In this letter a new method based on modified self-organizing maps is presented for the reconstruction of deep ocean current velocities from surface information provided by satellites. This takes advantage local correlations in data-space to improve accuracy reconstructed velocities. Unlike previous attempts reconstruct data, our makes no assumptions regarding structure water column, nor underlying dynamics flow field. Using satellite observations velocity, sea-surface height and...

Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management flood risk monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only radar images inputs. In order to determine whether other meteorological parameters wind would improve forecasts, we trained model on fusion of and velocity produced by weather forecast model. The network was compared similar...

We present ORCAst, a multi-stage, multi-arm network for Operational high-Resolution Current forecAsts over one week. Producing real-time nowcasts and forecasts of ocean surface currents is challenging problem due to indirect or incomplete information from satellite remote sensing data. Entirely trained on real data in situ measurements drifters, our model learns forecast global using various sources ground truth observations multi-stage learning procedure. Our encoder-decoder architecture...

The uncertainty quantification in sub-seasonal wind speed forecasting is important for risk assessment and decision-making. One way to improve dynamical forecast skills regress information from forecasts of large-scale fields surface by a supervised learning model. For such statistical downscaling approach, Tian et al. (2024) demonstrated that spatially independent stochastic perturbations based on model residuals can the representation ensemble dispersion. However, this...

Sampling from climate models to generate ensembles of predictions is computationally expensive (Hawkins et al., 2015). Climate model are used understand probabilities climatic events and identify internal variability in models. In the short term, uncertainty inter-annual dominate (Smith 2019). A typical approach address these uncertainties use large non-learned, physical numerical global circulation (GCM) (Eade 2014). These allow for statistical analysis distributions determination model.Our...

Weather forecast downscaling, the problem of recovering accurate local predictions given a lower resolution forecast,  is commonly used in operational NWP pipelines. Its purpose to recover some sub-grid processes that could not be represented by underlying numerical model due limited resolution. This misrepresentation provokes statistical mismatches between observation data gathered from stations and nearest grid point simulation.Using downscaling typically requires making...

Abstract Satellite‐based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, space‐borne altimetry provides valuable Sea Surface Height (SSH) measurements, used to estimate surface geostrophic currents. Due sensor technology employed, important gaps occur in SSH observations. Complete maps are produced using linear Optimal Interpolations (OI) such as widely Data Unification Altimeter Combination System ( duacs ). On other hand,...

Abstract. Studies of coastal seas in Europe have noted the high variability CO2 system. This variability, generated by complex mechanisms driving fluxes, complicates accurate estimation these mechanisms. is particularly pronounced Baltic Sea, where fluxes not been characterized as much detail open oceans. In addition, joint availability situ measurements and sea-surface satellite data limited area. this paper, we used SOMLO (self-organizing multiple linear output; Sasse et al., 2013)...

Abstract In this study we focused on estimating the pressure partial of CO 2 ( p ) in entire Baltic Sea which can be considered a coastal area its entirety. We used self‐organizing multiple linear output (SOMLO) method to estimate ocean surface from remotely sensed sea temperature, chlorophyll , colored dissolved organic matter, net primary production, and mixed‐layer depth. Uncertainties estimates include sparsity situ data train algorithms, particular, for some sectors seasons. For...

We present a novel approach named ITCOMP SOM that uses iterative self-organizing maps (SOM) to progressively reconstruct missing data in highly correlated multidimensional dataset. This method was applied for the completion of complex oceanographic data-set containing glider from EYE Levantine experiment EGO project. provided reconstructed temperature and salinity profiles are consistent with physics phenomenon they sampled. A cross-validation test performed validated approach, providing...

Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary are computationally technically expensive nature. Here we used a less-expensive, well-established methodology of self-organizing maps (SOMs) along with hidden Markov model (HMM) profiles inorganic (SPIM). The concept the proposed work is benefit from all available data sets through use fusion methods machine learning...

Observing the vertical dynamic of phytoplankton in water column is essential to understand evolution ocean primary productivity under climate change and efficiency CO2 biological pump. This usually made through in-situ measurements. In this paper, we propose a machine learning methodology infer distribution pigments from surface satellite observations, allowing their global estimation with high spatial temporal resolution. After imputing missing values iterative completion Self-Organizing...

Abstract The oceans have a very important role in climate regulation due to their massive heat storage capacity. Thus, for the past decades, been observed by satellites better understand dynamics. Satellites retrieve several data with various spatial resolutions. For instance, sea surface height (SSH) is low-resolution field where temperature (SST) can be retrieved much higher one. These two physical parameters are linked link that learned super-resolution machine-learning algorithm. In this...

Abstract. In this article, we present the first climatological map of air–sea CO2 flux over Baltic Sea based on remote sensing data: estimates pCO2 derived from satellite imaging using self-organizing classifications along with class-specific linear regressions (SOMLO methodology) and remotely sensed wind estimates. The have a spatial resolution 4 km both in latitude longitude monthly temporal 1998 to 2011. fluxes are estimated two types products, i.e. reanalysis winds higher-resolution...

Abstract In situ observations are vital to improving our understanding of the variability and dynamics ocean. A critical component ocean circulation is strong, narrow, highly variable western boundary currents. Ocean moorings that extend from seafloor surface remain most effective efficient method fully observe these For various reasons, mooring instruments may not provide continuous records. Here we assess application Iterative Completion Self-Organizing Maps (ITCOMPSOM) machine learning...

Abstract. Studies of coastal seas in Europe have brought forth the high variability CO2 system. This variability, generated by complex mechanisms driving fluxes makes their accurate estimation an arduous task. is more pronounced Baltic Sea, where not been as highly detailed open oceans. In adition, joint availability in-situ measurements and sea-surface satellite data limited area. this paper, a combination two existing methods (Self-Organizing-Maps Multiple Linear regression) used to...

We propose a neural network approach to produce probabilistic weather forecasts from deterministic numerical prediction. The developed method is applicable any gridded forecast including the recent machine learning prediction model outputs. To postprocess multiple lead times using single model, we introduce time embedding that encodes shift in biases as progresses. apply our operational outputs Global Deterministic Prediction System up ten-day times. trained predict METAR in-situ surface...

The Sea Surface Height (SSH) is an important variable of the ocean state. It currently estimated by satellites measuring return time a radar pulse. Due to this remote sensing technology, nadir-pointing altimeters take measures vertically, only along their ground tracks. Recovering fully gridded SSH fields involves challenging spatiotemporal interpolation. most widely used operational product, Data Unification and Altimeter Combination System (DUACS), combines data from several through linear...

Skillfully forecasting the evolution of tropical cyclones (TC) is crucial forthe human populations in areas at risk, and an essential indicator a storm’spotential impact Maximum Sustained Wind Speed, often referred to asthe cyclone’s intensity. Predicting future intensity ongoing storms istraditionally done using statistical-dynamical methods such as (D)SHIPS andLGEM, or byproduct fully dynamical models HWRF model.Previous works have shown that deep learning based on...

One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in neural network architecture. A popular prior locality, where atmospheric data processed with local interactions, like 3D convolutions or attention windows Pangu-Weather. On other hand, some works have shown great success without this locality principle, at cost a much higher parameter count. In paper, we show that processing Pangu-Weather computationally...