds in the marine atmosphere, but the amount of data is often too large and complex to be useful in climate simulations. Data reduction and inference methods provide a way that to reduce the complexity and dimensionality of datasets generated from high-resolution Large Eddy Simulations.

In this study we use network analysis, (the δ-Maps method) to study the complex interaction between liquid water, droplet number and vertical velocity in Large Eddy Simulations of Marine Boundary Layer clouds. δ-Maps identifies domains that are spatially contiguous and possibly overlapping and characterizes their connections and temporal interactions. The objective is to better understand microphysical properties of marine boundary layer clouds, and how they are impacted by the variability in aerosols. Here we will capture the dynamical structure of the cloud fields predicted by the MIMICA Large Eddy Simulation (LES) model. The networks inferred from the different simulation fields are compared between them (intra-comparisons) using perturbations in initial conditions and aerosol, using a set of four metrics. The networks are then evaluated for their differences, quantifying how much variability is inherent in the LES simulations versus the robust changes induced by the aerosol fields. 

How to cite: Ricard, L., Nenes, A., Runge, J., and Georgakaki, P.: Exploring microphysical properties of marine boundary layer clouds through network analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13307, https://doi.org/10.5194/egusphere-egu21-13307, 2021.

Clouds are a key factor in Earth's energy budget and thus significantly affect climate and weather predictions. These effects are dominated by shallow warm clouds (shown by Sherwood et al., 2014, Zelinka et al., 2020) which tend to be small and heterogenous. Therefore, remote sensing of clouds and three-dimensional (3D) volumetric reconstruction of their internal properties are of significant importance.

Recovery of the volumetric information of the clouds relies on 3D radiative transfer, that models 3D multiple scattering. This model is complex and nonlinear. Thus, inverting the model poses a major challenge and typically requires using a simplification. A common relaxation assumes that clouds are horizontally uniform and infinitely broad, leading to one-dimensional modeling. However, generally this assumption is invalid since clouds are naturally highly heterogeneous. A novel alternative is to perform cloud retrieval by developing tools of 3D scattering tomography. Then, multiple satellite images of the clouds are acquired from different points of view. For example, simultaneous multi-view radiometric images of clouds are proposed by the CloudCT project, funded by the ERC. Unfortunately, 3D scattering tomography require high computational resources. This results, in practice, in slow run times and prevents large scale analysis. Moreover, existing scattering tomography is based on iterative optimization, which is sensitive to initialization.

In this work we introduce a deep neural network for 3D volumetric reconstruction of clouds. In recent years, supervised learning using deep neural networks has led to remarkable results in various fields ranging from computer vision to medical imaging. However, these deep learning techniques have not been extensively studied in the context of volumetric atmospheric science and specifically cloud research.

We present a convolutional neural network (CNN) whose architecture is inspired by the physical nature of clouds. Due to the lack of real-world datasets, we train the network in a supervised manner using a physics-based simulator that generates realistic volumetric cloud fields. In addition, we propose a hybrid approach, which combines the proposed neural network with an iterative physics-based optimization technique.

We demonstrate the recovery performance of our proposed method in cloud fields. In a single cloud-scale, our resulting quality is comparable to state-of-the-art methods, while run time improves by orders of magnitude. In contrast to existing physics-based methods, our network offers scalability, which enables the reconstruction of wider cloud fields. Finally, we show that the hybrid approach leads to improved retrieval in a fast process.

How to cite: Sde-Chen, Y., Schechner, Y. Y., Holodovsky, V., and Eytan, E.: Deep Learning for Three-Dimensional Volumetric Recovery of Cloud Fields, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13334, https://doi.org/10.5194/egusphere-egu21-13334, 2021.

We can observe a global decrease of well maintained weather stations by meteorological services and governmental institutes. At the same time, environmental sensor data is increasing through the use of opportunistic or remote sensing approaches. Overall, the trend for environmental sensor networks is strongly going towards automated routines, especially for quality-control (QC) to provide usable data in near real-time. A common QC scenario is that data is being flagged manually using expert knowledge and visual inspection by humans. To reduce this tedious process and to enable near-real time data provision, machine-learning (ML) algorithms exhibit a high potential as they can be designed to imitate the experts actions. 

