Northern Carroll County Snowfall

A comparison of the seasonal snowfall between here at my house (extreme north central Carroll County) and the COOP station in Millers (extreme northeast Carroll County).

We’re both at about the same elevation, Millers is 860 feet. I believe it is around 800 ft here at the house, although we have lots of nearby hills and valleys.

Comparing the seasons of common data, we seem to track each other very closely, so I think it may be a good proxy for my years of missing data, and gauging what the snowfall range is here from season to season. Miller’s has a mean snowfall of 34.1″.

Want to guess how much snow I’ll get this year? Enter the contest!

Season CDS Millers
2018-2019 37.8″” 35.3″
2017-2018 34.1″ 33.4″
2016-2017 22.9″ 19.6″
2015-2016 43.7″ 43.0″
2014-2015 44.7″ 47.7″
2013-2014 78.0″ 83.4″
2012-2013 30.2″
2011-2012 18.1″
2010-2011 35.5″
2009-2010 (Note 1) 87.6″
2008-2009 18.6″
2007-2008 18.0″
2006-2007 22.3″
2005-2006 28.0″
2004-2005 30.9″
2003-2004 37.5″
2002-2003 (Note 2) 67.4″
2001-2002 8.2″
2000-2001 29.4″
1999-2000 30.9″
1998-1999 25.7″
1997-1998 18.0″
1996-1997 22.2″
1995-1996 (Note 3) 80.5″
1996-1997 22.2″
1994-1995 12.0″
1993-1994 44.1″
1992-1993 50.2″
1991-1992 10.4″
1990-1991 23.3″
1989-1990 33.8″
1988-1989 13.3″

Note 1:
The infamous Winter Of 2009-2010. We had the three large blizzards, one in December (I recall 30″) and two in February (I recall 24-30″ each). Millers reports 29.9″ in December and 52.2″ in February.
After the February storms:


Note 2:
Millers reports 35.9″ in February 2003. We had a large snowfall here. Pictures:

Note 3:
My house was built Nov 1995 – May 1996. I remember visiting the construction site many times over the winter, and there was often snow.

Winter 2019-2020 Snowfall

High atop Parr’s Ridge in northern Carroll County, MD at about 800 ft elevation.

Contest!
Guess The Amount of Snow At Casa De Smolinski (and DCA/BWI/IAD)

October Total: 0.0″

Friday November 8, 2019
Morning snow flurries.

November Total: 0.0″

2018-2019 Season To Date Total: 0″

Previous seasons:
Winter 2018-2019 37.8″
Winter 2017-2018 34.1″
Winter 2016-2017 22.9″
Winter 2015-2016 43.7″
Winter 2014-2015 44.7″
Winter 2013-2014 78.0″

Winter 2009-2010: No details, but the seasonal total was about 100″ with three major blizzards.

Setting up OP25 Scanner Trunking on an Ubuntu Virtual Machine

After a multi day (week?) saga of trying to get op25 to run on a Raspberry Pi, I decided to give it a try on a linux virtual machine, and had much better results. For the radio hardware I used one of the ubiquitous RTL SDR Dongles.

https://www.amazon.com/gp/product/B0129EBDS2/ref=ox_sc_act_title_1?tag=blackcatsyste-20

I used this guide as a reference / starting point: https://www.hagensieker.com/wordpress/2018/07/17/op25-for-dummies/

Here’s what I did to get things running:

I’m using VirtualBox for the VM setup. I did it on macOS, it should work the same way under Windows.

Create a new linux VM. I gave it 8G of RAM (perhaps overkill) and a 30G volume.

Download the Ububtu installation ISO: https://www.ubuntu.com/download/desktop

Go to Settings -> Storage for the new VM, select the ISO as the optical disc image.

Boot the VM, and install. I won’t go into the installation details, in general I found the defaults worked fine.

I installed the Guest Additions so I could cut and paste between the OS and VM.

Next I installed gqrx so I could check out that the RTL Dongle was working. The stock Ububtu I installed did not come with it:

sudo apt install gqrx-sdr

I plugged in the RTL Dongle, went to Devices -> USB in the VM menu, and assigned the dongle to the VM for use. (Don’t forget this step!)

