Craig D. Smith*, Environment and Climate Change Canada, Saskatoon, Canada,

Transcription

1 Exploring the utility of snow depth sensor measurement qualifier output to increase data quality and sensor capability: lessons learned during WMO SPICE Craig D. Smith*, Environment and Climate Change Canada, Saskatoon, Canada, Samuel Buisan, AEMET, Zaragoza, Spain Anna Kontu, FMI, Sodankylä, Finland Lauren Arnold, Environment and Climate Change Canada, Saskatoon, Canada Javier Alastrué, AEMET, Zaragoza, Spain José Luís Collado, AEMET, Zaragoza, Spain Samuel Morin, Météo-France, Grenoble, France Rodica Nitu, Environment and Climate Change Canada, Toronto, Canada Yves-Alain Roulet, MétéoSuisse, Payerne, Switzerland *corresponding author Abstract: During the WMO SPICE intercomparison project, two snow depth sensors under test (SUT) had the capability to output measurement qualifier information for each measurement made by the sensor. The SR50A from Campbell Scientific, an ultrasonic sensor, outputs a quality value associated with the uncertainty of the measurement as determined by the sensor s internal signal processing. The SHM30 from Lufft/Jenoptik, an optical sensor, outputs a value related to the strength of the return optical signal. Each of these measurement qualifiers are influenced by different environmental factors and can be used in various ways to either improve the snow depth data quality or increase the utility of the sensor. For the SR50A, understanding the impact of precipitation, precipitation rates, wind, blowing snow, and other circumstances on quality values could improve quality control procedures and increase data quality. For the SHM30, the signal strength output, which changes with changing optical properties of the target, could be used to identify the occurrence of light snowfall even before the accumulation is measureable by the sensor. 1. Introduction Some snow depth sensors are capable of outputting a sensor measurement qualifier that may be used to improve (or at least assess) the measurement quality or even increase the measurement capability of the sensor. This analysis looks at measurement qualifier information from two different sensors that were involved in the WMO-SPICE intercomparison and assessment: the Campbell Scientific SR50A (or SR50ATH, depending on site) and the Jenoptik/Lufft SHM30. These two sensors have different measurement principles (the SR50A being ultrasonic and the SHM30 being optical) and therefore their derived measurement qualifiers serve different purposes. In this analysis, we attempt to determine some of the environmental factors that influence the quality values returned by the SR50A and make some reference as to how the sensor quality values impact data quality control. The SHM30 signal strength data

2 collected during SPICE, on the other hand, are used to explore methods to increase the utility of the instrument to detect the presence of snow during transition periods. The SR50A quality values are a function of the sensor s proprietary interpretation of the sonic echo quality used in the measurement process. The manufacturer provides a scale to interpret the meaning of the quality value such that the measurement will fall into one of four categories: Good, Reduced Echo Strength, High Uncertainty and Not Able to Read. This analysis looks at the frequency distribution of the SR50A quality values as measured at the Formigal, Col de Porte, Sodankylӓ, and CARE SPICE sites as they relate to site environmental conditions, and then cross references those frequency distributions with the SPICE quality control (QC) flags (which identify data as out-of-range or suspicious). The signal strength (or signal intensity) output from the SHM30 is a measure of the wave intensity of the signal reflected from the surface target. The signal strength is influenced by the temperature of the instrument but largely depends on the optical properties of the surface target. It was demonstrated by de Haijj (2011) how responses in sensor signal strength could be used to identify the presence of new snow on bare targets (and conversely when snow covered targets become bare) and following this, suggests a technique to use the signal strength output to eliminate false alarms in snow depth measurements. This current analysis looks at how the SHM30 signal strength changes during the transition from a bare to a snow covered target (and vice versa) using a variety of surface target types during the SPICE intercomparison. The SHM30 sensors were installed at CARE (over grey plastic targets), Sodankylä (over green artificial turf targets), and Col de Porte (over natural ground consisting of mowed grass). Results explore the sensor s capability to function in this capacity and how it is impacted by the type of surface target. Many of the results of these analyses are only summarized here but included in full in the SPICE final report. 2. Results 2.1 SR50A/TH Quality Number Analysis At Formigal, the height of the SR50A installation is 4 m above the bare ground and snow depths during the analysis season of 2014/2015 exceeded 250 cm. The frequency distribution of the sensor quality numbers showed that only 71% of the data was classified as Good, nearly 27% had Reduced Echo Strength and almost 2% was High Uncertainty. The percentage of Good data was substantially lower here than at the other sites and there was a definite seasonal pattern in the distribution, with lower percentages of Good data occurring during the melt period (59% in April and 47% in May). Associated with this was a simultaneous increase in the percentage of data categorized as Reduced Echo rather than an increase in the frequency of High Uncertainty. The impact of precipitation on the frequency distribution was done using the SPICE Site Event Data Set (SEDS) and the Site Non-Event Data Set (SNEDS), derived from the SPICE reference gauge, to differentiate between high probability precipitation occurrences and non-occurrences. The results (Figure 1) show a

