Article
Meteorology & Atmospheric Sciences
Emy Alerskans, Eigil Kaas
Summary: Six adaptive, short-term post-processing methods were evaluated for correcting systematic errors in numerical weather prediction forecasts. The combination of moving average and lead time Kalman filter was found to perform the best across different NWP models.
METEOROLOGICAL APPLICATIONS
(2021)
Article
Instruments & Instrumentation
Wuzhi Min, Hui Zhao, Yingzhi Li, Liang Qin, Lan Cheng
Summary: In this study, a filtering scheme was proposed to extract high-precision sensor information. The scheme utilized a model-less prediction filter based on the principle of Kalman gain, which compensated for gain measurement noise and adjusted process noise. Compared to various Kalman filter methods, the proposed algorithm demonstrated better accuracy in the steady state. The high precision performance and effectiveness of the model-less prediction filter were verified under a digitally controlled linear power supply.
REVIEW OF SCIENTIFIC INSTRUMENTS
(2023)
Article
Green & Sustainable Science & Technology
Christina Brester, Viivi Kallio-Myers, Anders Lindfors, Mikko Kolehmainen, Harri Niska
Summary: The effective integration of solar PV output into overall energy consumption planning and control depends on accurate PV forecasting. However, the availability of numerical weather prediction (NWP) data poses a challenge in training data-driven PV forecasting models. In this study, an artificial neural network (ANN) is trained on weather observations and tested on NWP data, showing better performance than a physical model.
Article
Meteorology & Atmospheric Sciences
Yurii Batrak
Summary: Introduction of new sea ice data assimilation framework in HARMONIE-AROME numerical weather prediction system has shown a reduction in analyzed and forecasted ice surface temperature root mean square error (RMSE) by 0.4 degrees C on average, with effects persisting for up to 24 h forecast. This positive impact highlights the importance of using sea ice data assimilation to improve the accuracy of short-range numerical weather predictions in the Arctic.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2021)
Article
Astronomy & Astrophysics
Michael R. Goodliff, Steven J. Fletcher, Anton J. Kliewer, Andrew S. Jones, John M. Forsythe
Summary: The Gaussian assumption is commonly used in data assimilation and numerical weather prediction systems, but combining lognormally distributed random variables with Gaussian distributions can better capture error interactions. Dynamically changing the formulation of the data assimilation system based on distribution changes allows for more accurate assimilation of observational data. Using machine learning techniques to detect and predict distribution changes can improve data assimilation analysis errors compared to using a single distribution type for the entire dataset.
EARTH AND SPACE SCIENCE
(2022)
Article
Engineering, Marine
C. T. Liong, K. H. Chua
Summary: This paper presents a data assimilation framework for vessel motion prediction in real-time, combining artificial neural network (ANN) and Ensemble Kalman Filter (EnKF). The framework improves prediction accuracy by incorporating measured data with ANN predictions, especially for experimental measurements with higher uncertainties.
Article
Geosciences, Multidisciplinary
Li Xiang, Jie Xiang, Jiping Guan, Lifeng Zhang, Zenghui Cao, Jilu Xia
Summary: This study proposes a spatiotemporal convolutional network (STCNet) for local weather prediction post-processing, which uses feature extraction and time-series processing to improve the accuracy of local weather prediction.
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Engineering, Civil
Pengcheng Zhao, Quan J. Wang, Wenyan Wu, Qichun Yang
Summary: This paper proposes a two-step calibration approach that combines the strengths of joint probability models and the useful information included in the ensemble spread. In the first step, the ensemble mean is calibrated using a seasonally coherent calibration model. In the second step, the ensemble forecasts are re-calibrated to incorporate the ensemble spread information. The results show that forecasts calibrated using the two-step calibration approach have better skills.
JOURNAL OF HYDROLOGY
(2022)
Article
Chemistry, Analytical
Yuxi Li, Gang Hao
Summary: This paper proposes an improved modified model predictive control algorithm by combining the Sage-Husa adaptive Kalman filter (SHAKF), the cubature Kalman filter (CKF), and the back-propagation neural network (BPNN) to mitigate the negative impacts of system noise on energy-optimal adaptive cruise control (EACC) and achieve further energy reduction.
Article
Energy & Fuels
Lorenzo Donadio, Jiannong Fang, Fernando Porte-Agel
Summary: The study proposed two hybrid NWP and ANN models for wind power forecasting over complex terrain, where one directly predicts wind power and the other predicts wind speed first and then converts it to power. Model 2 performed well, showing lower error rates compared to other models in the same category.
