Argo Data Mean Field Modeling

Honors (Bachelor's)StatisticsMathematicsUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/147404/1/wuhanm.pd

An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations

Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models

Typhoon-ocean interaction in the western North Pacific : Part 1

Author Posting. © The Oceanography Society, 2011. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 24 no. 4 (2011): 24–31, doi:10.5670/oceanog.2011.91.The application of new technologies has allowed oceanographers and meteorologists to study the ocean beneath typhoons in detail. Recent studies in the western Pacific Ocean reveal new insights into the influence of the ocean on typhoon intensity.This work is supported by grants from the Office of Naval Research, N00014- 10-WX-20203 (Black), N00014-08-1- 0656 (Centurioni), N00014-08-1-0577 (D’Asaro), N00014-09-1-0816 (D’Asaro), N00014-10-WX-21335 (Harr), N00014-08-1-0614 (Jayne), N00014- 09-1-0133 (Lee), N00014-08-1-0560 (Lien), N00014-10-1-0313 (student support), N00014-08-1-0658 (Rainville), N00014-08-1-0560 (Sanford); the National Oceanic and Atmospheric Administration NA17RJ1231 (Centurioni); and the National Science Foundation OCE0549887 (D’Asaro)

Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks

Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad hoc components that are not well constrained by theory or data. One such component is the eddy diffusivity model, where vertical turbulent fluxes of a quantity are parameterized from a variable eddy diffusion coefficient and the mean vertical gradient of the quantity. In this work, we improve a parameterization of vertical mixing in the ocean surface boundary layer by enhancing its eddy diffusivity model using data-driven methods, specifically neural networks. The neural networks are designed to take extrinsic and intrinsic forcing parameters as input to predict the eddy diffusivity profile and are trained using output data from a second moment closure turbulent mixing scheme. The modified vertical mixing scheme predicts the eddy diffusivity profile through online inference of neural networks and maintains the conservation principles of the standard ocean model equations, which is particularly important for its targeted use in climate simulations. We describe the development and stable implementation of neural networks in an ocean general circulation model and demonstrate that the enhanced scheme outperforms its predecessor by reducing biases in the mixed-layer depth and upper ocean stratification. Our results demonstrate the potential for data-driven physics-aware parameterizations to improve global climate models

Modelling circulation dynamics in the northern Baltic Sea

Circulation and surface layer dynamics are of significant importance, for example, when considering how hazardous substances or nutrients are transported in the sea. The earliest studies mapping circulation patterns in the northern Baltic Sea were done before the Second World War and were based on lightship observations. Although the number of available observation points was low, these studies showed that there is a cyclonic long-term surface circulation pattern in the northern subbasins. Even today, there are considerable research gaps and uncertainties in knowledge. For example, observational data still has insufficient coverage, descriptions of processes in numerical models need tuning to the conditions of the Baltic Sea and model forcing data can have large uncertainties. With modern analysis methods and new observational datasets, gaps in the current understanding of Baltic Sea circulation patterns can be identified and analyzed. In this thesis, circulation dynamics were investigated in the northern Baltic Sea with numerical hydrodynamic modelling. The complex dynamics of the brackish Baltic Sea put hydrodynamic models to the test. Several different model configurations were applied and developed further, including a high-resolution configuration of the NEMO (Nucleus for European Modelling of the Ocean) model for the Gulf of Finland (GoF). Methods such as machine learning algorithms, new data from automated observational platforms and ensemble forecasting were applied. Circulation patterns in the GoF were investigated with the self-organizing map (SOM) algorithm. The cyclonic circulation pattern visible in earlier studies was not seen in the GoF in the overall means calculated from the model results for the studied periods 2007–2013 and 2012–2014. SOM analysis of currents in the GoF revealed that they are highly variable and complex. There was significant inter-annual and intra-annual variability in the circulation patterns. A connection between wind forcing and the characteristic patterns from the SOM analysis was found. Analysis emphasized the estuary-like nature of the GoF. The results showed that circulation in the GoF changes rapidly between normal estuarine circulation and reverse estuarine circulation. The fact that the dominant wind direction is from the southwest supports this reversal. The cyclonic mean circulation pattern seems to appear only if the normal estuarine circulation is common enough for it to emerge during the averaging period. Small changes to wind direction distribution can have a significant effect on the longterm circulation patterns. Upwelling events on a timescale of days to weeks can also affect long-term circulation patterns. The NEMO model proved to be a suitable tool for the studies of circulation in the northern sub-basins of the Baltic Sea. It quality seems comparable to other models commonly used in the GoF and Bothnian Sea. The GoF is still a challenging environment for circulation modelling. Salinity gradients in the GoF are still not reproduced in a satisfactory manner by the models. More information is required on how well the models reproduce true circulation patterns and, for example, upwelling frequency and intensity. The need for accurate model inputs, especially wind forcing, was demonstrated. The value of observations (especially the better spatial coverage of current measurements) was once again emphasized. Furthermore, the results highlighted that care must be taken to make sure that models and observations represent the same thing when they are compared

