Dear Stan,
You are right – meteorological data is important to us in so many
ways. I was recently intrigued by this article written in Alaska –
about the extreme weather we experienced in Australia last summer. See http://akprogressive.blogspot.com/2009/03/warning-lights-are-flashing-australia.html
where the writer, Michael Richmond uses metric data to analyse the
factors affecting fires in Australia, in California, and in Alaska.
Using metric data makes all locations readily comparable.
Cheers,
Pat Naughtin
Author of the ebook, Metrication Leaders Guide, that you can obtain
from http://metricationmatters.com/MetricationLeadersGuideInfo.html
PO Box 305 Belmont 3216,
Geelong, Australia
Phone: 61 3 5241 2008
Metric system consultant, writer, and speaker, Pat Naughtin, has
helped thousands of people and hundreds of companies upgrade to the
modern metric system smoothly, quickly, and so economically that they
now save thousands each year when buying, processing, or selling for
their businesses. Pat provides services and resources for many
different trades, crafts, and professions for commercial, industrial
and government metrication leaders in Asia, Europe, and in the USA.
Pat's clients include the Australian Government, Google, NASA, NIST,
and the metric associations of Canada, the UK, and the USA. See http://www.metricationmatters.com
for more metrication information, contact Pat at [email protected]
or to get the free 'Metrication matters' newsletter go to: http://www.metricationmatters.com/newsletter
to subscribe.
On 2009/09/20, at 17:37 , STANLEY DOORE wrote:
National and regional climate data are derived from single point
local observations. NOAA-EDS archives such local obs which have
fairly standard obs site layouts to provide for consistency.
However, site selection for new solar energy acquisition systems
must account for local effects such as shading from trees,
obstructions from buildings and from mountains and valleys.
Topology and surface characteristics are important for wind
turbine site selection. For example, winds off the northeast coast
of the US are much better for turbines than winds off the southeast
coast. Funneling effects due to terrain for winds such as west
Texas and near Palm Springs CA are characteristically excellent for
turbine sites and, wind turbines have been built in those areas
years ago.
WeatherBug lets users choose SI or conventional unit readout.
Weatherbug also includes obs from local schools and other locations
to provide a very comprehensive data set. Schools use the data for
teaching purposes. Bob Ryan, former President of the American
Meteorological Society, was a driving force in expanding the school
weather observation program to help expand interest in science for
kids.
Stan Doore
----- Original Message ----- From: "James R. Frysinger" <[email protected]
>
To: "U.S. Metric Association" <[email protected]>
Sent: Saturday, September 19, 2009 7:27 PM
Subject: [USMA:45847] Re: Weather data for solar and wind power
calculations
Indeed, data is available on national and regional scales that can
indicate in general the feasibility of establishing solar and wind
energy farms. However, they suffer a bit due to their course
granularity. That is a more significant concern for wind farms, where
local topography can create large divergences from the predictions due
to lee and funneling effects.
My site is in a Category II region, which is generally not favorable
for
capturing wind energy. My local data happens to support that at the
location of the weather station for the time period of the data I
reported. I have since moved the station to a site with no lee
effects,
so the data since then might be slightly better. But I doubt that it
would show a wind turbine to be feasible even there.
One of the things I like about WeatherLink is that it reads out and
archives mostly in SI units. There are a few exceptions. Insolation is
reported in watts per square meter (W/m2) but solar energy is reported
in langleys (1 La = 41.84 J/m2). UV dose is reported in MEDs (1 MED =
125 mJ/cm2 = 1.25 kJ/m2). (MED stands for Minimum Erythemal Dose.) UV
intensity is reported on the UV index scale used by the World
Meteorology Organization. Of course, once I pull the data files up
into
a spreadsheet I can create columns that convert the UV dose and solar
energy columns to read in SI units.
I have a couple of other nice features available. WeatherLink uses an
algorithm to calculate evapotramspiration (in millimeters). Comparing
daily rainfalls and evapotranspiration numbers provides indication
(within the limits of the algorithm) on the net gain or loss of
water to
the soil. I also have a second thermometer outside that provides soil
temperature. Since my main use for this weather data is agricultural
in
nature, I have the soil thermometer 10 cm below the surface and the
anemometer 2.0 m above the surface of the soil. Were I actually
interested in erecting a wind turbine, I would be better off sampling
the wind at an elevation 10 m above soil level.
I might point out that our new house, being built next to our cabin
here, has a ridgepole orientation that is essentially E-W. At some
future date we or our heirs could mount solar collectors on its
southern
face.
Jim
STANLEY DOORE wrote:
Climatology for locating wind turbine systems is well-established
and published.
Similarly, climatology for solar isolation is readily available
and, it includes cloud cover as well as for clear skies.
These data should be used in planning and locating energy
collection systems. The best type of system should be selected
based on these available data.
The type of solar energy collection system - roof top/distributed
or concentrated/centralized - are in competition. Each has
advantages and disadvantages.
However, there is a hugh number of roofs which can be covered
with thin-film solar photovoltaic collectors (panels) which would
provide direct input to the building and, excess electricity could
be fed back to the grid. This would eliminate transmission loss and
not take land space that huge concentrated solar energy collection
systems would.
Thin-film solar collector and installation costs are dropping
rapidly and, they can be located where wind farms are not
practical. However, wind farms can generate electrical energy
where solar systems are not practical.
