Foehn winds are accelerated, warm and dry winds that can have significant environmental impacts as they descend into the lee of a mountain range. For example, in the McMurdo Dry Valleys in Antarctica, foehn events can cause ice and glacial melt and destabilise ice shelves, which if lost, resulting in a rise in sea level. Consequently, there is a strong interest in a deeper understanding of foehn winds and their meteorological signatures. Most current automatic detection methods rely on rule-based methodologies that require static thresholds of meteorological parameters. However, the patterns of foehn winds are hard to define and differ between alpine valleys around the world. Consequently, data-driven solutions might help create more accurate detection and prediction methodologies. 

State-of-the-art machine learning approaches to this problem have shown promising results but follow a supervised learning paradigm. As such, these approaches require accurate labels, which for the most part, are being created by imprecise static rule-based algorithms. Consequently, the resulting machine-learning models are trained to recognise the same static definitions of the foehn wind signatures. 

In this paper, we introduce and compare the first unsupervised machine-learning approaches for detecting foehn wind events. We focus on data from the Mc Murdo Dry Valleys as an example, however, due to the unsupervised nature of these approaches, our solutions can recognise a more dynamic definition of foehn wind events and are therefore, independent of the location. The first approach is based on multivariate time-series clustering, while the second utilises a deep autoencoder-based anomaly detection method to identify foehn wind events. Our best model achieves an f1-score of 88%, matching or surpassing previous machine-learning methods while providing a more flexible and inclusive definition of foehn events. 

How to cite: Milz, T., Hofsteenge, M., Katurji, M., and Vetrova, V.: Foehn Wind Analysis using Unsupervised Deep Anomaly Detection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10256, https://doi.org/10.5194/egusphere-egu23-10256, 2023.

Mesoscale eddies are gyrating currents in the ocean and have horizontal scales from 10 km up to 100 km and above. They transport water mass, heat, and nutrients and therefore are of interest among others to marine biologists, oceanographers, and geodesists. Usually, gridded sea level anomaly maps, processed from several radar altimetry missions, are used to detect eddies. However, operational processors create multi-mission (processing level 4) SLA grid maps with an effective spatiotemporal resolution far lower than their grid spacing and temporal resolution. 

This drawback leads to erroneous eddy detection. We, therefore, investigate if the higher-resolution along-track data could be used instead to solve the problem of classifying the SLA observations into cyclonic, anticyclonic, or no eddies in a more accurate way than using processed SLA grid map products. With our framework, we aim to infer a daily two-dimensional segmentation map of classified eddies. Due to repeat cycles between 10 and 35 days and cross-track spacing of a few 10 km to a few 100 km, ocean eddies are clearly visible in altimeter observations but are typically covered only by a few ground tracks where the spatiotemporal context within the input data is highly variable each day. However conventional convolutional neural networks (CNNs) rely on data without varying gaps or jumps in time and space in order to use the intrinsic spatial or temporal context of the observations. Therefore, this is a challenge that needs to be addressed with a deep neural network that on the one hand utilizes the spatiotemporal context information within the modality of along-track data and on the other hand is able to output a two-dimensional segmentation map from data of varying sparsity. Our approach with our architecture Teddy is to use a transformer module to encode and process the spatiotemporal information along with the ground track's sea level anomaly data that produces a sparse feature map. This will then be fed into a sparsity invariant convolutional neural network in order to infer a two-dimensional segmentation map of classified eddies. Reference data that is used to train Teddy is produced by an open-source geometry-based approach (py-eddy-tracker [1]). 

The focus of this presentation is on how we implemented this approach in order to derive two-dimensional segmentation maps of classified eddies with our deep neural network architecture Teddy from along-track altimetry. We show results and limitations for the classification of eddies using only along-track SLA data from the multi-mission level 3 product of the Copernicus Marine Environment Monitoring Service (CMEMS) within the 2017 - 2019 period for the Gulf Stream region. We find that using our methodology, we can create two-dimensional maps of classified eddies from along-track data without using preprocessed SLA grid maps.

[1] Evan Mason, Ananda Pascual, and James C. McWilliams, “A new sea surface height–based code for oceanic mesoscale eddy tracking,” Journal of Atmospheric and Oceanic Technology, vol. 31, no. 5, pp. 1181–1188, 2014.

How to cite: Bolmer, E., Abulaitijiang, A., Fenoglio-Marc, L., Kusche, J., and Roscher, R.: Framework for creating daily semantic segmentation maps of classified eddies using SLA along-track altimetry data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13367, https://doi.org/10.5194/egusphere-egu23-13367, 2023.

