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Image © Barry Osborne 2017. Used with permission.


This post uses the Raspberry Pi sense hat to display the AuroraWatch UK alert status. It assumes you already have Raspbian installed on a Raspberry Pi, networking configured and enabled, and the sense hat fitted to the Pi.

The current status level is easily obtained from the AuroraWatch UK API with a custom python module. First, ensure you have the necessary python modules installed from the Raspbian software archive:

sudo apt-get update
sudo apt-get install python-lxml python-requests python-six

Then install two custom python modules. We will install them into the user profile, so run these commands as the user pi:

pip install --user --no-deps atomiccreate
pip install --user --no-deps aurorawatchuk

Finally, download the python script which fetches the status and displays it on the sense hat (I used bit.ly to shorten the original GitHub URL):

wget https://bit.ly/awuk-status-on-sense-hat -O awuk_sense_hat.py

The AuroraWatch UK status can then be displayed on the sense hat with this simple command:

python awuk_sense_hat.py

The program will monitor the current status and set the LED colours to show the status level (green, yellow, amber or red). For more information about the AuroraWatch UK status level meaning and interpretation see http://aurorawatch.lancs.ac.uk/alerts/. If you don't have a sense hat (I don't) it is even possible to try this with the sense hat emulator.

Credits

The geomagnetic activity status is courtesy of AuroraWatch UK and uses data from the SAMNET and AuroraWatchNet magnetometers.See also the AuroraWatch UK conditions of use for the API.

Thanks to Barry Osborne for testing the software with a real Raspberry Pi sense hat, and for providing the photograph.
Image © Barry Osborne 2017. Used with permission.


This post uses the Raspberry Pi sense hat to display the AuroraWatch UK alert status. It assumes you already have Raspbian installed on a Raspberry Pi, networking configured and enabled, and the sense hat fitted to the Pi.

The current status level is easily obtained from the AuroraWatch UK API with a custom python module. First, ensure you have the necessary python modules installed from the Raspbian software archive:

sudo apt-get update
sudo apt-get install python-lxml python-requests python-six

Then install two custom python modules. We will install them into the user profile, so run these commands as the user pi:

pip install --user --no-deps atomiccreate
pip install --user --no-deps aurorawatchuk

Finally, download the python script which fetches the status and displays it on the sense hat (I used bit.ly to shorten the original GitHub URL):

wget https://bit.ly/awuk-status-on-sense-hat -O awuk_sense_hat.py

The AuroraWatch UK status can then be displayed on the sense hat with this simple command:

python awuk_sense_hat.py

The program will monitor the current status and set the LED colours to show the status level (green, yellow, amber or red). For more information about the AuroraWatch UK status level meaning and interpretation see http://aurorawatch.lancs.ac.uk/alerts/. If you don't have a sense hat (I don't) it is even possible to try this with the sense hat emulator.

Credits

The geomagnetic activity status is courtesy of AuroraWatch UK and uses data from the SAMNET and AuroraWatchNet magnetometers.See also the AuroraWatch UK conditions of use for the API.

Thanks to Barry Osborne for testing the software with a real Raspberry Pi sense hat, and for providing the photograph.

A Python module for the AuroraWatch UK API

The first goal in the journey to create an automated all-sky camera system for AuroraWatch UK was to interface to the camera using Python. If you missed update #1 you can read it at here.

One of the requirements of the camera software is to be able to change between different recording settings, for instance, in response to solar elevation and the AuroraWatch UK status level. Computing solar elevation is easily acheived with the astral module. For the AuroraWatch UK status level I began with fetching the status XML document but soon realised a much better approach was to write a Python module. The module automatically fetches the various XML documents, parses them and caches the results, both to memory and to disk.

I won't repeat the instructions for installing and using the module - that information is already given in an ipython notebook that you can view at https://github.com/stevemarple/python-aurorawatchuk/blob/master/aurorawatchuk/examples/aurorawatchuk_api.ipynb

A Python module for the AuroraWatch UK API

The first goal in the journey to create an automated all-sky camera system for AuroraWatch UK was to interface to the camera using Python. If you missed update #1 you can read it at here.

One of the requirements of the camera software is to be able to change between different recording settings, for instance, in response to solar elevation and the AuroraWatch UK status level. Computing solar elevation is easily acheived with the astral module. For the AuroraWatch UK status level I began with fetching the status XML document but soon realised a much better approach was to write a Python module. The module automatically fetches the various XML documents, parses them and caches the results, both to memory and to disk.