I ran gqrx, and verified the dongle was working.
gqrx

Next, before installing op25, I had to install git:

sudo apt install git

Then grab op25:

git clone https://github.com/boatbod/op25.git

Switch to the op25 directory that was just created:

cd op25

And install it:

./install.sh

Then install gnuplot so you can see the spectrum and constellation plots:

sudo apt-get install gnuplot-x11

Go to the apps directory for op25:

cd op25/gr-op25_repeater/apps

Next you need to go to https://www.radioreference.com/ and locate the details on the trunking system for your area. Several control frequencies may be listed for your system, you need to find the currently active one. In my case, I checked them all until I found one frequency that was continuously transmitting, 852.9375 MHz.

Now, the next part was perhaps the most difficult, you need to determine the correct ppm error for your RTL dongle. All dongles seem to be off, some more than others. Mine turned out to be off by a LOT. The other tutorials I read gave examples with ppm errors of around 2 or 3. I spent a lot of time trying small values like that, and even up to 20 or 30, without success.

I brought up the spectrum plot in op25 (hit the 1 key) and looked at the spikes, representing transmissions, and checked them against my R-7000 receiver. It was confusing at first, trying to match things up. I eventually realized my dongle was off by a huge factor – about 150 kHz at 853 MHz. I ended up using a ppm value of 173, and that seems to be working. Your value will likely be different, but carefully use the spectrum plot to determine what it is, or at least get close. Then you can iterate up and down by 1 ppm. Another recommendation I read, and used, was to set the offset (used to avoid the 0 Hz spike) to zero for initial testing.

Here’s the command to run op25 with a control frequency of 852.9375 MHz, ppm of 172, and an offset of 0 Hz:

./rx.py –args ‘rtl’ -N ‘LNA:47’ -S 2400000 -f 852.9375e6 -o 0 -q 172

I found I still need to use the , and . keys to shift the received frequency offset around until the program started to decode data correctly (the tsbks value will start incrementing). Again I used the spectrum plot to
help center the control frequency.

When properly tuned, the constellation plot looks like this, hit the 2 key to bring it up:

Once that worked, the next step was to find the NAC value, which is displayed in the op25 program, in my case it was 0x661.

In the apps directory, open the trunk.tsv file in the LibreOffice editor built into Ubuntu, it opens as a spreadsheet. I edited it as follows, entering in a system name, setting the control channel and NAC values. I left the modulation alone (CQPSK) and entered a new tags file name, we’ll create that file next.

I then duplicated the tompkins.tsv file, renamed the duplicate carroll.csv to match what I entered in trunk.tsv, and then opened it in LibreOffice.

It’s a bit tedious, but you have to enter in each talkgroup tag number and name. I just went down the list of talkgroups in radio reference, and it took a few minutes. Part of the list:

Once that was done, I ran op25 again. You can append 2> followed by a filename, to route error messages to a file, so they do not clutter the screen:

./rx.py --args 'rtl' -N 'LNA:47' -S 2400000 -o 25000 -q 181 -T trunk.tsv -V -2 -U 2> stderr.2

I am using an offset of 25 kHz (25000 Hz), and notice I now had to change the ppm to -181, the RTL dongle drifted that much in a few hours!

Update, I also got it working with the AirSpy, which turned out to be very easy. I just had to install the AirSpy support with:

sudo apt install airspy

Running it is as easy as:

./rx.py --args "airspy" -q 3 -N 'IF:12,MIX:12,LNA:12' -S 2500000 -V -2 -U -T trunk.tsv

As you can see, the AirSpy is much more accurate, the ppm value is only 3.

I still need to optimize the gain settings, but this is working nicely. Much better reception than the RTL dongle, as you can imagine. Hmm… unfortunately, op25 is freezing after a while with the AirSpy. Need to investigate this…

Another update:

I decided to install Ububtu on an older i3 laptop. I resized the Windows 10 drive, and freed up 200GB (perhaps excessive, but the drive is 750GB, and I don’t really use the laptop much anymore for Windows) for the linux partition.

I followed the above steps and got gqrx running first, then op25. I have not tried with the AirSpy yet, but even with the RTL dongle, things are improved, the audio quality and overall reception are noticeably better.

