#metrology — Public Fediverse posts

WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
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https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
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https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
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https://deepmind.google/science/weatherlab/ <-- shared data
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https://github.com/google-deepmind/weathernext <-- shared GitHub repository
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H/T @juliet Rothenberg | Product Director of Earth & Resilience AI at Google
[this post should not be considered an endorsement of a particular organisation or their approach]
“[The Google WeatherNext AI team] are showing how the WeatherNext AI model from Google DeepMind and Google Research has achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, the WeatherNext Cyclones model gives forecasters an extra day’s worth of predictive accuracy- delivering an advance equivalent to roughly a decade of historical meteorological progress 🌀
Here is how WeatherNext is transforming cyclone forecasting:
• Gaining an Extra Day of Advanced Warning: WN 3-day forecasts are as good as what prior models were able to provide for 2-day forecasts, giving critical time for emergency response.
• Overcoming Traditional Trade-offs: WN bridges the gap between massive global atmospheric currents (which steer a cyclone's path) and fine-grained thermodynamic processes around its core (which drive its intensity) into a single AI model.
• Unprecedented Ensemble Scale: Using Functional Generative Networks (FGNs), WN now generates 1,000-member ensembles in less than a minute on a TPU to capture rare, consequential tail-risks like sudden rapid intensification – which means forecasters can see a broader range of possible scenarios.
• Real-World Impact: During the 2025 Atlantic hurricane season, the WN model helped the National Hurricane Center (NHC) make a historic forecast for Hurricane Melissa by predicting rapid intensification and landfall five days in advance.
[The] teams are open sourcing the operationalized models (WeatherNext Cyclones and WeatherNext 2), alongside a compact version (WeatherNext 2-mini) that can run on a single TPU in a free public Colab notebook – all with a goal of empowering local organizations worldwide.
Weather affects everyone. By combining advanced AI with the real-world expertise of human forecasters, we can build a collaborative ecosystem that saves lives and helps communities adapt to a changing climate…”
#Google #DeepMind #GoogleResearch #AI #ensembles #FunctionalGenerativeNetworks #WeatherNext #cyclone #operationalised #model #modeling #forecasting #spatialanalyis #spatiotemporal #track #intensity #windstructure #hurricane #weather #climate #metrology #cyclonetrack #risk #hazard #emergencyresponse #planning #tool #earlywarning #scale #magnitude #path #track #thermodynamic #scenarios #opensource #impacts #tropicalcyclones #WeatherNextCyclones #weathermodel #atmospheric #predictions #mitigation #warning #robust #publicsafety #infrastructure
@Google | @WeatherNext

[Open] Data Related To Flood Mapping [Canada]
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https://natural-resources.canada.ca/science-data/science-research/natural-hazards/flood-mapping/data-related-flood-mapping <-- shared link to technical details
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https://app.geo.ca/en-ca/map-browser/record/a13a2575-5bda-4bfd-a9b1-5bd2dd583f09 <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
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https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc <-- shared data-portal link, Canada Flood Susceptibility Index
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https://doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
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https://doi.org/10.1002/2017WR020917 <-- shared (2017) paper
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H/T @Michael DePue | VP & AtkinsRéalis Fellow for Water Resources Engineering | PE, PMP, CFM
“At the Canadian Water Resources Association National Conference in Winnipeg, colleagues shared insights from Canada's Flood Hazard Identification and Mapping Program. This initiative has seen over 400 flood mapping projects and more than 1,000 flood hazard maps produced, supported by a substantial investment of $164.2 million from 2024 to 2028.
Two key datasets:
• The Canada Flood Map Inventory, which records the locations of flood hazard maps and provides information on how to access them.
• The national Flood Susceptibility Index, a machine-learning assessment of flood-prone areas, including regions that have not been mapped in detail.
When these two layers are combined on a single screen, it becomes clear where future mapping efforts should be directed — specifically, areas with high susceptibility that currently lack detailed maps…”
#water #hydrography #flood #flooding #risk #hazard #model #modeling #fedscience #publicsafety #humaninpacts #opendata #Canada #GIS #spatial #mapping #damage #infrastructure #floodmapping #prediction #spatialanalysis #spatiotemporal #historic #current #future #preduction #extremeweather #metrology #rainfall #precipitation #atmosphericriver #FloodMapInventory #CFM #floodhazard #FloodSusceptibilityIndex #floodprone #research #susceptibility
@NRCAN

