Results 71 to 80 of about 2,350 (166)

Validation of ANCHOR Ionospheric Data Assimilation Model Using GIRO Ionosondes Within PyVALION Software

Radio Science, Volume 61, Issue 8, August 2026.
Abstract Accurate specification of the ionosphere is essential for mitigating the impacts of space weather on communication, navigation, and surveillance systems. ANCHOR is a newly developed ionospheric data assimilation (DA) model from the U.S. Naval Research Laboratory that fuses measurements from ionosondes, radio occultation, and Global Navigation ...
Victoriya V. Forsythe, Katherine A. Zawdie, Dustin Hickey, Angeline G. Burrell   +3 more
wiley   +1 more source

Short‐Term Hourly Weather Forecasting Using PredRNN With Image Preprocessing

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed.
Hoang Tran, Hao Li, Vinh Ngoc Tran, Tam V. Nguyen, Manh‐Hung Le, Thanh Duc Dang, Hong Xuan Do, Hung T. Pham, Tan Bui‐Thanh, L. Ruby Leung   +9 more
wiley   +1 more source

TITAN‐LSTM: A Weather Radar Nowcasting Tool

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Precipitation nowcasting is essential for a wide range of meteorological and hydrological applications, and weather radar plays a central role in this task. Extrapolation algorithms such as TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting) provide efficient storm tracking but do not directly use rainfall rate fields, while
Andrea S. Viteri López, Carlos A. Morales Rodriguez   +1 more
wiley   +1 more source

Improving Extreme Precipitation Forecasts via Cascade Binary Classification With Diffusion Post‐Processing

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Accurate forecasting of extreme precipitation remains a critical challenge, owing to its heavy‐tailed distribution and severe class imbalance that limit conventional deep learning approaches. This study presents a two‐stage cascade‐diffusion framework for extreme precipitation forecasting over eastern China (110°–130°E, 20°–40°N).
Leyan Dai, Huiling Yuan, Yiheng Liu, Xinyue Li, Xuanhao Gu, Jiajun Li, Yang Zhao   +6 more
wiley   +1 more source

Disentangling Spatial‐Temporal Features for Controllable Factors Learning in Precipitation Nowcasting

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Precipitation nowcasting refers to the high‐resolution forecasting of rainfall and hydrometeors within 0–6 hr according to the official definition of the World Meteorological Organization, which has relied on numerical models for decades. Recently, artificial intelligence (AI) has shown promise in addressing precipitation nowcasting.
Nan Yang, Chong Wang, Ruikai Yang, Xiaofeng Li   +3 more
wiley   +1 more source

Cultural geography of heat: Artistic experiments with heat’s animacies

Geographical Research, Volume 64, Issue 3, August 2026.
A sustained, cultural geographical engagement with heat is long overdue. I advance this agenda by engaging with the work of contemporary artists. Artists are responding to uneven conditions of global heating by exploring heat’s animacies: how heat interweaves with the practices and textures of life.
Sasha Engelmann
wiley   +1 more source

Enhanced oceanic fog nowcasting through satellite-based recurrent neural networks

Big Earth Data
The presence of fog in offshore regions poses significant hazards to navigation and aviation, making fog nowcasting indispensable for various industries, including oil and gas.
Sahel Mahdavi, Meisam Amani, Terry Bullock, Steven Beale   +3 more
doaj   +1 more source

Cointegration in a MIDAS Regression

Oxford Bulletin of Economics and Statistics, Volume 88, Issue 4, Page 712-725, August 2026.
ABSTRACT Mixed data sampling (MIDAS) cointegration models are used to analyse variables observed at different frequencies. In this paper, we start from an assumed autoregressive distributed lag (ADL) model for high‐frequency observations, and derive the resulting representation when the dependent variable is only observed at a lower frequency.
H. Peter Boswijk, Philip Hans Franses
wiley   +1 more source

COMPARATIVE ANALYSIS OF TWO-STEP AND QUASI MAXIMUM LIKELIHOOD ESTIMATION IN THE DYNAMIC FACTOR MODEL FOR NOWCASTING GDP GROWTH IN INDONESIA

Barekeng
Economic activity data is needed quickly to make policy decisions, but this data suffers from publication delays. Gross Domestic Product (GDP) data is released within five weeks after the end of the quarter.
Gilbert Alvaro Souisa, Reyner M. Leiwakabessy, Salma Damayanti, Mohammad Zanuar F Terim, Shelma M Pelu   +4 more
doaj   +1 more source

Physics‐Embedded Deep Learning Reveals the Memory Effect of Ionospheric Electron Density in Response to Solar EUV Forcing

Geophysical Research Letters, Volume 53, Issue 14, 28 July 2026.
Abstract Precise characterization of the electron density profile is crucial for optimizing high frequency communications. Reconstruction models often assume an instantaneous ionospheric response to solar forcing or using time delays, neglecting the time‐integrated cumulative “memory effect” of plasma dynamics.
Hongwei Gong, Hanxian Fang, Die Duan, Chao Xiao, Jintai Li, Chenghao Li, Hongtao Huang, Ganming Ren, Chuyue Zhou, Han Wu   +9 more
wiley   +1 more source