Further Weather Analysis (Weather Studies of Connecticut)

In 2020 I began a prolonged study of the weather patterns in central Connecticut, where I live, dating back 25 years to 1995. As it took me two years to compile and slowly assemble first the Weather Calendars of each year and then analyze the 35 Weather Patterns, it was not until 2022, in mid-September, that I finally prepared the Comprehensive Study for publication. This led to inclusion of data from 2021 and 2022, outside the study period, but incompletely. Based on this, I then, in Farrell’s Almanac, made a year’s worth of predictions which I then anxiously waited to see if they panned out. This volume, accordingly, will continue yearly analysis of the weather patterns of each year from 2021-2023, listing them under the main headings of one of each Weather Pattern. Predictions that came to pass or failed will be treated under each year’s data, with analysis as to why they failed. At the end will follow updated graphs for the years given, added to the original graphs where possible save for charts such as the Severity Analysis Chart. I will also make my chief argument in greater depth than I was able to do in the original Comprehensive Study, where it was given as a mere hypothesis: as, however, it has been borne out by many of the Weather Pat-terns in the two years succeeding, it has more confidence and can be presented with somewhat more force. This argument is that, far from Global Warming, the climate change we are experi-encing is not really climate change at all, but a reassertion of earlier patterns of weather behave-iour after an extreme climax in severity, and that emissions have almost no say in the matter as the cycles are behaving according to cyclic pattern and are not exacerbating. The primary purpose of this volume is to lay forth as a verified scientific method the mode of prediction described in the Comprehensive Study’s introduction and used all through it, name-ly the calculation of sequences of recurrence of particular features of the identified 35 annual Weather Patterns such as time of month, severity and doubling, then to use the intervals between recurrences to identify the next likely stage in the continuation of the sequence. Larger and more regular cycles such as the Winter and Summer Heat cycles or the 17-year cycle of the May heats and August heats depend entirely on observation of the previous position in the cycle and an assumption the cycle will repeat roughly the position. Long-term data was also used to verify the accuracy within southern New England of weather proverbs, predictions then being made according to whether the data bore out the proverb. I describe this sequence-interval prediction method as the Sequence Interval Model (SIM) and lay it forth in the hope that it will be useful in predicting the weather, as many of the patterns I have observed I am apparently the first to notice.