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#weatherprediction — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #weatherprediction, aggregated by home.social.

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  1. I turned a Raspberry Pi into a tiny weather intelligence powerhouse

    A Raspberry Pi Zero 2 W paired with a Sense HAT V2 runs a fully self-contained, edge-native machine learning weather station with no cloud, GPU, or heavy ML frameworks. The system uses pure NumPy implementations of Recursive Least Squares, Kalman filtering, conformal prediction, and drift detection, staying under 150 MB RAM. The station continuously updates its own statistical model of local atmospheric conditions, produces calibrated uncertainty intervals, and renders animated forecasts on […]

    kemal.yaylali.uk/i-turned-a-ra

  2. I turned a Raspberry Pi into a tiny weather intelligence powerhouse

    A Raspberry Pi Zero 2 W paired with a Sense HAT V2 runs a fully self-contained, edge-native machine learning weather station with no cloud, GPU, or heavy ML frameworks. The system uses pure NumPy implementations of Recursive Least Squares, Kalman filtering, conformal prediction, and drift detection, staying under 150 MB RAM. The station continuously updates its own statistical model of local atmospheric conditions, produces calibrated uncertainty intervals, and renders animated forecasts on […]

    kemal.yaylali.uk/i-turned-a-ra

  3. I turned a Raspberry Pi into a tiny weather intelligence powerhouse

    A Raspberry Pi Zero 2 W paired with a Sense HAT V2 runs a fully self-contained, edge-native machine learning weather station with no cloud, GPU, or heavy ML frameworks. The system uses pure NumPy implementations of Recursive Least Squares, Kalman filtering, conformal prediction, and drift detection, staying under 150 MB RAM. The station continuously updates its own statistical model of local atmospheric conditions, produces calibrated uncertainty intervals, and renders animated forecasts on […]

    kemal.yaylali.uk/i-turned-a-ra

  4. I turned a Raspberry Pi into a tiny weather intelligence powerhouse

    A Raspberry Pi Zero 2 W paired with a Sense HAT V2 runs a fully self-contained, edge-native machine learning weather station with no cloud, GPU, or heavy ML frameworks. The system uses pure NumPy implementations of Recursive Least Squares, Kalman filtering, conformal prediction, and drift detection, staying under 150 MB RAM. The station continuously updates its own statistical model of local atmospheric conditions, produces calibrated uncertainty intervals, and renders animated forecasts on […]

    kemal.yaylali.uk/i-turned-a-ra

  5. I have a question 4 meteorologists here. Weather prediction seems way less reliable lately. For example 2 different well-known websites list temps 10 degrees apart. Even within a single website the home page says one temp but the hourly says something 7-10 degrees different for the exact same time. Last week a heavy rain shower popped up that wasn't shown on any website. What is going on? Is it lack of weather instruments due to defunding?something else? #weather #meteorology #WeatherPrediction

  6. I have a question 4 meteorologists here. Weather prediction seems way less reliable lately. For example 2 different well-known websites list temps 10 degrees apart. Even within a single website the home page says one temp but the hourly says something 7-10 degrees different for the exact same time. Last week a heavy rain shower popped up that wasn't shown on any website. What is going on? Is it lack of weather instruments due to defunding?something else? #weather #meteorology #WeatherPrediction

  7. I have a question 4 meteorologists here. Weather prediction seems way less reliable lately. For example 2 different well-known websites list temps 10 degrees apart. Even within a single website the home page says one temp but the hourly says something 7-10 degrees different for the exact same time. Last week a heavy rain shower popped up that wasn't shown on any website. What is going on? Is it lack of weather instruments due to defunding?something else? #weather #meteorology #WeatherPrediction

  8. I have a question 4 meteorologists here. Weather prediction seems way less reliable lately. For example 2 different well-known websites list temps 10 degrees apart. Even within a single website the home page says one temp but the hourly says something 7-10 degrees different for the exact same time. Last week a heavy rain shower popped up that wasn't shown on any website. What is going on? Is it lack of weather instruments due to defunding?something else? #weather #meteorology #WeatherPrediction

  9. Một dự án thú vị sử dụng AI! Người dùng đã kết hợp dự báo thời tiết cục bộ và Llama3.1 8B để chọn trang phục cho cả tuần. Hệ thống dùng thư viện meteostat dự đoán nhiệt độ, sau đó Llama3.1 gợi ý đồ mặc phù hợp, thậm chí phát ra báo thức mỗi sáng!

