Table of Contents
A curious community member recently asked about my process for predicting the weather, particularly here in Dayboro. Before I dive into that, let me address a common question: How and what do I do for weather predictions?
I approach weather forecasting with a method inspired by early 20th-century techniques refined with modern technology and data. Let’s walk through this detailed process, mirroring the approach of a famed historical weather predictor, though with contemporary twists.
How accurate is Dayboro.AU
Some concerned citizens here in Dayboro are worried that I do not possess enough qualifications due to the lack of letters behind my name and the lack of advertising them. They are also concerned about the Dunning-Kruger effect. This only applies to those not speaking towards the narrative of the CO2 agenda. Those who claim the Dunning-Kruger effect applies to me might want to look in the mirror… instead of chewing mainstream propaganda.
So let’s look at that, shall we?
The Dunning-Kruger effect is a cognitive bias that occurs when people with limited expertise or experience overestimate their abilities and skills in a particular field. It results from a combination of two factors:
- Low expertise: People who lack knowledge or skills in a specific domain may not realize how much they don’t know.
- Overconfidence – This happens because people tend to inflate their abilities and skills, leading them to overestimate their performance.
Let’s start by comparing what I predict, what the BOM predicts, and what is recorded. Then, we can proceed to item 1 and item 2 of the Dunning-Kruger effect.
Comparison Analysis of BOM versus Mine.
High Temperature Accuracy:
- Local (My) predictions have a higher pass rate (93.33%) compared to BOM predictions (86.67%).
Low-Temperature Accuracy:
- Local (My) predictions also have a higher pass rate (70.00%) compared to BOM predictions (63.33%).
POP (Probability of Precipitation) Accuracy:
- BOM predictions are more accurate, with a pass rate of 70.00%, compared to Local (My) predictions, which have a pass rate of 63.33%.
Conclusion:
- Local (My) predictions are more accurate for both high and low-temperature predictions than BOM predictions.
- BOM predictions are more accurate in predicting the probability of precipitation (POP) than local (My) predictions.
How about that? If I compare the predicted MM by BOM versus the Actual, I would also win that pass/fail score. I have no doubt that many will say cherry-picking, only a tiny subset, etc., which is fair. Because it is hard to accept that you might be wrong, it is a predicted reply.
Before I show you numbers and how that comparison came about, let me take you on a journey…
How do I collect data for forecasts?
Local Weather Station Data: In Dayboro, the data I get from my weather station is critical. It is sort of how this all started to get out of control. I have been looking at weather patterns and cycles since my grandad showed me some, so that is about 40-plus years ago ;-). Because of the CO2 madness and climate hype, I have been a bit more vocal, and the last 25 years have been an exciting ride.
So, let’s have a look at what data we collect.
Types of Data Collected:
- Temperature: The station measures outdoor and indoor temperatures. It is equipped with a radiation shield to reduce the heating effects of solar radiation on temperature readings.
- Humidity: Measures both outdoor and indoor humidity levels with an accuracy of ±3%.
- Barometric Pressure: Measures atmospheric pressure, which is used to predict weather changes and storm patterns. The data is adjusted for elevation.
- Wind Speed and Direction: Includes an anemometer for measuring wind speed (with updates as frequent as every 2.5 seconds) and a wind vane for wind direction.
- Rainfall: Equipped with a self-emptying tipping bucket rain gauge that measures rain with 0.01 inch or 0.2 mm resolution.
- Solar Radiation: Measures solar radiation and is used for calculating evapotranspiration, helpful for agriculture and gardening.
- UV Radiation: Measures the sun’s ultraviolet light intensity, providing useful data for understanding exposure levels in different weather conditions.
- Dew Point and Heat Index: Calculated from temperature and humidity data, providing additional insights into how hot or cold it feels outside.
I also collect Particulate Matter (PM2.5, PM1, PM10), Ozone, Formaldehyde, Carbon Dioxide, Volatile Organic Compounds (VOC) and noise.
Leaf Wetness Sensor:
- Purpose: Measures the amount of dew and precipitation left on foliage. The leaf wetness sensor mimics the characteristics of a natural leaf, allowing it to measure the moisture content that real leaves would absorb.
- Applications: Crucial for agriculture and gardening as it helps understand and manage plant diseases, which can be influenced by wetness levels on the leaf surface. It’s also used to determine the best times to apply pesticides and herbicides, as specific treatments are more effective when leaves are dry.