Subsurface temperature estimation of mesoscale eddies in the Northwest Pacific Ocean from satellite observations using a residual muti-channel attention convolution network

The mesoscale eddies are prevalent oceanic circulation phenomena, exerting significant influence on various aspects of the marine environment including energy transfer, material transport and ecosystem dynamics in the Northwest Pacific Ocean. However, due to sparse vertical observational data, the understanding of the three-dimensional temperature structure of individual cases of mesoscale eddies remains limited. In recent years, utilizing surface remote sensing observations to estimate subsurface temperature anomaly has been crucial for comprehending the intricate multi-dimensional dynamic processes in the ocean. Consequently, this paper proposes an eddy residual multi-channel attention convolution network (ERCACN) with the adaptive threshold and designs the combination of various surface features to estimate the eddy subsurface temperature anomaly (ESTA). By integrating results with climatic temperature, thermal structures containing 46 levels at depths up to 1000 m could be obtained, achieving excellent daily temporal resolution and 0.25° spatial resolution. Validation using independent Argo profiles from 2016 to 2017 reveals that the combination of multiple surface variables outperforms univariate methods, and the ERCACN model demonstrates superior performance compared to other approaches. Overall, with an 8% error deemed acceptable, the ERCACN model achieves a precision of 88.08% in estimating ESTA. This method provides a novel perspective for other essential oceanic variables, contributing to a better perception of the global climate system