Watching A #NOAA #Webinar on Flash Droughts
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https://noaaresearch.webex.com/wbxmjs/joinservice/sites/noaaresearch/meeting/download/9b3e684d45ca47fc9469070eabd9a142?MTID=m2fa4a8af7bd8647fc48619af5eeecb5a <-- shared NOAA Summer Science Series individual webinar
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https://www.drought.gov/what-is-drought/flash-drought <-- shared NOAA overview technical article
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https://www.star.nesdis.noaa.gov/star/NOAAScienceSeminars.php <-- subscribe to the NOAA Summer Science Series
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https://doi.org/10.1038/s41612-024-00618-0 <-- shared paper
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https://communities.springernature.com/posts/the-prevalent-life-cycle-of-agricultural-flash-droughts <-- shared technical article (derived from paper above)
H/T @Jeffrey Basara PhD, MBA | Chair and Professor - Department of Environmental, Earth, and Atmospheric Sciences, University of Massachusetts Lowell | Co-Founder - American Prime Sustainable Solutions
[Flash floods? not TOO hard to conceptualise.
Flash drought? harder to 'get my head around', but H/T / presenter does an excellent job!]
"Not all droughts are the same. In some cases, drought rapidly intensifies at subseasonal to seasonal scales with significant impacts to agriculture and water resources along with the increased propensity for heatwaves and wildfires. Like all droughts, flash drought begins with a precipitation deficit. However, both evaporative demand and soil moisture are critical flash drought variables, and identifying and monitoring the desiccation of the terrestrial surface is key for determining flash drought development and associated impacts. While recent advances in knowledge and monitoring of flash drought have occurred, fundamental questions remain in the state of the science. What are the overall mechanistic relationships between atmospheric demand, evaporative stress, terrestrial desiccation, and precipitation that drive the progression of flash drought? Do regional characteristics of the environment impact the evolution of flash drought? What are the scales of predictability for flash drought? Finally, how will flash drought frequency and intensity evolve in a changing climate system"
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"Flash drought intensifies rapidly due to changes in precipitation, temperature, wind, and radiation. These changes in the weather increase evapotranspiration and lower soil moisture. Flash droughts can cause extensive damage to agriculture, economies, and ecosystems if they are not predicted and discovered early..."
#water #hydrology #fedscience #publicgood #hydrologicdrought #waterdeficit #spatialanalysis #spatiotemporal #watersecurity #risk #hazard #humanimpacts #streamflow #riverflow #groundwater #surfacewater #climate #weather #climatechange #extremeweather #atmosphere #metrology #regional #global #farming #agriculture #fluvial #pluvial #rainfall #precipitation #cloudcover #energy #heat #temperature #ET #evapotranspiration #farming #agriculture #foodsecurity #waterresources #dynamicsystems #watermanagement #flashdrought #drought #susceptibility #monitoring #prediction #model #modeling
@noaa

From Space To Field - Putting satellite insights into growers’ hands [OpenET]
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https://irrigationtoday.org/features/from-space-to-field/ <-- shared technical media article
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https://etdata.org/ <-- shared OpenET home page
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https://www.nasa.gov/image-article/openet-satellite-based-water-data-resource/ <-- shared NASA technical article
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https://youtu.be/Rbobf6aurLs?si=Q0kN9eiZH0gT8O5m <-- shared OpenET overview video
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#OpenET #openscience #opendata #publicgood #publicservice #nonprofit #fedscience #fedservice #evapotranspiration #ET #Landsat #OLI #OperationalLandImager #groundwater #water #hydrology #spatialanalysis #spatiotemporal #agriculture #USA #farming #waterresources #watermanagement #planning #watersecurity #satellite #remotesensing #earthobservation #model #modeling #irrigation #metrology #raster #cost #economics #efficiency #foodproduction #mobileapp #FARMS #view #download #usecase #reporting
#OpenET #USGS #USDA @NASA #USGS_EROS

An exciting first meeting of the Special Interest Group for #TimeandFrequency took place at the University of Amsterdam, focusing on #metrology and the #science of accurate #time.

SIG-TFN looked into some challenging use cases from redefining the System of Units (SI), detecting #gravitational waves, testing new #physics and supporting European-wide commercial #services such as very accurate time stamps for banks to validate high-frequency trading.

Find out more
👉 https://connect.geant.org/2024/10/30/sig-tfn-meet-for-the-first-time

For examples, 1 #kg of liquid water at room termperature has 1 #litre of #volume. Therefore, 1 #tonne of water fits into a cube with sides of 1 #m. One #calorie is the amount of #heat or #energy required to raise the #temperature of 1 #gram (or 1 #cc or #CubicCentimetre) of water by 1 #degreeCelsius or 1 #K.

#Metrology