Winter 2018-2019 Snowfall

High atop Parr’s Ridge in northern Carroll County, MD at about 800 ft elevation.

Contest!
Guess The Amount of Snow At Casa De Smolinski (and DCA/BWI/IAD)

Seasonal Snowfall Contest Entries

TSMagnum  26.9
Groveton  28.8
Snowbrow  35.2
mjbuffettfan  38
BigCountry  42.2
beadalou  42.3
Terpiecat  42.7
Xtrain21  44.4
ck5416  45.1
taylort2  45.8
(no name) 47.2
cdklktr  47.5
alrob8  48.3
Sneakyfeets  48.5
SSB  51.3
Sarandipity885  53.4
eric654  54.5
gelezinis vilkas  56.7
83worldtraveler  58.7
Kate commenting  59.3
asimovian  61.3
nomini  63.2
parksndc  64.7
I Love Terpiecat  65.0
cameraman  65.4
Snownomore  69.9
Autumn_Forge  67.3
chrisofthebeagles  71.9
Rex Block  73.3
Merry Mildest  75.0
walter-in-fallschurch  75.1
WBsnowchic  77
surewhynot  79
speedo311yo  88.0
PBH  88
Scrabble Girl   98.6
Chris Smolinski 100.0
The Adorable Miniature Snowplows  101

Mean: 61.0
Median: 59.3

(No, I can’t win my own contest, I thought it would to fun to add my own guess, which is just the approximate total snowfall the last time we had a solar minimum winter)

October Total: 0.0″

Thursday November 15, 2018 – Friday November 16, 2018:
7.0″ of heavy snow on the 15th.
An additional 1.25″ of snow measured early on the morning of the 16th, which fell overnight

Wednesday November 28, 2018
Snow flurries. No accumulation.

November Total: 8.25″

December Total: 0.0″

Wednesday January 9, 2019 – Thursday January 10, 2019
A dusting of snow.

Saturday January 12, 2019 – Sunday January 13, 2019
2.5″ of snow

Thursday January 17, 2019 – Friday January 18, 2019
1.5″ of snow

Saturday January 19, 2019
0.5″ of snow,then sleet, then rain.

Friday January 25, 2019
Morning snow flurries.

Tuesday January 29, 2019
3.5″ of fairly heavy snow.

Wednesday January 30, 2019
0.4″ from a snow squall.

January Total: 8.4″

Friday February 1, 2019
2.0″ of dry snow.

Monday February 11, 2019 – Tuesday February 12, 2019
5.5″ snow, ending as sleet and rain.

Sunday February 17, 2019 – Monday February 18, 2019
Glaze of freezing rain.

Wednesday February 20, 2019
4.5″ of wet snow, followed by some freezing mist. About 0.1″ ice build up on trees.

February Total: 12.0″

Friday March 1, 2019
2.9″ of snow.

Saturday March 2, 2019
1.5″ of wet snow, also some rain and sleet.

Sunday March 3, 2019
4.75″ of heavy snow.

March Total: 9.15″

2018-2019 Season To Date Total: 37.8″

Previous seasons:
Winter 2017-2018 34.1″
Winter 2016-2017 22.9″
Winter 2015-2016 43.7″
Winter 2014-2015 44.7″
Winter 2013-2014 78.0″

Winter 2009-2010: No details, but the seasonal total was about 100″ with three major blizzards.

Variation In Precipitation At Dulles International Airport (IAD) 1964-2017

Previously I wrote about the variation In precipitation At Washington Reagan National Airport (DCA) and Baltimore Washington International Airport (BWI) to see if there was a significant change over the years due to Climate Change, and now it’s time to look at Dulles International Airport.

The IAD dataset starts later than that for DCA and BWI, and starts from April 1, 1960. As with the DCA and BWI data, there appear to be a few days with missing data, as for some years there is only data for 364 days. The actual number of days per year of data was taken into account when computing means. Also, large amounts of data are not available for various dates 1960 through 1963, and of course there is no data for the remainder of 2018. So the plots are from 1964 through 2017.

Each of the graphs can be clicked to be viewed full size.