In short, climatology plays a significant role in types used for
locating energy generation. In some places, both types may be cost-
effective.
Data are readily available from the National Oceanographic and
Atmospheric Administration's (NOAA) Environmental Data Service (EDS).
The International System of Units (SI) should be used exclusively
to make comparisons among the various systems to simplify
understanding.
Stan Doore
----- Original Message ----- From: "James R. Frysinger" <[email protected]
>
To: "U.S. Metric Association" <[email protected]>
Sent: Saturday, September 19, 2009 11:14 AM
Subject: [USMA:45845] Re: Weather data for solar and wind power
calculations
One sees occasional anomalies in "raw" data, John. Yes, that one
instantaneous insolation number is nearly the theoretical maximum
at the
top of the atmosphere AND at the Sun's declination. I have not
searched
the table of numbers; that value might have been recorded at night!
It
is impossible to say what caused the instrument to record that
particular value: a reflection, electronic noise, a flash of
lightning, ...
The data does show that our farm would be a very poor candidate for
harvesting the wind. Just looking at the large fraction of time in
which
the winds are below or just at minimum turbine needs dissuades such
notions. Our low solar average insolation likewise does not
encourage me
to erect large solar arrays to ease TVA's load, but of course it
suffices for powering remote electric fences.
Jim
John M. Steele wrote:
Interesting data. Your solar sensor may be a bit "optimistic."
The maximum value is essentially equal to the accepted value for
solar incidence at the top of the earth's atmosphere. At sea
level, for overhead sun the value is usually taken as 950 - 1000 W/
m². Since it is never "overhead" at your latitude, the number
would be slightly lower. (If you were on top of a substantial
mountain, it could be a little higher, but not at your altitude).
On the wind data, even your highest 10 minute value would put you
well down in the cube law region and at roughly 7.5% of rated
power (usually generators require 12 - 14 m/s for rated power).
--- On *Fri, 9/18/09, James R. Frysinger /<[email protected]>/
* wrote:
From: James R. Frysinger <[email protected]>
Subject: [USMA:45842] Weather data for solar and wind power
calculations
To: "U.S. Metric Association" <[email protected]>
Date: Friday, September 18, 2009, 10:46 PM
I have a Davis Vantage Pro2 weather station and console, read out
and archived with WeatherLink software.
Here is some actual weather data that might interest those
concerned
with harnessing solar or wind power. The data was collected on my
farm in Middle Tennessee. To four places, the weather station
location then was 35.9785N, 085.5087W; it has since been moved
to a
better, nearby site. This old site was somewhat shielded by
nearby
buildings, but still the data is interesting (at least to me).
The data is collected every few seconds and recorded in 10 min
bins.
Each bin's value then represents an average over that 10 min
period,
but a few channels also record "instantaneous" high and low
values
during that period as well. I have pulled the 52599 bins (10 min
each) of data that I obtained for 2008 into a spreadsheet, in
which
I have started some additional statistical analysis.
My data follows, without regard to significant figures. Note that
wind speed data is sensed and transmitted in 1 mi/h intervals.
The
logger is set up to convert those to kilometers per hour and it
then
rounds it to the nearest 0.1 km/h. Thus wind speeds are
archived in
1.61 km/h intervals to the nearest 0.1 km/h.
Solar (essentially no effective shadowing by nearby structures):
minimum insolation 0 W/m2
maximum insolation 1180 W/m2 (10 min average)
maximum insolation 1331 W/m2 ("instantaneous")
average insolation 165.02 W/m2 (10 min average)
Wind (two one story buildings in the south to northwest sector):
minimum speed 0 km/h
maximum speed 20.9 km/h (10 min average)
maximum speed 57.9 km/h ("instantaneous")
average speed 1.66 km/h (10 min average)
Wind bin data:
speed freq. log frequency
(km/h) 0.0 27658 4.44
1.6 10401 4.02
3.2 6681 3.82
4.8 3920 3.59
6.4 2033 3.31
8.0 1036 3.02
9.7 488 2.69
11.3 218 2.34
12.9 90 1.95
14.5 42 1.62
16.1 21 1.32
17.7 7 0.85
19.3 3 0.48
20.9 1 0
The best fit straight line for the log frequency data is
log f = 4.55 - 0.21v/(km/h)
Those who love to play with numbers can cube the speeds (I
recommend
converting to m/s first), then doing a numerical integration
using
the above frequency data to determine the mean power available in
the wind here. Or the regression equation above can be used to do
the integration if you're a hard-core mathematician. Either some
more information will be needed.
The elevation here is 375 m above sea level. The average sea
level
pressure (obtained by altitude correction to sea level by the
logger) was 1017.8 kPa (min 981.3 hPa, max 1046.4 hPa). The
average
relative was humidity of 71 % (min 17 %, max 98 %). The average
temperature was 14.54 °C (min -13.4 °C, max 35.0 °C). These are
all
10 min bin averages. You will need these numbers to correct to
actual barometric pressure and to calculate the density of air
here
(on the average, sort of).
Hopefully this will give someone some interesting real-world
numbers
to play with.
Jim
-- James R. Frysinger
632 Stony Point Mountain Road
Doyle, TN 38559-3030
(C) 931.212.0267
(H) 931.657.3107
(F) 931.657.3108
--
James R. Frysinger
632 Stony Point Mountain Road
Doyle, TN 38559-3030
(C) 931.212.0267
(H) 931.657.3107
(F) 931.657.3108