Forests are a significant carbon sink of the total carbon dioxide (CO₂) emitted by humans. Climate change is expected to impact forest systems, and their role in the terrestrial carbon cycle in several ways – for example, the fertilization effect of increased atmospheric CO₂, and the lengthening of the growing season in northern temperate and boreal areas may increase forest productivity, while more frequent extreme climate events such as storms and windthrows or drought spells, as well as wildfires might reduce disturbances return period, hence increasing forest land loss and reduction of the carbon stored in the vegetation and soils. In addition, forest management in response to an increased demand for wood products and fuel can affect the carbon storage in ecosystems and wood products. State-of-the-art Dynamic Global Vegetation Models (DGVMs) simulate the forest responses to environmental and human processes, however running these models globally for many climate and management scenarios becomes challenging due to computational restraints. Integration of process-based models and machine learning methods through emulation allows us to speed up computationally expensive simulations. In this work, we explore the use of machine learning to surrogate the LPJ-GUESS DGVM. This emulator is spatially-aware to represent forests across the globe in a flexible spatial resolution, and consider past climate and forest management practices to account for legacy effects. The training data for the emulator is derived from dedicated runs of the DGVM sampled across four dimensions relevant to forest carbon and yield: atmospheric CO₂ concentration, air Temperature, Precipitation, and forest Management (CTPM). The emulator can capture relevant forest responses to climate and management in a lightweight form, and will support the development of the coupled socio-economic/ecologic model of the land system, namely LandSyMM (landsymm.earth). Other relevant scientific applications include the analysis of optimal forestry protocols under climate change, and the forest potential in climate change mitigation.

How to cite: Natel de Moura, C., Belda, D. M., Antoni, P., and Arneth, A.: A machine learning emulator for forest carbon stocks and fluxes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16287, https://doi.org/10.5194/egusphere-egu23-16287, 2023.

The terrestrial carbon cycle is one of the largest sources of uncertainty in climate projections. The terrestrial carbon sink which removes a quarter of anthropogenic CO2 emissions; is highly variable in time and space depending on climate. Previous studies have found that data-driven models such as random forest, artificial neural networks and long short-term memory networks can be used to accurately model Net Ecosystem Exchange (NEE) and Gross Primary Productivity (GPP) accurately, which are two important metrics to quantify the direction and magnitude of CO2 transfer between the land surface and the atmosphere. Recently, a new class of machine learning models called transformers have gained widespread attention in natural language processing tasks due to their ability to learn from large volumes of sequential data. In this work, we use Transformers to model NEE and GPP from 1996-2022 at 39 Flux stations in the ICOS Europe network using ERA5 reanalysis data. We can compare our results with traditional machine learning approaches to evaluate the generalisability and predictive performance of transformers for carbon flux modelling.

How to cite: Mazumder, S. and Prasad, A.: Modeling the Variability of Terrestrial Carbon Fluxes using Transformers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-582, https://doi.org/10.5194/egusphere-egu23-582, 2023.

Data Assimilation (DA) is a challenging and expensive computational problem targetting hidden variables in high-dimensional spaces. 4DVar methods are widely used in weather forecasting to fit simulations to sparse observations by optimization over numerical model input. The complexity of this inverse problem and the sequential nature of common 4DVar approaches lead to long computation times with limited opportunity for parallelization. Here we propose using machine learning (ML) algorithms to replace the entire 4DVar optimization problem with a single forward pass through a neural network that maps from noisy and incomplete observations at multiple time points to a complete system state estimate at a single time point. We train the neural network using a loss function derived from the weak-constraint 4DVar objective, including terms incorporating errors in both model and data. In contrast to standard 4DVar approaches, our method amortizes the computational investment of training to avoid solving optimization problems for each assimilation window, and its non-sequential nature allows for easy parallelization along the time axis for both training and inference. In contrast to most previous ML-based data assimilation methods, our approach does not require access to complete, noise-free simulations for supervised learning or gradient-free approximations such as Ensemble Kalman filtering. To demonstrate the potential of our approach, we show a proof-of-concept on the chaotic Lorenz'96 system, using a novel "1.5D Unet" architecture combining 1D and 2D convolutions.

How to cite: Zinchenko, V. and Greenberg, D.: Training Deep Data Assimilation Networks on Sparse and Noisy Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12458, https://doi.org/10.5194/egusphere-egu23-12458, 2023.

