Friday, September 12, 2014

Why Kp is a poor indicator for auroral alerts

What is Kp?

Kp is a an index to represent the planetary geomagnetic activity. The name originates from "planetarische Kennziffer" (German for planetary index). It has 10 main levels, numbered from 0 (no geomagnetic activity) to 9 (extreme geomagnetic activity). The intermediate ranges are subdivided with a + or - suffix, making 28 levels in total (0, 0+, 1-, 1, 1+, 2-, 2, 2+, ... 8, 8+, 9-, 9).

The Kp index is calculated for three-hourly intervals, beginning at UT midnight. To calculate Kp the daily "solar-quiet" (Sq) variation is removed from the measurements of magnetic field strength. Then the difference between the largest and smallest values is computed. By looking up the difference in a conversion table the local K index can be found. Stations at different latitudes (or more properly magnetic latitudes) have different conversion tables.

There is a pronounced diurnal variation in the K index at a single station, with intervals close to local midnight being substantially more disturbed in comparison with those centered on local noon; see For this reason the planetary Kp index is computed from magnetic variations recorded by 13 magnetometers located around the world.

Why Kp is a poor indicator of for auroral alerts

The Kp index only indicates geomagnetic activity within a 3 hour interval, which is too long to be useful for auroral alerts. Geomagnetic conditions can have recovered to a calm state long before the next Kp index is computed. This is particularly true for aurora caused by a substorm, as the entire substorm cycle (including the growth, expansion and recovery phases) is typically 2 to 4 hours duration; see

It must also be noted that the official Kp values are not available in real-time. Not only must the data from the 13 stations be collected the quiet-day curves for each of the magnetometers must be calculated for that calendar month, a process which cannot begin until the month ends. Therefore any references to Kp for the current month will be to unofficial estimates. The most reliable estimate of Kp is probably obtained from NOAA, which is derived from a worldwide distribution of magnetometers, see

In summary: Kp is not available in real-time. The 3 hour interval means current auroral activity may be much lower than an estimated Kp value suggests.

Estimated Kp can differ significantly from the local K index

As noted above, the pronounced daily variation of the K index means that there can be considerable difference between the average planetary geomagnetic activity (Kp) and that observed locally (K). The plot below shows the difference between the local K index measured by the British Geological Survey magnetometer at Eskdalemuir and Kp.

Difference between Eskdalemuir local K index and Kp
Difference between K index measured at Eskdalemuir and Kp.

The plot shows that in general there is good agreement but at certain times the K index can be higher or lower than Kp.

In summary: global measurements of Kp are not always valid indications of  regional geomagnetic activity.

What measurements should be used?

One of the better measurements is using the K index as long as it is from a nearby magnetometer or one on a similar geomagnetic longitude. It is still limited by the 3 hour resolution of the K index so it will not always be accurate for real-time measurements.

An alternative measurement is to consider the rate of change of the magnetic field, commonly referred to by its mathematical notation dB/dt ("D B by D T"). This measurement is of particular interest to operators of pipelines and long power distribution networks since it indicates the levels of geomagnetically-induced currents which might be expected. British Geological Survey publish real-time dB/dt measurements.These measurements require magnetically quiet sites as human interference is likely to cause sudden spikes with a high dB/dt value, limiting its usefulness to observatory measurements.

AuroraWatch UK publishes a real-time auroral activity measurement. It is the H component deviation, which is the difference between the current H component magnetic field strength and the expected value for the time of day taken from the "quiet-day curve". Unlike Kp this measurement is computed hourly and so can indicate a return to low activity values sooner than Kp. An accurately fitted quiet day curve is required to make the deviation measurement.

Data credits

Eskdalemuir K indices were downloaded from the British Geological Survey Kp data was downloaded from GFZ-Potsdam

Saturday, May 17, 2014

Calunium v2.1a

I've made a few minor improvements to the Calunium design, partly to satisfy compatibility between Calunium and Arduino shields and partly to fix a few minor hardware errors.

