기후 모델링은 지구 기후 시스템에 대한 수학적 표현을 개발하고 적용하는 것으로, 대기, 해양, 육지 및 빙권 구성 요소를 통합하여 과거, 현재 및 미래의 기후 상태를 시뮬레이션합니다. 이러한 모델은 다양한 공간적 및 시간적 규모에서 작동하며, 물리적, 화학적 및 생물학적 과정을 통합하여 기후 변동성을 분석하고 예측합니다. 기후 변화 impacts, and assess feedback mechanisms. Advances in computational methods, parameterization schemes, and data assimilation techniques drive improvements in model accuracy and resolution. The following page compiles the latest peer-reviewed publications and patented technologies that advance climate modeling 방법론, enhance model components, and refine projections critical for scientific research and policy formulation.
본 자료는 기후 모델링에 관한 전 세계 영어 논문 및 특허를 엄선하여 정리한 것으로, 다양한 온라인 과학 저널에 게재된 자료들을 기후 모델, 지구 순환 모델(GCM), 지구 시스템 모델, 기후 시뮬레이션, 대기 모델, 해양 모델, 결합 모델, 복사 강제력, 기후 민감도, 매개변수화, 기후 예측, 앙상블 모델링, 기후 피드백, 기후 변동성, 다운스케일링, 기후 시나리오, 탄소 순환 모델, 기후 예측, 모델 초기화, 기후 데이터 동화, 기후 강제력, 기후 평형, 과도기적 기후 반응, 기후 원격 상관, 기후 편향 보정, 기후 모델 상호 비교, 기후 역학 및 기후 불확실성 등의 주제로 분류하여 제공합니다.
an integrated architectural strategy for life-cycle carbon assessment and resilience optimization
Published on 2026-10-24 by @OXFORD
Abstract: AbstractBackground: High-density cities are facing the dual challenges of decarbonisation and climate resilience under the global climate crisis and the frequent occurrence of extreme weather. This study proposes an integrated architectural strategy of “multi-dimensional corridor circulation and decarbonisation”, which integrates life-cycle carbon assessment of urban connecting corridors, urban microclimate simulation and resilience optimisation of physical-social systems, and aims to achiev[...]
Our summary: This study proposes an integrated architectural strategy for high-density cities to address decarbonisation and climate resilience. It combines life-cycle carbon assessment, urban microclimate simulation, and resilience optimization. The results indicate significant carbon reduction and improved thermal environments while maintaining connectivity in urban corridors.
decarbonisation, life-cycle assessment, urban microclimate, resilience optimization
Publication
Methods for optimising the atmospheric radiative forcing of aircraft flight operations on climate by forecasting and validating aircraft contrail form
Patent published on the 2026-09-24 in US under Ref US20260287785 by DURANT ADAM [GB] (Durant Adam [gb])
Abstract: Disclosed is a method for determining an atmospheric radiative forcing difference by optimising or preventing contrail formation caused by an aircraft. The method comprises receiving one or more weather parameters to determine contrail forecast data; receiving one or more flight parameters associated with aircraft to determine flight data; determining tentative atmospheric radiative forcing quantity, along tentative flight trajectory, based on contrail forecast data and flight data; altering one[...]
Our summary: This method optimizes aircraft flight operations to reduce atmospheric radiative forcing by forecasting contrail formation. It involves analyzing weather and flight parameters to determine an optimal flight trajectory. The approach is validated using imagery data to assess the impact on radiative forcing differences.
contrail formation, atmospheric radiative forcing, flight trajectory optimization, weather parameters
Patent
Volumetric content data processing systems and methods
Patent published on the 2026-09-23 in EP under Ref EP4811290 by TNO [NL] (Subramanyam Shishir [nl], Cernigliaro Gianluca [nl])
Abstract: A volumetric content data processing system for processing first volumetric content data comprising first volumetric geometry data and first volumetric attribute data is described wherein the processing system comprises a memory and at least one processor, coupled to the memory, wherein the at least one processor is configured to: -perform a first encoding process (ENC-1(G)) on the first volumetric geometry data to obtain encoded volumetric geometry data; perform a first decoding process (DEC-1([...]
Our summary: A volumetric content data processing system processes first volumetric geometry and attribute data. It encodes and decodes volumetric geometry data to generate video attribute data. The system utilizes trained neural networks to evaluate and encode video attributes into a bitstream.
volumetric data processing, encoding, decoding, neural networks
Patent
Encrypted data management apparatus, conversion key generation apparatus, decryption apparatus, encrypted data conversion system, encrypted data manag
Patent published on the 2026-07-23 in WO under Ref WO2026154691 by MITSUBISHI ELECTRIC CORP [JP] (Hayasaka Kenichiro [jp], Kawai Yutaka [jp])
Abstract: This encrypted data management apparatus comprises a conversion unit that, on the basis of a conversion source secret key for decrypting a conversion source ciphertext that is generated by encrypting a plaintext by using a GCM encryption function, and a conversion destination secret key for decrypting a conversion destination ciphertext that is generated by converting the conversion source ciphertext by using a first conversion key for converting the conversion source ciphertext, generates an in[...]
