Eine Einleitung in Die Methodik des Ramans Spectra Fiber Optics Distributed Temperature Sensing is enthalten. Fiber-optic Raman distributed temperature sensing (DTS) is a laser light-based measurement technology for measuring temperatures.
INTRODUCTION
SNOW AVALANCHE PROTECTION
- Permanent Protection Measures
- Temporary Protection Measures
- Today’s limits of Avalanche Protection
- Recent Year’s Achievements
From 1977 to 2006, 703 people were killed in avalanches in Switzerland during recreational activities, either in the backcountry or off-piste near ski resorts (Harvey and Zweifel, 2008). The accidents occurred in the backcountry and in ski resorts and were caused by a multitude of distinct avalanche events (www.bergrettung.at).
SNOW STABILITY EVALUATION AND AVALANCHE WARNING
- Snow Avalanche Formation
- Types of Snow Avalanches and of Failure Mechanisms
- Assessment of Snow Avalanche Dangers
- Snow Avalanche Forecasting
The Swiss Avalanche Warning Agency at the SLF and forecasters in France also use models to determine and obtain the snowpack's layer characteristics. The level is defined as significant when the snowpack's stability situation is highly variable and only specific exposures are indicated as dangerous in the bulletin.
SNOW‐ AND AVALANCHE RESEARCH
SNOW PHYSICAL BACKGROUND WITH AN EMPHASIS ON TEMPERATURE
- Heat Transport within an Alpine Seasonal Snow pack
- Energy Exchange at the Snow Surface
- Snow Metamorphism and Bond Formation
- Temperature Related Snow Mechanical Properties
Timescales of metamorphism differ between upper and lower parts of the snowpack due to different temperature settings. The water content in snow and water accumulations within the snowpack and on the ground contribute to the formation of wet sheet piles (McClung and Schaerer, 1993).
TEMPORAL AND SPATIAL VARIABILITY OF SNOW PACK PROPERITES
- Characteristic’s of a Snow Cover’s Variability
- Relevance of a Snow Cover’s Variability to Avalanche Forecasting
- Studying Snow Cover Variability – A Brief Literature Review
Furthermore, although to a much lesser extent, research has been conducted to investigate the changes in the snowpack variability over time (Birkeland and Landry, 2002, Hendrikx et al., 2009). He found that daily fluctuations in air temperatures affected temperatures in the upper parts of the snowpack.
MEASURING AND MODELLING SNOW PACK TEMPERATURES
- Standard Methods for In‐situ Temperature Measurements in Snow
- Snow Cover Models
- Special Temperature Devices
The models use input meteorological data to calculate the physical state of the snowpack. When measured temperatures within the snowpack are available, they are used for model validation rather than as input data (Bartelt et al., 2002).
THEORETICAL BACKGROUND
- Basic Principles of the Method
- Setting up a DTS System at a Site
- Assessing Instrument‐ and System Performance
The two components of the Raman band are called Stokes and Anti-Stokes. One pulse must reach the end of the fiber and backscatter back to the instrument box before the next can be emitted. Which type of instrument is most appropriate depends on the character of the field application (Tyler et al., 2009).
Note that the temperature resolution achievable with a specific instrument also depends on the length of the fiber optic cable along which the measurements are made. Two constant temperature environments are needed, one at the beginning and one at the end of the cable (Tyler et al., 2009); for example, to extract matching sections as described above (chapter 4.1.1). In general, the accuracy of the measured ratio between Stokes- and Anti-Stokes scattering and their respective temperatures is proportional to the number of photons that are collected in the detector in the instrument box.
CASE STUDY ON MAMMOTH MOUNTAIN, CA
- Study Site and Experimental Set‐up
- Data Collection
Therefore, snowfall is lower on the eastern side of the Sierra range, as it lies in the rain shadow. In terms of avalanche climatic zones, which are defined by the temperatures, amounts of snowfall, typical densities of the snow covers and size of snow temperature gradients in an area, the Sierra Nevada can be assigned in the Coastal Zone (Mock and Birkeland, 2000). Both ends of the multimode fiber were accessible in the housing where they could be connected to a DTS instrument box, enabling dual measurements.
Instead of an ice or water bath, a piece of snowpack was chosen as the calibration medium. The beginning and end of the loop of fibers were placed in the container; the turn box, at the end of the cable sheath, was kept on a coil out of the snow and close to the container. Data were stored on board the instrument box and downloaded to a computer for processing and analysis at the end of each measurement session.
DATA POST PROCESSING – QUALITY CONTROL AND CORRECTIONS
INSTRUMENT DRIFT
For each time step, the difference between the lag-corrected thermistor temperatures and the average temperatures measured by the DTS was calculated. Each offset-corrected trace was in good agreement with the traces of the corresponding reference device in both sections, at the 55 m ground coil and at the 30 m calibration coil. Discrepancies between DTS temperatures and thermologger temperatures are due to instrument drift over time.
The small deviations between the absolute values and the model DTS temperatures and the thermolog temperatures are the result of a strong and variable temperature gradient in the calibration section at appropriate times. The deviations from the reference device traces shown in the February data do not only result from instrument drift over time, but also include a temperature offset caused by incorrect calibration (see below, chapter 4.2.3.2, for further explanation). Deviations in the absolute values and pattern of DTS temperatures from the tologger temperatures at the beginning of the February track are the result of a strong temperature gradient in the calibration section at this time.