Article
Meteorology & Atmospheric Sciences
Zhongrui Wang, Lili Lei, Jeffrey L. Anderson, Zhe-Min Tan, Yi Zhang
Summary: Two convolutional neural network-based localization methods are proposed in this article, which can better capture the structures of the Kalman gain and generate improved analyses and forecasts in cycling assimilations.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Article
Construction & Building Technology
Yanzhe Zhang, Yong Ding, Jianqing Bu, Lina Guo
Summary: The paper proposes a modification to the square root UKF method (MSRUKF) by utilizing QR decomposition, ensuring unconditional numerical stability and improving the tracking capability for time-variant parameters by incorporating an adaptive forgetting factor.
STRUCTURAL CONTROL & HEALTH MONITORING
(2023)
Article
Multidisciplinary Sciences
Weina Chen, Zhong Yang, Shanshan Gu, Yizhi Wang, Yujuan Tang
Summary: This paper proposes an adaptive transfer alignment method based on observability analysis to improve the navigation accuracy of airborne pods. By analyzing the observability of each state variable in the electronic system and using a transfer alignment filter algorithm with adaptive adjustment factor, the influence of weakly observable state variables on the entire filter is reduced, thus improving the estimation accuracy of transfer alignment.
SCIENTIFIC REPORTS
(2022)
Review
Chemistry, Multidisciplinary
Dah-Jing Jwo, Amita Biswal, Ilayat Ali Mir
Summary: As artificial intelligence becomes more prevalent, several machine learning methodologies, such as artificial neural networks (ANN), are gaining popularity. ANN can be used as a black-box modeling strategy without the need for a detailed physical model of the system. Extensive research has been conducted on ANN to enhance navigation performance.
APPLIED SCIENCES-BASEL
(2023)
Article
Mathematics
Francisco Garcia Riesgo, Sergio Luis Suarez Gomez, Enrique Diez Alonso, Carlos Gonzalez-Gutierrez, Jesus Daniel Santos
Summary: This study utilized fully convolutional neural networks to address the complexities of solar Shack-Hartmann wavefront sensor correlations, comparing networks that use sensor images and correlations images as inputs. The results showed an improvement in phase recovery performance with the image-to-phase approach, achieving up to 93% similarity in recovering turbulence from high-altitude layers.
Article
Environmental Sciences
O. Naughton, A. Donnelly, P. Nolan, F. Pilla, B. D. Misstear, B. Broderick
SCIENCE OF THE TOTAL ENVIRONMENT
(2018)
Article
Environmental Sciences
Connie O'Driscoll, Jose L. J. Ledesma, John Coll, John G. Murnane, Paul Nolane, Eva M. Mockler, Martyn N. Futter, Liwen W. Xiao
SCIENCE OF THE TOTAL ENVIRONMENT
(2018)
Review
Energy & Fuels
Conor Sweeney, Ricardo J. Bessa, Jethro Browell, Pierre Pinson
WILEY INTERDISCIPLINARY REVIEWS-ENERGY AND ENVIRONMENT
(2020)
Article
Meteorology & Atmospheric Sciences
Joao M. Correia, Frank McDermott, Conor Sweeney, Eadaoin Doddy, Seanie Griffin
METEOROLOGICAL APPLICATIONS
(2020)
Editorial Material
Meteorology & Atmospheric Sciences
Conor Sweeney
Article
Geosciences, Multidisciplinary
Chris D. Jones, Jonathan E. Hickman, Steven T. Rumbold, Jeremy Walton, Robin D. Lamboll, Ragnhild B. Skeie, Stephanie Fiedler, Piers M. Forster, Joeri Rogelj, Manabu Abe, Michael Botzet, Katherine Calvin, Christophe Cassou, Jason N. S. Cole, Paolo Davini, Makoto Deushi, Martin Dix, John C. Fyfe, Nathan P. Gillett, Tatiana Ilyina, Michio Kawamiya, Maxwell Kelley, Slava Kharin, Tsuyoshi Koshiro, Hongmei Li, Chloe Mackallah, Wolfgang A. Mueller, Pierre Nabat, Twan van Noije, Paul Nolan, Rumi Ohgaito, Dirk Olivie, Naga Oshima, Jose Parodi, Thomas J. Reerink, Lili Ren, Anastasia Romanou, Roland Seferian, Yongming Tang, Claudia Timmreck, Jerry Tjiputra, Etienne Tourigny, Kostas Tsigaridis, Hailong Wang, Mingxuan Wu, Klaus Wyser, Shuting Yang, Yang Yang, Tilo Ziehn
