Courtyard wicket gates

Courtyard wicket gate half open

View East Through Wicket Gate

This small courtyard has been described on its own page: “A Heat-control Courtyard”.

Built to help control the indoor climate, it is enclosed by solid walls and solid gates made of a sandwich of fibre and polystyrene.
At times when free circulation of air is wanted, the gates can be latched open. That has the disadvantage that dogs and small children can pass in and out.
I have now made the control of air separate from the control of traffic by adding a wicket gate in each gateway.

The first photo (above) is a view of the courtyard as one would enter it from the west. Both main gates are open. The west wicket gate stands partly open, and the east wicket gate is closed.

Courtyard wicket gate bolted open.

West solid gate closed and wicket gate bolted open.

The second photo, taken from just inside the courtyard, shows the west main gate closed to prevent the flow of air. The wicket gate is fully open, as it would be in that case, secured there by its drop bolt.

Courtyard seen through the east wicket gate

Courtyard through east wicket gate

 

 

 

 

In the third photo, the courtyard is seen through the open east main gate and the bars of the closed east wicket gate.

These photos were taken on the 1st of August 2017 at 10:30 am. They show the courtyard receiving sunshine that passes over the roof of the house, as it does during winter mornings. Some sunshine is direct, some reflecting diffusely off the wall, and some reflecting brightly off mirrors of aluminium foil.

Two thermometer screens can be seen in the third photo. I am monitoring temperatures to find if the courtyard is affecting the indoor climate. As an experiment, I keep the main gates open or closed in alternate months. When gates were open in a particular month of the first year, they are closed in that month of the next year.


The wicket gates are made of welded, pre-galvanised steel tube in the style “Pool’nPlay Flattop”, powder-coated in white. They were supplied and installed in July 2017 by Bluedog Fences for $1793.

July 2017 fine with cold nights

July morning photo of Manilla from the lookout

Manilla Prospect in July

Through most of the month, days were fine and sunny, but some days, mainly in the middle, were cloudy and some had a little rain. The highest reading, on the 16th, was only 7.4 mm.
No days were remarkable except the 28th which, at 23.7°, equalled the record for July set 31/07/14. It was 6.1° above normal.
Frosts (below +2.2° in the screen) happened on 23 mornings, 6 more than normal. However, the coldest morning, at -2.6°, was not nearly as cold as the record of -5.1° set in 2002.

Weather log

Comparing July months

Unlike July 2016, which had been cloudy with warm nights, this July was fine with cold nights. Days, at 18.1°, were not quite as warm as in July 2013 (18.9°), the warmest in the new century.
Moisture was scarce, as in the record-making July of 2002. Readings that reflected low moisture were:

Daily minimum temperature very low: +1.2° (2002: 0.9°);
Very many frosts: 23 (2002: 27);
Very low percentage of cloudy mornings: 29% (2002: 23%);
Very low early morning dew point: -1.4° (2002: -1.4°);
Very wide daily temperature range: 16.9° (2002: 18.5°);
Very low rainfall: 13.2 mm (2002: 1.0 mm).

Relative humidity in the early mornings, normally 90% in July, was 74%. That was the lowest July value in my 13-year record.
Despite the total rainfall of 13.2 mm (16th percentile) being far below the July average (41 mm), there are still no shortages of rainfall for groups of months. The most recent serious shortage was nearly two years ago. In October 2015, the 30-month total to that date (1216 mm) was still down at the 6th percentile. That shortage was carried over from an earlier extreme event: the 85 mm summer rainfall of 2013-14 that was 142 mm below average.

Climate graph for July


Data. A Bureau of Meteorology automatic rain gauge operates in the museum yard. From 17 March 2017, 9 am daily readings are published as Manilla Museum, Station 55312.  These reports use that rainfall data when it is available. All other data, including subsoil at 750 mm, are from 3 Monash Street, Manilla.

3-year trends to July 2017

Fine with a wide daily temperature range

3-year climate trends to July 2017

July raw anomaly data (orange)

In July 2017 the largest anomaly was the very wide daily temperature range (middle right graph). This was linked to the daily minimum temperature anomaly (lower left graph) falling suddenly very low.
All moisture indicators pointed to aridity (upwards), and the anomalies of both daily maximum temperature and subsoil temperature were high.

 Fully smoothed data (red)

The latest available fully-smoothed data point, January 2017, showed continued warming in the anomalies of maximum, minimum and subsoil temperatures. These were coming to a peak: the maximum and minimum perhaps in February, but subsoil not for several months.
Moisture anomaly variables were near a peak of aridity. Dew point had peaked (low) in November, cloudiness (low) and daily temperature range (high) in January, with rainfall (low) likely in February.