The spatio-temporal reconstruction of a dynamical process from some observationaldata is at the core of a wide range of applications in geosciences. This is particularly true for weather forecasting, operational oceanography and climate studies. However, the re35 construction of a given dynamic and the prediction of future states must take into ac36 count the uncertainties that affect the system. Thus, the available observational measurements are only provided with a limited accuracy. Besides, the encoded physical equa38 tions that model the evolution of the system do not capture the full complexity of the real system. Finally, the numerical approximation generates a non-negligible error. For these reasons, it seems relevant to calculate a probability distribution of the state system rather than the most probable state. Using recent advances in machine learning techniques for inverse problems, we propose an algorithm that jointly learns a parametric distribution of the state, the dynamics governing the evolution of the parameters, and a solver. Experiments conducted on synthetic reference datasets, as well as on datasets describing environmental systems, validate our approach.

How to cite: Lafon, N., Naveau, P., and Fablet, R.: Uncertainty quantification in variational data assimilation with deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15994, https://doi.org/10.5194/egusphere-egu23-15994, 2023.

Soil temperature and soil moisture variations have large effects on ecological processes in the soil. To investigate and understand these processes, high-resolution data of soil temperature and soil moisture are required. Here, we present an approach to generate data of soil temperature and soil moisture continuously in space, depth, and time for a 400 km² study area in the Fichtel Mountains (Germany). As reference data, measurements with 1 m long soil probes were taken. To cover many different locations, the available 15 soil probes were shifted regularly in the course of one year. With this approach, around 250 different locations in forest sites, on meadows and on agricultural fields were captured under a variety of meteorological conditions. These measurements are combined with readily available meteorological data, satellite data and soil maps in a machine learning approach to learn the complex relations between these variables. We aim for a model which can predict the soil temperature and soil moisture continuously for our study area in the Fichtel Mountains, with a spatial resolution of 10 m x 10 m, down to 1 m depth with segments of 10 cm each and in an hourly resolution in time. Here, we present the results of our pilot study where we focus on the temperature and moisture change within the depth down to 1 m at one single location. To take temporal lags into account, we construct a Long Short-Term Memory network based on meteorological data as predictors to make temperature and moisture predictions in time and depth. The results indicate a high ability of the model to reproduce the time series of the single location and highlight the potential of the approach for the space-time-depth mapping of soil temperature and soil moisture.

How to cite: Baumberger, M., Adorf, L., Haas, B., Meyer, N., and Meyer, H.: Modelling Soil Temperature and Soil Moisture in Space, Depth, and Time with Machine Learning Techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11958, https://doi.org/10.5194/egusphere-egu23-11958, 2023.

Time transfer functions describe the change of state variables over time in geoscientific numerical simulation models. The identification of these functions is an essential but challenging step in model building. While traditional methods rely on qualitative understanding or first order principles, the availability of large spatio-temporal data sets from direct measurements or extremely detailed physical-based system modelling has enabled the use of machine learning methods to discover the time transfer function directly from data. In this study we explore the feasibility of this data driven approach for numerical simulation of the co-evolution of soil, hydrology, vegetation, and grazing on landscape scale, at geological timescales. From empirical observation and hyper resolution (1 m, 1 week) modelling (Karssenberg et al, 2017) it has been shown that a hillslope system shows complex behaviour with two stable states, respectively high biomass on deep soils (healthy state) and low biomass on thin soils (degraded or desertic state). A catastrophic shift from healthy to degraded state occurs under changes of external forcing (climate, grazing pressure), with a transient between states that is rapid or slow depending on system characteristics. To identify and use the time transfer functions of this system at hillslope scale we follow four procedural steps. First, an extremely large data set of hillslope average soil and vegetation state is generated by a mechanistic hyper resolution (1 m, 1 week) system model, forcing it with different variations in grazing pressure over time. Secondly, a machine learning model predicting the rate of change in soil and vegetation as function of soil, vegetation, and grazing pressure, is trained on this data set. In the third step, we explore the ability of this trained machine learning model to predict the rate of system change (soil and vegetation) on untrained data. Finally, in the fourth step, we use the trained machine learning model as time transfer function in a forward numerical simulation of a hillslope to determine whether it is capable of representing the known complex behaviour of the system. Our findings are that the approach is in principle feasible. We compared the use of a deep neural network and a random forest. Both can achieve great fitting precision, although the latter performs much faster and requires less training data. Even though the machine learning based time transfer function shows differences in the rates of change in system state from those calculated using expert knowledge in Karssenberg et al. (2017), forward simulation appeared to be possible with system behaviour generally in line with that observed in the data from the hyper resolution model. Our findings indicate that discovery of time transfer functions from data is possible. Next steps need to involve the use observational data (e.g., from remote sensing) to test the approach using data from real-world systems.

Karssenberg, D., Bierkens, M.F.P., Rietkerk, M., Catastrophic Shifts in Semiarid Vegetation-Soil Systems May Unfold Rapidly or Slowly. The American Naturalist 2017. Vol. 190, pp. E145–E155.

