Xiong’an National Climate Observatory was officially inaugurated on April 1 in Xiong’an New Area, Hebei. The facility – China’s 27th national climate observatory – will transmit basic meteorological observation data in real time to the TianQing meteorological big data cloud platform, supporting climate monitoring and assessment, high-resolution forecasting, early warning and sector-specific meteorological services.

National climate observatories in China are ground-based, comprehensive stations designed for long-term, continuous and integrated observation of the climate system and its interactions. They also serve as platforms for scientific research, collaboration and talent development.

The new observatory is expected to support regional priorities in Xiong’an, including ecological protection of Baiyangdian Lake, urban safety operations and the development of emerging sectors such as the low-altitude economy, all of which require higher-precision and continuous climate observations.

Continue reading China inaugurates 27th national climate observatory in Hebei at Meteorological Technology International.

The UK Met Office has launched a significant upgrade to its forecasting system, introducing new modeling capabilities designed to improve forecast accuracy, clarity and usability across multiple sectors.

The update is the most substantial scientific improvement to the agency’s forecasting systems in more than three years and is the first major advancement delivered using its new supercomputer. The Met Office said the changes will produce forecasts that more closely reflect real-world conditions, particularly for precipitation, cloud and fog.

Forecasts will now show rain and snow with greater realism, while improved modeling of cloud and fog is expected to provide more accurate guidance for travel planning and operations.

Continue reading Met Office rolls out major forecasting system upgrade at Meteorological Technology International.

The National Institute of Solar Energy (NISE) and Grid Controller of India have signed a memorandum of understanding to jointly carry out a research project on hybrid day-ahead and intra-day solar forecasting for large solar parks in India.

The agreement was signed by Prof. Mohammad Rihan, director general of NISE, and Manoj Kumar Agrawal, executive director of the National Load Despatch Centre (NLDC), with representatives from both organizations present, including GRID-India chairman and managing director Samir Chandra Saxena.

The project will be delivered in partnership with renewable energy developers including Adani Green Energy, NTPC Green Energy, Tata Power Renewable Energy, ReNew and Serentica Renewables.

Continue reading India partners on hybrid solar forecasting project for large solar parks at Meteorological Technology International.

Probability-based forecasts can better inform weather-related decision-making, according to new research from the UK Met Office.

The study brings together 25 years of research to explain why probabilistic forecasting is becoming central to weather prediction. Funded by the Public Weather Service and published in the Royal Meteorological Society’s Weather, it examines how probability-based forecasts that account for uncertainty in predicted weather patterns can support more informed decisions.

The peer-reviewed research also suggests that public understanding of probabilistic forecasts should not be a barrier to their wider use, challenging earlier assumptions.

Probability forecasts are based on ensemble forecasting, a different approach from traditional deterministic forecasts often presented on television.

Continue reading Probability-based forecasting central to future weather prediction, Met Office says at Meteorological Technology International.

China has launched a coordinated observation experiment to improve understanding of mountain snowfall processes and enhance winter forecasting capabilities. The North China Mountain Snowfall Cloud Microphysics–Surface Properties experiment began on December 11, 2025, in Chongli, Hebei Province.

The two-month experiment will collect firsthand datasets, including comprehensive snowflake microphysical data and snow-surface high-spectral emissivity data. Its findings are expected to support improvements in numerical weather prediction (NWP) models and strengthen snow disaster early warning capabilities across North China and nationwide.

Chongli was selected as the observation site due to its complex topography and distinctive climate. Han Wei, deputy chief engineer of the China Meteorological Administration (CMA) Earth System Modeling and Prediction Center (CEMC), said, “Chongli, as a typical mountainous snowfall region in North China with complex terrain and a unique climate, features well-structured snowfall cloud systems and diverse snowflake forms, making it a natural laboratory for studying microphysics and dynamic processes of mountain snowfall.”

The experiment employs a multiscale, multiplatform, highly coordinated observation approach to investigate the microphysical, dynamic and radiative characteristics of snowfall cloud systems.

Continue reading China completes coordinated mountain snowfall observation experiment in North China at Meteorological Technology International.

New research from the UK’s Met Office has revealed the potential of machine learning-based weather models to revolutionize global seasonal forecasting.

The peer-reviewed paper Skilful global seasonal predictions from a machine learning weather model trained on reanalysis data, published in npj Climate and Atmospheric Science, assesses the viability of applying an ML weather model to global seasonal forecasts, which are vital for understanding global weather patterns.

According to the Met Office, seasonal forecasts – which look at the likely conditions for the next three months – can provide valuable insights for long-term planning and decision making, including for agriculture, water resources and public health.

Continue reading Met Office research shows machine learning can boost seasonal forecasting capabilities at Meteorological Technology International.

A study led by scientists from the University of Chicago, in collaboration with New York University and the University of California Santa Cruz, has found that neural networks cannot forecast weather events beyond the scope of existing training data, which might leave out weather that’s unprecedented in recorded history such as 200-year floods, unprecedented heat waves or massive hurricanes.

According to their research, published in Proceedings of the National Academy of Sciences, this is because neural networks only predict based on patterns from the past. The researchers highlight that this limitation is particularly important as researchers incorporate neural networks into operational weather forecasting, early warning systems and long-term risk assesments.

Continue reading University of Chicago analyzes AI’s ability to predict unprecedented weather events at Meteorological Technology International.

A team of researchers from the University of Pennsylvania and Microsoft Research has developed Aurora, a machine-learning model with predictive capabilities for air quality, ocean waves, tropical cyclone tracks and weather.

Research team

The research was supported by the Department of Energy’s Advanced Scientific Computing Research program (DE-SC0024563) and the Engineering & Physical Sciences Research Council Prosperity Partnership between Microsoft Research and the University of Cambridge (EP/T005386/1).

Their findings have been published in Nature. The team was led by Paris Perdikaris, associate professor in the Department of Mechanical Engineering and Applied Mechanics in the School of Engineering and Applied Science at the University of Pennsylvania. 

Continue reading VIDEO: University of Pennsylvania and Microsoft Research develop machine-learning weather prediction model at Meteorological Technology International.

A study, led by Professor Mike Kendall, head of the University of Oxford’s Department of Earth Sciences, has compared the earthquake signals during two eruptions of Ontake Volcano in Japan to investigate the use of a new monitoring technique for early warning of a volcanic eruption. The study, named ‘Changes in seismic anisotropy at Ontake volcano: a tale of two eruptions’, has been published in the journal Seismica.

The research team compared a seismic signal called shear-wave splitting from the two eruptions – one small, one explosive – and has been able to demonstrate that this parameter varies depending on the size of the eruption.

Continue reading University of Oxford researchers investigate use of seismic signals in volcanic eruption prediction at Meteorological Technology International.

Hanadi Rifai, Moores Professor of Civil and Environmental Engineering and director of the University of Houston’s Hurricane Resilience Research Institute, has created a numerical computer model to help scientists and environmental experts understand how water moves in estuaries and evaluate extreme weather events and natural hazards. The model is the focus of an article in the journal Environmental Science and Pollution Research.  

Monitoring local pollutants

Rifai spent two decades examining Galveston Bay – its tides, currents and how fresh and salty water mix, continually extending knowledge about predicting water levels, pollution spread and how ecosystems stay balanced.

Continue reading University of Houston unveils hydrology model to evaluate extreme weather at Meteorological Technology International.