클라우드 컴퓨팅 환경기반에서 수치 모델링과 머신러닝을 통한 지구과학 자료생성에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2022. 8. 조양기.To investigate changes and phenomena on Earth, many scientists use high-resolution-model results based on numerical models or develop and utilize machine learning-based prediction models with observed data. As information technology advances, there is a need for a practical methodology for generating local and global high-resolution numerical modeling and machine learning-based earth science data. This study recommends data generation and processing using high-resolution numerical models of earth science and machine learning-based prediction models in a cloud environment. To verify the reproducibility and portability of high-resolution numerical ocean model implementation on cloud computing, I simulated and analyzed the performance of a numerical ocean model at various resolutions in the model domain, including the Northwest Pacific Ocean, the East Sea, and the Yellow Sea. With the containerization method, it was possible to respond to changes in various infrastructure environments and achieve computational reproducibility effectively. The data augmentation of subsurface temperature data was performed using generative models to prepare large datasets for model training to predict the vertical temperature distribution in the ocean. To train the prediction model, data augmentation was performed using a generative model for observed data that is relatively insufficient compared to satellite dataset. In addition to observation data, HYCOM datasets were used for performance comparison, and the data distribution of augmented data was similar to the input data distribution. The ensemble method, which combines stand-alone predictive models, improved the performance of the predictive model compared to that of the model based on the existing observed data. Large amounts of computational resources were required for data synthesis, and the synthesis was performed in a cloud-based graphics processing unit environment. High-resolution numerical ocean model simulation, predictive model development, and the data generation method can improve predictive capabilities in the field of ocean science. The numerical modeling and generative models based on cloud computing used in this study can be broadly applied to various fields of earth science.지구의 변화와 현상을 연구하기 위해 많은 과학자들은 수치 모델을 기반으로 한 고해상도 모델 결과를 사용하거나 관측된 데이터로 머신러닝 기반 예측 모델을 개발하고 활용한다. 정보기술이 발전함에 따라 지역 및 전 지구적인 고해상도 수치 모델링과 머신러닝 기반 지구과학 데이터 생성을 위한 실용적인 방법론이 필요하다. 본 연구는 지구과학의 고해상도 수치 모델과 머신러닝 기반 예측 모델을 기반으로 한 데이터 생성 및 처리가 클라우드 환경에서 효과적으로 구현될 수 있음을 제안한다. 클라우드 컴퓨팅에서 고해상도 수치 해양 모델 구현의 재현성과 이식성을 검증하기 위해 북서태평양, 동해, 황해 등 모델 영역의 다양한 해상도에서 수치 해양 모델의 성능을 시뮬레이션하고 분석하였다. 컨테이너화 방식을 통해 다양한 인프라 환경 변화에 대응하고 계산 재현성을 효과적으로 확보할 수 있었다. 머신러닝 기반 데이터 생성의 적용을 검증하기 위해 생성 모델을 이용한 표층 이하 온도 데이터의 데이터 증강을 실행하여 해양의 수직 온도 분포를 예측하는 모델 훈련을 위한 대용량 데이터 세트를 준비했다. 예측모델 훈련을 위해 위성 데이터에 비해 상대적으로 부족한 관측 데이터에 대해서 생성 모델을 사용하여 데이터 증강을 수행하였다. 모델의 예측성능 비교에는 관측 데이터 외에도 HYCOM 데이터 세트를 사용하였으며, 증강 데이터의 데이터 분포는 입력 데이터 분포와 유사함을 확인하였다. 독립형 예측 모델을 결합한 앙상블 방식은 기존 관측 데이터를 기반으로 하는 예측 모델의 성능에 비해 향상되었다. 데이터합성을 위해 많은 양의 계산 자원이 필요했으며, 데이터 합성은 클라우드 기반 GPU 환경에서 수행되었다. 고해상도 수치 해양 모델 시뮬레이션, 예측 모델 개발, 데이터 생성 방법은 해양 과학 분야에서 예측 능력을 향상시킬 수 있다. 본 연구에서 사용된 클라우드 컴퓨팅 기반의 수치 모델링 및 생성 모델은 지구 과학의 다양한 분야에 광범위하게 적용될 수 있다.1. General Introduction 1 2. Performance of numerical ocean modeling on cloud computing 6 2.1. Introduction 6 2.2. Cloud Computing 9 2.2.1. Cloud computing overview 9 2.2.2. Commercial cloud computing services 12 2.3. Numerical model for performance analysis of commercial clouds 15 2.3.1. High Performance Linpack Benchmark 15 2.3.2. Benchmark Sustainable Memory Bandwidth and Memory Latency 16 2.3.3. Numerical Ocean Model 16 2.3.4. Deployment of Numerical Ocean Model and Benchmark Packages on Cloud Clusters 19 2.4. Simulation results 21 2.4.1. Benchmark simulation 21 2.4.2. Ocean model simulation 24 2.5. Analysis of ROMS performance on commercial clouds 26 2.5.1. Performance of ROMS according to H/W resources 26 2.5.2. Performance of ROMS according to grid size 34 2.6. Summary 41 3. Reproducibility of numerical ocean model on the cloud computing 44 3.1. Introduction 44 3.2. Containerization of numerical ocean model 47 3.2.1. Container virtualization 47 3.2.2. Container-based architecture for HPC 49 3.2.3. Container-based architecture for hybrid cloud 53 3.3. Materials and Methods 55 3.3.1. Comparison of traditional and container based HPC cluster workflows 55 3.3.2. Model domain and datasets for numerical simulation 57 3.3.3. Building the container image and registration in the repository 59 3.3.4. Configuring a numeric model execution cluster 64 3.4. Results and Discussion 74 3.4.1. Reproducibility 74 3.4.2. Portability and Performance 76 3.5. Conclusions 81 4. Generative models for the prediction of ocean temperature profile 84 4.1. Introduction 84 4.2. Materials and Methods 87 4.2.1. Model domain and datasets for predicting the subsurface temperature 87 4.2.2. Model architecture for predicting the subsurface temperature 90 4.2.3. Neural network generative models 91 4.2.4. Prediction Models 97 4.2.5. Accuracy 103 4.3. Results and Discussion 104 4.3.1. Data Generation 104 4.3.2. Ensemble Prediction 109 4.3.3. Limitations of this study and future works 111 4.4. Conclusion 111 5. Summary and conclusion 114 6. References 118 7. Abstract (in Korean) 140박

Improving the thermocline calculation over the global ocean

According to the typical thermal structure of the ocean, the water column can be divided into three layers: the mixed layer, the thermocline and the deep layer. In this study, we provide a new methodology, based on a function adjustment to the temperature profile, to locate the minimum and maximum depths of the strongest thermocline. We first validated our methodology by comparing the mixed layer depth obtained with the method proposed here with three other methods from previous studies. Since we found a very good agreement between the four methods we used the function adjustment to compute the monthly climatologies of the maximum thermocline depth and the thermocline thickness and strength in the global ocean. We also provide an assessment of the regions of the ocean where our adjustment is valid, i.e., where the thermal structure of the ocean follows the three-layer structure. However, there are ocean regions where the water column cannot be separated into three layers due to the dynamic processes that alter it. This assessment highlights the limitations of the existing methods to accurately determine the mixed layer depth and the thermocline depth in oceanic regions that are particularly turbulent such as the Southern Ocean and the northern North Atlantic, among others. The method proposed here has shown to be robust and easy to apply.</p