जलवायु मॉडलिंग में पृथ्वी की जलवायु प्रणाली के गणितीय निरूपणों का विकास और अनुप्रयोग शामिल है, जिसमें अतीत, वर्तमान और भविष्य की जलवायु स्थितियों का अनुकरण करने के लिए वायुमंडलीय, महासागरीय, स्थलीय और क्रायोस्फेरिक घटकों को एकीकृत किया जाता है। ये मॉडल कई स्थानिक और लौकिक पैमानों पर कार्य करते हैं, और जलवायु परिवर्तनशीलता का विश्लेषण करने, पूर्वानुमान लगाने आदि के लिए भौतिक, रासायनिक और जैविक प्रक्रियाओं को शामिल करते हैं। जलवायु परिवर्तन 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 प्रौद्योगिकियों that advance climate modeling methodologies, enhance model components, and refine projections critical for scientific research and policy formulation.
यह जलवायु मॉडलिंग पर अंग्रेजी में प्रकाशित विश्वव्यापी प्रकाशनों और पेटेंटों का हमारा नवीनतम संकलन है, जिसमें कई वैज्ञानिक ऑनलाइन जर्नल शामिल हैं। इन्हें जलवायु मॉडल, सामान्य परिसंचरण मॉडल (जीसीएम), पृथ्वी प्रणाली मॉडल, जलवायु अनुकरण, वायुमंडलीय मॉडल, महासागर मॉडल, युग्मित मॉडल, विकिरण बल, जलवायु संवेदनशीलता, पैरामीटराइजेशन, जलवायु प्रक्षेपण, समूह मॉडलिंग, जलवायु प्रतिक्रिया, जलवायु परिवर्तनशीलता, डाउनस्केलिंग, जलवायु परिदृश्य, कार्बन चक्र मॉडल, जलवायु पूर्वानुमान, मॉडल आरंभीकरण, जलवायु डेटा आत्मसात्करण, जलवायु बल, जलवायु संतुलन, क्षणिक जलवायु प्रतिक्रिया, जलवायु टेलीकनेक्शन, जलवायु पूर्वाग्रह सुधार, जलवायु मॉडल अंतरतुलना, जलवायु गतिशीलता और जलवायु अनिश्चितता पर वर्गीकृत और केंद्रित किया गया है।
Machine learning systems and methods for improved statistical downscaling for extreme weather event modeling using generative diffusion models
Patent published on the 2026-05-21 in US under Ref US20260141139 by INSURANCE SERVICES OFFICE INC [US] (Sundar Rahul [in], Hu Yucong [ca], Parashar Nishant [in], Blanchard Antoine [us], Dodov Boyko [us])
Abstract: [0000] Machine learning systems and methods for extreme weather event modeling using generative diffusion models are provided. The system includes a weather modeling processor and a weather modeling engine executed by the processor. The weather modeling engine causes the processor to: receive a dataset including a plurality of vorticity samples; process the dataset using a deterministic mean model having a temporal attention unit to model spatial, cross-channel, and temporal dependencies using d[...]
Our summary: The system employs machine learning for improved modeling of extreme weather events. It processes vorticity samples using a deterministic mean model with temporal attention. A reverse diffusion model captures fine-scale features and generates denoised outputs for downscaling.
machine learning, statistical downscaling, extreme weather, generative diffusion models
Patent
seals as meltwater monitors
Published on 2026-05-19 by Alice Drinkwater @NATURE
Abstract: Communications Earth & Environment, Published online: 19 May 2026; doi:10.1038/s43247-026-03609-6Measuring meltwater coming off polar glaciers can help us to understand how climate change is impacting Antarctic ice sheets, but this meltwater is difficult to observe and track over time. Dr Zheng and colleagues solved this problem by using data collected by tagged seals. The seals were equipped with tags that measured temperature, salinity, and pressure, building a picture of Antarctic ice-she[...]
Our summary: Researchers used tagged seals to monitor meltwater from Antarctic glaciers. The seals provided data on temperature, salinity, and pressure. Findings indicate that meltwater rises in winter, influencing climate models.
meltwater monitoring, Antarctic ice sheets, tagged seals, climate modeling
Publication
Method and system for constructing a water inflow forecasting model in typical karst landscape watershed
Patent published on the 2026-05-07 in LU under Ref LU603752 by GUIZHOU NEW METEOROLOGICAL TECH CO LTD [CN] (Luo Naixing [cn], Xia Xiaoling [cn], Zeng Liping [cn])
Abstract: The present invention discloses a method and system for constructing a typical Karst Landscape watercraft forecasting model, which relates to the technical field of prediction model construction, and includes performing downscaling study of weather history data based on existing weather observation data; based on the typical hydrological section observation data, the runoff data of the subflow area outlet section is calculated, and using the subflow area surf ace rain and runoff, a linear regres[...]
