Everybody talks about the weather but nobody does anything about it.

- Mark Twain

Click thumbnails for a larger image

Current Year to Date numerical data with projections based on historical data since 1895.

On the left are Annual and Oct-Sept Rainy Season totals to date, an estimate for the whole period and the year's rank since 1895. Lower rankings are wetter. Below is average annual and remaining Average - YtD. Below are Historical and Rainy Season Min/Max and year.

On the right is the data date and current month total.

Below are two comparisons of current month to estimates based on the historical data. The Trapezoid Estimate uses a linear estimate based on the slope of the previous, current and next month averages. The Polynomial Function uses a 4^{th} order polynomial to fit a curve through the prior two, current and next two months' averages.

Monthly & Annual Average Rain, Current Month and Year to Date and Minimum, Average and Maximum annual rainfall since 1895. North Pacific coastal towns can be a bit damp at times.

A very useful app for precipitation prognostication is Flowx. The free version is better than many paid apps I tried. Using the app, you can decide the best time for walking the dog, marketing, hiking, flying, etc. The missus uses the free version and I, the Silver.

It's a very polished app with a simple, but flexible interface. We were even able to use it to determine a forward berth on a cruise should be OK by looking at general wave patterns. Highly recommended.

A lie gets half way around the world before the truth has a chance to get its pants on.

- Winston Churchill

Annual Rain Totals and trend since 1895. Annual totals over the years *with data*† has a decreasing* trend, about 0.058in/yr. A few wet years, as occurred around 1900, could negate or reverse the trend.

OTOH, a polynomial function shows that rain is increasing for the last couple of decades. Data over a longer time frame would give higher reliability, *unless the data is chaotic*, in which case making predictions is a fools errand. And therein lies the danger of predicting the future based on infinitesimally short data sets.

By choosing the period over which the data is analyzed, rain is either decreasing at 0.058" per year or increasing at 0.006" or or 0.010" or 0.132" per year. The last two values differ by a factor of 13 which is achieved by selecting the period, 50 or 45 years respectively. That, dear reader, is exactly how climate alarmists achieve the desired result: **manipulate the data!**

See Climategate: Ten Years Later

A little chicanery helps. Stephen Schneider — creator of the journal Climatic Change and one of the founding members of the UN’s Intergovernmental Panel on Climate Change (IPCC) — wrote in 1971 "It is found that even an increase by a factor of 8 in the amount of CO2, which is highly unlikely in the next several thousand years, will produce an increase in the surface temperature of less than 2°K." then said in 1989 "We have to get some broad-based support, to capture the public’s imagination. That, of course, entails getting loads of media coverage. So we have to offer up scary scenarios, make simplified, dramatic statements, and make little mention of any doubts we might have. This “double ethical bind” which we frequently find ourselves in cannot be solved by any formula.** Each of us has to decide what the right balance is between being effective and being honest.** I hope that means being both." [Emphasis added]

An excellent article on how wrong are climate alarmist dire predictions: Climate Change Fears Of Teen Activist Are Empirically Baseless

† data exists for ≈130 years of the past hundreds of millennia

* f(x) sign is reversed as data table is current to 1895. Graph is plotted in reverse data order

See Climategate: Ten Years Later

A little chicanery helps. Stephen Schneider — creator of the journal Climatic Change and one of the founding members of the UN’s Intergovernmental Panel on Climate Change (IPCC) — wrote in 1971 "It is found that even an increase by a factor of 8 in the amount of CO2, which is highly unlikely in the next several thousand years, will produce an increase in the surface temperature of less than 2°K." then said in 1989 "We have to get some broad-based support, to capture the public’s imagination. That, of course, entails getting loads of media coverage. So we have to offer up scary scenarios, make simplified, dramatic statements, and make little mention of any doubts we might have. This “double ethical bind” which we frequently find ourselves in cannot be solved by any formula.

An excellent article on how wrong are climate alarmist dire predictions: Climate Change Fears Of Teen Activist Are Empirically Baseless

† data exists for ≈130 years of the past hundreds of millennia

* f(x) sign is reversed as data table is current to 1895. Graph is plotted in reverse data order

The graph shows rainfall by month from 1895 to present. There is no pattern or trend in year to year rainfall. The dotted lines are the data mean. It can be seen that rainfall may be below average for several years and then ping-pong above and below. Or an exceedingly wet month can occur after a year of below average rain as occurred in December 2016, presaging the 2^{nd} wettest year in Florence's history.

“plus ça change, plus c'est la même chose”

- Jean-Baptiste Alphonse Karr

A great many bits are committed to *climate change.*

The climate has always changed.

The climate will always change.

The graph averages monthly year to year change from 1895 to present in four periods. The legend caption is the last year of the period. While the period volatility is quite pronounced, the overall volatility is remarkably constant, up a very little, down a very little over a century and a quarter.

“...avoid prophesying beforehand because it is much better to prophesy after the event has already taken place”

- Winston Churchill

A frequent topic of conversation is whether it's a *wet* or *dry* year and the likelihood of the current trend continuing.

The top graph shows the grouping of a control period and how it correlates with another period and a trend. A Positive slope means that the control period presages a similar dependent period, a Negative slope the inverse. The more tightly grouped the higher the correlation. Widely scattered points indicate a chaotic relationship.

The 2^{nd} graph shows the same data in a linear format. In some periods there is a correlation and some none.

In Other Words, *your guess is as good as mine.*

"There are three kinds of lies: lies, damned lies, and statistics." - Benjamin Disraeli

DISCLAIMER: Flaws: Data is annualized and compartmentalized into months. There may be others.

Data from Oregon State and Community Collaborative Rain, Hail & Snow Network