I won't repeat the instructions for installing and using the module - that information is already given in an ipython notebook that you can view at https://github.com/stevemarple/python-aurorawatchuk/blob/master/aurorawatchuk/examples/aurorawatchuk_api.ipynb

ZWO ASI 174MC colour USB 3.0 camera. Image from ZWO website.
Recently AuroraWatch UK purchased a ZWO ASI174MC colour USB 3.0 camera to (hopefully) record images of the aurora. The aim is to build an all-sky imager. To minimise the cost the camera will be connected to a Raspberry Pi single-board computer and be located outside in a waterproof enclosure. This particular camera was chosen for several reasons:
  • No mechanical shutter to wear out. 
  • The large pixel size (5.86um) should give good sensitivity. 
  • The sensor has an IR cut-off filter fitted (filters cannot be used with the fish-eye lens). 
  • The manufacturer provides support to access the camera from Linux. 
At present the Linux support from ZWO is limited to a software development kit (SDK) which provides a C language library and header file. This is sufficient for a Raspberry Pi to be able to interface with the camera. However my preferred approach is to write the image capture and control software in Python since that will simplify development and access to the AuroraWatch UK API. I therefore decided to write a Python wrapper for the C library. This is not something I have done before and after a little experimentation with ctypes, Cython and CFFI and I settled on using ctypes. You can find the source code for my Python wrapper on Github. It's also available on pypi so you can install it simply by running "pip install zwoasi".

I've written a short demo program to illustrate how to access the camera from Python. The camera can operate in two modes. The first, snapshot mode, takes a single exposure. The second, video mode,  continually takes exposures which can be downloaded from the camera as and when required. At first glance either mode would appear suitable for the purpose of taking a single exposure once a minute. My main concern is how to set the exposure correctly for an automated system which will run in a variety of different lighting levels (twilight, nighttime, and possibly even daytime). Fortunately this particularly camera provides an auto-exposure mode which can adjust the exposure time and gain settings according to the ambient light levels. It does this by analysing the image histogram after each exposure. The documentation doesn't explain the auto-exposure mode but I suspected that the camera would have to be operated in video mode for this to work. A quick test proved this to be correct. How well the auto-exposure algorithm works at night has yet to be discovered; since it is an astronomy camera I'm optimistic that it will be suitable and save me from having to implement an exposure control algorithm.

Sample ASI174MC image
Sample image from ASI174MC and Fukinon FE185C057HA-1 2/3" fisheye lens.
The complete control system is intended to timestamp and watermark images, change the recording cadence according to AuroraWatch UK status and solar elevation, record temperature and humidity, and control heating and fans.




ZWO ASI 174MC colour USB 3.0 camera. Image from ZWO website.
Recently AuroraWatch UK purchased a ZWO ASI174MC colour USB 3.0 camera to (hopefully) record images of the aurora. The aim is to build an all-sky imager. To minimise the cost the camera will be connected to a Raspberry Pi single-board computer and be located outside in a waterproof enclosure. This particular camera was chosen for several reasons:
  • No mechanical shutter to wear out. 
  • The large pixel size (5.86um) should give good sensitivity. 
  • The sensor has an IR cut-off filter fitted (filters cannot be used with the fish-eye lens). 
  • The manufacturer provides support to access the camera from Linux. 
At present the Linux support from ZWO is limited to a software development kit (SDK) which provides a C language library and header file. This is sufficient for a Raspberry Pi to be able to interface with the camera. However my preferred approach is to write the image capture and control software in Python since that will simplify development and access to the AuroraWatch UK API. I therefore decided to write a Python wrapper for the C library. This is not something I have done before and after a little experimentation with ctypes, Cython and CFFI and I settled on using ctypes. You can find the source code for my Python wrapper on Github. It's also available on pypi so you can install it simply by running "pip install zwoasi".

I've written a short demo program to illustrate how to access the camera from Python. The camera can operate in two modes. The first, snapshot mode, takes a single exposure. The second, video mode,  continually takes exposures which can be downloaded from the camera as and when required. At first glance either mode would appear suitable for the purpose of taking a single exposure once a minute. My main concern is how to set the exposure correctly for an automated system which will run in a variety of different lighting levels (twilight, nighttime, and possibly even daytime). Fortunately this particularly camera provides an auto-exposure mode which can adjust the exposure time and gain settings according to the ambient light levels. It does this by analysing the image histogram after each exposure. The documentation doesn't explain the auto-exposure mode but I suspected that the camera would have to be operated in video mode for this to work. A quick test proved this to be correct. How well the auto-exposure algorithm works at night has yet to be discovered; since it is an astronomy camera I'm optimistic that it will be suitable and save me from having to implement an exposure control algorithm.

