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#weatherprediction — Public Fediverse posts

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

  1. Kemal's @[email protected] ·

    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

    #ai #datascience #edgeai #embeddedsystems #iot #machinelearning
  2. Pustam | पुस्तम | পুস্তম🇳🇵 @[email protected] ·

    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

    #navierstokes #dynamics #thermodynamics #moistureequation #verticalvelocity #sigmavelocity
  3. Rainer Zufall @[email protected] ·

    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

    #bigtech #weatherprediction #climatemodelling