How to cite: Pomarol Moya, O. and Karssenberg, D.: Machine learning for data driven discovery of time transfer functions in numerical modelling: simulating catastrophic shifts in vegetation-soil systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4454, https://doi.org/10.5194/egusphere-egu23-4454, 2023.

With fast parallel computing hardware, particularly GPUs, becoming more accessible in the geosciences the now efficiently running deep learning techniques are ready to handle larger amounts of recorded observation and satellite derived data and are able to learn complex structures across time-series. Thus, a suitable deep learning setup is able to generate highly-resolved weather forecasts in real-time and on demand. Forecasts of irradiance and radiation can be challenging in machine learning as they embrace a high degree of diurnal and seasonal variation.

Continuously extended PV/solar power production grows into one of our most important fossil-fuel free energy sources. Unlike the just recently emerging PV power observations, solar irradiance offers long time-series from automized weather station networks. Being directly linked to PV outputs, forecasting highly resolved solar irradiance from nowcasting to short-range plays a crucial role in decision support and managing PV.

In this study, we investigate the suitability of several deep learning techniques adopted and developed to a set of heterogeneous data sources on selected locations. We compare the forecast results to traditional – however computationally expensive - numerical weather prediction models (NWP) and rapid update cycle models. Relevant input features include 3D-fields from NWP models (e.g.: AROME), satellite data and products (e.g.: CAMS), radiation time series from remote sensing, and observation time time-series (site observations and close sites). The amount of time-series data can be extended by a synthetic data generator, a part of our deep learning framework. Our main models investigated includes a sequence-to-sequence LSTM (long-short-term-memory) model using a climatological background model or NWP for post-processing, a Graph NN model, and an analogs based deep learning method. Furthermore, a novel neural network model based on two other ideas, the IrradianceNet and the PhyDNet, was developed. IrradPhyDNet combines the skills of IrradianceNet and PhyDNet and showed improved performance in comparison to the original models.

Results obtained by the developed methods yield, in general, high forecast-skills. For selected case studies of extreme events (e.g. Saharan dust) all novel methods could outperform the traditional methods.  Different combinations of inputs and processing-steps are part of the analysis.

How to cite: Papazek, P., Schicker, I., and Gfähler, P.: Comparison of LSTM, GraphNN, and IrradPhyDNet based Approaches for High-resolution Solar Irradiance Nowcasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13143, https://doi.org/10.5194/egusphere-egu23-13143, 2023.

Over the last decades, space oceanography missions, particularly altimeter missions, have greatly advanced our ability to observe sea surface dynamics. However, they still struggle to resolve spatial scales below ~ 100 km. On a global scale, sea surface current are derived from sea surface height by a geostrophical assumption. While future altimeter missions should improve the observation of sea surface height, the observation of sea surface current using altimetry techniques would remains indirect. In the other hands, recent works have considered the use of AIS (automated identification system) as a new mean to reconstruct sea surface current : AIS data streams provide an indirect observational models of total currents including ageostrophic phenomenas. In this work we consider the use of the supervised learning framework 4DVARNet, a supervised data driven approach that allow us to perform multi-modal experiments : We focus on an Observing System Simulation Experiment (OSSE) in a region of the Gulf-Stream and we show that the joint use of AIS and sea surface height (SSH) measurement could improve the reconstruction of sea surface current with respect to product derived solely from AIS or SSH observations in terms of physical and time scale resolved. 

How to cite: Benaïchouche, S., Le Goff, C., Boussidi, B., Rousseau, F., and Fablet, R.: Multi-modal data assimilation of sea surface currents from AIS data streams and satellite altimetry using 4DVARNet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14051, https://doi.org/10.5194/egusphere-egu23-14051, 2023.

Mass accumulation rates of sediments[g/cm²/yr] or sedimentation rates[cm/yr] on the seafloor are important to understand various benthic properties, like the rate of carbon sequestration in the seafloor and seafloor geomechanical stability. Several machine learning models, such as random forests, and k-Nearest Neighbours have been proposed for the prediction of geospatial data in marine geosciences, but face significant challenges such as the limited amount of labels for training purposes, skewed data distribution, a large number of features etc. Previous model predictions show deviation in the global sediment budget, a parameter used to determine a model's predicitve validity, revealing the lack of accurate representation of sedimentation rate by the state of the art models. 

Here we present a semi-supervised deep learning methodology to improve the prediction of sedimentation rates, making use of around 9x10⁶  unlabelled data points. The semi-supervised neural network implementation has two parts: an unsupervised pretraining using an encoder-decoder network. The encoder with the optimized weights from the unsupervised training is then taken out and fitted with layers that lead to the target dimension. This network is then fine-tuned with 2782 labelled data points, which are observed sedimentation rates from peer-reviewed sources. The fine-tuned model then predicts the rate and quantity of sediment accumulating on the ocean floor, globally.