    #AI #Llama3_1 #WeatherPrediction #OutfitPicker #LocalLLaMA #TechProject
    #AIDựĐoán #DựBáoThờiTiết #ChọnTrangPhục #HọcMáy

    reddit.com/r/LocalLLaMA/commen

  10. 🚜 The Farmers' Almanac bids a tearful #goodbye by putting on a grand #circus of ads, subscriptions, and calendars you'd never use. Clearly, they're hoping you'll get lost in the clutter and accidentally buy something. 🎪 Who knew predicting the weather required this much spam? 🌧️📅
    farmersalmanac.com/fond-farewe #FarmersAlmanac #AdSpam #WeatherPrediction #ClutteredCalendars #HackerNews #ngated

  11. 🚜 The Farmers' Almanac bids a tearful #goodbye by putting on a grand #circus of ads, subscriptions, and calendars you'd never use. Clearly, they're hoping you'll get lost in the clutter and accidentally buy something. 🎪 Who knew predicting the weather required this much spam? 🌧️📅
    farmersalmanac.com/fond-farewe #FarmersAlmanac #AdSpam #WeatherPrediction #ClutteredCalendars #HackerNews #ngated

  12. 🚜 The Farmers' Almanac bids a tearful #goodbye by putting on a grand #circus of ads, subscriptions, and calendars you'd never use. Clearly, they're hoping you'll get lost in the clutter and accidentally buy something. 🎪 Who knew predicting the weather required this much spam? 🌧️📅
    farmersalmanac.com/fond-farewe #FarmersAlmanac #AdSpam #WeatherPrediction #ClutteredCalendars #HackerNews #ngated

  13. 🚜 The Farmers' Almanac bids a tearful #goodbye by putting on a grand #circus of ads, subscriptions, and calendars you'd never use. Clearly, they're hoping you'll get lost in the clutter and accidentally buy something. 🎪 Who knew predicting the weather required this much spam? 🌧️📅
    farmersalmanac.com/fond-farewe #FarmersAlmanac #AdSpam #WeatherPrediction #ClutteredCalendars #HackerNews #ngated

  14. 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

  15. 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

  16. 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

  17. Convective storm detection (Storm 🌪️)

    Convective storm detection is the meteorological observation, and short-term prediction, of deep moist convection. DMC describes atmospheric conditions producing single or clusters of large vertical extension clouds ranging from cumulus congestus to cumulonimbus, the latter producing thunderstorms associa...

    en.wikipedia.org/wiki/Convecti

    #ConvectiveStormDetection #Storm #Tornado #StormChasing #RadarMeteorology #WeatherPrediction

  18. Researchers unveil Aardvark, an AI-powered weather prediction system that uses thousands of times less computing power and delivers results much faster than current methods. A game-changer for #AI and #WeatherTech! 🌦️ #AI #MachineLearning #WeatherPrediction #Innovation

  19. Researchers unveil Aardvark, an AI-powered weather prediction system that uses thousands of times less computing power and delivers results much faster than current methods. A game-changer for #AI and #WeatherTech! 🌦️ #AI #MachineLearning #WeatherPrediction #Innovation

  20. Researchers unveil Aardvark, an AI-powered weather prediction system that uses thousands of times less computing power and delivers results much faster than current methods. A game-changer for #AI and #WeatherTech! 🌦️ #AI #MachineLearning #WeatherPrediction #Innovation

  21. Researchers unveil Aardvark, an AI-powered weather prediction system that uses thousands of times less computing power and delivers results much faster than current methods. A game-changer for #AI and #WeatherTech! 🌦️ #AI #MachineLearning #WeatherPrediction #Innovation

  22. "NOAA Global Systems Laboratory, NOAA Physical Science Laboratory, The Cooperative Institute for Earth Systems Research in Environmental Sciences and Data Science, and NOAA Office of Science and Technology all provided financial support for the workshop."

    An intricate apparatus and national asset now being destroyed by obsessed simpletons.

    #WeatherPrediction

    journals.ametsoc.org/view/jour

  23. "NOAA Global Systems Laboratory, NOAA Physical Science Laboratory, The Cooperative Institute for Earth Systems Research in Environmental Sciences and Data Science, and NOAA Office of Science and Technology all provided financial support for the workshop."

    An intricate apparatus and national asset now being destroyed by obsessed simpletons.

    #WeatherPrediction

    journals.ametsoc.org/view/jour

  24. "NOAA Global Systems Laboratory, NOAA Physical Science Laboratory, The Cooperative Institute for Earth Systems Research in Environmental Sciences and Data Science, and NOAA Office of Science and Technology all provided financial support for the workshop."

