Article
Multidisciplinary Sciences
Mickael D. Chekroun, Ilan Koren, Honghu Liu, Huan Liu
Summary: This article presents a unified stochastic framework to rectify the oscillations of nonlinear time delay systems and enrich their temporal variabilities through nonlinear responses to stochastic perturbations. Two paradigms of noise-driven chaos, low-dimensional stretch-and-fold mechanism and high-dimensional swarm-like behaviors, are identified. The theory is applied to cloud delay models and successfully reproduces the complex temporal variabilities of cloud simulations.
Article
Environmental Sciences
Ian Boutle, Wayne Angevine, Jian-Wen Bao, Thierry Bergot, Ritthik Bhattacharya, Andreas Bott, Leo Duconge, Richard Forbes, Tobias Goecke, Evelyn Grell, Adrian Hill, Adele L. Igel, Innocent Kudzotsa, Christine Lac, Bjorn Maronga, Sami Romakkaniemi, Juerg Schmidli, Johannes Schwenkel, Gert-Jan Steeneveld, Benoit Vie
Summary: This study compares 10 single-column (SCM) models and 5 large-eddy simulation (LES) models for a radiation fog case. The SCM models tend to overdevelop fog, while the quality of LES model results is variable. The ability of a model to accurately simulate the subtle balance between water lost to the surface and water condensed into fog strongly determines the forecast quality.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2022)
Article
Meteorology & Atmospheric Sciences
Mark Weber, Kurt Hondl, Nusrat Yussouf, Youngsun Jung, Derek Stratman, Bryan Putnam, Xuguang Wang, Terry Schuur, Charles Kuster, Yixin Wen, Juanzhen Sun, Jeff Keeler, Zhuming Ying, John Cho, James Kurdzo, Sebastian Torres, Chris Curtis, David Schvartzman, Jami Boettcher, Feng Nai, Henry Thomas, Dusan Zrnic, Igor Ivic, Djordje Mirkovic, Caleb Fulton, Jorge Salazar, Guifu Zhang, Robert Palmer, Mark Yeary, Kevin Cooley, Michael Istok, Mark Vincent
Summary: The research discusses the potential benefits of using polarimetric phased-array radar (PAR) for NOAA's future national operational weather radar network, highlighting improvements in severe-weather warning performance and reduction in casualties through adaptive volumetric scanning.
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2021)
Review
Oncology
Maria Alieva, Amber K. L. Wezenaar, Ellen J. Wehrens, Anne C. Rios
Summary: Live-cell imaging provides a deeper understanding of cancer response to treatment by offering spatial, molecular, and morphological data over time. It has been applied to uncover tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. The unique opportunity of time-lapse imaging in capturing the interactivity and motility of immunotherapies is also discussed. Recent technological advancements in multidimensional imaging and multi-omics data integration have allowed for the connection of single-cell dynamics to molecular phenotypes. These advancements significantly contribute to our understanding of tumour targeting and have implications for next-generation precision medicine.
NATURE REVIEWS CANCER
(2023)
Article
Geosciences, Multidisciplinary
Xingying Huang, Andrew Gettelman, William C. Skamarock, Peter Hjort Lauritzen, Miles Curry, Adam Herrington, John T. Truesdale, Michael Duda
Summary: This study uses a state-of-the-art storm-resolving global climate model (SIMA-MPAS) to simulate storms and precipitation, showing more realistic regional climate variability and fine-scale features. Comparisons with traditional regional climate models demonstrate the advantages of SIMA-MPAS.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Physics, Applied
Daiki Harada, Perawut Chinnavornrungsee, Songkiate Kittisontirak, Nuwong Chollacoop, Sasiwimon Songtrai, Kobsak Sriprapha, Jun Yoshino, Tomonao Kobayashi
Summary: Many PV systems are connected to power grids, which can become unstable due to fluctuations in PV output. Numerical weather prediction models are used to forecast solar irradiance and manage the grid effectively. By conducting a sensitivity analysis, the optimal combination of schemes for accurate forecasting in tropical regions, such as Thailand, has been determined, resulting in an increase in correlation coefficient from 0.773 to 0.814.