The developed semi-supervised neural network provide better predictions than supervised models trained only on labelled data. The predictions from the semi-supervised neural network are compared with that of the supervised neural network with and without dimensionality reduction(using Principle Component Analysis).

How to cite: Parameswaran, N., Gonzalez, E., Bur­wicz-Ga­ler­ne, E., Greenberg, D., Wallmann, K., and Braack, M.: Semi-supervised feature-based learning for prediction of Mass Accumulation Rate of sediments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15684, https://doi.org/10.5194/egusphere-egu23-15684, 2023.

One of the many ways in which anthropogenic climate change impacts our planet is
rising sea levels. The rate of sea level rise (SLR) across the oceans is,
however, not uniform in space or time and is influenced by a complex interplay
of ocean dynamics, heat uptake, and surface forcing. As a consequence,
short-term (years to a decade) regional SLR patterns are difficult to model
using conventional deterministic approaches. For example, the latest climate
model projections (called CMIP6) show some agreement in the globally integrated
rate of SLR but poor agreement when it comes to spatially-resolved
patterns. However, such forecasts are valuable for adaptation planning in
coastal areas and for protecting low lying assets.
Rather than a deterministic modeling approach, here we explore the possibility
of exploiting the high quality satellite altimeter derived record of sea surface
height variations, which cover the global oceans outside of ice-infested waters
over a period of 30 years. Alongside this rich and unique satellite record,
several data-driven models have shown tremendous potential for various
applications in Earth System science. We explore several data-driven deep
learning approaches for sea surface height forecasts over multi-annual to
decadal time frames. A limitation of some machine learning approaches is the
lack of any kind of uncertainty quantification, which is problematic for
applications where actionable evidence is sought. As a consequence, we equip
our models with a rigorous measure of uncertainty, namely conformal prediction which
is a model and dataset agnostic method that provides calibrated predictive
uncertainty with proven coverage guarantees. Based on a 30-year satellite
altimetry record and auxiliary climate forcing data from reanalysis such as
ERA5, we demonstrate that our methodology is a viable and attractive alternative
for decadal sea surface height forecasts.

How to cite: Lehmann, N., Bamber, J., and Zhu, X.: Global Decadal Sea Surface Height Forecast with Conformal Prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16597, https://doi.org/10.5194/egusphere-egu23-16597, 2023.

Monitoring the state of the sea is a fundamental task for economic activities in the coastal zone, such as transport, tourism and infrastructure design. In recent years, regular wave height monitoring for marine risk assessment and mitigation has become unavoidable as global warming impacts in more intense and frequent swells.
In particular, the Mediterranean Sea has been considered as one of the most responsive regions to global warming, which may promote the intensification of hazardous natural phenomena as strong winds, heavy precipitation and high sea waves. Because of the high density population along the Mediterranean coastlines, heavy swells could have major socio-economic consequences. To reduce the impacts of such scenarios, the development of more advanced monitoring systems of the sea state becomes necessary.
In the last decade, it has been demonstrated how seismometers can be used to measure sea conditions by exploiting the characteristics of a part of the seismic signal called microseism. Microseism is the continuous seismic signal recorded in the frequency band of 0.05 and 0.4 Hz that is likely generated by interactions of sea waves together and with seafloor or shorelines.
In this work, in the framework of i-WaveNET INTERREG project, we performed a regression analysis to develop a model capable of predicting the sea state in the Sicily Channel (Italy) using microseism, acquired by onshore instruments installed in Sicily and Malta. Considering the complexity of the relationship between spatial sea wave height data and seismic data measured at individual stations, we used supervised machine learning (ML) techniques to develop the prediction model. As input data we used the hourly Root Mean Squared (RMS) amplitude of the seismic signal recorded by 14 broadband stations, along the three components, and in different frequency bands, during 2018 - 2021. These stations, belonging to the permanent seismic networks managed by the National Institute of Geophysics and Volcanology INGV and the Department of Geosciences of the University of Malta, consist of three-component broadband seismometers that record at a sampling frequency of 100 Hz.
As for the target, the significant sea wave height data from Copernicus Marine Environment Monitoring Service (CMEMS) for the same period were used. Such data is the hindcast product of the Mediterranean Sea Waves forecasting system, with hourly temporal resolution and 1/24° spatial resolution. After a feature selection step, we compared three different kinds of ML algorithms for regression: K-Nearest-Neighbors (KNN), Random Forest (RF) and Light Gradient Boosting (LGB). The hyperparameters were tuned by using a grid-search algorithm, and the best models were selected by cross-validation.  Different metrics, such as MAE, R² and RMSE, were considered to evaluate the generalization capabilities of the models and special attention was paid to evaluate the predictive ability of the models for extreme wave height values.
Results show model predictive capabilities good enough to develop a sea monitoring system to complement the systems currently in use.

