After a week and a half in Berkeley and a quick trip to Anchorage for work, I finally feel like things are getting back to normal. Last weekend we bought a bunch of stuff at the Farmer’s Market, including a small basket of raspberries. I had planned to make some raspberry bars, but when I looked at them today, they seemed a bit soft for something like that. On Sunday we picked a half gallon of blueberries from the power line, so making preserves from the combination seemed like a good idea. Here’s the recipe I used:

Ingredients:

• 1 small basket fresh raspberries (approximately 2 cups)
• Same weight of fresh blueberries (~2 cups)
• 1 apple, peeled, cored, and chopped small
• Juice from ½ lemon
• 1 T water
• 3 cups sugar

Cooking:

• Bring fruit and liquids to a boil.
• Boil 5 minutes to soften apples.
• Add sugar and mix.
• Purée in food processor.
• Return to boil.
• Boil 10–15 minutes until the pectin is ready (it gels on the back of a spoon as it cools)

Canning:

• Pour or ladle into hot canning jars (2 pint jars for this quantity), boil in hot water bath for 10 minutes.

I’m not sure if the apple is necessary, but the recipe I was looking at included pectin and I didn’t have any. The Interwebs informed me that apples contain a lot of pectin, so I threw that in the pot as well. The food processor step was included because I wasn’t convinced the apples would get mushed up enough to disappear in the mix.

Yesterday I looked at how wind might be affecting my bicycling to and from work. Today I’ll examine the idea that Miller Hill is confounding the effect of wind on average speed by excluding this portion of the trip from the analysis. To do this, I include a bounding box comparison in the SQL statement that extracts the wind factors for track points. The additional WHERE condition looks like this:

ST_Within(point_utm, ST_SetSRID(ST_MakeBox2D(ST_Point(454861,7193973), ST_Point(458232,7199159)), 32606))

The same ST_Within test is used in the calculation of average speed for each of the trips from work to home. After compiling the wind factors and average speeds, we compare the two using R. Here are the updated results:

lm(formula = mph ~ wind, data = data)

Residuals:
     Min       1Q   Median       3Q      Max
-1.87808 -0.55299  0.04038  0.62790  1.19076

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  16.8544     0.2176  77.442   <2e-16 ***
wind          0.3896     0.2002   1.946   0.0683 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9445 on 17 degrees of freedom
Multiple R-squared: 0.1822,     Adjusted R-squared: 0.1341
F-statistic: 3.788 on 1 and 17 DF,  p-value: 0.06834

This time around the model and both coefficients are statistically significant (finally!), and “wind factor” is positively correlated with my average speed over the part of the route that doesn’t include Miller Hill and Railroad drive. It’s not a major contributor, but it does explain approximately 18% of the variation in average speed.

I decided to look at wind a little more deeply after yesterday’s bike ride home. It seemed clear to me that the wind was strongly at my back for much of the route. It wasn’t my fastest ride home, but it was close, and it didn’t feel like I was working all that hard.

Here’s the process. First, examine all my bicycling tracks individually, using PostGIS’s ST_Azimuth function to calculate the direction I was traveling at each point. The query uses another of the new window functions (lead) in PostgreSQL 8.4.

SELECT
    point_id, dt_local,
    ST_Azimuth(
        point_utm,
        lead(point_utm) OVER (PARTITION BY tid ORDER BY dt_local)
    ) / (2 * pi()) *360
FROM points
WHERE tid = TID
ORDER BY dt_local;

Then, for each point, find the direction the wind was blowing. This is a pretty slow query, but I haven’t found a better way to compare timestamps in the database to find the closest record. This technique, based on converting both timestamps to “epoch,” which is the number of seconds since January 1st, 1970, is faster than using an interval type of operation (like: WHERE obs_dt - POINT_DT BETWEEN interval '-3 minutes' AND interval '3 minutes').

