Researchers at Arizona State University have used satellite-borne lightning detectors to measure a record-setting lightning megaflash that streaked across the Great Plains for 515 miles during a major thunderstorm in October 2017. Its horizontal reach surpassed the previous record-holder by 38 miles yet went unnoticed until this re-examination of satellite observations of the storm.

Leveraging geostationary satellites

Satellite-borne lightning detectors in orbit since 2017 have made it possible to continuously detect lightning and measure it accurately at continental-scale distances. Parked in geostationary orbit, the National Oceanic and Atmospheric Administration’s GOES-16 satellite detects around one million lightning flashes per day.

Continue reading Arizona State University discovers record-setting lightning with geostationary satellites at Meteorological Technology International.

Cyprus’s Research & Innovation Foundation (RIF), in collaboration with the Deputy Ministry of Research, Innovation and Digital Policy, has launched the ‘AI in Government’ funding program to support “digital transformation in the public sector through the deployment of artificial intelligence”.

Funding early warning systems

The program is addressed to Cypriot businesses and other entities, and is intended to support the development of innovative AI solutions that address specific challenges faced by public authorities.

The first two challenges of the program are issued by the Department of Meteorology and focus on an early warning system for extreme weather events and agrometeorological support for the agricultural sector.

Continue reading Cyprus launches AI funding program to develop early warning system for extreme weather at Meteorological Technology International.

The first MetOp Second Generation satellite (MetOp-SG-A1), which also carries the Copernicus Sentinel-5 mission, is scheduled to launch on August 12 on board an Ariane 6 rocket from Europe’s Spaceport in French Guiana.

MetOp Second Generation mission

The MetOp-SG mission will take over from the first-generation MetOp satellites to improve the accuracy of European weather forecasts for periods ranging from 12 hours to 10 days. According to the ESA, it will not only ensure the continuity of global observations from polar orbit for weather forecasting and climate analysis, but it will do it even better.

Unlike the original MetOp series of three successive single satellites, the all-new MetOp-SG mission comprises three successive pairs of satellites.

Continue reading MetOp-SG-A1 satellite to launch in August at Meteorological Technology International.

Researchers at North Carolina State University have devised a machine learning (ML) method to improve large-scale climate model projections and demonstrated that the tool makes models more accurate at both the global and regional level.

These findings were facilitated by the National Science Foundation, under grants 2151651 and 2152887 and published in the paper, ‘A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs’, in the journal Scientific Data – Nature. The paper was co-authored by Emily Hector, an assistant professor of statistics at NC State; Brian Reich, the Gertrude M Cox distinguished professor of statistics at NC State; and Reetam Majumder, an assistant professor of statistics at the University of Arkansas.

Continue reading Machine learning method improves extreme weather projections at Meteorological Technology International.

NOAA’s Global Systems Laboratory (GSL) is to release an experimental, AI-powered short-term weather forecast model trained on three years of High Resolution Rapid Refresh (HRRR) short-term weather forecast model data, for testing by NOAA’s National Weather Service (NWS) in August.

Developing the AI-powered model

The model, named HRRR-Cast, is NOAA’s first regional experimental AI forecast system and a key component of NOAA’s broader Project EAGLE, a long-term project to provide NOAA and the US weather enterprise with the ability to rapidly test, develop and identify the most promising AI models for global to regional ensemble forecasting.

“HRRR-Cast has been shared with colleagues in NOAA’s Environmental Modeling Center for evaluation and potential integration into a demonstration forecast system,” said project manager Isidora Jankov, GSL’s scientific computing branch chief.

Continue reading NOAA Research develops AI-powered weather forecast model at Meteorological Technology International.

Dr Kianoosh Yousefi, assistant professor of mechanical engineering in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas, has received an Office of Naval Research 2025 Young Investigator Program (YIP) award of US$742,345 over three years to improve hurricane forecasting with machine learning (ML).

At UT Dallas, researchers are studying sea spray, particularly spume, or foam, droplets, in the lab to develop a model based on machine learning (ML) to improve hurricane forecasting. The model incorporates the effects of the spray generation function, which quantifies the rate at which droplets form.

Continue reading University of Texas engineer awarded US$740,000 to improve storm predictions with machine learning at Meteorological Technology International.

The Indian Ministry of Earth Sciences (MoES) has developed 14 meteorological tools and digital services to build a climate-resilient and scientifically empowered India. Revealed by Union Minister Dr Jitendra Singh, these tools included rainfall monitoring and crop-weather calendars; advanced weather forecasting systems like the Bharat Forecast System – Extended Range Prediction (BharatFS-ERP) tool; high-resolution rainfall datasets; updated wave atlases and seabed charts; air quality forecasting systems; marine biodiversity reports, and seismic microzonation studies of four Indian cities.

Decadal progress report

Marking the 19th foundation day of the ministry at an event held in the national capital, the Indian Meteorological Department’s (IMD) Dr Jitendra Singh noted that the number of Doppler weather radars in the country had increased from 15 to 41 in the last 10 years and that seismic and weather stations, upper-air observation systems, lightning detection networks and rain gauges have all more than doubled.

Continue reading India unveils 14 meteorological tools as it celebrates 19th anniversary of Ministry of Earth Sciences founding at Meteorological Technology International.

Researchers at Northwestern Polytechnical University in China have proposed a novel deep learning-based framework that they say improves the accuracy of regional forecasts, even when data is limited. The study has been published in Atmospheric and Oceanic Science Letters.

Developing the AI model

The method integrates three major systems: the use of semantic segmentation models originally designed for medical image analysis; a learnable Gaussian noise mechanism that improves the model’s robustness; and a cascade prediction strategy that breaks the forecasting task into manageable stages.

“Our goal was to make regional forecasting smarter, faster and more reliable, even in data-limited scenarios,” said associate professor Congqi Cao, corresponding author of the study.

Continue reading AI model improves accuracy of five-day regional weather forecasting at Meteorological Technology International.

R M Young Company has announced it is to host its inaugural webinar, ‘Reimagining Snowfall: Introduction to Snodar’, which will take place on Thursday, August 21, 2025, at 8:00am and 1:00pm EDT. This event marks the beginning of a three-part educational series focused on tools and techniques for winter weather monitoring.  

The first session will spotlight Snodar, R M Young’s latest lidar-based snow depth sensor, designed to deliver reliable performance in harsh winter environments. Led by Andy Oliver, director of product and applications management, and Ed DiFilippo, vice president of global sales and marketing at R.M.

Continue reading R M Young Company to host first-ever webinar at Meteorological Technology International.

A research team led by associate professor Yosuke Matsumoto from the Institute for Advanced Academic Research at Chiba University in Japan has begun testing the use of soft x-ray imaging to measure magnetic reconnection rates in Earth’s magnetosphere, which, it says, could pave the way to more accurate space weather predictions.

The study, co-authored by Ryota Momose from Chiba University and Prof. Yoshizumi Miyoshi from Nagoya University, was made available online on June 23, 2025, and was published in Volume 52, Issue 12 of the journal Geophysical Research Letters on June 28, 2025.

Supercomputer simulations

In the study, the researchers proposed leveraging the soft x-rays that are naturally emitted when solar wind particles interact with the boundaries of the magnetosphere to remotely measure reconnection rates across much larger regions than previously possible.

Continue reading Chiba University-led research team uses x-ray imaging to improve space weather predictions at Meteorological Technology International.