    An intricate apparatus and national asset now being destroyed by obsessed simpletons.

    #WeatherPrediction

    journals.ametsoc.org/view/jour

  25. "NOAA Global Systems Laboratory, NOAA Physical Science Laboratory, The Cooperative Institute for Earth Systems Research in Environmental Sciences and Data Science, and NOAA Office of Science and Technology all provided financial support for the workshop."

    An intricate apparatus and national asset now being destroyed by obsessed simpletons.

    #WeatherPrediction

    journals.ametsoc.org/view/jour

  26. More than 40% of all tropical activity in a typical season occurs after September 10, so there’s plenty of precedent for storms 🌪️ edition.cnn.com/2024/09/06/wea

    #PolarisDawn #WeatherPrediction

  27. More than 40% of all tropical activity in a typical season occurs after September 10, so there’s plenty of precedent for storms 🌪️ edition.cnn.com/2024/09/06/wea

    #PolarisDawn #WeatherPrediction

  28. More than 40% of all tropical activity in a typical season occurs after September 10, so there’s plenty of precedent for storms 🌪️ edition.cnn.com/2024/09/06/wea

    #PolarisDawn #WeatherPrediction

  29. More than 40% of all tropical activity in a typical season occurs after September 10, so there’s plenty of precedent for storms 🌪️ edition.cnn.com/2024/09/06/wea

    #PolarisDawn #WeatherPrediction

  30. The rain forecast has been extremely unreliable lately. 100% predicted even hours away, then nothing.

    I think a lot of weather models take past weather patterns into account, and that just doesn't work in a world with climate change.

    #climatechange #weather #arkansasweather #weatherpatterns #weatherprediction #meteorology #climatecollapse

  31. The rain forecast has been extremely unreliable lately. 100% predicted even hours away, then nothing.

    I think a lot of weather models take past weather patterns into account, and that just doesn't work in a world with climate change.

    #climatechange #weather #arkansasweather #weatherpatterns #weatherprediction #meteorology #climatecollapse

  32. The rain forecast has been extremely unreliable lately. 100% predicted even hours away, then nothing.

    I think a lot of weather models take past weather patterns into account, and that just doesn't work in a world with climate change.

    #climatechange #weather #arkansasweather #weatherpatterns #weatherprediction #meteorology #climatecollapse

  33. The rain forecast has been extremely unreliable lately. 100% predicted even hours away, then nothing.

    I think a lot of weather models take past weather patterns into account, and that just doesn't work in a world with climate change.

    #climatechange #weather #arkansasweather #weatherpatterns #weatherprediction #meteorology #climatecollapse

  34. Our #ECMWF machine learning weather forecast is public! 🌦️

    Right on time for the weekend, too!

    It's an alpha version for now, because the whole team is working hard to wrestle every iota of performance from our graph NN #AIFS.

    But even as an alpha version, it's playing in the big leagues.

    I am so excited to finally share this with you! 🚀

    I'll link our blog post and the forecast below. Make sure to share this with your colleagues and machine-learning-curious friends!

    Still can't believe how far these people have made it in this short time, running four machine learning models operationally, with one developed in-house.

    The Blog: ecmwf.int/en/about/media-centr

    Your Weekend forecast: charts.ecmwf.int/?facets=%7B%2

    More to come soon!

    #MachineLearning #AI #WeatherPrediction #MLOps

  35. Our #ECMWF machine learning weather forecast is public! 🌦️

    Right on time for the weekend, too!

    It's an alpha version for now, because the whole team is working hard to wrestle every iota of performance from our graph NN #AIFS.

    But even as an alpha version, it's playing in the big leagues.

    I am so excited to finally share this with you! 🚀

    I'll link our blog post and the forecast below. Make sure to share this with your colleagues and machine-learning-curious friends!

    Still can't believe how far these people have made it in this short time, running four machine learning models operationally, with one developed in-house.

    The Blog: ecmwf.int/en/about/media-centr

    Your Weekend forecast: charts.ecmwf.int/?facets=%7B%2

    More to come soon!

    #MachineLearning #AI #WeatherPrediction #MLOps

  36. Our #ECMWF machine learning weather forecast is public! 🌦️

    Right on time for the weekend, too!

    It's an alpha version for now, because the whole team is working hard to wrestle every iota of performance from our graph NN #AIFS.

    But even as an alpha version, it's playing in the big leagues.

    I am so excited to finally share this with you! 🚀

    I'll link our blog post and the forecast below. Make sure to share this with your colleagues and machine-learning-curious friends!