Disconnect VCC from the ISP header

The ISP header is a 6 pin male header which can be used to program the microcontroller, such as to upload the bootloader. On Calunium the VCC pin of this header is wired to the same supply voltage used by the microcontroller, ie +3.3V or +5V depending upon the operating volatge selected. This makes sense, especially when used when programmers such as Atmel's AVR Dragon which use the pin to set the HIGH voltage level on the SCK and MOSI control lines. Unfortunately it seems that on Arduino devices (such as the Ethernet shield) this pin is always wired to +5V. This is true even for the latest hardware versions which should be aware of the operating voltage via the IOREF pin. To avoid the possibility of connecting the +3.3V and +5V supplies I added a jumper. For normal operation the shunt can be omitted. When using an ISP header programmer that needs to know the operating voltage a shunt can be added.

Pull-down resistor on D13 FET

Calunium uses a field-effect transistor (FET) to switch on the D13 LED. This is to avoid load on the D13 SCK line when it is used for SPI communications. I learnt this trick from Freetronics who do something similar with their range of clones. In normal use, when D13 is an output, everything works fine. However, when it is an input, such as during RESET state or bootloader programming, the FET gate can float to potential such that the LED is switched on. A 1 megaohm pull down resistor connected to D13 is sufficient to prevent this happening without loading the SCK line. It also helps prevent damage by electrostatic discharge. The latest Calunium design now has room to add this 1 megaohm resistor. On older boards an 0805 surface mount resistor can be fitted between the gate and source of the 2N7000 FET, or alternatively between the D13 and adjacent ground pin.

Auto-reset behaviour corrected for MCP2200 USB interface

I recently added the option of fitting the MCP2200 USB-serial interface. Despite suggestions to the contrary this useful USB  device can automatically reset the Arduino when the device is opened. This auto-reset behaviour as it is known is used to activate the bootloader when uploading sketches. The intention was that auto-reset could be disabled by removing a shunt from the AUTO RESET jumper. Unfortunately an error in the schematic meant that auto-reset could only be disabled when using the FTDI interface, not the MCP2200 option. The latest version corrects this error.

Silkscreen label corrected

On Calunium v2.1 a jumper was added to select FTDI or USB power. Due to a rotation of the component the correct position for the shunt is opposite to that indicated by the silkscreen label. The latest version corrects the text.

Open source

The Eagle PCB design files for Calunium are available on Github and are licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License. Files to allow Calunium to be used with the Arduino 1.0 IDE are available on  Github and are licensed under the Gnu General Public License v2.

Monday, March 24, 2014

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 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.

Wednesday, February 12, 2014

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


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.


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.

Sunday, October 27, 2013

Magnetometer progress report: link indicator LED

I recently added a new feature to the magnetometer remote sensor unit firmware. To help indicate when it is within radio communication range of the base unit an LED is turned on at the start of a message communication. When the sensor unit receives the acknowledgement from the base unit of successful message receipt the LED is switched off. In normal operation the LED should blink briefly every 30 seconds, after each sampling interval. If the LED remains on it indicates a problem with the radio link. For the initial installation the sampling time can be reduced to 5 seconds for to obtain faster feedback of whether communication is successful or not.

Improvements for battery-powered operation

Indicating link errors by using an LED is convenient during installation but the power wasted after installation is not compatible with battery-powered operation. To save power the LED is used only the first 15 minutes of operation. Only user-initiated reset actions (power on or reset switch pressed) cause the LED to be used, resets from by the watchdog timer or brown-out detector are ignored. The cause of the reset is detected by examining the microcontroller's status register (MCUSR).

Communication timeout feature added

I've also added a timeout which detects when communication has been lost for an extended period. The microcontroller system is rebooted in the hope that the error is recoverable. Loss of communication, along with low battery status, can also be sensed and reported by running the latest version of the data plotting software on the Raspberry Pi. If either error is detected a message can be sent via email, Twitter or Facebook.

Saturday, October 19, 2013

Cloud detector: a review of progress so far

The hardware

My cloud detector has been running outside for over 5 months now. Overall I'm very pleased with how it works. The battery-powered hardware is based on the AuroraWatchNet magnetometer design, which uses my own Calunium microcontroller development board. The remote sensor board and fluxgate sensor are omitted. I've added a Melexis MLX90614 non-contact infra-red thermometer to measure the sky temperature. Clear skies should give low temperatures whilst clouds are expected to have warmer temperatures, although still colder than the ambient temperature. The MLX90614 also outputs the sensor temperature, which should be close to the ambient temperature. I also added a Honeywell HIH-6131-021 humidity sensor which has an I2C interface. Do not confuse with the similar sounding version which has an SPI interface! The HIH-6130 also provides and ambient temperature measurement. To avoid direct contact with water the humidity sensor is placed at the bottom of the enclosure with a hole underneath to expose it to air. This hole also functions as a breather hole and ensures that the internal pressure matches atmospheric pressure. Since this hole was drilled there have not been any more incidents of water ingress.