Here we address these three common challenges when applying ML for QC: 1) Robustness to missing values in the input data. 2) Availability of training data, i.e. manual quality flags that mark erroneous data points. And 3) Generalization of the model regarding non-stationary behavior of one  experimental system or changes in the experimental setup when applied to a different study area. We approach the QC problem and the related issues both as a supervised and an unsupervised learning problem using deep neural networks on the one hand and dimensionality reduction combined with clustering algorithms on the other.

We compare the different ML algorithms on two time-series datasets to test their applicability across scales and domains. One dataset consists of signal levels of 4000 commercial microwave links distributed all over Germany that can be used to monitor precipitation. The second dataset contains time-series of soil moisture and temperature from 120 sensors deployed at a small-scale measurement plot at the TERENO site “Hohes Holz”.

First results show that supervised ML provides an optimized performance for QC for an experimental system not subject to change and at the cost of a laborious preparation of the training data. The unsupervised approach is also able to separate valid from erroneous data at reasonable accuracy. However, it provides the additional benefit that it does not require manual flags and can thus be retrained more easily in case the system is subject to significant changes. 

In this presentation, we discuss the performance, advantages and drawbacks of the proposed ML routines to tackle the aforementioned challenges. Thus, we aim to provide a starting point for researchers in the promising field of ML application for automated QC of environmental sensor data.

How to cite: Polz, J., Schmidt, L., Glawion, L., Graf, M., Werner, C., Chwala, C., Mollenhauer, H., Rebmann, C., Kunstmann, H., and Bumberger, J.: Supervised and unsupervised machine-learning for automated quality control of environmental sensor data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14485, https://doi.org/10.5194/egusphere-egu21-14485, 2021.

The development of atmospheric parameterizations based on neural networks is often hampered by numerical instability issues. Previous attempts to replicate these issues in a toy model have proven ineffective. We introduce a new toy model for atmospheric dynamics, which consists in an extension of the Lorenz'63 model to a higher dimension. While neural networks trained on a single orbit can easily reproduce the dynamics of the Lorenz'63 model, they fail to reproduce the dynamics of the new toy model, leading to unstable trajectories. Instabilities become more frequent as the dimension of the new model increases, but are found to occur even in very low dimension. Training the neural network on a different learning sample, based on Latin Hypercube Sampling, solves the instability issue. Our results suggest that the design of the learning sample can significantly influence the stability of dynamical systems driven by neural networks.

How to cite: Balogh, B., Saint-Martin, D., and Ribes, A.: A toy model to investigate stability of AI-based dynamical systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-647, https://doi.org/10.5194/egusphere-egu21-647, 2021.

Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258

How to cite: Irrgang, C., Saynisch-Wagner, J., Dill, R., Boergens, E., and Thomas, M.: Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-783, https://doi.org/10.5194/egusphere-egu21-783, 2021.

Ensemble predictions are essential to characterize the forecast uncertainty and the likelihood of an event to occur. Stochasticity in predictions comes from data and model uncertainty. In deep learning (DL), data uncertainty can be approached by training an ensemble of DL models on data subsets or by performing data augmentations (e.g., random or singular value decomposition (SVD) perturbations). Model uncertainty is typically addressed by training a DL model multiple times from different weight initializations (DeepEnsemble) or by training sub-networks by dropping weights (Dropout). Dropout is cheap but less effective, while DeepEnsemble is computationally expensive.

We propose instead to tackle model uncertainty with SWAG (Maddox et al., 2019), a method to learn stochastic weights—the sampling of which allows to draw hundreds of forecast realizations at a fraction of the cost required by DeepEnsemble. In the context of data-driven weather forecasting, we demonstrate that the SWAG ensemble has i) better deterministic skills than a single DL model trained in the usual way, and ii) approaches deterministic and probabilistic skills of DeepEnsemble at a fraction of the cost. Finally, multiSWAG (SWAG applied on top of DeepEnsemble models) provides a trade-off between computational cost, model diversity, and performance.

