scholarly journals Process‐based climate model development harnessing machine learning: II. model calibration from single column to global

2020 ◽  
Author(s):  
Frédéric Hourdin ◽  
Daniel Williamson ◽  
Catherine Rio ◽  
Fleur Couvreux ◽  
Romain Roehrig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Calibration ◽  
Climate Model ◽  
Model Development ◽  
Single Column

scholarly journals Process-based climate model development harnessing machine learning: II. model calibration from single column to global

2020 ◽  
Author(s):  
Frédéric Hourdin ◽  
Danny Williamson ◽  
Catherine Rio ◽  
Fleur Couvreux ◽  
Romain Roehrig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Calibration ◽  
Climate Model ◽  
Model Development ◽  
Single Column

scholarly journals Process‐based climate model development harnessing machine learning: I. a calibration tool for parameterization improvement

2020 ◽  
Author(s):  
Fleur Couvreux ◽  
Frédéric Hourdin ◽  
Daniel Williamson ◽  
Romain Roehrig ◽  
Victoria Volodina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climate Model ◽  
Model Development ◽  
Calibration Tool

scholarly journals Process-based climate model development harnessing machine learning: I. a calibration tool for parameterization improvement

2020 ◽  
Author(s):  
Fleur Couvreux ◽  
Frédéric Hourdin ◽  
Danny Williamson ◽  
Romain Roehrig ◽  
Victoria Volodina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climate Model ◽  
Model Development ◽  
Calibration Tool

scholarly journals Process-based climate model development harnessing machine learning: III. The Representation of Cumulus Geometry and their 3D Radiative Effects

2020 ◽  
Author(s):  
Najda Villefranque ◽  
Danny Williamson ◽  
Fleur Couvreux ◽  
Frédéric Hourdin ◽  
Jacques Gautrais ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climate Model ◽  
Model Development ◽  
Radiative Effects

scholarly journals The ICON Single-Column Mode

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 906
Author(s):  
Ivan Bašták Ďurán ◽  
Martin Köhler ◽  
Astrid Eichhorn-Müller ◽  
Vera Maurer ◽  
Juerg Schmidli ◽  
...  
Keyword(s):  
Data Storage ◽  
Model Development ◽  
Computational Cost ◽  
Three Dimensional ◽  
Shallow Convection ◽  
Modeling Framework ◽  
Additional Tool ◽  
Eddy Simulation ◽  
Single Column ◽  
Stratocumulus Clouds

The single-column mode (SCM) of the ICON (ICOsahedral Nonhydrostatic) modeling framework is presented. The primary purpose of the ICON SCM is to use it as a tool for research, model evaluation and development. Thanks to the simplified geometry of the ICON SCM, various aspects of the ICON model, in particular the model physics, can be studied in a well-controlled environment. Additionally, the ICON SCM has a reduced computational cost and a low data storage demand. The ICON SCM can be utilized for idealized cases—several well-established cases are already included—or for semi-realistic cases based on analyses or model forecasts. As the case setup is defined by a single NetCDF file, new cases can be prepared easily by the modification of this file. We demonstrate the usage of the ICON SCM for different idealized cases such as shallow convection, stratocumulus clouds, and radiative transfer. Additionally, the ICON SCM is tested for a semi-realistic case together with an equivalent three-dimensional setup and the large eddy simulation mode of ICON. Such consistent comparisons across the hierarchy of ICON configurations are very helpful for model development. The ICON SCM will be implemented into the operational ICON model and will serve as an additional tool for advancing the development of the ICON model.

Assessment of Machine Learning-Based Audiovisual Quality Predictors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Mythili K. ◽  
Manish Narwaria
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Quality Assessment ◽  
Computational Models ◽  
Model Development ◽  
Multimedia Communication ◽  
Current Approach ◽  
Learning Ability ◽  
Data Uncertainty ◽  
Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

scholarly journals Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1044
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Predictive Maintenance ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Building Maintenance ◽  
Collection Data ◽  
Iot Devices

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

Stepwise improvement of hydrological models using satellite-based evaporation and total water storage estimations

2021 ◽  
Author(s):  
Markus Hrachowitz ◽  
Petra Hulsman ◽  
Hubert Savenije
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Model Development ◽  
Water Storage ◽  
Spatiotemporal Variability ◽  
Model Structure ◽  
Hydrological Models ◽  
Total Water ◽  
Spatiotemporal Model ◽  
Multiple Variables

<p>Hydrological models are often calibrated with respect to flow observations at the basin outlet. As a result, flow predictions may seem reliable but this is not necessarily the case for the spatiotemporal variability of system-internal processes, especially in large river basins. Satellite observations contain valuable information not only for poorly gauged basins with limited ground observations and spatiotemporal model calibration, but also for stepwise model development. This study explored the value of satellite observations to improve our understanding of hydrological processes through stepwise model structure adaption and to calibrate models both temporally and spatially. More specifically, satellite-based evaporation and total water storage anomaly observations were used to diagnose model deficiencies and to subsequently improve the hydrological model structure and the selection of feasible parameter sets. A distributed, process based hydrological model was developed for the Luangwa river basin in Zambia and calibrated with respect to discharge as benchmark. This model was modified stepwise by testing five alternative hypotheses related to the process of upwelling groundwater in wetlands, which was assumed to be negligible in the benchmark model, and the spatial discretization of the groundwater reservoir. Each model hypothesis was calibrated with respect to 1) discharge and 2) multiple variables simultaneously including discharge and the spatiotemporal variability in the evaporation and total water storage anomalies. The benchmark model calibrated with respect to discharge reproduced this variable well, as also the basin-averaged evaporation and total water storage anomalies. However, the evaporation in wetland dominated areas and the spatial variability in the evaporation and total water storage anomalies were poorly modelled. The model improved the most when introducing upwelling groundwater flow from a distributed groundwater reservoir and calibrating it with respect to multiple variables simultaneously. This study showed satellite-based evaporation and total water storage anomaly observations provide valuable information for improved understanding of hydrological processes through stepwise model development and spatiotemporal model calibration.</p>

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