How to cite: Cannavo', F., Minio, V., Saitta, S., Alparone, S., Borzì, A. M., Cannata, A., Ciraolo, G., Contrafatto, D., D’Amico, S., Di Grazia, G., and Larocca, G.: Machine Learning and Microseism as a Tool for Sea Wave Monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7492, https://doi.org/10.5194/egusphere-egu23-7492, 2023.

The ambient noise tomography (ANT) method is widely adopted to reconstruct shear-wave velocity anomalies and to generate high-resolution images of the crust and upper-mantle. A critical step in this process is the extraction of surface-wave dispersion curves from cross-correlation functions of continuous ambient noise recordings, which is traditionally performed manually on the dispersion spectrograms through human-machine interfaces. Picking of dispersion curves is sometimes prone to bias due to human interpretation. Furthermore, it is a laborious and time-consuming task that needs to be resolved in an automatized manner, especially when dealing with dense seismic network of nodal geophones where the large amount of generated data severely hinders manual picking approaches. In the last decade, several studies successfully employed machine learning methods in Earth Sciences and across many seismological applications. Early studies have shown versatile and reliable solutions by treating dispersion curve extraction as a visual recognition problem. 

We review and adapt a specific machine learning approach, deep convolutional neural networks, for use on dispersion spectrograms generated with the usual frequency-time analysis (FTAN) processing on ambient noise cross-correlations. To train and calibrate the algorithm we use several available datasets acquired from previous experiments across different geological settings. The main dataset consists of records acquired with a dense local geophone network (150 short period stations sampling at 250 Hz) deployed for one month in October 2021. The dataset has been acquired during the volcanic unrest of the Vulcano-Lipari complex, Italy. The network also accounts for additional 17 permanent broadband stations (sampling at 100 Hz) maintained by the National Institute of Geophysics and Volcanology (INGV) in Italy. We evaluate the performance of the dispersion curves extraction algorithm. The automatically-picked dispersion curves will be used to construct a shear-wave velocity model of the Vulcano-Lipari magmatic plumbing system and the surrounding area of the Aeolian archipelago.

How to cite: Stumpp, D., Amir Jiwani-Brown, E., Barat, C., Lupi, M., Muñoz, F., Planes, T., and Savard, G.: Nodal Ambient Noise Tomography and automatic picking of dispersion curves with convolutional neural network: case study at Vulcano-Lipari, Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13322, https://doi.org/10.5194/egusphere-egu23-13322, 2023.

Since PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) directly threatens public health, in order to take appropriate measures(prevention) in advance, the Korea Ministry of Environment(MOE) has been implementing PM₁₀ forecast nationwide since February 2014. PM_2.5 forecasts have been implemented nationwide since January 2015. The currently implemented PM forecast by the MOE subdivides the country into 19 regions, and forecasts the level of PM in 4 stages of “Good”, “Moderate”, “Unhealthy”, and “Very unhealthy”.

Currently PM air quality forecasting system operated by the MOE is based on a numerical forecast model along with a weather and emission model. Numerical forecasting model has fundamental limitations such as the uncertainty of input data such as emissions and meteorological data, and the numerical model itself. Recently, many studies on predicting PM using artificial intelligence such as DNN, RNN, LSTM, and CNN have been conducted to overcome the limitations of numerical models.

In this study, in order to improve the prediction performance of the numerical model, past observational data (air quality and meteorological data) and numerical forecasting model data (chemical transport model) are used as input data. The machine learning model consists of DNN and Seq2Seq, and predicts 3 days (D+0, D+1, D+2) using 6-hour and 1-hour average input data, respectively. The PM_2.5 concentrations predicted by the machine learning model and the numerical model were compared with the PM_2.5 measurements.

The machine learning models were trained for input data from 2015 to 2020, and their PM forecasting performance was tested for 2021. Compared to the numerical model, the machine learning model tended to increase ACC and be similar or lower to FAR and POD.

Time series trend was showed machine learning PM forecasting trend is more similar to PM measurements compared with numerical model. Especially, machine learning forecasting model can appropriately predict PM low and high concentrations that numerical model is used to overestimate.

Machine learning forecasting model with DNN and Seq2Seq can found improvement of PM forecasting performance compared with numerical forecasting model. However, the machine learning model has limitations that the model can not consider external inflow effects.

In order to overcome the drawback, the models should be updated and added some other machine learning module such as CNN with spatial features of PM concentrations.