3 decrease in the frequency of Good quality values and the concurrent increase in the frequency of Reduced Echo, High Uncertainty and Not Able to Read quality values. Following this, the percentage of Good quality values decreased to 41% when snowfall accumulations exceeded 2 cm during a 30 minute period. Precipitation No Precipitation Figure 1: Frequency distribution of measurement quality values for the SR50A at Formigal during the 2014/2015 winter season for precipitating events (left) defined by the SEDS and non-precipitating events (right) defined by the SNEDS. Similar results are seen for Col de Porte where the SR50ATH is mounted at a height of 4 m to accommodate a snow depth that exceeded 170 cm during the 2014/2015 intercomparison. At Col de Porte, the percentage of Good quality values for the season was 87% with 12% of the observations categorized as Reduced Echo. However, the seasonal shift in quality values during snow melt that occurred at Formigal did not occur at Col de Porte. Precipitation and non-precipitation occurrences were defined using a high quality precipitation product (Morin et al., 2012) rather than the SPICE SEDS/SNEDS which were unavailable due to the absence of the SPICE reference gauge at this site. Like Formigal, precipitation did result in a shift in the frequency distribution (Figure 2) although not as drastic as at Formigal. Also, similar to Formigal, the frequency of Good values decreased to 48% during snowfall with accumulations exceeding 2 cm during a 30 minute period.

4 Precipitation No Precipitation Figure 2: Frequency distribution of measurement quality values for the SR50ATH at Col de Porte during the 2014/2015 winter season for precipitating events (left) and nonprecipitating events (right). At Sodankylӓ, two SR50ATH sensors are mounted at a height of 2 m with the maximum snow depth for either season just exceeding 80 cm. The frequency of Good quality values is much higher than at the alpine sites averaging over 91% (for the two sensors). Precipitation has a notable effect on the distribution as shown in Figure 3, which is broken down by sensor and by season. Here, the precipitation and non-precipitation events are defined by the SPICE SEDS and SNEDS. On average, the drop in Good quality values with the occurrence of precipitation was approximately 7.5% with the majority of that shift being an increase in the Reduced Echo category. As with the previous sites, the frequency of Good quality values decreases with increased precipitation rate. For this analysis, precipitation rate was established with the SEDS and divided into rates above and below 1 mm/hr. Precipitation rates greater than 1 mm/hr resulted in a decrease in the Good quality values of 3-8% as compared to precipitation rates less than 1 mm/hr.