Our summary: The invention presents a method and system for constructing a water inflow forecasting model in karst landscapes. It utilizes historical weather and hydrological data to enhance prediction accuracy through deep learning algorithms. The model incorporates cross-validation and error thresholds for improved generalization and flexibility across various watersheds.
water forecasting, karst landscape, deep learning, model optimization
Patent
Precipitation downscaling with limited ground-observation data
Patent published on the 2026-05-06 in EP under Ref EP4737951 by FUJITSU LTD [JP] (Ushijima-mwesigwa Hayato [us], Wong Hon Yung [us], Dai Ting-yu [us])
Abstract: A method to train a diffusion model for satellite observation precipitation data downscaling may include obtaining high-resolution (HR) ground observation precipitation data that has a first resolution. The method may include obtaining corresponding low-resolution (LR) satellite observation precipitation data that has a second resolution lower than the first resolution. The method may include upsampling the LR satellite observation precipitation data that has the second resolution to generate up[...]
Our summary: The method trains a diffusion model for downscaling satellite precipitation data using limited ground observations. It involves obtaining high-resolution ground data and low-resolution satellite data, then upsampling the latter. Training residuals are generated and denoised to update the diffusion model for improved predictions.
Precipitation downscaling, diffusion model, satellite observation, ground observation
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
Biaxial Constitutive Relation and Strength Criterion of Envelope Materials for Stratospheric Airships
Published on 2026-02-03 by Zhanbo Li, Yanchu Yang, Rong Cai, Tao Li @MDPI
Abstract: The performance upgrading of stratospheric airships hinges on breakthroughs in the mechanical properties of envelope materials. As a multi-layer composite, the envelope’s load-bearing layer exhibits orthotropic and nonlinear mechanical behaviors owing to its unique structure and manufacturing process. To overcome the limitations of traditional testing methods and classical strength criteria in characterizing envelope materials, this paper presents a systematic investigation of typi[...]
Our summary: This study investigates the mechanical properties of stratospheric airship envelope materials using modified biaxial testing methods. It develops constitutive models and a five-parameter strength criterion to predict material failure. The findings enhance the engineering design and strength prediction of these materials.
Biaxial testing, Constitutive models, Envelope materials, Strength criterion
Publication
A Comparison of the RCP 4.5 and RCP 8.5 Scenarios (2021–2050) Using the MUSLE Model
Published on 2026-02-03 by Damian Badora, Rafa? Wawer, Aleksandra Krl-Badziak, Beata Bartosiewicz, Jerzy Kozyra @MDPI
Abstract: This study aims to assess how climate change will affect the intensity of soil erosion in the Vistula River basin by the mid-21st century. A simulation framework based on the SWAT–MUSLE model was applied, calibrated, and validated against observed streamflow data and driven by climatic forcings from the EURO-CORDEX ensemble (the RACMO22E, HIRHAM5, and RCA4 models forced by EC-EARTH GCM) under the RCP 4.5 and RCP 8.5 scenarios. Simulations were conducted at a daily time step for the[...]
Our summary: This study evaluates the impact of climate change on soil erosion in the Vistula River basin using the SWAT-MUSLE model under RCP 4.5 and RCP 8.5 scenarios. Simulations indicate increased sediment yield relative to baseline values, with significant seasonal variations. The findings highlight the need for targeted soil protection measures and infrastructure maintenance in response to projected erosion trends.
RCP scenarios, soil erosion, MUSLE model, climate change
Publication
The Prediction of Low-Level Jet Using Machine Learning Based on Turbulence Observations and Remote Sensing
Published on 2026-02-02 by Minghao Chen, Yan Ren, Hongsheng Zhang, Wei Wei, Weiqi Tang, Jiening Liang, Xianjie Cao, Pengfei Tian, Lei Zhang @MDPI
Abstract: Low-level jets (LLJs) are common strong wind structures in the atmospheric boundary layer. They have important impacts on aviation safety, wind energy utilization and pollutant dispersion. However, the formation mechanisms of LLJs are complex. Traditional parameterization schemes and numerical models still show limitations in forecasting LLJ occurrence and resolving their structures. In this study, wind lidar, near-surface turbulence and gradient meteorological observations from the Semi-Arid Cl[...]
Our summary: This study develops a machine learning framework to predict low-level jet (LLJ) occurrence, height, and intensity using multi-source atmospheric data. It employs LightGBM and CatBoost algorithms, achieving high accuracy in predictions. The results enhance understanding of boundary layer processes and have implications for aviation safety and wind energy utilization.
Machine Learning, Low-Level Jet, Turbulence Observations, Remote Sensing
Publication
