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#weatherprediction — Public Fediverse posts

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

  1. 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

  2. 🚜 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

  3. 🚜 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

  4. 🚜 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

  5. 🚜 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  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. "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

  20. "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

  21. "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

  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. 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

  25. 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

  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. 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

  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