This
section describes where I got the information that I used to develop those
professional opinions and long-term forecasts that make up my Sunshine Guides. It also explains the long and tortuous road
from the raw data to the finished product.
If you have little or no interest in such matters, please feel free to
skip this subject entirely. You won’t
hurt my feelings a bit.
The proximate
source of my forecasts is the rather extensive personal library of climatic
data and information that I have compiled in over fifty years of more or less
intensive collection of such things.
This collection is better then that in many university librariesCand much of it was copied in many university
libraries. I have spent way too much
time in various libraries, copying sources laboriously by hand, photographing
documents, and feeding an endless supply of coins into library copying
machines.
The
most useful of these libraries have been the Library of Congress in Washington
(DC), the National Climatic Data Center (NCDC) library in Asheville (NC), and
the libraries of the
The
ultimate source of my forecasts, of course, has been the thousands of
publications published (andCall too oftenCnow out
of print) by the hundreds of various national meteorological and climatological
organizations in nations throughout the world.
Sometimes, you can order publications directly from these organizations,
but it is ultimately an unsatisfying and patience-wearing exercise. The Library of Congress gets copies of most
of them in the long run, anyway.
Useful
intermediate sources include the many compilations made by scholars from those
primary sources. Among the most useful
have been the Handbuch der
Klimatologie by Köppen
and Geiger; the two WWII-era compilations by the British Meteorological Office,
Tables of Temperature, Relative Humidity, and Precipitation for the World, and
Tables of Temperature, Relative Humidity, Precipitation and Sunshine for the
World; the pricey but invaluable sixteen-volume World Survey of
Climatology, edited by Landsberg; the U. S. Naval
Weather Service’s World-wide Airfield Summaries (use with care, contains
estimates as well as data of record); and the World Meteorological Organization=s two compilations, Climatological Normals for the Period 1931-1960, and Climatological
Normals for the Period 1961-1990.
Finally,
there are the extremely useful compilations issued by the NCDC in various
non-print formats. These include the
exhaustive Global Climatic Historical Network (GCHN); the International
Station Meteorological Climate Summary (ISMCS); the Global Daily Summary
(GDS) for 1977-1991; and the
This,
then, is where I get the raw data for my forecasts. The next section deals with what exactly I do
with this data once I have it.
It
would be nice if, for any given destination, there was a single set of climatic
data that was the official set for that destination. If would make my work so much simpler. All I would have to do is to reprint that
data-set (with the appropriate citation of credit, of course) and my work would
be done. Unfortunately, this is most
definitely not the case.
Most
destinations have dozens of sites scattered over their area for which climatic
records of varying lengths have been compiled.
In most cases, they are all official, in that the records appear in the
official publications of that nation’s meteorological organization. I think that I once counted twenty-three for
the Phoenix (AZ) metropolitan area, alone.
Some of these sites have long records, some have short ones. Some are in areas that travelers are likely
to visit, and some are not. In addition,
there are the numerous sites operated by airfields, military bases,
universities, and agricultural experiment farms. In many cases, these latter records are better
and more detailed than the nation’s meteorological agency sites.
Not
only that, but not all of this data is equally useful. Records may be fragmented, with no
measurements being made at certain times of year or certain times of day. This is very common in some areas of harsh
weather. Instruments may be badly
located, or observers badly trained.
Quite often, estimates will be printed and passed off as actual
readings. Numerical errors creep in at
all levels (you can safely disregard a temperature of -44° offered for the central
Because
there are many possible sources of widely-varying quality, it is necessary to
make decisions as to which particular sources will be utilized in the final
numbers, and what weight to give to each.
Over the years, I have developed a computer program which enables me to
do just this with some ease.
Once
I have decided which sources to use (the average is about eight), I give each
source an array of weightsCone weight for each parameter that is being
evaluated. My program allows for
fifty-six parameters, which means up to 56 weights for each source. These weights are based on my professional
judgment as to how useful that particular source is in regard to that
particular parameter.
The
published value for a particular month and parameter (let’s say, January
snowfall) is multiplied by the weight assigned to that combination of source
and parameter. The total weighted value
for all sources used is then divided by the total of the weights to get the
value that will appear in the table.
