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  1. Google DeepMind and Google Research have launched a new AI weather forecasting model that delivers hourly predictions ⚡ with unprecedented detail. The system uses real-time satellite data and weather station observations for improved accuracy 🌦️ across multiple resolutions. Read the article to learn how it's changing weather prediction.

    true-tech.net/google-weatherne

    #Google #WeatherNext3 #AIWeatherForecasting #GoogleDeepMind #WeatherPrediction

    true-tech.net/google-weatherne

  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 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

  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?

  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. 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

  10. 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

  11. 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

  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. 🚜 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

  15. 🚜 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

  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. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. "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

  28. "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

  29. "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

  30. "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

  31. "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

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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

  39. 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

  40. 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

  41. 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