Soil Moisture Sensor:
- Purpose: Measures the volumetric water content in soil, continuously monitoring soil moisture levels.
- Applications: This is vital for agricultural sites to manage irrigation more efficiently and to understand water usage and plant needs at different growth stages. It helps prevent over-irrigation and optimize water consumption, which is crucial for conserving water and ensuring the health of the crops.
Soil Temperature Sensor:
- Purpose: Measures the temperature of the soil at the root level, which can differ significantly from air temperature.
- Applications: Soil temperature is critical to plant germination and growth rates. Monitoring soil temperature helps make informed decisions about planting times and can also influence the activity of pests and soil bacteria, which are temperature-dependent.
Global Data Integration
I integrate information from a network of around 400-1500 weather stations in the area per quadrant (I keep forgetting the exact number), also from worldwide, to enhance the local data. This global data helps track large-scale weather patterns that might impact local weather, providing a broader context for long-range forecasting. This data is shared across:
- Citizen Weather Observer Program (CWOP): This program reports real-time weather data to NOAA, contributing to weather modelling and forecasts (e.g., BOM uses).
- Weather Underground (Wunderground): A popular weather website and mobile app.
- PWSweather: A network for personal weather stations.
- AWEKAS: A European network for personal weather stations.
- GFS: (Global Forecast System) developed by the National Centers for Environmental Prediction (NCEP) in the US, GFS is a global weather model known for its free accessibility and long-range forecasts (up to 16 days).
- ECMWF: (European Centre for Medium-Range Weather Forecasts) widely regarded as one of the most accurate global weather models, ECMWF provides forecasts up to 10 days ahead and is known for its advanced data assimilation techniques.
- NAM (North American Mesoscale Forecast System) is a higher-resolution model focused on North America. It provides detailed forecasts up to 84 hours ahead and is beneficial for predicting regional weather events.
- WOW: (Weather Observation Website) a UK-based weather network.
That is a lot of data that helps identify global warming and how much of it is man-made (not really).
Satellite Data: Satellites offer a comprehensive view of weather systems from space, providing invaluable data on:
- Cloud cover and types: Helps in predicting rainfall and storm intensity.
- Sea surface temperatures: Influential in forming weather patterns and storm development.
- Solar radiation: Necessary for understanding heat dynamics and potential impacts on temperature.
Data Analysis Techniques
Real-Time Monitoring and Historical Analysis: The data collected is analysed in real-time to provide current weather conditions and compared with historical data to forecast future trends. Historical analysis helps understand how certain patterns lead to specific weather outcomes.
Geospatial Analysis: Using Geographic Information Systems (GIS), data is analysed spatially and temporally to observe how weather patterns move and change over time and space. Location awareness is crucial for tracking storms or changes in weather fronts. My Boltek card got old, and my new servers do not have the correct plug to plug the card in; a new setup is quite expensive; hence, I no longer track the storms. I did build an alternative but lost interest in it :-).
Statistical Methods: Various statistical methods, such as regression analysis, are used to understand the relationships between different weather variables and to predict future conditions based on these relationships.
Seasonal and Cyclical Trends: I can identify seasonal variations and cyclical trends by analysing long-term data, which is essential for accurate long-range forecasting. Seasonal and Cyclical trends analysis includes understanding phenomena like El Niño or La Niña cycles and their impact on local weather. There is a common misunderstanding that El and La impact the weather; they are just indicators, not weather patterns.
Integration into Forecasting Models
Model Calibration: The collected data calibrates forecasting models, ensuring they reflect current environmental conditions as closely as possible. Calibration involves adjusting model parameters until the output from the model aligns with observed data.
Continuous Updates: The models are continuously updated with new data, which allows for dynamic adjustments to forecasts as new information becomes available. This is crucial for maintaining forecast accuracy, especially in changing weather conditions.
Introduction to Long-Term Weather Forecasting
Long-term weather forecasting involves predicting weather patterns weeks, months, or even years in advance. It’s a sophisticated science that combines historical data, astronomical observations, and advanced mathematical models to predict future weather conditions.
Late last year, I was pointed to Weather Prophet, who uses the Inigo Jones method. This sparked my interest, mainly because it was posted that he was the only one who could accurately predict when it would rain. I promptly got banned from his site when I showed him everybody could do it, giving it time and resources. During that process, I figured I would start posting some just for fun.