Our summary: The apparatus manages encrypted data through a conversion unit. It generates intermediate tags for message authentication. The process involves using secret keys for decryption and conversion.
encrypted data management, conversion key generation, decryption apparatus, GCM encryption
Patent
Fire and source prediction using dynamic weighting-based ensemble modeling and probabilistic cause determination
Patent published on the 2026-07-15 in EP under Ref EP4776193 by TATA CONSULTANCY SERVICES LTD [IN] (Mohite Jayantrao [in], Sawant Suryakant Ashok [in], Singh Dineshkumar Jang Bahadur [in], Pandit Ankur [in], Pappula Srinivasu [in])
Abstract: In traditional ensemble modeling multiple individual models are trained on the same set of input variables without considering variations in spatial and temporal resolutions. A method and system for dynamic forest fire prediction and source prediction is proposed using dynamic weighting-based ensemble modeling and probabilistic cause determination. The adaptive ensemble model dynamically re-weights multiple base models, each optimized for different data scales, to provide accurate predictions ba[...]
Our summary: A dynamic ensemble modeling method is proposed for accurate fire and source prediction. The model adapts to variations in spatial and temporal data resolutions by re-weighting base models. Continuous monitoring of environmental variables allows for recalibrating predictions and determining probable causes of forest fires.
ensemble modeling, fire prediction, dynamic weighting, probabilistic cause determination
Patent
Predicting cardiologist level clinical explanations for electrocardiogram signal classification using deep learning
Patent published on the 2026-07-15 in EP under Ref EP4776295 by TATA CONSULTANCY SERVICES LTD [IN] (Jose Akshay Kiran [in], Ukil Arijit [in], Deb Trisrota [in], Pal Arpan [in])
Abstract: [0001] This disclosure relates generally to a method and system for predicting cardiologist level clinical explanations for electrocardiogram signal classification using deep learning. Conventional methods for explainability of artificial intelligence models are influenced by data distribution and do not provide clinical level explanations in cardiac healthcare. The method disclosed provides clinical level explanations for rhythm diagnosis from ECG signals. The method validates that clinical con[...]
Our summary: The method predicts cardiologist-level clinical explanations for ECG signal classification using deep learning. It generates synthetic ECG signals with generative adversarial networks for validation. A clinical concept identifier model is trained to predict clinical concepts in classified ECG signals.
deep learning, electrocardiogram, clinical explanations, generative adversarial networks
Patent
Fire and source prediction using dynamic weighting-based ensemble modeling and probabilistic cause determination
Patent published on the 2026-07-09 in US under Ref US20260192141 by TATA CONSULTANCY SERVICES LTD [IN] (Mohite Jayantrao [in], Sawant Suryakant Ashok [in], Singh Dineshkumar Jang Bahadur [in], Pandit Ankur [in], Pappula Srinivasu [in])
Abstract: [0000] In traditional ensemble modeling multiple individual models are trained on the same set of input variables without considering variations in spatial and temporal resolutions. A method and system for dynamic forest fire prediction and source prediction is proposed using dynamic weighting-based ensemble modeling and probabilistic cause determination. The adaptive ensemble model dynamically re-weights multiple base models, each optimized for different data scales, to provide accurate predict[...]
Our summary: A method for forest fire and source prediction is proposed using dynamic weighting-based ensemble modeling. The adaptive model re-weights base models to enhance prediction accuracy based on varying data scales. Continuous monitoring and probabilistic cause determination provide insights into factors contributing to fire outbreaks.
ensemble modeling, fire prediction, dynamic weighting, probabilistic determination
Patent
Inter-Comparison of Deep Learning Models for Flood Forecasting in Ethiopia’s Upper Awash Basin
Published on 2026-02-03 by Girma Moges Mengistu, Addisu G. Semie, Gulilat T. Diro, Natei Ermias Benti, Emiola O. Gbobaniyi, Yonas Mersha @MDPI
Abstract: Flood events driven by climate variability and change pose significant risks for socio-economic activities in the Awash Basin, necessitating advanced forecasting tools. This study benchmarks five deep learning (DL) architectures, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and a Hybrid CNN–LSTM, for daily discharge forecasting for the Hombole catchment in the Upper Awash Basin (UAB) using 40 years of hy[...]
Our summary: This study benchmarks five deep learning architectures for daily discharge forecasting in Ethiopia s Upper Awash Basin. The Hybrid CNN–LSTM model achieved the best performance, while all deep learning models outperformed traditional baselines. Findings suggest deep learning methods enhance flood early-warning systems, though challenges in peak-flow magnitude prediction remain.
Deep Learning, Flood Forecasting, Hydrometeorological, Model Evaluation
Publication