Note:

Fully smoothed data – Gaussian smoothing with half-width 6 months – are plotted in red, partly smoothed data uncoloured, and raw data for the last data point in orange. January data points are marked by squares.
Blue diamonds and the dashed blue rectangle show the extreme values in the fully smoothed data record since September 1999.

Normal values are based on averages for the decade from March 1999.* They appear on these graphs as a turquoise (turquoise) circle at the origin (0,0). A range of anomalies called “normal” is shown by a dashed rectangle in aqua (aqua). For values in degrees, the assigned normal range is +/-0.7°; for cloudiness, +/-7%; for monthly rainfall, +/-14 mm.

 * Normal values for rainfall are based on averages for the 125 years beginning 1883.

Annual Rainfall Extremes at Manilla NSW: II

II. Platykurtic, Bimodal Annual Rainfall

Histogram annual rainfall frequency Manilla NSW

Manilla’s 135 years of rainfall readings yield the graph above. There are several features to notice.

A ragged pattern

Despite having as many as 135 annual rainfall values, the graph is still ragged. Some of the 20 mm “bins” near the middle have less than 2% of the observations, while others have over 5%. The pattern has not yet become smooth.

It is not near a normal distribution

Rainfall is thought of as a random process, likely to match a curve of normal distribution. On the first two graphs I have drawn the curve of normal distribution that best fits the data.

Smoothed annual rainfall frequency Manilla NSW

In this second graph, I have smoothed out the ragged shape of the plotted data, using a 9-point Gaussian smoothing. You can see more clearly where the actual curve (black) and the normal curve (magenta) differ. The dotted red line shows the differences directly:

The peak is low;
The shoulders, each side of the “peak”, are high;
Both of the tails are thin.

These three features describe a platykurtic curve: one with low kurtosis. This fact makes the highest and lowest annual rainfalls at Manilla less extreme than would be expected in a normal distribution.

Another departure from normality is that the curve is skewed: the tail on the left is shorter than the one on the right. That is a positive skew, but it is small. (By contrast, most of the rainfall distributions for individual months at Manilla have large positive skew. In them, the peak is well below the mean, and a tail extends to rare high values.)

In summary, four of the leading features of the shape of Manilla’s annual rainfall distribution are:

Mean or average: 652 mm per year.
Standard Deviation (measuring spread or scatter): 156 mm.
Skewness: 0.268 (slightly positive).
Kurtosis: -0.427 (strongly platykurtic).


A platykurtic curve matches the Manilla annual rainfall frequency curve to some extent.

The sum of two Gaussian curves gives a much better match.


Fitting a platykurtic near-normal curve

Much of the poor fit of a normal curve to the data is due to the data having a platykurtic distribution. Being platykurtic produces a reduced peak, high shoulders, and thin tails, as was noted.

Smoothed rainfall frequency and a platykurtic curve

In the third graph, I have drawn (in magenta) a new model distribution that is platykurtic. It is a transform of the normal distribution with a weighted sinusoidal correction. The new curve fits much better up both flanks of the data curve. It cannot be made to fit in the peak area between 500 mm and 820 mm.

Fitting a bimodal model made of two normal curves

The shoulders of the smoothed rainfall distribution curve (black) are not simply high; they are higher than the  zone in the middle where the peak would normally be. There is a major mode (peaking at 5.1%) on the left, a minor mode (3.9%) on the right, and an antimode (3.7%) between them.

Smoothed rainfall frequency and a bimodal curve

Continue reading

House June warmth profiles: III

Part III: Daily temperature cycles, east wing

Graph showing the daily temperature cycles for five days at mid-winter

This five-day period was a testing time for the unheated solar-passive house. Days were at their shortest, some nights were frosty, and overcast persisted for two days. It fell within a cold, wet, and cloudy winter.

This post is about the single-storied east wing of the house. It is the main part of the house, with most of the clearstory windows.

Back to Part I: Average temperature values.

Back to Part II: Daily temperature cycles, west wing

Observations

View of the house from the street

House From the Street

In this wing, seen on the left in the photo, five thermometer stations define a profile in height. They are:

Subsoil in the heat bank beneath the house;
On the floor slab;
On the room wall;
In the clearstory space;
OUTDOORS, in a Gill Screen, 1.5 metres above the ground and eight metres from the house.

During the five days I made 84 observations at each station at intervals as shown. They define the daily temperature cycles. I observed the amount of cloud in Octas (eighths of the sky) at the same intervals.

Table of east wing temperatures.This table lists for each thermometer station the five-day values of the average, maximum, and minimum temperatures, and the temperature range.