Sample ASI174MC image
Sample image from ASI174MC and Fukinon FE185C057HA-1 2/3" fisheye lens.
The complete control system is intended to timestamp and watermark images, change the recording cadence according to AuroraWatch UK status and solar elevation, record temperature and humidity, and control heating and fans.



Mar
25

Power over ethernet (PoE) magnetometer and cloud detector

arduino, AuroraWatch UK, AuroraWatchNet, Calunium, cloud detector, Magnetometer Commenti disabilitati su Power over ethernet (PoE) magnetometer and cloud detector 

Combined magnetometer and cloud detector
Combined magnetometer and cloud detector.


To improve the performance and stability of the AuroraWatchNet magnetometers I recently began experimenting with a power over ethernet (PoE) version. With restrictions on power consumption lifted considerable performance improvements are possible. As a result I have developed the magnetometer hardware specifically to support a power over ethernet version. Another instrument I've been developing is a cloud detector. This too should benefit from a power over ethernet version. One problem I encountered was with dew settling on the sensor but fitting a heater is incompatible with battery-powered operation. Since both the magnetometer and cloud detector use almost the same hardware I decided to design an Arduino-compatible 'shield' that could be used to support both systems.

Combined magnetometer and cloud detector hardware

Combined magnetometer and cloud detector PCBs
Printed circuit boards for the sensor shield (left), the IR or humidity sensor (top right)
and fluxgate magnetometer (bottom right). Click for annotated version.
The complete system requires six circuit boards (five for the wireless version). The first is the microcontroller board, I use my Calunium Arduino clone. I hope that in future it will be possible to use an off-the-shelf Arduino Mega2560 instead but the current firmware relies on Calunium's real-time clock to generate the hardware interrupts which control the sampling interval. There is also a sensor shield, the Arduino Ethernet shield (omitted on the battery-powered wireless version), and one board for each of the sensors (fluxgate magnetometer, IR temperature and humidity).

The sensor shield is based on the existing design and retains the option of battery-powered operation with radio communication. The magnetometer sensor and analogue-to-digital converter must be powered at 5V, which requires a level-shifting circuit when the microcontroller is powered from 3.3V, which is the case when operating from batteries. For power over ethernet use the microcontroller must also operate at 5V for compatibility with the Arduino Ethernet shield so the level shifting is not required. It is kept however as it provides buffering between the two circuit boards; 1.5m is a considerable distance for an I2C bus. The cloud detector uses a non-contact infra-red temperature sensor operating at 3.3V so a level-shifting circuit is required for PoE operation where the microcontroller is connected to 5V. (I've ignored the fact that a 5V version of the sensor exists since it isn't readily available in the UK). The sensor shield allows a humidity sensor to be connected so that estimates of the clear sky and cloudy sky temperatures can be made. As before, an on-board LM61 temperature sensor monitors the system temperature. The new sensor shield also adds a header to fit an Embedded Adventures lightning sensor module. I don't have one of these at the moment so I can't be certain it will work and there is no software support for it in the existing firmware. Fitting it at the same time as the cloud detector sensors will require long break-away headers to be soldered to the bottom of the lightning sensor module.

Fluxgate magnetometer sensor PCB
Fluxgate magnetometer sensor mounted on its sensor PCB.
Click for annotated version.

The fluxgate sensor is fitted on its own PCB which contains the analogue to digital converter. For PoE operation a linear voltage regulator is used to convert from 9V to the 5V supply it requires. For battery operation a MAX619 DC-DC charge pump boosts the battery voltage to 5V. Almost all of the temperature variation can be removed by placing the PCB at the bottom of a 1m length of soil pipe. The pipe is buried to a depth of 0.85m with its axis vertical. In the magnetometer-only system the microcontroller, sensor shield and ethernet shield (or batteries for the wireless version) are fitted onto a wooden frame to hold them into the top part of the tube. Positioning the rest of the system away from the fluxgate sensor helps to avoid unwanted effects from any ferro-magnetic components (such as the batteries), it also aids access and enables wireless data transmission.

Humidity sensor PCB
Honeywell HIH6131 humidity sensor mounte on its sensor PCB.
The same PCB design is also used for the MLX90614 IR temperature sensor.
Click for annotated version.

