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

- Mark Twain





Year to Date & Current Month

YearToDate
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 Rainy Season Oct-Sept totals, an estimate for the whole period and the year's rank since 1985. Lower numbers are wetter.
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 and next month averages. The Polynomial Function uses a 4th order polynomial to fit a curve through the prior two and next two months' averages.


Monthly & Annual Average Rain

RainYtD

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.


Annual Rain Totals

AnnualTotals

Annual Rain Totals and trend since 1895. Total rain over the years has a decreasing trend, about 6.7%/yr. A few above average years could negate or reverse the trend.
OTOH, a polynomial function shows that rain is increasing for the last decade. Note the R² value, a measure of how well the function correlates to the data, is higher for the polynomial than the linear function. Data over a longer time frame should give higher R² coefficients, 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 nearly infinitesimally short data sets.

Annual Volatility

RainByYear

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 average of the data and 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 2nd wettest year in Florence's history.


Historical Trends

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

- Jean-Baptiste Alphonse Karr

RainTrends

A great many bits are committed to climate change.
The climate has always changed. The climate will always change.
The graph shows the average month to month change from 1985 broken down into fourths. The legend caption is the last year of the segment. While the month to month volatility is quite pronounced for each fourth, the overall volatility is remarkably constant, up a very little, down a very little over a century and a quarter.


Prognostication

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

- Winston Churchill

AnnualCorrelation
DepVsControl

A frequent topic of conversation is whether it's a wet or dry year and the likelihood of the current trend continuing.
The top graphs show 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 2nd graph shows the same data in a linear format. In some periods there is a correlation and some none.

IOW, your guess is as good as mine.


"There are three kinds of lies: lies, damned lies, and statistics." - Benjamin Disraeli
DISCLAIMER: One flaw in the analysis: Rain data is annualized and compartmentalized into months. There may be others.
Data from Oregon State and Community Collaborative Rain, Hail & Snow Network