The first graph is the average daily precipitation. This is total amount of rain per year divided by the number of days in the year. This graph shows a slight increase, from 0.110 to 0.115 inches per day:

Next is a graph of the number of days per year with precipitation, followed by its inverse, the number of days without. The number of days with precipitation has increased from about 114 to 121, or 6 more days. The increases at DCA and BWI were 5 days, and they was over a larger number years. It currently rains about 115 days per year at DCA, and 118 days per year at BWI.

This leads to the next graph, the average rainfall on days when it actually rained. There is no apparent change at all – this graph is flat. So while it rains about 6 more days a year at IAD, the amount of rain, on days when it does rain, is the same. Actually if you squint, it looks like it might be very so slightly decreasing, but it is subtle. And probably going to be different once you add in another year’s worth of data.

Next, we can look at the standard deviation in rainfall amounts on days when it rains, standard deviation is amount of variation of a set of data values around the mean (average). My previous post has a link for more information about standard deviation, as well as the best standard deviation joke for math nerds.

The standard deviation rose from about 0.48 to 0.50, about half the increase seen at BWI. By comparison, the standard deviation was unchanged at DCA. If you torture the data enough, you can find possible reasons for the change. I notice there was a step change / increase in the standard deviation from 2005-2014, which seems to have since stopped. You can also spot a period in the 1990s when the standard deviation was low. I am sure 20 people can find 20 more possible reasons. It’s like a Rorschach test for meteorologists. Here’s the standard deviation plot:

Finally, we can look at the number of days that rained and had rainfall over two standard deviations (using the standard deviation for that year), treating this as significant or extreme rainfall events. The trend of this is completely flat:

To summarize the analysis of rainfall events at DCA, BWI, and IAD:

  • The number of days with rain is increasing, 5 or 6 more days per year, over the last half century or more, at all three airports.
  • The average annual measured rainfall is either decreasing (DCA) or increasing (BWI, IAD), depending on which station you look at.
  • The average amount of rain on days when it does rain is either decreasing (DCA), increasing (BWI) or exactly the same (IAD).
  • The number of days with significantly high rainfall, exceeding two standard deviations, is either very slightly increasing (BWI) or unchanged (DCA, IAD), depending on which airport you look at.
  • Variation In Precipitation At Baltimore Washington International Airport (BWI) 1937-2018

    Previously I wrote about the Variation In Precipitation At Washington Reagan National Airport (DCA) 1945-2018 to see if there was a significant change over the years due to Climate Change, and now it’s time to look at BWI Airport.

    The BWI dataset starts earlier than tat for DCA, and runs from July 1, 1937 to July 18, 2018. As with the DCA data, there appear to be a few days with missing data, as for some years there is only data for 364 days. The actual number of days per year of data was taken into account when computing means. Also, some data is not plotted for 1945 or 2018, namely the number of days with/without rain for those years, as a full year of data is not available.

    Each of the graphs can be clicked to be viewed full size.

    The first graph is the average daily precipitation. This is total amount of rain per year divided by the number of days. It shows an increase from roughly 0.108 to 0.121 inches per day from 1939 to 2018. That’s an increase of about 12%.

    Next is a graph of the number of days per year with precipitation, followed by its inverse, the number of days without. The number of days with precipitation has increased from about 113 to 118, or 5 more days. This is the same increase we saw at DCA, although it seems rain about 3 fewer days per year at DCA vs BWI (You picked the right airport to leave near, Walter):

    That leads to the next graph, the average rainfall on days when it actually rained. This shows an increase from about 0.35 inches to 0.37 inches. (The trend at DCA was actually negative, from 0.37 to 0.35 inches in fact. Exactly opposite. Hmm…):

    Next, we can look at the standard deviation in rainfall amounts on days when it rains, standard deviation is amount of variation of a set of data values around the mean (average). My previous post has a link for more information about standard deviation, as well as the best standard deviation joke for math nerds. The standard deviation in rainfall amounts at BWI is increasing. Looking at the graph, the increase seems to be mostly due to an increase during the 2005-2015 period, which has since ended. Is it a short term variation due to random processes, or part of a long term shift?