The daily cycles

Subsoil

Continue reading

June 2017 not as wet as in 2016

Close-up Australian magpie

Thieving Magpie

The month began cool, but became warm in the second half. The only unusual daily temperature was the early morning reading of 12.0° on the 29th, 10.0° above normal. There were ten frosts, when there are normally thirteen. On several mornings there was fog in the valley.
Seven days (normally six) registered rain over 0.2 mm. Significant falls came around the 12th and the 29th. On the 29th, the reading was 23.4 mm, but the rain extended over more than one day, totalling 39 mm. It was neither steady nor heavy, but unusually persistent. At Tamworth, rain fell in 27 hours out of 30.

Weather log for June 2017

Comparing June months

June of 2016 had been the wettest and most cloudy of the new century, with warm nights and cold days to match. This June, while moist, was close to normal. It was very like June 2015 and June 2014.
The month’s total rainfall of 62.8 mm was at the 75th percentile, well above the June average of 44 mm. There are no shortages of rainfall for groups of months to this date.

Climate for June months


Data. A Bureau of Meteorology automatic rain gauge operates in the museum yard. From 17 March 2017, 9 am daily readings are published as Manilla Museum, Station 55312.  These reports use that rainfall data when it is available. Since that gauge records “0.2 mm” on many rainless days, I cannot call those days rain days if the monthly count of rain days is not to show a sudden jump to record-breaking numbers.

All other data, including subsoil at 750 mm, are from 3 Monash Street, Manilla.

3-year trends to June 2017

Parametric plots of smoothed climate variables at Manilla
A 13-month “Mackellar cycle”

3-year climate trends to June 2017

June raw anomaly data (orange)

In June 2017 the daily maximum temperature was normal. Moisture variables were low on the graphs, showing rather high moisture. Both daily minimum temperature and subsoil temperature were high. For each variable, the raw value was close to the smoothed value of June 2016, just twelve months earlier.

 Fully smoothed data (red)

The latest available fully-smoothed data point, December 2016, showed warming and drying. Only the dew point anomaly had just passed a “dry” peak. Smoothed subsoil temperature anomaly, which had reached a record low value in November, began to rise, like both of the air temperature anomalies.

The Mackellar cycle

Manilla’s climate variables often move in the cycle of “droughts and flooding rains” from Dorothea Mackellar’s poem “My Country”.*

In that cycle, temperature and moisture move together: hot with dry, cold with wet. On my graphs, hot is to the right. The top four graphs have dry at the top. (I count daily temperature range anomaly as a moisture indicator: high values show dryness.)

The “Mackellar cycle” drives the anomaly values up and down the blue trend lines that skew from cold-and-wet at the lower left to hot-and-dry at the upper right. The path is seldom straight, as any lead or lag of moisture will curve it into an ellipse.

Ellipses on the graphs show the cycle has been strong for two years since the winter of 2015. Its period has been very short: only twelve or thirteen months. Daily maximum air temperature anomaly reached a peak in February of both 2016 and 2017 (hot in late summer-autumn), and reached a trough in August-September 2016 (cold in late winter-spring).

On the top four graphs the cycle advances around an ellipse clockwise. A peak of dryness (up) comes several months before the related peak of daily maximum temperature anomaly (right). Similarly, wetness (down) comes before low temperature (left). I have posted already about the way this cycle skewed the seasons in 2016.

The two graphs at the bottom contain only temperatures. Circles on those graphs show that both the daily minimum temperature anomaly and the subsoil temperature anomaly have been lagging the daily maximum temperature anomaly by several months during these last two years (and not before).


In a post to a “weatherzone” forum, I have annotated (in green) the graph for Dew Point Anomaly versus Daily Max Temp Anomaly. It is the one that shows most clearly the elliptical trace caused by the cycles. That forum thread: “Climate Driver Discussion 2017 (Enso, IOD, PDO, SAM etc.)” has almost no reports of climate cycles observed in Australia.


Note:

Fully smoothed data – Gaussian smoothing with half-width 6 months – are plotted in red, partly smoothed data uncoloured, and raw data for the last data point in orange. January data points are marked by squares.
Blue diamonds and the dashed blue rectangle show the extreme values in the fully smoothed data record since September 1999.

Normal values are based on averages for the decade from March 1999.* They appear on these graphs as a turquoise (turquoise) circle at the origin (0,0). A range of anomalies called “normal” is shown by a dashed rectangle in aqua (aqua). For values in degrees, the assigned normal range is +/-0.7°; for cloudiness, +/-7%; for monthly rainfall, +/-14 mm.

 * Normal values for rainfall are based on averages for the 125 years beginning 1883.


*By arrangement with the Licensor, The Dorothea Mackellar Estate, c/- Curtis Brown (Aust) Pty Ltd.