Before that, I was doing some guesstimates based upon nature, history and sun cycles, not as “comprehensive” as Inigo Jones, or David for that matter, and yes, he is still the authority on this and, in my view “the one to go to”.
Inigo Jones incorporated astronomical influences into his predictions. Sure, he was called a nutter, a cooker etc., so I am among good company in that regard. Here are some basic principles and how I apply these principles to local weather forecasting here in Dayboro:
Astronomical Calculations and Observations
Mathematical Formulas and Astronomical Data: I use mathematical models and astronomical tables to calculate the positions and movements of celestial bodies. This helps in understanding their potential impact on local weather conditions. (“Astronomical Algorithms” by Jean Meeus, The Jet Propulsion Laboratory’s Development Ephemeris (JPL DE) etc)
Instrumental Observations: Similar to Jones’s use of a magnetometer to record magnetic field variations, I employ modern instruments to measure atmospheric conditions precisely. These measurements are supplemented by telescopic observations of sunspots from SWX and other celestial phenomena, providing a more detailed understanding of their correlation with weather patterns. I do not use the magnetometer; I get that data from INTERMAGNET (International Real-time Magnetic Observatory Network).
Influence of Solar and Planetary Cycles
Temperature Trends: The warming graph I frequently reference is compiled by Dr. Roy Spencer. It’s crucial to my analyses and is the sole dataset I use for temperature trends. I respect its objectivity; it shows the temperature as it is, whether it’s rising or falling. You can explore his work on his website.
Sunspot Cycles: Just as Inigo Jones studied variations in sunspot cycles identified by Eduard Bruckner, I monitor these cycles closely. These solar activities, particularly the well-known 11-year sunspot cycle, play a significant role in shaping climatic conditions in Dayboro, influencing the frequency and intensity of droughts and floods. I follow the studies of Prof Valentina Zharakova, who has written extensively about Grand Solar minimums and solar cycles.
Planetary Magnetic Fields: The interactions between Earth’s magnetic field and those of Jupiter and Saturn are crucial to my forecasts. These planetary giants exert a subtle yet measurable influence on Earth’s weather patterns. By analyzing their magnetic fields, I can anticipate changes in weather dynamics, particularly those related to extreme weather events. Well, so I try.
Cyclical Weather Patterns
- Identified Cycles: Building on Jones’s methodology, I focus on several vital cycles that have historically affected Queensland’s climate, including Dayboro:
- The 22-year Cycle: This cycle doubles the 11-year sunspot cycle and relates closely to variations in temperature and rainfall patterns, which are crucial for agricultural planning in Dayboro.
- The 35-year and 60-year Cycles: Associated with the magnetic fields of Jupiter and Saturn, respectively, these cycles are linked to the severity and duration of droughts and floods, as well as extremes of temperature and rainfall.
- The 178-year Cycle: Influenced by Uranus’s magnetic field, this cycle is considered in assessing long-term climate trends in Dayboro, aiding in preparations for significant climatic shifts.
These are some of the cycles we see; we can see more extensive cycles, so we have been saying we are in a 2000+ weather cycle. You can see weather patterns shift towards it might have been during the birth of a dude that is quite famous among larger groups… they call him Jesus. When he was born, due to different variations to the “birds and bees” we are accustomed to, that area was all green, and we see it greening up again.
Validation and Review.
Occasionally, I review what is predicted and what is fact, as in recorded. Here is one for April. I put a score of plus and minus 2C. If it is outside that range, it gets a fail. For rain, I do 50%; if it is outside that, it is a fail. I do not see 0.2mm as rain; it is dew.
This weather prediction was made a month in advance using the Inigo Jones method, which I am still learning. I tune the models as I go, still thinking that the rain has a 63% accuracy; on a month out, using Inigo is pretty ok. Some folks can do it more accurately, but I am using computers as someone said it can not be done using computers… so the challenge is on LOL.
The short-cycle model
This model uses earth-observed data to create weather forecasts for about seven days out. So, this does not include planetary stuff like the Inigo Jones predictions.
This is how the data is scored.
- Local High Comparison: Checks if the local predicted high is within ±2°C of the actual recorded high.
- Local Low Comparison: Check if the local predicted low is within ±2°C of the actual recorded low.
- Local Rain Comparison: Check if the predicted rainfall is within ±1mm of the recorded low.