    Next we can look at the number of days that rained and had rainfall over two standard deviations (using the standard deviation for that year), treating this as significant or extreme rainfall events. The trend of this is completely flat:

    But, you might ask, isn’t the standard deviation slightly increasing each year? What if we use a fixed standard deviation value, like 0.50 which seems to be the mean value? Doing that, you do see an increase from about 9 to 11 days per year:

    Next up… Dulles Airport (which unfortunately has the shortest dataset of all three major airports)

    Variation In Precipitation At Washington Reagan National Airport (DCA) 1945-2018

    Recently the weather, at least precipitation wise, in the DCA area has been variable. It was very wet and rainy, then we had dry conditions for several weeks with essentially no rain. Now, it is very wet again. Are we seeing extreme (some may say historic) changes in the weather? Or are these just the usual variations?

    Are rainfall events are becoming less common, but more extreme? That is, it rains less often, but we get more rain when it does rain, rather than getting rainfall spread out evenly over time as it used to be. And maybe we’re getting more rain overall. Or possibly less rain, those two claims seems to depend on recent weather memory.

    To check this claim, data for Washington Reagan National Airport (DCA) was downloaded from the NOAA NCDC site and analyzed in several ways. You can download this data yourself, if you wish: https://www.ncdc.noaa.gov/cdo-web/

    The dataset runs from July 1, 1945 to July 18, 2018. There appear to be a few days with missing data, as for some years there is only data for 364 days. The actual number of days per year of data was taken into account when computing means. Also, some data is not plotted for 1945 or 2018, namely the number of days with/without rain for those years, as a full year of data is not available.

    Each of the graphs can be clicked to be viewed full size.

    First, is there a significant long term trend in the amount of precipitation at DCA? No, there does not seem to be (if you squint you may see a very small decline over time, the sign of this slope likely changes from year to year with normal variability in rainfall):
    Mean daily precipitation

    Second, are rain events becoming less common, but with higher rainfall totals from those events? That would mean we are seeing fewer days with rain, but more rain on those days. The following two graphs show the number of days without any rain, and the number of days with rain, defined as 0.01″ or more. First the number of days without rain. Which is not increasing, but actually decreasing:
    Days per year without precipitation

    And the number of days per year with rain, which of course is just the inverse of the previous graph. It’s raining 5 or 6 more days per year (sorry, Walter):
    Days per year with precipitation

    Third, what about the rainfall totals on days when it actually rains. Is that increasing, leading to more extreme rain events? No, it isn’t. It is actually decreasing, which makes sense considering the mean rainfall per year is essentially steady, and it is raining a few more days out of the year:
    Mean precipitation for days with precipitation

    OK, maybe it is mostly the same, but we’re getting a few more extreme rainfall events per year? Let’s look at the standard deviation of the rainfall amounts, again only for days when it actually rains.

    Standard deviation is amount of variation of a set of data values around the mean (average), there is an explanation here: https://en.wikipedia.org/wiki/Standard_deviation If you get this joke, you understand standard deviation: “Yo mama is so mean she has no standard deviation”

    Hmm, no, that is also steady:
    Standard deviation of daily precipitation

    One more thing, look at the number of days with very extreme rainfall. How about the number of days where the rainfall exceeded two standard deviations? That also seems to be flat:
    Days above two standard deviations

    Anything else we can check to see if precipitation is indeed getting more extreme in Washington DC? So far, it doesn’t seem to be.

    Note, the purpose of this analysis was not to try and discredit man made climate change aka AGW, which is certainly real. Only to see if claims of a noticeable effect on the precipitation patterns in the DC area can be confirmed, which does not seem to be the case.

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    Listening To Pirate Radio Stations from South America

    Looking for a new DX challenge? In addition to shortwave pirate stations in the USA, and Europe (Europirates as we call them), there’s a relatively new group of pirate radio stations being heard in North America, those from South America.

    It’s really only been the previous year that we’ve confirmed that there’s a significant number of pirate radio stations in South America that can be received here. Radio Pirana has been known for some time, and I believe thee were a few reports of it, and at least one other station that I cannot remember the name of, but that’s about it. For years there have been logs of very weak UNID stations heard on the 43 meter band (6800-7000 kHz), presumed to be pirates of some sort, and it is possible some of these were South American pirates.

    Most of these stations use homemade transmitters, often of the “Lulu” design, with a IRF510 or similar MOSET RF final stage. That means they are generally in the 15 or 20 watt carrier range, although some are higher power. That also means that unless otherwise noted, all of these stations use AM mode, and in general the frequency is highly variable, easily varying 100 Hz or more from night to night, or even during transmissions.

