Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation

This paper presents an approach for employing artificial neural networks (NN) to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation. The assimilation methods are tested in the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localization of balloon soundings. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the analysis from the local ensemble transform Kalman filter (LETKF). After the training process, the method using the MLP-NN is seen as a function of data assimilation. The NN were trained with data from first three months of 1982, 1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle using MLP-NN were performed with synthetic observations for January 1985. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses is of order 0.02. The simulations show that the major advantage of using the MLP-NN is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-NN is 90 times faster than cycle assimilation with LETKF for the numerical experiment.Comment: 17 pages, 16 figures, monthly weather revie

Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model

Numerical weather prediction (NWP) uses atmospheric general circulation models (AGCMs) to predict weather based on current weather conditions. The process of entering observation data into mathematical model to generate the accurate initial conditions is called data assimilation (DA). It combines observations, forecasting, and filtering step. This paper presents an approach for employing artificial neural networks (NNs) to emulate the local ensemble transform Kalman filter (LETKF) as a method of data assimilation. This assimilation experiment tests the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localizations of meteorological balloons. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLPs) networks are applied. After the training process, the method, forehead-calling MLP-DA, is seen as a function of data assimilation. The NNs were trained with data from first 3 months of 1982, 1983, and 1984. The experiment is performed for January 1985, one data assimilation cycle using MLP-DA with synthetic observations. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses are of order 10–2. The simulations show that the major advantage of using the MLP-DA is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-DA analyses is 90 times faster than LETKF cycle assimilation with the mean analyses used to run the forecast experiment

Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering

The book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured meshes are introduced to capture the important dynamics that operate over a range of length scales. Deep learning techniques enable rapid and accurate modelling simulations and pave the way towards both real-time forecasting and overall optimisation control over time, thus improving profitability and managing risk. The use of data assimilation techniques incorporates information from experiments and observations to reduce uncertainties in predictions and improve predictive accuracy. Targeted observation approaches can be used for identifying when, where, and what types of observations would provide the greatest improvement to specific model forecasts at a future time. Such targeted observations are important as they will allow the most effective use of available monitoring resources. The combination of deep learning and data assimilation enables a rapid and accurate response in emergencies. The technologies discussed here can be also used to determine the sensitivity of outputs to various operational conditions in engineering and management, thus providing reliable information to both the public and policy-maker

Machine learning in weather prediction and climate analyses : applications and perspectives

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research - photovoltaic and wind energy, atmospheric physics and processes; in climate research - parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting

Fast ocean data assimilation and forecasting using a neural-network reduced-space regional ocean model of the north Brazil current

Data assimilation is computationally demanding, typically many times slower than model forecasts. Fast and reliable ocean assimilation methods are attractive for multiple applications such as emergency situations, search and rescue, and oil spills. A novel framework which performs fast data assimilation with sufficient accuracy is proposed for the first time for the open ocean. Speed improvement is achieved by performing the data assimilation on a reduced-space rather than on a full-space. A surface 10km resolution hindcast of the North Brazil current from the Regional Ocean Modelling System (ROMS) serves as the full-space state. The target variables are sea surface height, sea surface temperature, and surface currents. A dimension reduction of the full-state is made by an Empirical Orthogonal Function analysis while retaining most of the explained variance. The dynamics are replicated by a state-of-the-art neural network trained on the truncated principal components of the full-state. An Ensemble Kalman filter assimilates the data in the reduced-space, where the trained neural network produces short-range forecasts from perturbed ensembles. The Ensemble Kalman filter of the reduced-space is successful in reducing the root mean squared error by ∼ 45% and increases the correlations between state variables and data. The performance is similar to other full-space data assimilation studies. However, the computations are three to four orders of magnitude faster than for other full-space data assimilation schemes. The forecast of ocean variables is a computationally demanding task in terms of speed and accuracy. This framework manages to create fast forecasts in ∼ 30 seconds, once data have been assimilated. The forecasts are obtained using the trained neural network. We performed additional experiments using data and forecasts from July 2015 and January 2016. The analysis and forecasts in our framework yield a higher skill score and high spatial correlation when compared to the operational dataset Global Ocean Physics Analysis and Forecast by the UK MetOffice. Forcing the neural network with 10 m surface winds in order to improve the total surface currents forecast was considered. There is no additional skill in the forecasts using wind forcing because of the low Ekman component compared to the dominant geostrophic currents. The reduced model approach could be a useful tool when full physics regional models are not available to make a forecast.Open Acces