This sounds cumbersome, but in actual practice it goes fairly quickly.
More
than half of the table values have been treated in this way. If they appear to be the same as some
published value, it is usually due to a rather unusual agreement among all the
sources, or to simple rounding in print (the computer uses sixteen significant
figures) of the table values.
It
is this process that makes the results a matter of professional judgment rather
than a matter of fact. It also avoids
copyright infringement, since I make it a point never to use a single source
for any parameter. That and their
presentation as forecasts rather than records, enables me justify my own copyright. I decide how much weight to give to each
individual source.
Not
every destination has records for all of the twenty or so climatic
characteristics that I use in my Sunshine Guides. Where no records
exist, it is necessary to produce the table values by some means other than
multiple weighted extracts. One of these
means is interpolation. Care must be
exercised in the use of interpolation, however, because not all climatic
characteristics are suitable for interpolation.
I do not use it at all for precipitation values, humidity values, or
fog. It has only limited value for
temperature characteristics. It is most
useful for sky data, since (except in mountainous areas) that sort of
characteristic does not change rapidly with distance.
If a
destination has no sky data, I will use interpolation from surrounding sites to
produce values for that destination.
This is an accepted climatological procedure, and is used by the
meteorological services of many nations.
All maps using isopleths and isorithms use this technique. The weights that I give to the various
sources are in proportion to their similarity to and to their distance from the
destination in question.
I
use interpolation much less than I used to, because I have replaced it with a
technique that I call “regional ratios”.
This technique is based on two hypotheses: 1) certain climatological
characteristics are related; and 2) these relationships are more similar in
nearby areas than in distant ones.
An
example of the first hypothesis is that the percentage of sunshine is related
(although not necessarily linearly) to the percentage of cloud coverCor more precisely its inverse, the percentage of clear
sky. An example of the second hypothesis
is that the relationship between sunshine and clear skies at
This
relationship can be expressed in the formula:
SS(x):CS(x) = SS(y):CS(y)
Here,
SS is the percentage of sunshine, CS is the percentage of clear sky (100% minus
the percentage of cloud cover), x is the station for which no sunshine data is
available, and y is a nearby site where it is available. Both sites have cloud-cover data. By solving for SS(x) from a number of nearby
sites, and assigning the proper weights, a value for sunshine can be produced
for use in the tables. Since the ratios
become imprecise at low numerical percentage values, the computer program
switches over to incremental ratios in these situations. This is a far from perfect technique, but it
is demonstrably better than interpolation.
Many
climatic characteristics are related to one another, although usually much less
obviously than sunshine and clear skies.
Frequency of precipitation at level x is related both to total precipitation
and to frequency at level y. The
relationship is non-linear. My table
characteristic, “Reasonably Dry Days” involves a level of roughly a tenth of an
inch. Since that measurement is rarely
made outside of the
Many
of the table values involve simple (or, not so simple) calculations. The hours of daylight values are calculated
using the latitude, longitude, and elevation values. The techniques use simple spherical
trigonometry. The algorithms may be
found in most elementary astronomy texts or manuals of navigation.
Data
in metric form are entered into the database directly. The program converts data in other formats
into metric units. It will print in
either metric format or American Standard.
Values
for relative humidity are often calculated from temperature values and values
for vapor pressure or dew-point. This is
necessary because relative humidity is so strongly temperature dependent.
If you get the idea that all of these procedures result in
values that are not very precise, you are absolutely right. But this is in keeping with the realities of
actual atmospheric variability. Air
temperatures at shoe-top level may be tens of degrees different from air
temperatures at forehead level. Showers
fall at the airport, but not downtown—and vice
versa.
Don’t confuse precision with accuracy. For pi, the value of twenty-two sevenths
is far more accurate than a value of 3.15472861. The latter is more precise, but less
accurate. My Guides
are as accurate as I can get them—precision is not that important. Just remember that weather conditions vary
from morning to night, from day to day, from year to year, and from place to
place at any instant in time—often over very short distances. What you need is a professional judgment—and
that is what I give you.