We believe that the method we present will become a common tool to generate large ensembles at a fraction of the current cost. Additionally, the possibility of sampling DL models allows the design of data-driven/emulated stochastic model components and sub-grid parameterizations.

Reference

Maddox W.J, Garipov T., Izmailov P., Vetrov D., Wilson A. G., 2019: A Simple Baseline for Bayesian Uncertainty in Deep Learning. arXiv:1902.02476

How to cite: Haddad, Y., Defferrard, M., and Ghiggi, G.: The SWAG solution for probabilistic predictions with a single neural network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2401, https://doi.org/10.5194/egusphere-egu21-2401, 2021.

Deep Learning (DL) has the potential to revolutionize numerical weather predictions (NWP) and climate simulations by improving model components and reducing computing time, which could then be used to increase the resolution or the number of simulations. Unfortunately, major progress has been hindered by difficulties in interfacing DL with conventional models because of i) programming language barriers, ii) difficulties in reaching stable online coupling with models, and iii) the inability to exploit the horizontal spatial information as classical convolutional neural networks can’t be used on spherical unstructured grids.

We present a solution to perform spatial convolutions directly on the unstructured grids of NWP models. Our convolution and pooling operations work on any pixelization of the sphere (e.g., Gauss-Legendre, icosahedral, cubed-sphere) provided a mesh or the pixel’s locations. Moreover, our solution allows mixing data from different grids and scales linearly with the number of pixels, allowing it to ingest millions of inputs from 3D spherical fields.

We show that a proper treatment of the spherical topology and geometry of the Earth (as opposed to a projection to the plane, cylinder, or cube) i) yields geometric constraints that provide generalization guarantees (i.e., the learned function does not depend on its localization on the Earth), and ii) induces prior biases that facilitate learning. We demonstrate that doing so improves prediction performance at no computational overhead for data-driven weather forecasting. We trained autoregressive ResUNets on five spherical samplings, covering those adopted by the major meteorological centers.

We believe that the proposed solution can find immediate use for post-processing (e.g., bias correction and downscaling), model error corrections, linear solvers pre-conditioning, model components emulation, sub-grid parameterizations, and many more applications. To that end, we provide open-source and easy-to-use code accompanied by tutorials.

How to cite: Defferrard, M., Feng, W., Bolón Brun, N., Lloréns Jover, I., and Ghiggi, G.: Deep Learning on the sphere for weather/climate applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2681, https://doi.org/10.5194/egusphere-egu21-2681, 2021.

Recent developments in machine learning (ML) have demonstrated impressive skills in reproducing complex spatiotemporal processes. However, contrary to data assimilation (DA), the underlying assumption behind ML methods is that the system is fully observed and without noise, which is rarely the case in numerical weather prediction. In order to circumvent this issue, it is possible to embed the ML problem into a DA formalism characterised by a cost function similar to that of the weak-constraint 4D-Var (Bocquet et al., 2019; Bocquet et al., 2020). In practice ML and DA are combined to solve the problem: DA is used to estimate the state of the system while ML is used to estimate the full model. 

In realistic systems, the model dynamics can be very complex and it may not be possible to reconstruct it from scratch. An alternative could be to learn the model error of an already existent model using the same approach combining DA and ML. In this presentation, we test the feasibility of this method using a quasi geostrophic (QG) model. After a brief description of the QG model model, we introduce a realistic model error to be learnt. We then asses the potential of ML methods to reconstruct this model error, first with perfect (full and noiseless) observation and then with sparse and noisy observations. We show in either case to what extent the trained ML models correct the mid-term forecasts. Finally, we show how the trained ML models can be used in a DA system and to what extent they correct the analysis.

Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models, Nonlin. Processes Geophys., 26, 143–162, 2019

Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization, Foundations of Data Science, 2 (1), 55-80, 2020

Farchi, A., Laloyaux, P., Bonavita, M., and Bocquet, M.: Using machine learning to correct model error in data assimilation and forecast applications, arxiv:2010.12605, submitted. 

How to cite: Farchi, A., Laloyaux, P., Bonavita, M., and Bocquet, M.: Using machine learning to correct model error in data assimilation and forecast applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4007, https://doi.org/10.5194/egusphere-egu21-4007, 2021.

In this work we compare the performance of machine learning metamodels of different scale for the prediction of pasture grass nitrogen response rate using a case study across different locations in New Zealand. We first used a range of soil, plant and management parameters known to affect grass growth and/or nitrogen response. These generated a complete factorial that enabled us to run virtual nitrogen response rate experiments, using the APSIM simulation model, in eight locations around the country. We included 40 years of weather data to capture the effect of weather variability on response rate. This created a large database with which to train machine learning models. We created local, regional, and nation-wide models using Random Forest and tested them on known and unknown locations. To evaluate the models, we first calculated the RMSE, MAE and R² and then determined if the distributions of the predictions were statistically different using the Mann-Whitney U test. Finally, we explore the generalizability of the models using the error metrics and the results of the statistical test.

How to cite: Pylianidis, C., Snow, V., Overweg, H., and Athanasiadis, I. N.: Comparing machine learning metamodels of different scale for pasture nitrogen response rate prediction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8279, https://doi.org/10.5194/egusphere-egu21-8279, 2021.

Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.

We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.

We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.

To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.

How to cite: van der Kooij, E., Schleiss, M., Taormina, R., Fioranelli, F., Lugt, D., van Hoek, M., Leijnse, H., and Overeem, A.: Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12814, https://doi.org/10.5194/egusphere-egu21-12814, 2021.

Permafrost thaw is considered one of the major climate feedback processes and is currently a significant source of uncertainty in predicting future climate states. Coverage of in-situ meteorological and land-surface observations is sparse throughout the Arctic, making it difficult to track the large-scale evolution of the Arctic surface and subsurface energy balance. Furthermore, permafrost thaw is a highly non-linear process with its own feedback mechanisms such as thermokarst and thermo-erosion. Land surface models, therefore, play an important role in our ability to understand how permafrost responds to the changing climate. There is also a need to quantify freeze-thaw cycling and the incomplete freezing of soil at depth (talik formation). One of the key difficulties in modeling the Arctic subsurface is the complexity of the thermal regime during phase change under freezing or thawing conditions. Modeling heat conduction with phase change accurately requires estimation of the soil freeze characteristic curve (SFCC) which governs the change in soil liquid water content with respect to temperature and depends on the soil physical characteristics (texture). In this work, we propose a method for replacing existing brute-force approximations of the SFCC in the CryoGrid 3 permafrost model with universal differential equations, i.e. differential equations that include one or more terms represented by a universal approximator (e.g. a neural network). The approximator is thus tasked with inferring a suitable SFCC from available soil temperature, moisture, and texture data. We also explore how remote sensing data might be used with universal approximators to extrapolate soil freezing characteristics where in-situ observations are not available.

How to cite: Groenke, B., Langer, M., Gallego, G., and Boike, J.: Learning Soil Freeze Characteristic Curves with Universal Differential Equations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13409, https://doi.org/10.5194/egusphere-egu21-13409, 2021.

Estimating the probability of a wildfire occurring at a specific location on a given day comes with the challenge that it not only depends to a high degree on weather conditions and soil moisture, but also on the presence of an ignition source [1]. A commonly used index to assess wildfire risks is the Canadian Fire Weather Index [2], which does, however, not model the presence of an ignition source. 

