Eddy covariance flux measurements and micrometeorological methods have become standard in assessing the exchange of greenhouse gases (GHG) and reactive trace gases between forest ecosystems and the atmosphere (Baldocchi, 2014; Burba, 2013; Keronen
The challenge to keep up measurements in northern climate regions during winter months with the occurrence of snow and ice has been discussed by Makkonen
Measurement of turbulent air movements utilising 3D ultrasonic anemometers has become a standard task at flux tower sites. In winter conditions, icing of the ultrasonic anemometer sensor heads leads to loss of data and introduces measurement gaps of some short periods up to months given that winter conditions may last from October to April. Different heating strategies were reported in a comparison study. Applying a constant heating strategy improved the annual data coverage by 64% and led to a data loss of 3.2% by removing biased data due to the heating. Utilising an intermittent heating strategy reduced the loss of data even further (Goodrich
Different brands of ultrasonic anemometers have a possibility to heat the sensor heads to melt ice or snow and keep them operable also during freezing periods. While heating is desired to be operable it also leads to biases in measurements because heat-driven buoyancy is part of the turbulent flow. Therefore, artificial added turbulence by heating should be minimised (Goodrich
In this technical paper, we aim to quantify the temperature ranges when icing occurs using statistical methods and online measured data from the SMEAR Estonia station. We hypothesised that 1) icing takes place at different temperature ranges, 2) we can determine the temperatures with the highest anemometer sensor head icing probability and 3) a regulation feedback loop algorithm can minimise data loss.
We conducted our micrometeorological measurements at the SMEAR Estonia station (Noe
We used the meteorological equipment deployed on a 130 m high atmospheric measurement mast where ultrasonic anemometers together with shielded and ventilated PT-100 temperature sensors (Metek uSonic-3 class A, Metek PT-100, Metek GmbH, Germany) are located at 30, 50, 70, 90, and 110 metres above ground level (further on instrumentation heights). The system measures at a frequency of 10 Hz and stores raw data into files covering 30-minute periods. The ultrasonic anemometers have the possibility to heat the sensor heads. Our data acquisition system analyses data transfer online and detects measurement errors which lead to automated data flagging within the raw data files. The state of the measurement can be “no error”, “error”, “heated”, or “heated and error”. In case an error occurs, the system uses the last valid dataset to write it to the raw data.
Depending on the measured temperature and the number of errors, the data acquisition system switches on sensor head heating when the measured temperature is below a threshold temperature
We used data from August 2014 until October 2019 to assess the amount of heated data sets in the raw data. For the temperature at ground level (Figure 2) 10-minute averaged data from the same period were used. We filtered the raw data files according to the heating flags for each month whenever heating occurred. This led to separate files per month where only the datasets with the flag “heated” or “heated and error” were stored. With these files, we then calculated the fraction of heated 10 Hz datasets and compared it to all datasets logged during that month. This step was repeated for all the months we investigated and was used to estimate the median time of anemometer sensor head heating. In the next step, we calculated probability density functions (PDF) from the heated datasets to determine the probability of the temperature ranges that led to anemometer sensor head icing. Because these are mostly mixture distributions with several peaks, we further determined the PDF’s peaks representing the temperatures where the highest probability of icing occurred. As a last step, we determined the lengths of heating intervals in seconds which gives a robust estimate on the potential data loss due to anemometer sensor head heating. Data processing and analyses were conducted using Python 3.x (Python Software Foundation,
At the SMEAR Estonia station, we measured temperatures below zero degrees Celsius (Figure 2) from October to April, if measured above the forest canopy on the atmospheric mast. On ground level (2 m above the surface), temperatures can fall below zero for short periods also during May. Therefore, ice formation occurs frequently on ultrasonic anemometer sensor heads which are exposed to the ambient weather conditions during these months.
