Tag: Mobile weather station

MeteoTracker, the mini weather station for monitoring the thermal potential of the atmosphere during flight

MeteoTracker is a portable mini weather station designed for taking measurements on the move.

In the context of a fruitful collaboration between the creators of MeteoTracker and a group of free flight enthusiasts, a series of specially made functionalities for free flight have been implemented on the MeteoTracker web platform.

In fact, this data collection method brings significant innovation to the free flight scene, more than in others, in terms of real-time and a posteriori analysis and monitoring of the thermal quality within the flight area.

Indeed, it is possible to assess the existence and intensity of ascending and descending motions in the atmosphere, thanks to the measurement of the thermal and hygrometric profile.

MeteoTracker is very easy to install on the flight vehicle and acquires temperature, humidity and pressure (altitude) data every few metres during flight.

The APP and MeteoTracker web platform enable the visualization and analysis of measured data, in real-time and a posteriori, through various representations:

  • skew-T charts
  • time-domain charts
  • visualization on a map with a chromatic scale
  • numerical visualization
  • statistics

In this way, free flight enthusiasts have an unprecedented source of knowledge for quantifying the thermal quality of the flight area at high spatial resolution, both for the purposes of real-time monitoring and for subsequent processing aimed at improving forecasting tools.

What is MeteoTracker?

MeteoTracker is a mini weather station that is as compact as a computer mouse. It is placed on the exterior of the vehicle used for free flight (or on the land-based vehicle: car, bicycle, scooter, etc.) by means of the magnetic base or other fastening system. It acquires the temperature, humidity and pressure data (weatherpoint), with a spatial interval of just a few metres.

Each weatherpoint is visualised, geotagged and sent to the cloud in real-time by the MeteoTracker APP, to which the mini weather station is connected via a Bluetooth Low Energy (BLE) connection.

Each weatherpoint then flows to the MeteoTracker database and can be analysed and visualised with advanced graphical and statistical tools (maps, charts and interrelated numeric formats).

A distinctive characteristic of the mini weather station is the Radiation Error Correction Systems (RECS), a patented system that corrects the typical error caused by solar radiation without requiring the use of cumbersome shielding structures.

In turn, the absence of shielding structures allows the sensors ample exposure to ventilation and, therefore, significant measurement speed. This is essential for the correct measurement of thermal variations encountered on the move (we detected a variation of up to 10°C over 35 seconds of travel, a distance of 400 m).

Current limitations in the thermo-hygrometric characterisation of the flight area

Those who practice free flight, in addition to relying on their own experience, must rely on local-scale weather prediction models. While these models are of fundamental usefulness, the mathematics on which they are based have an inbuilt limitation, that is, a notable approximation of the orography.

From a measurement perspective, the only source capable of providing real data on the vertical thermo-hygrometric profile of the atmosphere is the atmospheric sounding. However, radiosonde measurements have a very low spatial density (one sounding every several thousand square kilometres) and are, therefore, often far from the airfield.

Systematic and automatic thermo-hygrometric characterisation of portions of the atmosphere covered during flight sessions. What innovation can MeteoTracker bring to the world of free flight.

Measuring temperature differentials and understanding the factors that determine them is the most important exercise for a free flight pilot.

MeteoTracker enables the acquisition of accurate data with high spatial resolution during flight, making it possible to verify the hypotheses all pilots formulate in order to evaluate areas with the highest thermal potential.

The collection of hundreds of weatherpoints during each flight session can be translated, by way of subsequent SW elaborations, into simple and accurate indications of the thermal potential of the flight area (for example, icons indicating high, medium or low thermal quality which can be shared in real-time with one’s community). These indications are no longer based on forecasts but on real data. For example, a weather reconnaissance flight can provide a “thermal map” in real-time of a specific flight area. This mapping can be “updated” in real-time in subsequent flights based on the new measurements that are carried out.

In the future, in addition to its usefulness in terms of real-time monitoring, this approach could lead to the introduction of data analysis techniques such as machine learning. By comparing the forecast outputs from the models and the data collected with MeteoTracker, machine learning could generate processes for fine tuning forecast models. As a result, optimised forecasts for specific areas (those covered in flight) could be obtained.