The enclosure design used for the magnetometer isn't suitable for the cloud detector so the prototype cloud detector used a standard IP65 rated box, with the IR temperature sensor pointing upwards to view the sky. The humidity sensor was fitted above a breather hole in the bottom of the box. This concept will continue to be used for the cloud detector; the IR temperature sensor and humidity sensor are fitted to separate PCBs in the top and bottom of the box. To minimise costs the IR temperature and humidity sensors use the same PCB design. The mechanical design of the cloud detector part is something I'd like to improve upon, particularly to reduce the number of PCBs used. However the differing sensor requirements may prevent this.

For a combined system I plan to use the soil pipe to house the fluxgate sensor but locate the rest of the electronics in a separate box following the design of the prototype cloud detector.

Does it work?

I've not yet deployed a system outside but testing indicates the new printed circuit boards work as intended when used in power over ethernet mode. Battery-powered operation on this new version has not yet been tested.

Design files

The design files (hardware, firmware and software) are open source and can all be downloaded from the Github repository. A PDF version of the user manual describing how to construct and operate the magnetometer can be downloaded from http://aurorawatch.lancs.ac.uk/manual.pdf. At the time of writing only instructions to build the original FLC100 shield are included. Instructions to build the combined sensor shield described above will be added in due course.


Combined magnetometer and cloud detector
Combined magnetometer and cloud detector.


To improve the performance and stability of the AuroraWatchNet magnetometers I recently began experimenting with a power over ethernet (PoE) version. With restrictions on power consumption lifted considerable performance improvements are possible. As a result I have developed the magnetometer hardware specifically to support a power over ethernet version. Another instrument I've been developing is a cloud detector. This too should benefit from a power over ethernet version. One problem I encountered was with dew settling on the sensor but fitting a heater is incompatible with battery-powered operation. Since both the magnetometer and cloud detector use almost the same hardware I decided to design an Arduino-compatible 'shield' that could be used to support both systems.

Combined magnetometer and cloud detector hardware

Combined magnetometer and cloud detector PCBs
Printed circuit boards for the sensor shield (left), the IR or humidity sensor (top right)
and fluxgate magnetometer (bottom right). Click for annotated version.
The complete system requires six circuit boards (five for the wireless version). The first is the microcontroller board, I use my Calunium Arduino clone. I hope that in future it will be possible to use an off-the-shelf Arduino Mega2560 instead but the current firmware relies on Calunium's real-time clock to generate the hardware interrupts which control the sampling interval. There is also a sensor shield, the Arduino Ethernet shield (omitted on the battery-powered wireless version), and one board for each of the sensors (fluxgate magnetometer, IR temperature and humidity).

The sensor shield is based on the existing design and retains the option of battery-powered operation with radio communication. The magnetometer sensor and analogue-to-digital converter must be powered at 5V, which requires a level-shifting circuit when the microcontroller is powered from 3.3V, which is the case when operating from batteries. For power over ethernet use the microcontroller must also operate at 5V for compatibility with the Arduino Ethernet shield so the level shifting is not required. It is kept however as it provides buffering between the two circuit boards; 1.5m is a considerable distance for an I2C bus. The cloud detector uses a non-contact infra-red temperature sensor operating at 3.3V so a level-shifting circuit is required for PoE operation where the microcontroller is connected to 5V. (I've ignored the fact that a 5V version of the sensor exists since it isn't readily available in the UK). The sensor shield allows a humidity sensor to be connected so that estimates of the clear sky and cloudy sky temperatures can be made. As before, an on-board LM61 temperature sensor monitors the system temperature. The new sensor shield also adds a header to fit an Embedded Adventures lightning sensor module. I don't have one of these at the moment so I can't be certain it will work and there is no software support for it in the existing firmware. Fitting it at the same time as the cloud detector sensors will require long break-away headers to be soldered to the bottom of the lightning sensor module.

Fluxgate magnetometer sensor PCB
Fluxgate magnetometer sensor mounted on its sensor PCB.
Click for annotated version.

The fluxgate sensor is fitted on its own PCB which contains the analogue to digital converter. For PoE operation a linear voltage regulator is used to convert from 9V to the 5V supply it requires. For battery operation a MAX619 DC-DC charge pump boosts the battery voltage to 5V. Almost all of the temperature variation can be removed by placing the PCB at the bottom of a 1m length of soil pipe. The pipe is buried to a depth of 0.85m with its axis vertical. In the magnetometer-only system the microcontroller, sensor shield and ethernet shield (or batteries for the wireless version) are fitted onto a wooden frame to hold them into the top part of the tube. Positioning the rest of the system away from the fluxgate sensor helps to avoid unwanted effects from any ferro-magnetic components (such as the batteries), it also aids access and enables wireless data transmission.