    One important caveat: Since most of these stations use relatively low power, and due to the long distances involved, signal levels are generally weak, although occasionally when conditions are excellent (especially if there’s grayline propagation), they can put in stronger signals. I am fortunate to live in a rural area with relatively low noise/RFI levels, and have several high end receivers and large antennas. My primary setup for catching these stations is a netSDR receiver and a 670 foot Sky Loop antenna. You’re going to want to use the best receiver and antenna you can for catching these stations, you’re not likely to have good (or any) results with a portable SW radio, RTL dongle, or small/indoor antenna. Also, I record the entire 43 meter band nightly on my netSDR, and then go through the recordings each morning. This lets me catch stations that may only appear for a brief period of time. That said, you can still hear them with a reasonable HF setup, although it may take persistence, checking each night, until conditions permit reception.

    It’s well worth checking the Latin American Pirate logging forum on the HF Underground website, to see what is presently being heard. The HF Underground is the best way to keep up to date with the hobbyist radio scene in general, with dedicated forums for North American Pirates, Europirates, and of course radio in general.

    And for those of you into collecting QSLs – many of these stations are reliable QSLers!

    In general, the easiest station to hear is Lupo Radio from Argentina. It is on the air most evenings on 6973 kHz in AM mode. At least at my location, it puts in the strongest and most reliable signal. Usually in the SIO 222 to 333 range, sometimes stronger. There are frequent IDs. I use Lupo Radio as a “beacon” to gauge how good conditions are to South America on 43 meters.


    luporadio@hotmail.com

    Another station that is often on the air is RCW – Radio Compañía Worldwide from Chile. They use 6925.13 kHz, and their carrier is more stable and usually on this offset frequency, which makes it easier to determine that it’s likely you’re hearing them vs a US pirate station.


    rcwradio@gmail.com

    New to the scene is Radio Marcopolo on 6991 kHz.


    Marcopoloradio@hotmail.com

    Also new to the scene is an as yet UNID pirate from South America on 6934.9 kHz. I have received them for several weeks now in the local evenings, usually starting around the 2300-0300 UTC window. They put in a respectable signal (relatively speaking), strong enough for Shazam to ID songs. They have frequent breaks in their transmission, with the carrier often going off and on many times during a broadcast. They also occasionally transmit audio test tones, and sometimes seem to relay audio from licensed stations in Argentina such as Radio El Mundo. This could be someone testing a new transmitter? A new mystery to solve!

    Radio Dontri is somewhat unique in that they use USB mode, on 6955 kHz. They also send SSTV, which is sometimes easier to receive than music, and helps to verify that you’re actually hearing them, vs a US pirate on 6955. They tend to drift a lot, however, which can make decoding the SSTV transmissions challenging.


    radiodontri@gmail.com

    Outside the 43 meter band, there is Rádio Casa 8000 kHz. I have only received weak carriers from this station, although partly that may be because I do not frequently check for it, and it does not turn up on my overnight SDR recordings.

    Radio Triunfal Evangélica is other station outside of the 43 meter band, they use the nominal frequency of 5825 kHz, often closer to 5824.9 kHz. Again I have only received a carrier from them. As the name implies, they are a religious station, affiliated with a church.

    Now that we’ve talked about the pirate stations from South America, we should probably mention things you are likely to hear that are not pirates. Specifically, what we call Peskies (or Pesky as the singular), short for pescadores, the Spanish word for fishermen. Peskies generally use LSB mode, and can be heard on many frequencies in the 43 meter band, engaging in QSOs. Years ago, pirate listeners started to call these stations pescadores, since some of them were indeed fishermen, and could be heard discussing related matters. It might be better to think of most of them as freebanders/outbanders, much in the tradition of those transmitting on 11 meters. There’s a logging forum on the HFU dedicated to Peskies, if you’re interesting in learning more about them.

    Occasionally they use AM mode. We’ve logged several on 6965 kHz (+/- of course), that at first were thought to be pirates. But they never transmitted music, and after some discussions with DXers in South America, it was determined that they were more properly considered peskies.