TEMPERATURE OFFSETS
Poor thermal contact between a VWR thermometer and the calibration coil during calibration caused an additional offset error in the February DTS data. The instrument was told that the calibration temperature was inaccurate because the VWR thermometer data was not representative of the calibration coil. Fig.52: Offset errors, air temperatures and snow depths, February 2009 Figure 52 shows the evolution of temperature offsets related to air temperatures and snow depths above the calibration section.
Strongly varying measurements at the beginning of February correlated with a drop in air temperature on the night of February 4 to 5. At that time, the calibration section was in the upper part of the snowpack; Only around February 10, when the air temperature varied by 15 °C within half a day, did temperature differences between coil and logger show a response.
STEP LOSSES
The blue areas highlight changes in signal strength, which are especially visible in the Anti-Stokes signal and are the result of real temperature changes in the cable environment. Shortly before meter 550 (indicated by the GPS device in the photo), the cable also passed over a small, sharp ground element right next to the corner of the container. Under the heavy load of a deep snowpack, the cable would cross itself and bend over the small sharp ground element, possibly causing increased signal losses in the fiber.
Since measurements were taken in the dual mode, the instrument case could automatically correct for the step losses and reproduce the correct temperature values for the measurements from December to March. Two pieces of cable, at the same temperature, of a length of at least three times the spatial integration scale, one before and one after the step loss would have been required to allow corrections for its effects. Since temperatures along the loops in snow depths of 1 m (cable gauge 132 to 182) and 1.9 m (cable gauge 80 to 130) and along the ground loop below them (cable gauge 345 to 409) were measured before the step loss and equaled. unaffected by the error, they can still be included in later analyzes (chapter 5.3).
RESULTS AND DISCUSSION
DATA ACCURACY
Since small snow temperature variations over time could not be fully excluded in the reference section, especially in December/January when the snowpack was still shallow, temporal standard deviations over the course of only 24 hours were also calculated. The temporal standard deviations over the course of only 24 hours are also shown in Table 4. Tab.3: spatial standard deviations over a constant temperature 55 m section along the fiber optic cable, representative for a temporal resolution of 24 hours.
Tab.4: temporal standard deviations of a 2 m segment within a constant temperature section along the fiber optic cable, representative for measurements at a spatial resolution of 2 m and a temporal resolution of 5 min. Tab.5: temporal standard deviations of a 2 m segment within a constant temperature section along the fiber optic cable, representative for measurements at a spatial resolution of 2 m and a temporal resolution of 24 hours. Over the course of just 24 hours, the standard deviation in December/January is similar to that calculated for the following months.
GROUND TEMPERATURE VARIABILITY
- Spatial Variability of DTS Measured Ground Temperatures
- Evolution of Spatial Ground Temperature Variability in Time
The snow depth in the container was about 1.6 m on 30 December and about 2.5 m on 14 February. 83 and 84: spatial temperature distribution at the base of the snowpack on 30 December and 2 January. 85 and 86: spatial temperature distribution at the base of the snowpack on 3 January and 4 January.
87 and 88: spatial temperature distribution at the base of the snowpack on 6 January and 8 January. 89 and 90: spatial temperature distribution at the base of the snowpack on February 5 and February 15. 91 and 92: spatial temperature distribution at the base of the snowpack on March 8 and 10.
SNOW PACK TEMPERATURE VARIABILITY
No response of snow temperatures to the strongly cooling and then warming air temperatures (Fig. 75) could be detected in this part of the study field. During December/January, the distribution of temperatures in this part of the snowpack remained similar; absolute temperatures. Snow temperatures averaged over 24 hours from this part of the snowpack did not follow the changes in air temperatures at that time (Fig. 75).
The temperatures measured in this part of the snowpack in February did not respond to changing air temperatures at this time. The temperatures measured in this section of the February snowpack showed a response to changing air temperatures at this time. At the end of the season, the variation of snow temperatures in space decreased faster at 1.9 m above ground than at 1 m.
CONCLUSIONS
- STUDYING SNOW TEMPERATURE VARIABILITY
- INSTRUMENT PERFORMANCE
- SNOW TEMPERATURE VARIABILITY – CASE STUDY
- DTS SYSTEMS IN AVALANCHE RESEARCH
The absolute accuracy that can be achieved with this method strongly depends on the quality of the setup and the calibration technique in place. Tracing the exact vertical position of the cables is more difficult and was not attempted at Mammoth Mountain. During a single measurement session, the evolution of temperature distribution patterns can be compared, as well as the distribution of their absolute values.
Temporal and spatial temperature variations and the development of their spatial distribution at the base of the snow cover and in two different snow depths, at 1 m and 1.9 m above the ground, could be successfully monitored in high spatial resolution and continuously. on time. The spatial variability of temperatures developed differently in different snow heights in the same part of the city and in the same time period. Hendrikx, J., Birkeland, K., Clark, M.: Assessing changes in the spatial variability of snowpack fracture propagation propensity over time Cold Regions Science and Technology.
APPENDICES
APPENDIX A – Matlab Routines for DTS Data
APPENDIX B – Configuration Files
APPENDIX C – Instrument and Cable Examples