SELECT obs_dt, wdir, wspd
FROM observations
WHERE abs(extract(epoch from obs_dt) - extract(epoch from POINT_DT)) < 5 * 60
    AND wspd IS NOT NULL
    AND wdir IS NOT NULL
ORDER BY abs(extract(epoch from obs_dt) - extract(epoch from POINT_DT))
LIMIT 1;

Now I’ve got the direction I was traveling and the direction the wind is coming from. I wrote a Python function that returns a value from –1 (wind is in my face) to 1 (wind is at my back). The procedure is to convert the wind directions to unit u and v vectors and get the distance between the endpoints of each vector. The distances are then scaled such that wind behind the direction traveled range from 0 – 1, and from –1 – 0 for wind blowing against the direction traveled.

def wind_effect(mydir, winddir):
    """ Returns a number from 1 (wind at my back) to -1 (wind in my face)
        based on the directions passed in.

        Remember that wind direction is where the wind is *from*, so a
        wind direction of 0 and a mydir of 0 means the wind is in my face.
    """
    try:
        mydir = float(mydir)
        winddir = float(winddir)
    except:
        return(None)

    my_spd = 1.0
    wind_spd = 1.0
    u_mydir = -1 * my_spd * math.sin(math.radians(mydir))
    v_mydir = -1 * my_spd * math.cos(math.radians(mydir))
    u_winddir = -1 * wind_spd * math.sin(math.radians(winddir))
    v_winddir = -1 * wind_spd * math.cos(math.radians(winddir))
    distance = math.sqrt((u_mydir - u_winddir)**2 + (v_mydir - v_winddir)**2)
    factor = (1.41421356 - distance)
    if factor < 0.0:
        factor = factor / -0.58578644
    else:
        factor = factor / -1.41421356

    return(factor)

Finally, multiply this value by the wind speed at that time, and sum all these values for an entire bicycling track. The result is a “wind factor.” A positive wind factor means the wind was generally at my back during the ride, negative means it was blowing in my face. Yesterday’s ride home had the highest wind factor (1.07) among trips since June. So the wind really was at my back!

Can “wind factor” help predict average speed? Here’s the R and results:

$ R --save < wind_from_abr.R
> data<-read.table('wind_factor_from_abr',header=TRUE)
> model<-lm(speed ~ wind, data)
> summary(model)

Call:
lm(formula = speed ~ wind, data = data)

Residuals:
     Min       1Q   Median       3Q      Max
-0.90395 -0.46782 -0.04334  0.40286  0.85918

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  14.7796     0.1471  100.48   <2e-16 ***
wind          0.4369     0.2875    1.52    0.147
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5522 on 17 degrees of freedom
Multiple R-squared: 0.1196,     Adjusted R-squared: 0.06784
F-statistic:  2.31 on 1 and 17 DF,  p-value: 0.1469

Hmm. Not a whole lot of help here. The model is close to being statistically significant (although it’s not…), and it’s not very predictive (only 12% of the variation in average speed is explained by wind factor). However, the directionality of the (not quite statistically significant) wind coefficient is correct. A positive wind factor is (weakly) correlated with a higher average speed.

Thinking more about my route from work, I suspect that the route is actually two trips: the trip from ABR to the bottom of Miller Hill (4.8 miles) and the two mile trip over Miller Hill to our house. I’ll bet that wind becomes statistically significant if I only consider the first part of the trip: wind doesn’t have as much effect on a hill climb, and after making it over the top, the rest is a bumpy, gravel road where speed is determined more by safety than wind or how hard I’m pedalling. I think this might also resolve the question of why the ride home is so much easier than to work. It’s not because I’m glad to be out of work or because I’m carrying a lunchbox full of food to work, it’s because it’s downhill from ABR to the bottom of Miller Hill.

It’s interesting riding the same route back and forth to work every day. I have a perception that the wind is always in my face, and wondered if maybe the wind tends to be going one direction in the morning, and another in the afternoon when I’m riding home. Despite the fact that riding home seems much easier than riding to work, the wind always seems stronger.

If you look at the little map of my bicycling route from work on the right, you can see that the major portion of the trip is in a northwesterly (to work) or southeasterly (from work) direction. The long stretch that’s north/south is Miller Hill, and wind doesn’t really matter on that section of the route because it’s a steep hill. The color of the dots indicate speed from blue (slow) to red (fast).