We develop a machine learning model which discriminates between (1) the probability of a wildfire occurring given an ignition source, and (2) the probability of an ignition source being present, and inferences both. We first demonstrate the performance of our approach by estimating these probabilities on simulated data. With these simulations, we also assess the robustness of our model to machine learning-related challenges that arise with wildfire data, such as extreme class imbalance and label uncertainty. We then show the performance of our model trained on satellite-derived global wildfire occurrences between 2001 and 2017. The dataset FireTracks, which includes a comprehensive record of wildfire occurrences [3], is used as ground truth. Input features include weather data (ERA5 [4]) and population densities (GPW4 [5]). Finally we compare wildfire risk ratings computed with the Canadian Fire Weather Index to the probabilities estimated by our model.

References
[1] K. Rao et al., SAR-enhanced mapping of live fuel moisture content, Remote Sensing of Environment, 2020. 
[2] R. D. Field et al., Development of a Global Fire Weather Database. Natural Hazards and Earth System Sciences, 2015. 
[3] D. Traxl, FireTracks Scientific Dataset, 2021. (https://github.com/dominiktraxl/firetracks) 
[4] H. Hersbach et al., ERA5 hourly data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2018. 
[5] Center for International Earth Science Information Network - CIESIN - Columbia University, Gridded Population of the World, Version 4 (GPWv4): Population Density, NASA Socioeconomic Data and Applications Center (SEDAC), 2016.

How to cite: Pilot, J., Bui, T. B., and Boers, N.: Towards machine learning for the estimation of wildfire risk from weather and sociological data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13729, https://doi.org/10.5194/egusphere-egu21-13729, 2021.

Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.

How to cite: Polat, A. B., Akcay, O., and Balik Sanli, F.: Analysing Temporal Effects on Classification of SAR and Optical Images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14386, https://doi.org/10.5194/egusphere-egu21-14386, 2021.

The trying of automatic creation of the Kola Peninsula geomorphological map [after Grave M.K. et al., 1971] at the morphogenetic legend’s principle was provided based on the "random forest" classification technique. As input data a several geomorphometric variables were used only (the basic variables – elevation, slope angle, curvatures etc., and the relatively rare variables including spectral terrain variables – result of the decomposition of digital elevation model into 2D Fourier series). On the training data covering only 1.3 % study area with known labels for one of thirteen probable landform types, it were carried out the reconstruction of geomorphological boundaries and the automatic creation of the geomorphological map. The accuracy of resulting map was 81 % (area’s share with the correct classification result – the same landform type that expertly way defined). This result gives increasing of accuracy over “zero accuracy” (random guessing) more than x10. In general, a large visual similarity between the expertly created geomorphological map and the one created automatically based on the known typological affiliation of the landforms of a small part of the territory is also noticeable. Mistakenly recognized affiliation to one or another genetic type of landform in 19% of tries is rather not a problem, but a good opportunity to improve the predictive power of the model by targeted search of representative morphometric variables. We emphasize - the obtained accuracy of the model is achieved only when using variables extracted entirely from the DEM and calculated fully automatically. The use of data from tectonic and surficial geology maps, maps of quaternary deposits and other data sources can significantly improve the accuracy of the classification and bring it to the level of confident use of the model in practical work. As a by-product of landform classification by the random forest method - the characteristics that are most representative of the prediction of the genetic types of the landforms of the Kola Peninsula have been identified. Almost all of them turned out to be relatively rarely used focal geomorphometric variables. More standard and familiar parameters - slopes, aspect, curvatures are not characterized by significant representativeness. The predictive power of the model was considerably increased by using the spectral characteristics of the relief (parameters of periodicity of the elevation field, calculated by the sliding window method of two-dimensional discrete Fourier transform). The obtained results, we think, convince us that the possibilities of morphometric indicators alone in general geomorphological mapping are underestimated.

The study was supported by the Russian Science Foundation (project No. 19-77-10036).

How to cite: Kharchenko, S.: Automated recognition of the landforms origin for the Kola Peninsula based on morphometric variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15564, https://doi.org/10.5194/egusphere-egu21-15564, 2021.