MeteoTracker functionalities for free flight

In addition to the collection of data, MeteoTracker enables the visualization and analysis of data in various modes:

  • Data visualization on the APP (in real-time and in the archive), on charts, maps and in numeric format
  • Data visualization on the web platform (in real-time and in the archive), on skew-T and time-domain charts, maps and in numeric format. By selecting the area of interest on the map and the time interval in the relative field, it is possible to filter for, and analyse, specific weatherpoints.
  • Photo mode (measured weather data is included at the top of each image) for capturing the state of the sky and associating it with the weather data measured at that time

Tests Performed

Thanks to the valued collaboration with Francesco Fontanesi, Costantino Molteni and Alberto Sabbadini, MeteoTracker was tested in flight, in the skies over the Central and Northern Apennines, with sessions on a paraglider and glider aircraft.

This activity enabled us to best define the requirements and functionalities to be implemented in the web platform, in addition to permitting the collection of very interesting data.

The data and charts that were obtained from a session on a paraglider and on a glider aircraft are provided below.

Glider session of October 25th 2020

Paraglider session of October 17th 2020

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Mobile measurements. A field that is surprising, a source of significant weather knowledge, and has yet to be fully explored

What’s the temperature?

Will it be cold where we’re heading?

Where was the coldest place last night?

A quick look on the internet or at the screensaver of your smartphone would provide you with what would appear to be answers to these questions.

But is that really the temperature value that awaits us just outside of our homes, or in the place we’re travelling towards?

No, it isn’t. Or at least, it is in no way a given that it will be.

Let’s see why.

The data that comes from a fixed weather station network is generally accurate and reliable. Less so, the data we see on the screensaver of our smartphones, this is often due to interpolation as we seek to compensate for the lack of weather stations in specific locations.

But this is not the point.

No matter how accurate and reliable the temperature data may be, during certain common meteorological situations, the data derived from a fixed weather station will never be able to provide an exact representation of the thermal distribution across a reference geographical area.

For reasons well-known to meteorological experts – air temperature often has very notable spatial variability and the data measured at one specific point may be very different to that measured at a distance of several hundred metres, sometimes just tens of metres away.



We measured a difference of more than 10 °C between two points in the same village, at a distance of 400 m apart. Temperatures ranged from values well over zero to negative values. For this reason, specifying what the temperature was in Hauzenberg (Germany) during the night between 1st and 2nd January 2020 becomes rather complicated. The correct answer to this question would be all values between 4.9 °C and -5.3 °C.

Data Hauzenberg

The data measured by MeteoTracker in Hauzenberg (Germany) during the night between 1st and 2nd January 2020


At a lower latitude and during a different season, we came across some other very interesting data.

During the night of 7th July 2019, on a coastal stretch covering approximately 20 km in Southeastern Sardinia, we detected thermal variation ranging between 32.8 °C and 22.1 °C whose peak, in terms of rate of spatial variation, was recorded in the location of Geremeas where we measured a difference of 8.5 °C along a 400-meter section of the route (from 30.6 °C to 22.1 °C).

Data Hauzenberg

The thermal profile recorded during the first hour of 7th July 2019 along the Southeastern coast of Sardinia


Many other mobile data acquisition sessions, each with their own peculiarities, demonstrate that the extreme spatial variability of temperature data does not represent an anomaly at all rather a typical condition in common meteorological situations.

Aside from conditions of thermal inversion, that in many geographical areas prevail in the cold half of the year (but not only), very pronounced spatial temperature changes can also arise from breezes, interference between mountain ranges and sustained winds (Stau/Föhn), specific elevation profiles, the basic characteristics of the soil, the “urban heat island” effect and other territorial-environmental factors of anthropic origin.

On the one hand, these situations reveal the intrinsic limit of a fixed weather station network, whose information flow, in such cases, is greatly weakened; on the other hand, they highlight the potential and utility of mobile measurements, a source of weather knowledge that has yet to be fully explored and which assumes a significant contingent (item of data in that specific moment, at that specific point) and statistical (climatic characterisation at a high level of spatial resolution) value.

Nowadays, technological advances – from global connectivity to sensor improvements – enable mobile weather exploration by pursuing the vehicle as weather station paradigm, in which a means of transport becomes a mobile weather station through the use of low-cost devices.

But before the measurement system can become mobile, it must satisfy very specific requirements that are by no means a given.

We’ll talk about this in our next post.


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