5 Figure 3: Frequency distribution of measurement quality values for two sensors over two seasons (2013/2014 on the left and 2014/2015 on the right) at Sodankylӓ for precipitating events (left panels) defined by the SEDS and non-precipitating events (right panels) defined by the SNEDS. At CARE, the sensors are mounted at 2 m and the maximum snow depth was approximately 40 cm (25 cm) in 2013/2014 (2014/2015). Of the four sites, CARE exhibited the highest frequency of Good quality values, averaging approximately 97%. Figure 4 shows the difference in frequency distribution for precipitation and nonprecipitation. The impact of precipitation is even less than it is at Sodankylӓ and the impact of increased precipitation rate is almost nil. Also, with CARE being a windier site with greater occurrence of blowing snow, the impacts of increased wind speed on the quality number distribution were examined, but found to be negligible. The next phase of this analysis, now that we have some idea as to the causes of decreased SR50A quality numbers, was to cross-reference the frequency distribution with the SPICE data QC flags. The objective was to provide some insight on how to use the SR50A measurement quality values to improve quality control procedures. In theory, there should be a high correlation between instrument determined Reduced Echo and High Uncertainty quality values and values flagged as suspicious or erroneous by the SPICE QC process. This was tested by using contingency tables and the results are summarized here. When considering the entire dataset, there is a high percentage (> 72%) of data that is flagged as Good by both the instrument and the SPICE quality control process. There is a very small percentage (< 1%) that is flagged as Not Good (i.e. either Reduced Echo or High Uncertainty ) by the sensor and Suspicious by the SPICE QC process. This means that there is a large percentage of the data that both methods agree are Good and a very small percentage of the data that both methods agree are Not Good or Suspicious. The biggest discrepancy is at Formigal where approximately 27% of the data is qualified by the sensor as Not Good but is not being flagged as

6 Figure 4: Frequency distribution of measurement quality values for three sensors over two seasons (2013/2014 on the left and 2014/2015 on the right) at CARE for precipitating events (left panels) defined by the SEDS and non-precipitating events (right panels) defined by the SNEDS. Suspicious by the QC process. Overall, the percentage of data output as Good by the sensor and then identified as Suspicious by the QC process is usually (considerably) less than 0.3%. This is the portion of bad data that would be included in the data set if the data QC was based on quality numbers alone. When only considering data with a Good quality number, more than 99% of this data is also flagged as Good by the SPICE QC process. Most of the data (> 98%) with Reduced Certainty quality numbers are also flagged as Good by the SPICE QC process. Finally, over 88% of data with High Uncertainty quality numbers are also flagged as Good by the SPICE QC process. This means that a substantial amount of good data would be removed from the data set if the user relied only on the quality numbers for QC.

7 2.2 SHM30 Signal Strength Analysis Examples of the behavior of the SHM30 signal strength output in reaction to the first light snow on the bare targets are examined for the sensors at Sodankylä, CARE, and Col de Porte. Figure 5a shows the reaction of the SHM30 signal strength output to light snow on the green artificial turf targets at Sodankylä from October, The signal strength shows a distinct and rapid increase in response to light snow on 13-October (< 1 cm) which barely covers the surface target (Figure 5b). The signal strength is then shown to decrease as this light snow melts, returning close to a baseline value when the target becomes bare (Figure 5c), repeating the cycle for the next relatively light event (< 2 cm) on 14-October (Figure 5d). a) b) c) d) Figure 5: a) Behaviour of the SHM30 signal strength output over artificial turf targets at Sodankylä in response to b) light snowfall on 13-Oct, c) melting on 13-Oct, and d) new snow on 14-Oct.

8 The signal strength behavior is not the same at CARE, where surface targets are a light grey plastic. Figure 6 shows that the baseline value for the signal strength (when there is no snow on the target) is substantially higher than at Sodankylä and actually drops in reaction to the light snow that occurs on 14-Nov. Although the transition from bare target to snow covered target is detectable in the signal, it is much less distinct that at Sodankylä. Figure 6: Behaviour of the SHM30 signal strength output over grey plastic targets at CARE during first seasonal snowfall in Nov Over the mown grass target area at Col de Porte, the SHM30 signal strength reaction to new snow (Figure 7) is more similar to Sodankylä than CARE since the colour of the bare targets are quite similar. Col de Porte shows a very distinct jump in signal strength late into the day of 4-November as snow depth increases from 0 to 2.5 cm (after correction for the zero offset error, i.e. negative snow depth, shown in Figure 7). Following melt that occurs into 5-November, the signal strength returns close to a baseline value suggesting that the target becomes bare. The signal strength jumps again later on 5-November in response to the next snowfall event. Another interesting feature to point out in Figure 7 is the slight increase in snow depth immediately following melt early in the day on 5-November. The small increase in snow depth without a corresponding increase in signal strength is most likely a result of the grass rebounding after the snow has completely melted. This is an example of how the signal strength output could be used for quality control as demonstrated by de Haij (2011).

