Ab sofort bis zum 25. Juni können Sie Kommentare und Anregungen zum Entwurf des Nationalen Wiederherstellungsplans (NWP) über die Online-Beteiligungsplattform des Bundesumweltministeriums abgeben: https://beteiligung.bundesumweltministerium.de/de/nationaler-wiederherstellungsplan
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The Bulletin of the American Meteorological Society has published a critique of ML by Leonard A Smith and Alan Thorpe. It compares the role and value of physics-based and statistical models in weather and climate forecasting, and tempers the ebulition around AI/ML.
The article content is accessible to non-specialists in either ML or meteorology.
https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-25-0214.1/BAMS-D-25-0214.1.pdf
BIG news from #ECMWF: a portable version of our global forecasting model is now open source! Development of #IFS started in 1987 and just a year before entering its 4th decade it is now available to all.
This continues our journey that started a few years ago, when we made a first and growing number of several key model components available.
See the news article for more details: https://www.ecmwf.int/en/about/media-centre/news/2026/openifs-open-source
Code repository: https://github.com/ecmwf-ifs/openifs
ECMWF annual seminar 2026 is on "Advancing the assimilation of Earth system observations with new methodology and Machine Learning"
Registration is open:
https://events.ecmwf.int/event/513/overview
Advancing the assimilation of Earth system observations with new methodology and Machine Learning Data Assimilation continues to underpin all operational weather forecasting, initialising physics-based models as well as initialising and training data-driven models. The 2026 Annual Seminar will look at how the deployment of Machine Learning (ML) technology and innovative methodology is changing the way we assimilate observations for numerical weather prediction, atmospheric composition and...
@tinoeberl #ECMWF recognized “AI” #NWP is accurate for large scale ~2,000 km features only. Let’s put some science based constraints to the “AI” hype.
Je propose des cartes de divers paramètres clés du modèle numérique de prévisions #meteo (#PNT #NWP) GFS entraîné par apprentissage automatique (Machine Learning en anglais) via GraphCast de Google Inc.
Les données binaires initiales sont fournies par la #NOAA
Licence CC-BY 4.0.
#EUMETSAT via #businesswire:
"
Revolutionärer Metop-SGA1 überträgt bereits Instrumentendaten
"
"Weniger als drei Wochen nach dem Start von Metop-Satellit A1 der zweiten Generation (Metop-SGA1) am 13. August überträgt der Satellit bereits Daten von zwei seiner sechs Instrumente."
https://www.businesswire.com/news/home/20250902406888/de
https://www.eumetsat.int/revolutionary-metop-sga1-already-transmitting-instrument-data
2.9.2025
#EO #Erdbeobachtung #ESA #Europa #NWP #MetOpSG #MetOpSGA1 #MWS #Raumfahrt #RO #Satelliten #SpaceFlight #Wettervorhersage #Wettersatellit
Machine Learning is making a big impression in Numerical Weather Prediction these days. However, explicitly physics-based models still outperform AI models when it comes to extreme events. #meteorology #nwp
https://arxiv.org/abs/2508.15724
Hey sometimes my LinkedIn feed isn't all Facebook for office grunts.
Artificial intelligence (AI)-based models are revolutionizing weather forecasting and have surpassed leading numerical weather prediction systems on various benchmark tasks. However, their ability to extrapolate and reliably forecast unprecedented extreme events remains unclear. Here, we show that for record-breaking weather extremes, the numerical model High RESolution forecast (HRES) from the European Centre for Medium-Range Weather Forecasts still consistently outperforms state-of-the-art AI models GraphCast, GraphCast operational, Pangu-Weather, Pangu-Weather operational, and Fuxi. We demonstrate that forecast errors in AI models are consistently larger for record-breaking heat, cold, and wind than in HRES across nearly all lead times. We further find that the examined AI models tend to underestimate both the frequency and intensity of record-breaking events, and they underpredict hot records and overestimate cold records with growing errors for larger record exceedance. Our findings underscore the current limitations of AI weather models in extrapolating beyond their training domain and in forecasting the potentially most impactful record-breaking weather events that are particularly frequent in a rapidly warming climate. Further rigorous verification and model development is needed before these models can be solely relied upon for high-stakes applications such as early warning systems and disaster management.
Basics of Numerical Weather Prediction (NWP):
1. THE HORIZONTAL MOMENTUM EQUATION:
\[
\frac{d\mathbf{V}}{dt} + f\hat{k} \times \mathbf{V} = -\nabla \phi + \frac{\sigma}{p_s} \frac{\partial \phi}{\partial \sigma} \nabla p_s + \mathbf{F}
\]
2. THE CONTINUITY EQUATION:
\[
\frac{\partial p_s}{\partial t} + \nabla \cdot (p_s \mathbf{V}) + \frac{\partial}{\partial \sigma}(p_s \dot{\sigma}) = 0
\]
3. THE THERMODYNAMIC ENERGY EQUATION:
\[
\frac{1}{R} \frac{d}{dt} \left[ \sigma \frac{\partial \phi}{\partial \sigma} \right] + \frac{RT}{C_p p} \left[ p_s \dot{\sigma} + \sigma\dot{p_s} \right] = -Q
\]
4. HYDROSTATIC EQUATION:
\[
\frac{\partial \phi}{\partial \sigma} = -\frac{RT_v}{\sigma}
\]
5. SURFACE PRESSURE TENDENCY EQUATION:
\[\displaystyle
\frac{\partial p_s}{\partial t} = -\int_{0}^{1} \nabla\cdot (p_s \mathbf{V}) \, d\sigma
\]
6. MOISTURE EQUATION:
\[\displaystyle
\frac{\partial}{\partial t} (p_s q) + \nabla\cdot (p_s q \mathbf{V}) + \frac{\partial}{\partial \sigma} (p_s q \dot{\sigma}) = p_s S
\]
The six primary unknowns are: \(\mathbf{V}\) (horizontal wind velocity), \(p_s\) (surface pressure), \(T\) (temperature), \(q\) (specific humidity or moisture), \(\phi\) (geopotential), and \(\dot{\sigma}\) (sigma velocity or vertical velocity in \(\sigma\)-coordinates).
#NWP #Weather #NumericalWeatherPrediction #Meteorology #Climate #ClimateScience #Earth #EarthScience #ClimateChange #ClimateSciences #Science #WeatherPrediction #Humidity #Moisture #Pressure #Velocity #SurfacePressure #HydrostaticEquation #WeatherPrediction #Ocean #Atmosphere #AOS #ClimateDynamics #WeatherDynamics #Geopotential #SigmaVelocity #VerticalVelocity #MoistureEquation #Thermodynamics #Dynamics #NavierStokes
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