How to cite: Wang, K.-H., Lee, C.-Y., Lee, J.-Y., Jung, M.-W., Kim, D.-G., Han, S.-H., Choi, D.-R., and Yun, H.: Comparison of PM2.5 concentrations prediction model performance using Artificial Intelligence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12566, https://doi.org/10.5194/egusphere-egu23-12566, 2023.

Current numerical weather prediction models contain significant systematic errors, due in part to indeterminate ground forcing (GF). This study considers an optimal virtual GF (GF_o) derived by training observed and simulated datasets of 10-m wind speeds (WS₁₀) for summer and winter. The GF_o is added to an offline surface multilayer model (SMM) to revise predictions of WS₁₀ in China by the Weather Research and Forecasting model (WRF). This revision is a data-based optimization under physical constraints. It reduces WS₁₀ errors and offers wide applicability. The resulting model outperforms two purely physical forecasts (the original WRF forecast and the SMM with physical GF parameterized using urban, vegetation, and subgrid topography) and two purely data-based revisions (i.e., multilinear regression and multilayer perceptron). Compared with original WRF forecasting, using the GF_o scheme reduces the Root Mean Square Error (RMSE) in WS₁₀ across China by 25% in summer and 32% in winter. The frontal area index of GF_o indicates that it includes both the effects of indeterminate GF and other possible complex physical processes associated with WS₁₀.

How to cite: Feng, J.: Mitigate forecast error in surface wind speed using an offline single-column model with optimal ground forcing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10394, https://doi.org/10.5194/egusphere-egu23-10394, 2023.

The aircraft-based observation is one of the important anchor data used in the numerical weather prediction (NWP) models. Nevertheless, the bias has been noted in the temperature observation through several previous studies. As the performance on the hybrid four-dimensional ensemble variational (hybrid-4DEnVar) data assimilation (DA) system of the Korean Integrated Model (KIM) ⸺ the operational model in the Korea Meteorological Administration (KMA) ⸺ has been advanced, the need for the aircraft temperature bias correction (BC) has been confirmed. Accordingly, as a preliminary study on the BC, the static BC method based on the linear regression was applied to the KIM Package for Observation Processing (KPOP) system. However, the results showed there were limitations of a spatial discontinuity and a dependency on the calculation period of BC coefficients.

In this study, we tried to develop the machine learning-based bias estimation model to overcome these limitations. The MultiLayer Perceptron (MLP) based learning was performed to consider the vertical, spatial and temporal characteristics of each observation by flight IDs and phases, and at the same time to consider the correlation among observation variables. As a result of removing the predicted bias from the bias estimation model, the mean of the background innovation (O-B) decreases from 0.2217 K to 0.0136 K in a given test period. Afterwards, in order to verify the analysis field impact for BC, the bias estimation model will be grafted onto the KPOP system and then several DA cycle experiments will be conducted in the KIM.

How to cite: Kwon, H., Jeon, H., Kang, J., Kwon, I., and Park, S. K.: Bias correction of aircraft temperature observations in the Korean Integrated Model based on a deep learning approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12218, https://doi.org/10.5194/egusphere-egu23-12218, 2023.

Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. Traditionally, the parameter tuning has been done manually, which can be an extremely labourious task. Tuning the entire model usually requires adjusting a relatively large amount of parameters. In case of manual tuning, the need to balance a number of requirements at the same time can lead the tuning process being a maze of subjective choices. It is, therefore, desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. In this presentation we present how to optimise 20 key physical parameters having a strong impact on forecast quality. These parameters belong to the Stochastically Perturbed Parameters Scheme in the atmospheric model Open Integrated Forecasting System.

The results show that simultaneous optimisation of O(20) parameters is possible with O(100) algorithm steps using an ensemble of O(20) members, and that the optimised parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimised model. We find that the optimisation process is dependent on the starting values of the parameters that are optimised (starting from better suited values results in a better model). The results also show that the applicability of the tuned parameter values across different model resolutions is somewhat questionable since the model biases seem to be resolution-specific. Moreover, our optimisation algorithm tends to treat the parameter covariances poorly limiting its ability to converge to the global optimum.

How to cite: Tuppi, L., Ekblom, M., Ollinaho, P., and Järvinen, H.: Algorithmic optimisation of key parameters of OpenIFS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4817, https://doi.org/10.5194/egusphere-egu23-4817, 2023.

Machine learning approaches have been widely used for improving the representation of subgrid scale parameterizations in Earth System Models. In our study we target the Cloud Microphysics parameterization, in particular the two-moment bulk scheme of the ICON (Icosahedral Non-hydrostatic) Model. 