    Still can't believe how far these people have made it in this short time, running four machine learning models operationally, with one developed in-house.

    The Blog: ecmwf.int/en/about/media-centr

    Your Weekend forecast: charts.ecmwf.int/?facets=%7B%2

    More to come soon!

    #MachineLearning #AI #WeatherPrediction #MLOps

  37. Our #ECMWF machine learning weather forecast is public! 🌦️

    Right on time for the weekend, too!

    It's an alpha version for now, because the whole team is working hard to wrestle every iota of performance from our graph NN #AIFS.

    But even as an alpha version, it's playing in the big leagues.

    I am so excited to finally share this with you! 🚀

    I'll link our blog post and the forecast below. Make sure to share this with your colleagues and machine-learning-curious friends!

    Still can't believe how far these people have made it in this short time, running four machine learning models operationally, with one developed in-house.

    The Blog: ecmwf.int/en/about/media-centr

    Your Weekend forecast: charts.ecmwf.int/?facets=%7B%2

    More to come soon!

    #MachineLearning #AI #WeatherPrediction #MLOps

  38. I was wondering why #BigTech companies were pushing into #WeatherPrediction and #ClimateModelling, but this assessment by a Swiss official seems plausible.
    "Big Tech’s arrival on the weather forecasting scene is not purely based on scientific curiosity. [...] Our economies are becoming increasingly dependent on weather, especially with the rise of renewable energy. [...] Tech companies’ businesses are also linked to weather"
    technologyreview.com/2023/07/1

  39. I was wondering why #BigTech companies were pushing into #WeatherPrediction and #ClimateModelling, but this assessment by a Swiss official seems plausible.
    "Big Tech’s arrival on the weather forecasting scene is not purely based on scientific curiosity. [...] Our economies are becoming increasingly dependent on weather, especially with the rise of renewable energy. [...] Tech companies’ businesses are also linked to weather"
    technologyreview.com/2023/07/1

  40. I was wondering why #BigTech companies were pushing into #WeatherPrediction and #ClimateModelling, but this assessment by a Swiss official seems plausible.
    "Big Tech’s arrival on the weather forecasting scene is not purely based on scientific curiosity. [...] Our economies are becoming increasingly dependent on weather, especially with the rise of renewable energy. [...] Tech companies’ businesses are also linked to weather"
    technologyreview.com/2023/07/1

  41. The rise of machine learning in weather forecasting! ⛈

    We wrote a big science blog about the last year in AI & weather!

    How do we approach these rapid developments by NVIDIA, Huawei and even Google DeepMind?!

    Read about the last year in the context of AI for weather and what's ahead. We at the European Centre for Medium-Range Weather Forecasts put a bit of work in giving a really nice overview and running all the new open-source models for your convenience!

    Read the blog: ecmwf.int/en/about/media-centr

    Authors are: Matthew Chantry, Zied Ben Bouallègue, Linus Magnusson, Michael Maier-Gerber, and Jesper Dramsch.

    #WeatherPrediction #MachineLearning #python #DeepLearning #kaggle #career #tech #science #google #deepmind #nvidia #huawei

  42. The rise of machine learning in weather forecasting! ⛈

    We wrote a big science blog about the last year in AI & weather!

    How do we approach these rapid developments by NVIDIA, Huawei and even Google DeepMind?!

    Read about the last year in the context of AI for weather and what's ahead. We at the European Centre for Medium-Range Weather Forecasts put a bit of work in giving a really nice overview and running all the new open-source models for your convenience!

    Read the blog: ecmwf.int/en/about/media-centr

    Authors are: Matthew Chantry, Zied Ben Bouallègue, Linus Magnusson, Michael Maier-Gerber, and Jesper Dramsch.

    #WeatherPrediction #MachineLearning #python #DeepLearning #kaggle #career #tech #science #google #deepmind #nvidia #huawei

  43. The rise of machine learning in weather forecasting! ⛈

    We wrote a big science blog about the last year in AI & weather!

    How do we approach these rapid developments by NVIDIA, Huawei and even Google DeepMind?!

    Read about the last year in the context of AI for weather and what's ahead. We at the European Centre for Medium-Range Weather Forecasts put a bit of work in giving a really nice overview and running all the new open-source models for your convenience!

    Read the blog: ecmwf.int/en/about/media-centr

    Authors are: Matthew Chantry, Zied Ben Bouallègue, Linus Magnusson, Michael Maier-Gerber, and Jesper Dramsch.

    #WeatherPrediction #MachineLearning #python #DeepLearning #kaggle #career #tech #science #google #deepmind #nvidia #huawei