JAPANESE JOURNAL OF APPLIED PHYSICS
(2023)
Article
Multidisciplinary Sciences
M. G. Schultz, C. Betancourt, B. Gong, F. Kleinert, M. Langguth, L. H. Leufen, A. Mozaffari, S. Stadtler
Summary: The recent hype around artificial intelligence has renewed interest in applying successful deep learning methods in the field of meteorology. Evidence suggests that better weather forecasts can be achieved with the introduction of big data mining and neural networks. However, fundamental breakthroughs are needed before numerical weather models can be completely replaced by DL approaches.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2021)
Article
Geosciences, Multidisciplinary
Wenjie Zhang, Xiaoye Zhang, Hong Wang
Summary: This study evaluates the role of aerosol-radiation interaction (ARI) in meteorology prediction in China using the NWP model GRAPES_CUACE. The results indicate that the online calculation of ARI can significantly improve the accuracy of meteorology prediction, particularly in heavy pollution areas and stages.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Green & Sustainable Science & Technology
Dazhi Yang, Guoming Yang, Bai Liu
Summary: This study focuses on optimally combining quantiles of ensemble solar forecasts that have been post-processed. Numerical weather prediction (NWP) is used to provide dynamical ensemble irradiance forecasts for solar energy grid integration. However, these ensemble members often lack dispersion, leading to the use of statistical calibration techniques such as quantile regression (QR) and ensemble model output statistics (EMOS). Due to the numerous variants of QR and EMOS, it is unclear which variant performs best in different situations, motivating the combination of quantile forecasts. A framework for combining solar forecasts in the form of quantiles is proposed and demonstrated using a constrained quantile regression averaging scheme. Results show that combining quantiles is an effective strategy in improving the calibrated forecasts.
Article
Meteorology & Atmospheric Sciences
Yuxiao Chen, Jing Chen, Dehui Chen, Zhizhen Xu, Jie Sheng, Fajing Chen
Summary: A new simulated radar reflectivity calculation method has been developed in this study, significantly improving the reflectivity products and enhancing the reliability and accuracy of precipitation indication in the model. This method has good prospects for providing more information about forecasting precipitation and convective activity in operational models.
WEATHER AND FORECASTING
(2021)
Article
Geosciences, Multidisciplinary
Samuel Brenner, Christopher Horvat, Paul Hall, Anna Lo Piccolo, Baylor Fox-Kemper, Stephane Labbe, Veronique Dansereau
Summary: The size and interactions of individual ice floes in sea ice can greatly influence the turbulence within the ice-ocean boundary layer. This is important for understanding heat and momentum exchange between the atmosphere and ocean. A new modeling framework has been used to study the effects of floe-scale turbulence, and a parameterization method has been developed to represent this turbulence in climate models.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Environmental Sciences
Anna Napoli, Fabien Desbiolles, Antonio Parodi, Claudia Pasquero
Summary: Aerosols play a crucial role in climate through different feedback mechanisms, affecting radiation, clouds, and air column stability. This study focused on the altitude dependence of aerosol-induced cloud-mediated effects in the Great Alpine Region, showing that seasonal cloud cover, temperature, and precipitation are influenced by aerosol concentrations in the air column, with the overall cloud cover increase leading to surface cooling or warming depending on surface albedo. Different cloud types respond differently to aerosol levels, with convective clouds in summer potentially decreasing at high pollution levels due to increased air column stability.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2022)
Article
Environmental Sciences
Chang-Hung Lin, Ming-Jen Yang, Ling-Feng Hsiao, Jen-Her Chen
Summary: This study modified the scale-aware parameterization method to improve precipitation forecasting. The results show significant improvements in frontal precipitation forecasts, but there is a warm bias in synoptic-scale features. Further modifications are proposed to enhance the overall performance of medium-range weather forecasts.
Article
Computer Science, Information Systems
Chih-Ying Chen, Nan-Ching Yeh, Yao-Chung Chuang, Chuan-Yao Lin
Summary: In this study, a low-cost, low-power high-performance weather prediction system was developed using the Raspberry Pi platform, which can be used in atmospheric science education and local weather forecasting applications.
Article
Meteorology & Atmospheric Sciences
Philipp Hess, Niklas Boers
Summary: The accurate prediction of heavy rainfall events remains challenging for numerical weather prediction models. In this study, a U-Net-based deep neural network is used to learn heavy rainfall events from a NWP ensemble. A frequency-based weighting method is proposed to enable the learning of heavy rainfall events in the distributions' tails. Applying this method in post-processing improves the forecast skill of heavy rainfall events.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