Humidity sensor PCB
Honeywell HIH6131 humidity sensor mounte on its sensor PCB.
The same PCB design is also used for the MLX90614 IR temperature sensor.
Click for annotated version.

The enclosure design used for the magnetometer isn't suitable for the cloud detector so the prototype cloud detector used a standard IP65 rated box, with the IR temperature sensor pointing upwards to view the sky. The humidity sensor was fitted above a breather hole in the bottom of the box. This concept will continue to be used for the cloud detector; the IR temperature sensor and humidity sensor are fitted to separate PCBs in the top and bottom of the box. To minimise costs the IR temperature and humidity sensors use the same PCB design. The mechanical design of the cloud detector part is something I'd like to improve upon, particularly to reduce the number of PCBs used. However the differing sensor requirements may prevent this.

For a combined system I plan to use the soil pipe to house the fluxgate sensor but locate the rest of the electronics in a separate box following the design of the prototype cloud detector.

Does it work?

I've not yet deployed a system outside but testing indicates the new printed circuit boards work as intended when used in power over ethernet mode. Battery-powered operation on this new version has not yet been tested.

Design files

The design files (hardware, firmware and software) are open source and can all be downloaded from the Github repository. A PDF version of the user manual describing how to construct and operate the magnetometer can be downloaded from http://aurorawatch.lancs.ac.uk/manual.pdf. At the time of writing only instructions to build the original FLC100 shield are included. Instructions to build the combined sensor shield described above will be added in due course.


Feb
13

Performance comparison of power over ethernet (PoE) and battery magnetometers

arduino, AuroraWatch UK, AuroraWatchNet, Calunium, Magnetometer Commenti disabilitati su Performance comparison of power over ethernet (PoE) and battery magnetometers 

Introduction

I've been working to improve the performance and stability of the AuroraWatchNet magnetometers. It's apparent that both measurement noise and stability are considerably improved when the sensor is powered continually. Unfortunately with the sensor powered continually the batteries last for only a few weeks. I have therefore been testing a power over ethernet (PoE) version of the magnetometer. The hardware is essentially the same but the addition of the Arduino Ethernet shield requires the microcontroller to operate from 5V. Operating voltage is easily changed on my Calunium microcontroller board. With the power restrictions lifted the sampling interval can also be reduced. The test system has been operating reliably for almost two months, sampling every 5 seconds. With some minor configuration changes sampling every second is possible although I am not convinced the trade-off in measurement noise is worthwhile.

Performance comparison

Below is a plot comparing one hour of data from the new power over ethernet system with two of the existing battery-powered wireless models already in operation. The PoE system is on the same site as LAN1 but located nearer parked cars. I chose this period because it was free of man-made disturbances. I adjusted the baselines so that the plots overlap.Only the H component of the magnetic field is shown.

Power over ethernet compared with existing AWN magnetometers
Click for larger version.

The graph shows that the power over ethernet version has much smaller measurement errors; it can probably operate with ~0.1nT accuracy compared to ~10nT for the battery-powered version. It's such an improvement that I found the measurement accuracy was being limited by the available resolution of the analogue-to-digital converter. The battery-powered versions derive the sample value from the median of 15 samples (taken as a burst over 4 seconds) to reduce noise. To improve the resolution for the PoE version it operates by taking the mean of 15 samples. Further improvement to the resolution may be possible by taking advantage of the programmable gain amplifier that is built into the analogue-to-digital converter.

Let's see how it compares against some observatory-grade measurements. The plot below shows the same interval but this time uses data from the British Geological Survey magnetometers at Eskdalemuir and Hartland. I obtained the data from the SAMNET data archive at Lancaster University, where the data has already been converted into HEZ magnetic coordinates. AuroraWatchNet also operates with HEZ magnetic coordinates, although usually the E and Z sensors are not present. As before, the baselines have been adjusted so that the plots overlap.


PoE magnetometer compared with BGS magnetometers
Click for larger version.


The similarity between the different traces is striking. Some differences are to be expected since Eskdalemuir is approximately 140km north of Lancaster and Hartland is 350km SSW of Lancaster. This interval is interesting because it shows Pi2 pulsations, starting at about 01:15 and ending around 01:30. The period of the pulsations are about 90 to 120 seconds.