Cloud microphysics parameterization schemes suffer from an accuracy/speed tradeoff. The simplest schemes, often heavy with assumptions (such as the bulk moment schemes) are most common in operational weather prediction models. Conversely, the more complex schemes with fewer assumptions –e.g. Lagrangian schemes such as the super-droplet method (SDM)– are computationally expensive and used only within research and development. SDM allows easy representation of complex scenarios with multiple hydrometeors and can also be used for simulating cloud-aerosol interactions. To bridge this gap and to make the use of more complex microphysical schemes feasible within operational models, we use a data-driven approach. 

Here we train a neural network to mimic the behavior of SDM simulations in a warm-rain scenario in a dimensionless control volume. The network behaves like a dynamical system that converts cloud droplets to rain droplets–represented as bulk moments–with only the current system state as the input. We use a multi-step training loss to stabilize the network over long integration periods, especially in cases with extremely low cloud water to start with. We find that the network is stable across various initial conditions and in many cases, emulates the SDM simulations better than the traditional bulk moment schemes. Our network also performs better than any previous ML-based attempts to learn from SDM. This opens the possibility of using the trained network as a proxy for imitating the computationally expensive SDM within operational weather prediction models with minimum computational overhead. 

How to cite: Sharma, S. and Greenberg, D.: Machine Learning Parameterization for Super-droplet Cloud Microphysics Scheme, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5149, https://doi.org/10.5194/egusphere-egu23-5149, 2023.

Retrieving ozone from limb measurements necessitates the modeling of scattered light through the atmosphere.  However, accurately modeling multiple scattering (MS) during retrieval requires excessive computational resources; consequently, operational retrieval models employ approximations in lieu of the full MS calculation.  Here we consider an alternative MS approximation method, where we use radiative transfer (RT) simulations to train neural network models to predict the MS radiances.  We present our findings regarding the best-performing network hyperparameters, normalization schemes, and input/output data structures.  Using RT calculations based on measurements by the Ozone Mapping and Profiling Suite's Limb Profiler (OMPS/LP), we compare the accuracy of these neural-network models with both the full MS calculation as well as the current MS approximation methods utilized during OMPS/LP retrievals.

How to cite: Himes, M., Kramarova, N., Zhu, T., Mok, J., Bandel, M., Fasnacht, Z., and Loughman, R.: Neural network surrogate models for multiple scattering: Application to OMPS LP simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7281, https://doi.org/10.5194/egusphere-egu23-7281, 2023.

Physical parameterization is one of the major components of Numerical Weather Prediction system. In Korean Integrated Model (KIM), physical parameterizations account for about 30 % of the total computation time. There are many studies of developing neural network based emulators to replace and accelerate physics based parameterization. In this study, we develop a planetary boundary layer(PBL) emulator which is based on Shin-Hong (Hong et al., 2006, 2010; Shin and Hong, 2013, 2015) scheme that computes the parameterized effects of vertical turbulent eddy diffusion of momentum, water vapor, and sensible heat fluxes. We compare the emulator performance with Multi-Layer Perceptron (MLP) based architectures: simple MLP, MLP application version, and MLP-mixer(Tolstikhin et al., 2021). MLP application version divides data into several vertical groups for better approximation of each vertical group layers. MLP-mixer is MLP based architecture that performs well in computer vision without using convolution and self-attention. We evaluate the resulting MLP based emulator performance. MLP application version and MLP-mixer showed significant performance improvement over simple MLP.

How to cite: Jang, J., Oh, T.-J., An, S., Park, W., Na, I., and Kim, J.: Development of PBL Parameterization Emulator using Neural Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4695, https://doi.org/10.5194/egusphere-egu23-4695, 2023.

In the preparation of the global kilometre-resolution coupled ICON climate model, it is necessary to calibrate cloud microphysical parameters. Here we explore the avenue towards optimally calibrating such parameters using machine learning. The emulator developed by Watson-Parris et al. (2021) is employed in combination with a perturbed-parameter ensemble of limited-area atmosphere-only ICON simulations for the North Atlantic ocean. In a first step, the autoconversion scaling parameter is calibrated, using satellite-retrieved top-of-atmosphere and bottom-of-atmosphere radiation fluxes. For this purpose, limited area simulations of the north atlantic are performed with ICON. In which different cloud microphysical parameters are changed, in order to evaluate possible influences on the output of radiation fluxes.

How to cite: Eichholz, H. M., Kretzschmar, J., Watson-Parris, D., Umlauft, J., and Quaas, J.: Towards machine-learning calibration of cloud parameters in the kilometre-resolution ICON atmosphere model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5003, https://doi.org/10.5194/egusphere-egu23-5003, 2023.