These guide steps provide a detailed, step-by-step explanation of creating weather forecasts for Dayboro using the Inigo Jones method. This user manual style guide is your essential starting point for aspiring weather forecasters like Matt or anyone fascinated by meteorological predictions.
You can use it to create your method and data sets to compare against what we put up. We will not provide you with the raw data as it will encourage discussions about manipulated data, biased data, etc; it is not the discussion we should have. You must focus on mimicking the process with your method to create a forecast so you can audit it and point out where it arguably is not science or not working. We fully understand that it is easier to say, “It does not work; it is all a hoax because the internet says so”that is easy.
What we started doing is adding validation to our Inigo Jones forecasts.
Step 1: Understanding the Basics of the Inigo Jones Method
Inigo Jones, an Australian meteorologist, became renowned for his long-range weather predictions by meticulously studying astronomical and meteorological data. Central to his method is the belief that planetary interactions and solar activities significantly influence weather patterns on Earth.
Key principles include:
Sunspot Cycles: Monitoring 11, 22, 35, 60, and 178-year cycles correlated with planetary movements.
Planetary Magnetic Fields: Jupiter, Saturn, Uranus, and Neptune notably affect Earth's weather.
Astronomical Calculations: Utilizing precise astronomical algorithms to track planetary and lunar positions
To learn more about Inigo Jones visit:
Step 2: Data Collection and Preparation
Reliable forecasting hinges upon accurate and comprehensive data. The following datasets are foundational:
Astronomical Algorithms by Jean Meeus: Essential formulas for calculating planetary and solar positions accurately.
Planetary Events: Tracks planetary conjunctions and oppositions, which significantly impact weather.
Historical Weather Data: Events like floods, heatwaves, and storms.
Local Geographic and Climatic Data: Understanding Dayboro’s subtropical climate, average rainfall, temperatures, and geographical influences.
Step 3: Collecting Your Data
Obtain the following publicly available datasets:
Moon Phases: Full, new, and half-moon phases influencing weather conditions.
Planetary Positions: Detailed planetary alignment data.
Sunspot Data (SN_d_tot_V2.0): Daily sunspot numbers are critical in forecasting solar influences.
Solar Cycle Predictions: Essential for long-range forecasting and predicting solar activity peaks.
Historical Severe Weather Events: Analysis and pattern recognition for extreme events.
Step 4: Setting Up Your Forecast Model
A reliable forecast involves:
Temperature Chart
Construct temperature charts for each quarter:
The Sun entering cardinal signs (Aries, Cancer, Libra, Capricorn) dictates seasonal temperature shifts.
Air Movement Chart
These are established each time Mercury enters a new zodiacal sign. Mercury's quick orbital changes strongly affect wind patterns. Track its exact entry times to define the air movement forecast periods accurately.
Step 5: Moisture Chart Creation
Lunar phases notably influence moisture predictions. Each lunar phase—New Moon, First Quarter, Full Moon, and Last Quarter—marks distinct moisture periods. Use precise local time data to ensure accuracy.
Step 6: Pattern Recognition
Identify correlations by comparing collected astronomical data and historical weather records:
Planetary Alignments: Special conjunctions or oppositions of planets can signal major weather changes.
Sunspot Peaks: Increased sunspot activity correlates with significant weather fluctuations.
Moon Phases: Track lunar distances and phases; they significantly affect tides and atmospheric moisture levels.
Step 7: Forecasting with Astronomical Calculations
Using Jean Meeus’s Astronomical Algorithms, compute planetary and lunar positions. This is crucial for determining their exact influence on Earth's climate at specific times.
Step 8: Verification & Calibration
After forecasting, consistently verify predictions against observed data:
Track actual weather events versus predicted conditions.
Adjust your model based on outcomes and refine your forecasting accuracy continually.
Step 9: Continuous Verification and Adjustment
Weather forecasting is dynamic; constantly refine your method through ongoing verification against real-world outcomes. Cross-reference predictions with:
Observed severe weather events
Seasonal climatological summaries
Updated planetary and solar data
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