The probability of icing on the anemometer sensor heads was found to span from approximately +5 °C to −25 °C (Figure 3 and 4). Icing events with slightly positive temperatures happen due to a change from cold dry to warmer wet air masses when the anemometer sensor head is still below zero degrees. We calculated PDFs for each height (Figure 3) and each month when icing occurred (Figure 4) by pooling the data from all years. For all heights, the calculated mixture distributions were composed of two or three normal distributions (Wolfram Research, 2016). The PDFs maxima denote the temperatures or temperature ranges where icing of the anemometer sensor heads is most likely to happen. Given Figure 3, the highest probability of icing lies at temperatures between zero and −3 °C. The second temperature range with a medium probability for icing is between −5 °C and −15 °C, and finally the lowest probability but still with a distinctive peak in icing probability lies at the range between −15 and −25°C. The latter peak was most pronounced on the highest instrumentation levels (90 and 110 m) but also near the forest canopy at 30 m. Almost all instrumentation heights have mixed distributions with at least two or three normal distributions that can be expressed as
To determine the most probable icing temperatures from measured data we used mixed distributions limited to be combinations of normal distributions
Height m | Tempe-rature °C | Pearson χ2 p-value | Distribution |
---|---|---|---|
110 | −21.2 | 0.73 | |
−6.4 | |||
−0.6 | |||
90 | −22.3 | 0.04 | |
−3.7 | |||
70 | −11.1 | 0.91 | |
−0.4 | |||
50 | −10.1 | 0.54 | |
−1.3 | |||
30 | −19.6 | 0.90 | |
−7.9 | |||
−1.3 | |||
all | −9.4 | 0.00047 | |
−1.2 |
To get more insight, we further compiled the data with respect to the months when icing occurred and calculated PDFs for each month (Figure 4). Overall, in October and April, the months that usually mark the beginning and end of the ice forming period, showed only one peak of high probability, and the temperature range found was between +5 °C and −5 °C. November showed the largest deviation from the general pattern with four peaks, one between +2 °C and +1 °C, one at zero degrees, and one between −1 °C and −4 °C. November’s lowest temperature range was found between −5 °C and −15 °C with the lowest probability for icing. December followed with a single-peak pattern and the probability maximum was found at −3 °C, but employed a wide span from +5 °C to −15 °C. January employed a two-peak probability pattern with wide ranges of +5 °C to −13 °C for the peak with its maximum at −5 °C and the second peak lied between −13 °C and −25 °C with the maximum at −19 °C. February showed also a two-peak shape but its ranges were found narrower, +5 °C to −6 °C and −6 °C to −15 °C. March then moved back to a single-peak probability function pattern with its maximum at −2 °C and a span between +5 °C and −10 °C. Table 2 summarizes the PDF fitting procedure parameters for the monthly data and shows good to excellent matches for October, November, December and March (> 0.8). A medium good fit was found for February (> 0.5) but a small probability for March (< 0.3) and a very small probability for January (< 0.08). These findings support our hypothesis #2 that we can determine the most probable icing temperature ranges. Altogether, the highest probability for icing is slightly below zero degrees and mostly a problem in weather conditions which are expected to be more likely in Estonia with climate warming (IPCC, 2021; Jaagus & Mändla, 2014; Kupper
To assess probable icing temperatures per month from measured data we used mixed distributions limited to be combinations of normal distributions
Month | Temperature °C | Pearson χ2 p-value | Distribution |
---|---|---|---|
October | 0.6 | 0.88 | |
November | −6.9 | 0.99 | |
−2.3 | |||
0 | |||
1.6 | |||
December | −3.1 | 0.84 | |
January | −18.2 | 0.079 | |
−3.8 | |||
February | −9.5 | 0.56 | |
−0.9 | |||
March | −1.4 | 0.22 | |
April | −0.2 | 0.90 |
To ensure as small data loss as possible we analysed the time it takes for the ultrasonic anemometer to get its sensor heads ice free using the de-icing algorithm (Figure 1). Figure 5 shows a Box-Whisker chart where the length of sensor head heating for each month is visualised. We found that the median time of heating is around 30 seconds with a minimal time of 3 seconds and a maximal heating time of 72 seconds. We grade this result as very positive because assessing forest ecosystems’ fluxes from both eddy covariance and inverting the gas concentration profile measured at five instrumentation heights of the atmospheric tower in Järvselja over the whole year, there is need to decide how much data must be deleted and how many gaps filled in carbon cycle estimations because of the heating effect that introduces extra turbulence by buoyancy and thus leads to biases. The box width in Figure 5 is scaled in relation to the number of icing events in the given month and we can see that most ice formation events occur during December and January. Such short time periods allow us to create strategies of correction and compensation of added turbulence in the data processing beside the removal of heated data sets. Also, the determination of the flux footprint is, given such short-term icing events, based on very robust data even in winter. The short time intervals found confirm our hypothesis #3 and tell us that it cannot be considered to let the ultrasonic anemometer sensor heads remain frozen and risk long periods of data loss. The risk of biased GHG budgets due to heating periods of the ultrasonic anemometer sensor heads is very small or absent, if after the short heating period the data are discarded for some 5 or 10 minutes. It is further important to minimise the lack of data during winter conditions and especially when considering that the period with temperatures near zero degrees are important for the tree frost hardening and de-hardening processes at the onset and the end of winter and based on that determine the vegetation period.
We found that icing on micrometeorological measurement equipment takes place over a wide range of temperatures. The highest probability is near zero degrees, but it depends on the instrument height and the particular month. The lowest icing temperatures were recorded in January and February, but the largest variation in icing temperature ranges was observed in November. Given the possible time range of five months when frost and ice formation can occur frequently, it is necessary to estimate the times when heating of instrumentation is needed to ensure the lowest possible data loss. The median 30 seconds of heating to get the 3D sonic anemometer sensor head ice free proves the intermittent heating algorithm successful. The risk of missing out long time intervals during autumn, winter and spring would jeopardise the GHG budget calculations. To reduce the risk of biased carbon sequestration estimations, the forest ecosystems’ carbon and energy budgets need to be properly assessed. The quality of estimating the eddy tower’s footprint area has a high impact on the potential of applying the greenhouse gas budget and forest growth in carbon sequestration regulations. With the smart heating algorithm leading only to intermittent heating of the anemometer sensor heads, the risk of biased carbon sequestration estimations due to large data gaps is strongly reduced at SMEAR Estonia.