Summary: The models show reduced aerosol amounts in 2020, particularly over southern and eastern Asia, leading to increased surface shortwave radiation levels. However, the impact on near-surface temperature or rainfall during 2020-2024 is extremely small and not detectable in this initial analysis. Further regional analyses and closer attention to extremes are needed to evaluate the impact of COVID-19-related emission reductions on near-term climate.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Article
Ecology
Marina Reyne, Natasha E. McGowan, Jason Flanagan, Paul Nolan, Aurelie Aubry, Mark Emmerson, Ferdia Marnell, Neil Reid
Summary: The global amphibian crisis is driven by various stressors, with climate change likely influencing environmental suitability, ranges, reproduction, and phenology. This study focused on characterizing the bioclimatic-habitat niche space of the Natterjack toad in Europe and assessing the impact of climate on its environmental suitability and breeding behavior. Projections suggest that future climate change may increase fecundity in Ireland, with spawning potentially commencing earlier under high greenhouse gas emission scenarios.
ECOLOGY AND EVOLUTION
(2021)
Article
Environmental Sciences
Eadaoin Doddy Clarke, Seanie Griffin, Frank McDermott, Joao Monteiro Correia, Conor Sweeney
Summary: Careful consideration should be given to which reanalysis dataset to use when preparing analysis for a project, as the accuracy of the dataset is crucial for future planning in the renewable energy sector. While the newer ERA5 dataset generally outperforms others, the best performing dataset can vary depending on the variable of interest and location. Errors in these datasets highlight the importance of considering datasets tailored specifically for renewable energy resource modeling.
Editorial Material
Meteorology & Atmospheric Sciences
Eadaoin Doddy Clarke, Conor Sweeney
Article
Biodiversity Conservation
Marina I. Reyne, Kara Dicks, Jason Flanagan, Paul Nolan, Joshua P. Twining, Aurelie Aubry, Mark Emmerson, Ferdia Marnell, Sarah Helyar, Neil Reid
Summary: Habitat fragmentation and loss have negative impacts on population size and connectivity, which endanger species survival. This study investigated the genetic structure, gene flow, and dispersal of the Natterjack toad in Ireland, a species listed as Endangered. The results showed spatial structuring and genetic distances increasing with geographic distance, with different habitats and anthropogenic factors influencing gene flow. The high genetic diversity of the Natterjack toad population in Ireland highlights the importance of maintaining habitat connectivity and implementing management strategies to prevent further declines.
CONSERVATION GENETICS
(2023)
Article
Environmental Sciences
Enda O'Brien, Paul Nolan
Summary: The TRANSLATE project, established in 2021 by Met eireann, aims to provide standardized future climate projections for Ireland. This paper outlines the principles and methods used to generate the first set of such projections and presents selected results up to the end of the 21st century. Analysis of two separate ensembles of downscaled CMIP5 projections shows consistent results, increasing confidence in the methods used. The future projected fields show detail depending on local geography, while the change maps relative to the base period are smoother, reflecting the global climate change signal. Uncertainty is represented by different emission scenarios and climate sensitivities.
FRONTIERS IN CLIMATE
(2023)
Article
Energy & Fuels
Eadaoin Doddy Clarke, Conor Sweeney, Frank McDermott, Seanie Griffin, Joao Monteiro Correia, Paul Nolan, Laura Cooke
Summary: The study using climate model simulation data found that wind energy generation in Ireland is projected to decrease overall in the future, with more pronounced impacts by the late 21st century. Seasonally, there may be a slight increase in wind energy generation in winter, but a decrease in summer. The research also discovered a reversed pattern of duration at different levels of the power curve.
Article
Environmental Sciences
Lara Hawchar, Owen Naughton, Paul Nolan, Mark G. Stewart, Paraic C. Ryan
CLIMATE RISK MANAGEMENT
(2020)
Proceedings Paper
Multidisciplinary Sciences
Jason Flanagan, Paul Nolan, Ray McGrath, Christopher Werner
ADVANCES IN SCIENCE AND RESEARCH
(2019)