9 Figure 7: Behaviour of the SHM30 signal strength output over mown natural grass at Col de Porte during first seasonal snowfall in Nov The reactions of the SHM30 signal strengths to snow melt at season end, as the targets go from snow covered to snow free, are similar to the start of the season, although generally not as distinct. Figure 8 shows examples from each of the three sites. The drop in signal strength to baseline at Sodankylä (Figure 8a) isn t large but it is distinguishable from the noise. Once the target is snow free, the baseline signal becomes very apparent (as a flat line) and largely consistent. CARE (Figure 8b), on the other hand, shows a signal decrease during snow melt as the optical properties of the melting snow change (i.e. dirty snow and slush) but jump when the target finally becomes snow free. This jump is also distinguishable but the baseline values are quite noisy. Finally, the change in signal strength at Col de Porte (Figure 8c) is very subtle for unknown reasons but the small drop early on 20-April is related to the target area becoming snow free as shown in Figure 8d.

10 a) b) c) d) Figure 8: Behaviour of the SHM30 signal strength output as the snow covered targets transition to snow free at a) Sodankylä (artificial turf), b) CARE (plastic) and c) Col de Porte (mown natural grass). The site photo from Col de Porte in d) corresponds with the small drop in signal strength on 20-April (where the circled target area is determined from the visible laser beam). 3. Conclusions Analysis of the SR50A quality number output has shown that the occurrence of precipitation has the biggest impact on the sensor s self-diagnosed measurement quality. Although the algorithm that the sensor uses to diagnose measurement quality is proprietary, the sensor appears to have more difficulty making a Good measurement when it is snowing. This is exacerbated when snowfall rates are high. This could be because of hydrometeors in the sensor path or because of degradation of a solid reflector with accumulation of low density snow, as suggested in the instrument manual. Either way, the impacts are greater when the sensor installation is high (as at Formigal and Col de Porte) and decreases when the sensor installation is low (as at Sodankylä and CARE). In fact, the impact of snowfall and increased precipitation rate is negligible at CARE. A seasonal degradation of the quality numbers only seems to

11 occur at Formigal and is possibly related to the rapid deterioration of the target area during rapid ablation, possibly related to rainfall onto the snow surface. This needs to be explored further. Generally, at all sites, a decrease in the frequency of Good quality numbers usually means an increase in the frequency of Reduced Echo strength and not a large increase in the frequency of High Uncertainty numbers. Intercomparison between the sensor s quality number output and the SPICE quality control flags suggests a frequent overlap of what the sensor determines is a Good measurement and what the quality control process determines is a good measurement. Furthermore, a large proportion of sensor diagnosed Reduced Echo and High Uncertainty measurements are not flagged by the QC process. Although the frequency of the QC process flagging High Uncertainty measurements as suspicious increases a small amount, the overlap is still quite small with only about 12% of these measurements actually being flagged as suspicious. This suggests that using the instrument quality numbers for quality control would lead to the rejection of a substantial amount of good measurements. It is therefore suggested that the quality numbers only be used as a guide for quality control or as a diagnostic tool and that more guidance on the use of this data should be requested from the manufacturer. This analysis has also demonstrated the SHM30 s capability of detecting light snow events on bare targets (and conversely, the timing of the transition from snow covered to bare targets) through the behaviour of the signal strength output. This, however, was confirmed to be target dependent with the reaction in the signal strength output more distinct when the target colour is darker (i.e. green artificial turf or natural grass rather than grey plastic). The reaction of the sensor to snow on the grey plastic targets was quite different from the darker targets with the baseline (no snow) signal strength output substantially higher. New snow actually resulted in a decrease in signal strength, opposite of what was observed on the darker targets. Based on these results, if the detection of first and light snowfalls and the timing of zero snow cover are important to the sensor user, it is suggested that they use a target (whether natural or artificial) with optical properties similar to natural grass. References de Haij, Marijn: Field test of the Jenoptik SHM30 laser snow depth sensor, Technical report TR-325, Koninklijk Nederlands Meteorologisch Instituut, Morin, S., Lejeune, Y., Lesaffre, B., Panel, J.-M., Poncet, D., David, P., Sudul, M. : An 18-yr long ( ) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte,France, 1325 m alt.) for driving and evaluating snowpack models, Earth Syst. Sci. Data, 4, 13 21,