In the recent years, prediction modelling techniques have been widely used for modelling groundwater arsenic contamination. Determining the accuracy, performance and suitability of these different algorithms such as univariate regression (UR), fuzzy model, adaptive fuzzy regression (AFR), logistic regression (LR), adaptive neuro-fuzzy inference system (ANFIS), and hybrid random forest (HRF) models still remains a challenging task. The spatial data which are available at different scales with different cell sizes. In the current study we have tried to optimize the spatial resolution for best performance of the model selecting the best spatial resolution by testing various predictive algorithms. The model’s performance was evaluated based of the values of determination coefficient (R²), mean absolute percentage error (MAPE) and root mean square error (RMSE). The outcomes of the study indicate that using 100m × 100m spatial resolution gives best performance in most of the models. The results also state HRF model performs the best than the commonly used ANFIS and LR models.

How to cite: Bindal, S.: Mapping arsenic vulnerability at different spatial scales using statistical and machine learning models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15964, https://doi.org/10.5194/egusphere-egu21-15964, 2021.

This work is devoted to development of neural networks for identification of partial differential equations (PDE) solved in the land surface scheme of INM RAS Earth System model (ESM). Atmospheric and climate models are in the top of the most demanding for supercomputing resources among research applications. Spatial resolution and a multitude of physical parameterizations used in ESMs continuously increase. Most of parameters are still poorly constrained, many of them cannot be measured directly. To optimize model calibration time, using neural networks looks a promising approach. Neural networks are already in wide use in satellite imaginary (Su Jeong Lee, et al, 2015; Krinitskiy M. et al, 2018) and for calibrating parameters of land surface models (Yohei Sawada el al, 2019). Neural networks have demonstrated high efficiency in solving conventional problems of mathematical physics (Lucie P. Aarts el al, 2001; Raissi M. et al, 2020). 

We develop a neural networks for optimizing parameters of nonlinear soil heat and moisture transport equation set. For developing we used Python3 based programming tools implemented on GPUs and Ascend platform, provided by Huawei. Because of using hybrid approach combining neural network and classical thermodynamic equations, the major purpose was finding the way to correctly calculate backpropagation gradient of error function, because model trains and is being validated on the same temperature data, while model output is heat equation parameter, which is typically not known. Neural network model has been runtime trained using reference thermodynamic model calculation with prescribed parameters, every next thermodynamic model step has been used for fitting the neural network until it reaches the loss function tolerance.

Literature:

1.     Aarts, L.P., van der Veer, P. “Neural Network Method for Solving Partial Differential Equations”. Neural Processing Letters 14, 261–271 (2001). https://doi.org/10.1023/A:1012784129883

2.     Raissi, M., P. Perdikaris and G. Karniadakis. “Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.” ArXiv abs/1711.10561 (2017): n. pag.

3.     Lee, S.J., Ahn, MH. & Lee, Y. Application of an artificial neural network for a direct estimation of atmospheric instability from a next-generation imager. Adv. Atmos. Sci. 33, 221–232 (2016). https://doi.org/10.1007/s00376-015-5084-9

4.     Krinitskiy M, Verezemskaya P, Grashchenkov K, Tilinina N, Gulev S, Lazzara M. Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics. Atmosphere. 2018; 9(11):426.

5.     Sawada, Y.. “Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model.” ArXiv abs/1909.04196 (2019): n. pag.

6.     Shufen Pan et al. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. Hydrol. Earth Syst. Sci., 24, 1485–1509 (2020)

7.     Chaney, Nathaniel & Herman, Jonathan & Ek, M. & Wood, Eric. (2016). Deriving Global Parameter Estimates for the Noah Land Surface Model using FLUXNET and Machine Learning: Improving Noah LSM Parameters. Journal of Geophysical Research: Atmospheres. 121. 10.1002/2016JD024821.

How to cite: Chernyshev, R., Krinitskiy, M., and Stepanenko, V.: Applying neural network for identification of land surface model parameters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16355, https://doi.org/10.5194/egusphere-egu21-16355, 2021.