- BOM High Comparison: Check if the BOM predicted high is within ±2°C of the actual recorded high.
- BOM Low Comparison: Check if the BOM predicted low is within ±2°C of the actual recorded low.
- BOM POP Comparison: Check if the BOM predicted POP is 50% or more and rain is more than 0.2, or if the POP is less than 50% and rain is 0.2 or less.
Dayboro.AU Data
For my comparision I use the predicted rain in MM versus the actual rain, this essentially is harder to do than POP%.
Local (My) Predictions:
- High Temperature Comparison:
- Count of Pass: 28
- Count of Fail: 2
- Percentage of Pass: 93.33%
- Percentage of Fail: 6.67%
- Low Temperature Comparison:
- Count of Pass: 21
- Count of Fail: 9
- Percentage of Pass: 70.00%
- Percentage of Fail: 30.00%
- POP Comparison:
- Count of Pass: 19
- Count of Fail: 11
- Percentage of Pass: 63.33%
- Percentage of Fail: 36.67%
BOM Data
For the BOM comparison, I use POP%. Obviously, this allows for a more significant margin for error. To give them some advantage. Also, it was easier to get the data that way.
BOM Predictions:
- High Temperature Comparison:
- Count of Pass: 26
- Count of Fail: 4
- Percentage of Pass: 86.67%
- Percentage of Fail: 13.33%
- Low Temperature Comparison:
- Count of Pass: 19
- Count of Fail: 11
- Percentage of Pass: 63.33%
- Percentage of Fail: 36.67%
- POP Comparison:
- Count of Pass: 21
- Count of Fail: 9
- Percentage of Pass: 70.00%
- Percentage of Fail: 30.00%
Martin Armstrong
Martin Armstrong, known for his work in financial forecasting through Armstrong Economics, has occasionally explored the connections between economic cycles and broader natural phenomena, including weather and climate changes. His approach mainly revolves around identifying patterns and cycles that can predict economic trends, but this methodology is also applied to understanding how climatic changes can impact economic systems.
Armstrong’s Approach to Cycles in Weather and Economy
Armstrong’s primary focus is on economic cycles, specifically through his Economic Confidence Model, which posits that economic waves occur in regular cycles of approximately 8.6 years. However, he has also discussed how these economic patterns can align with or be influenced by climatic cycles:
Historical Correlations: Armstrong has examined historical data to show how significant climatic events correspond with economic conditions shifts. For instance, periods of climatic instability, such as the Little Ice Age, profoundly impacted agricultural output, affecting economic stability and progress in Europe.
Predictive Analysis: While not a climatologist, Armstrong uses similar cyclical analysis techniques to discuss potential future shifts in climate and their possible economic impacts. This includes looking at long-term weather patterns and their historical economic consequences to predict future trends.
Solar Cycles and Economics: Armstrong has noted the potential impacts of solar cycles on Earth’s climate and, by extension, on economic conditions. Solar activity can influence weather patterns globally, affecting crop yields, commodity prices, and financial stability.
Integration of Climatic Data: Armstrong sometimes integrates climatic data to enhance economic forecasts in his analyses. This is based on the premise that significant changes in weather patterns can lead to shifts in economic productivity, particularly in sectors like agriculture, energy, and insurance.
Global Economic Impact from Climate Events: Armstrong has discussed how global warming and other long-term climatic trends could influence economic patterns and policy decisions. This includes potential shifts in energy policies, carbon trading markets, and international agreements on environmental issues.
The Dunning-Kruger Effect addressed
It results from a combination of two factors, as I mentioned at the start of this post:
- Low expertise: People who lack knowledge or skills in a specific domain may not realize how much they don’t know.
- Overconfidence happens because people tend to inflate their abilities and skills, leading them to overestimate their performance.
It is a phenomenon where individuals with little to no knowledge or expertise in a specific subject confidently express opinions and cannot recognize their lack of knowledge or expertise.
You be the judge if it still applies….
Now if someone got his/her information from Main Stream Media and other organisations that are getting paid to say what they say, directly or indirectly and quotes the Dunning-Kruger Effect. I chuckle; it is hard to get angry at people like that. I guess 99% of the time, the second point of the Dunning-Kruger Effect applies to 100% of those who comment simply because they are more concerned about beer, booze and footy then actually doing their research. The smart ones can do both, and I respect them for it.