I took a look at the wind data from our weather station for the days I bicycled to work; for the two hours before and after each ride on those dates. The weather database has a binned summary of wind direction (each row in the table shows the number of five-minute observations where the wind was blowing in a particular cardinal direction) and an average speed. I multiplied the two for each hour during my rides, and then summed them over all my rides this year. The plot below shows what the data looks like.

And, for reference, the SQL query, data set and gnuplot script. It took me forever to figure out how to make gnuplot make a histogram of this data.

I was right about it being windier on my ride home from work. But, my perception that the wind is always in my face isn’t right. In both the morning and afternoon, there are two predominant wind directions, northwest (which would be at my back on the way home) and south-southeast (in my face). This is one of those cases where I notice when the wind is in my face, but when it’s at my back it doesn’t register.

At some point I’ll have to see if there’s any relationship between my average speed and the wind. At least then I’d have something to blame when I arrive at my destination with a slow time.

For the past ten days I’ve been collecting data from three sets of temperature sensors located in different places around the yard. There’s the sensor in the Rainwise weather station at the top of the dog yard gate, a collection of sensors out behind the house under the oil tank, and a set of sensors under a collection of yogurt containers on top of a foundation post on the west side of the house.

It’s not easy to get accurate temperature readings. You need to site the sensors where they’ll get a good reading (between 4 and 6 feet off the ground, out in the open and away from buildings and trees), keep the sensors from getting heated by the sun, and keep them dry both from rain and snow, as well as from condensation inside an enclosure. I’ve got the last one figured out, but siting and solar radiation are proving to be big challenges.

The plot below shows the hourly average temperature readings for all three sets of sensors over the last ten days since I added the west sensor.

The sensor atop the dog yard gate (the red line) is well sited in terms of it’s distance from large objects like buildings and trees, but it’s too high off the ground. It’s enclosed in a Gill multi-plate radiation shield, which is effective at reducing the effect of solar radiation when the wind is blowing. Compared with other temperature sensors in the region, this sensor is commonly several degrees warmer during the middle of the day, and I think this is because the shield isn’t keeping the sensor cool enough. We do seem to get less wind than in other places, and I think this is why the shield isn’t working as well as it should be. The sensor’s location away from everything does allow it to reach accurate minimum temperatures at night.

The sensor cluster behind the house is effectively shielded from the sun because it’s very close to the north side of the house, and even when the sun is in the north (in Fairbanks, the sun comes pretty close to circling the sky in summer) there are trees behind the house that keep it shaded. But it’s much too close to the house, and the location is far more sheltered than is appropriate. The moderating effect of being so close to the house reduces the diurnal temperature range, clipping the highs and lows compared to the data from the dog yard sensor.

On the west side of the house, I’ve got three sensors sitting on top of a foundation post (a telephone pole driven into the ground). There are several layers of yogurt container on top of the sensors, both to protect them from rain, but also in an attempt to reduce solar heating under the containers. The radiation shielding appears to be almost as effective as the commercial Gill shield over the dog yard sensors (the high temperature peak on the graph is very similar between the two), but something is keeping the temperature from dropping at night. The low temperatures from the west sensors are more than 5 degrees warmer than the dog yard sensor. I suspect the sensors aren’t high enough off the ground, and that the foundation post may be absorbing a lot of heat during the day and keeping the sensors artificially warm at night.

My plan is to place the west sensors inside the Stevenson shield pictured at the top of this post. I’ll raise it to between 4 and 6 feet in the air, and see how the temperatures compare with the dog yard sensors. I’m also working on a solar powered aspiration system in case the Stevenson screen doesn’t have enough of an effect on the high temperatures on sunny days. I haven’t quite worked it out yet, but the idea is to put a small computer fan on the top of a short piece of 4” plastic pipe that contain the sensors. When the sun is shining, the solar panel drives the fan, which pulls air up through the pipe and over the sensors. We’ll see if it’s needed in the next few days.