The Calunium board is running a firmware is a modified version of the magnetometer firmware. It communicates with the Raspberry Pi data logger using an 868MHz radio link, with the same signed communication protocol used by AuroraWatchNet. This means I can use the data recording software from AuroraWatchNet. It also inherits the signed over-the-air firmware update capability. In principle it should be possible to combine both magnetometer and cloud detector functions in one unit.

I started off using the 90°  field-of-view version of the non-contact IR thermometer but later switched to theMLX90614ESF-DCH-000-TU-ND variant which features a 12° field of view. Its greater height meant it was easier to fit into the cable grommet housing. However I think the FOV is too narrow and I plan to switch back to the original sensor when I have time.

Data processing

The data processing and plotting now uses the auroraplot library for Python, which I developed to process the AuroraWatchNet magnetometer data. On the graphs I plot the sky temperature measured by the non-contact IR thermometer and the ambient temperature measured by the humidity sensor. For monitoring purposes I also plot the sensor temperature of the IR thermometer (marked as "detector temperature"); this should be similar to the ambient temperature but its exposed position makes it more likely to undergo solar heating and radiative cooling. For good measure I also plot the system temperature, which is measured by an LM61 temperature sensor connected to the ATmega1284P's analogue to digital converter. This measurement is noisy but useful to check the system doesn't overheat on sunny summer days.

The plan is to estimate the upper and lower bounds that I would expect for the sky temperature, and from that derive an estimate of the cloud cover. I initially expected that with complete cloud cover the sky temperature would match that the lifted condensation level, which I estimate using the ambient temperature and relative humidity. I soon saw temperatures higher than the LCL temperature. Research literature indicates that the clouds can act as a mirror at long IR wavelengths and thus the expected temperature should include an effect of ground temperature too. On the plots this is shown as the effective cloud temperature. The clear sky temperature is derived from results found in research literature but none of the equations tried so far have been a great match. Other researchers have fitted parameters for their specific location (including altitude) by comparison with visual measurements. I have yet to try this but daily measurements are made at the nearby Hazelrigg Weather Station. The graphs are too cluttered for production use but help me to understand what is happening.

Example plots

Below are a selection of plots. You can see the entire archive (from 2013-07-14) at

Cloud detector data for 2013-09-03. The asterisk (*) indicates derived parameters (ie not directly measured).
The figure above include most of the effects that can be identified. After midnight there is thick cloud, which later clears for short periods. At around 0300 UT I think it must have rained, the sky temperature is almost identical to the detector temperature and the variability is much reduced. Whilst the sensor is wet no sensible conclusions about cloud cover can be drawn. At about 0700 UT the sensor clears of water and the sky temperature falls indicating clearer skies. The rest of the day is dry, with heavy cloud cover until 1400 UT. At around 1700 UT the skies are completely clear. The clear sky estimate is too low for this time. At 2000 UT the clouds return, with some clear patches.

Cloud detector, 2013-09-28
Cloud detector data for 2013-09-28 showing the effect of clear skies.
The figure above shows an almost completely cloud-free day. The higher variability in the sky temperature measurements between 1200-1600 UT corresponds to similar effects in the humidity data (shown below); both are direct measurements made by different sensors.

Relative humidity, 2013-09-28
Humidity data for 2013-09-28. Notice how the higher variability occurs at the same times as the sky temperature measurements.


During summer operation dew has not been a problem but now that the nights are colder I have noticed effects which I believe are due to the formation of dew on the sensor. In the first plot below the sky temperature apparently rises from around 2230 UT. The rise is smooth and during this period the ambient temperature is falling, which overnight is often a sign of clear skies. After midnight (second plot) the sky temperature increases slightly before falling sharply around 0630 UT. Sunrise on this day was 0622 UT.

Cloud detector, 2013-10-05Cloud detector, 2013-10-06
This pair of plots is believed to show dew formation on the sensor.