Conclusions

For a home-built citizen-science magnetometer which probably costs 25 times less than its observatory grade cousin I'm very happy with its performance. The detection of Pi2 pulsations means a low-cost magnetometer can now notify of substorm onset, not just the arrival of geomagnetic storms. A network of such devices has interesting possibilities for the study of magnetic field line resonances.

You might wonder why anyone would want to buy an observatory grade instrument, there are good reasons. At present I am relying on the sensor manufacturer's calibration, whilst an observatory grade instrument would be supplied with an official calibration certificate. The observatory instrument would also have better long-term baseline stability, lower temperature variation and higher cadence. Calibration is an issue I hope to tackle at a later date. For space-weather monitoring only short-term variations are of interest and
the better performance provided by an observatory system may not be needed.

Data credits

The Sub-Auroral Magnetometer Network data (SAMNET) is operated by the Space Plasma Environment and Radio Science (SPEARS) group, Department of Physics, Lancaster UniversityHartland and Eskdalemuir data is provided courtesy of the British Geological Survey.



Introduction

I've been working to improve the performance and stability of the AuroraWatchNet magnetometers. It's apparent that both measurement noise and stability are considerably improved when the sensor is powered continually. Unfortunately with the sensor powered continually the batteries last for only a few weeks. I have therefore been testing a power over ethernet (PoE) version of the magnetometer. The hardware is essentially the same but the addition of the Arduino Ethernet shield requires the microcontroller to operate from 5V. Operating voltage is easily changed on my Calunium microcontroller board. With the power restrictions lifted the sampling interval can also be reduced. The test system has been operating reliably for almost two months, sampling every 5 seconds. With some minor configuration changes sampling every second is possible although I am not convinced the trade-off in measurement noise is worthwhile.

Performance comparison

Below is a plot comparing one hour of data from the new power over ethernet system with two of the existing battery-powered wireless models already in operation. The PoE system is on the same site as LAN1 but located nearer parked cars. I chose this period because it was free of man-made disturbances. I adjusted the baselines so that the plots overlap.Only the H component of the magnetic field is shown.

Power over ethernet compared with existing AWN magnetometers
Click for larger version.

The graph shows that the power over ethernet version has much smaller measurement errors; it can probably operate with ~0.1nT accuracy compared to ~10nT for the battery-powered version. It's such an improvement that I found the measurement accuracy was being limited by the available resolution of the analogue-to-digital converter. The battery-powered versions derive the sample value from the median of 15 samples (taken as a burst over 4 seconds) to reduce noise. To improve the resolution for the PoE version it operates by taking the mean of 15 samples. Further improvement to the resolution may be possible by taking advantage of the programmable gain amplifier that is built into the analogue-to-digital converter.

Let's see how it compares against some observatory-grade measurements. The plot below shows the same interval but this time uses data from the British Geological Survey magnetometers at Eskdalemuir and Hartland. I obtained the data from the SAMNET data archive at Lancaster University, where the data has already been converted into HEZ magnetic coordinates. AuroraWatchNet also operates with HEZ magnetic coordinates, although usually the E and Z sensors are not present. As before, the baselines have been adjusted so that the plots overlap.


PoE magnetometer compared with BGS magnetometers
Click for larger version.


The similarity between the different traces is striking. Some differences are to be expected since Eskdalemuir is approximately 140km north of Lancaster and Hartland is 350km SSW of Lancaster. This interval is interesting because it shows Pi2 pulsations, starting at about 01:15 and ending around 01:30. The period of the pulsations are about 90 to 120 seconds.

Conclusions

For a home-built citizen-science magnetometer which probably costs 25 times less than its observatory grade cousin I'm very happy with its performance. The detection of Pi2 pulsations means a low-cost magnetometer can now notify of substorm onset, not just the arrival of geomagnetic storms. A network of such devices has interesting possibilities for the study of magnetic field line resonances.

You might wonder why anyone would want to buy an observatory grade instrument, there are good reasons. At present I am relying on the sensor manufacturer's calibration, whilst an observatory grade instrument would be supplied with an official calibration certificate. The observatory instrument would also have better long-term baseline stability, lower temperature variation and higher cadence. Calibration is an issue I hope to tackle at a later date. For space-weather monitoring only short-term variations are of interest and
the better performance provided by an observatory system may not be needed.

Data credits

The Sub-Auroral Magnetometer Network data (SAMNET) is operated by the Space Plasma Environment and Radio Science (SPEARS) group, Department of Physics, Lancaster UniversityHartland and Eskdalemuir data is provided courtesy of the British Geological Survey.





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