I know from the informal comparisons with visual cloud cover that I regularly make the cloud detector does generally work very well. It does not function during wet periods. As anticipated dew is becoming a problem during the colder nights and a dew heater will be required for reliable winter operation. Future development will be to add a heater, which will need a wired connection to the cloud detector. Once a wired connection is made the radio link appears superfluous so I plan to investigate the options for power-over-ethernet. The Arduino ethernet shield is one possibility, although its compatibility with 3.3V operation has not been established.

Thursday, September 26, 2013

Auroraplot: data processing software for AuroraWatchNet

With the dispatch of the AuroraWatch schools' magnetometers imminent I have implemented a  Python  toolkit to process the data. The numpy and matplotlib modules are used extensively. The toolkit provides an API to load data and perform various processing actions on it, such as plotting data. The concept is influenced by my previous Multi-instrument Analysis toolbox for Matlab. In addition to the loading and processing of magnetic field data auroraplot allows other data types to be added later.

Loading data

Data can be loaded with arbitrary start and end times very simply:

md = ap.load_data('AURORAWATCHNET', 'LAN1', 'MagData',

In this case the selected portion of data crosses midnight and two data files must be loaded, concatenated and trimmed to get the desired time range. This is performed automatically by auroraplot, the user need not be concerned with the format of the files or where they are located. It is even possible for the files to be downloaded on-the-fly using FTP or HTTP transfer protocols.

load_data returns an object (of type MagData) to the user containing the actual magnetic field data and various other metadata, such as a timestamp for each sample and the data units. Each object can store more multiple data channels but all data points must share the same timestamps, be of the same type and share the same units. Therefore it is not possible to store operating temperatures (units °C) in an object holding magnetic field strength (units tesla). The operating temperature data can be accessed as:

td = ap.load_data('AURORAWATCHNET', 'LAN1', 'TemperatureData',

Battery voltage (data type VoltageData) can be accessed in a similar same way.

Plotting data

High-level plot functions enable the data be be plotted very simply, for the magnetic field data loaded previously


will produce a matplotlib figure with a title and the axes labelled with the correct units. Temperature and voltage data are plotted in the same way.

I have created some tools to make working with numpy's datetime64 and timedelta64 objects more convenient, including rounding functions (round, ceil and floor) which round to an interval. They are useful for finding the start of an hour, or the end of a day. I have also created Locator and Formatter classes to sensibly label time axes using datetime64 times and timedelta64 intervals. Tick marks are located on the nearest second, minute, hour, day, month or year boundary (or multiple thereof) depending on the time interval being displayed. Thanks to matplotlib's structure the labels are automatically regenerated with the most appropriate time units when a plot is zoomed.

Other operations

Quiet-day curves

Other operations include the generation of quiet-day curves. These are the curves from which we measure geomagnetic activity and are of critical importance for AuroraWatch UK. There are are not flat but have a daily variation caused by the equatorial electrojet. The empirical algorithm selects the days (typically 5) with the least geomagnetic activity. A truncated Fourier series is used to guarantee that the quiet-day curves are cyclic, with the start and end points having the same magnitude and slope. This is essential otherwise our rolling plots would show up the discontinuities in the QDC at midnight, and would falsely cause step changes in the geomagnetic activity. An example QDC is shown below.

Quiet-day curve for magnetometer at Lancaster ,UK. This is derived from recorded data
and clearly shows the Sq current system caused by the equatorial electrojet.

From this we can see that even on a geomagnetically quiet day we would expect a 30nT variation in field strength seen by the magnetometer. The AuroraWatch threshold for minor geomagnetic activity is 50nT so this shows the importance of using a quiet-day curve instead of a flat line when calculating geomagnetic activity.

Stack plots

Stack plots (also called magnetograms) are a convenient representation for magnetic field data from a set of magnetometers separated in latitude.Data from the northernmost instruments is placed at the top and that from the southernmost at the bottom. An example stackplot is shown below:

Stackplot showing data from two Lancaster stations and from Ormskirk.
The magnetometer at Ormskirk is operated by the Met Office as part of a test. The stackplots will be more interesting as the network grows.

Open source

The source code is available under a BSD-type license from Github.You will need python, along with the numpy (version 1.7), matplotlib and scipy python modules.auroraplot has been tested under Debian Linux (64 bit version) and Raspbian on the Raspberry Pi.