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- Author or Editor: Pierre Durand x
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Abstract
A sea wind situation was analyzed during the COAST (Cooperative Operations with Acoustic Sounding Techniques) experiment. The thermal internal boundary layer (TIBL) which develops inland from the coast was investigated by an instrumented aircraft and fixed measurements, and by a two dimensional version of a third order turbulence closure model. The two-dimensional structure of the TIBL was demonstrated in the vertical, perpendicular to the shore. The mean quantities (temperature, humidity, and wind), as well as their turbulent moments, were computed and comparison made between experiment and model. The experimental mean fields were well reproduced by the, model. The turbulence fields were reproduced in their general features as well as in their magnitude, but not in local singularities.
Abstract
A sea wind situation was analyzed during the COAST (Cooperative Operations with Acoustic Sounding Techniques) experiment. The thermal internal boundary layer (TIBL) which develops inland from the coast was investigated by an instrumented aircraft and fixed measurements, and by a two dimensional version of a third order turbulence closure model. The two-dimensional structure of the TIBL was demonstrated in the vertical, perpendicular to the shore. The mean quantities (temperature, humidity, and wind), as well as their turbulent moments, were computed and comparison made between experiment and model. The experimental mean fields were well reproduced by the, model. The turbulence fields were reproduced in their general features as well as in their magnitude, but not in local singularities.
Abstract
The authors show how a capacitive device measuring moisture can be used aboard instrumented atmospheric aircraft as an alternate sensor for turbulence measurement. Using a calibrated Lyman-α sensor as a reference, the time response of the capacitive device can be improved in such a way that turbulent latent heat flux and moisture variance can be calculated with a good level of accuracy. This improvement is done by correction of the amplitude as well of the phase of the signal. These corrections are determined from in-flight measurements therefore, they take into account the time response of the sensor itself, as well as its airborne installation.
Abstract
The authors show how a capacitive device measuring moisture can be used aboard instrumented atmospheric aircraft as an alternate sensor for turbulence measurement. Using a calibrated Lyman-α sensor as a reference, the time response of the capacitive device can be improved in such a way that turbulent latent heat flux and moisture variance can be calculated with a good level of accuracy. This improvement is done by correction of the amplitude as well of the phase of the signal. These corrections are determined from in-flight measurements therefore, they take into account the time response of the sensor itself, as well as its airborne installation.
Abstract
A simple relation to diagnose the existence of a thermally driven down-valley wind in a shallow (100 m deep) and narrow (1–2 km wide) valley based on routine weather measurements has been determined. The relation is based on a method that has been derived from a forecast verification principle. It consists of optimizing a threshold of permanently measured quantities to nowcast the thermally driven Cadarache (southeastern France) down-valley wind. Three parameters permanently observed at a 110-m-high tower have been examined: the potential temperature difference between the heights of 110 and 2 m, the wind speed at 110 m, and a bulk Richardson number. The thresholds are optimized using the wind observations obtained within the valley during the Katabatic Winds and Stability over Cadarache for the Dispersion of Effluents (KASCADE) field experiment, which was conducted in the winter of 2013. The highest predictability of the down-valley wind at the height of 10 m (correct nowcasting ratio of 0.90) was found for the potential temperature difference at a threshold value of 2.6 K. The applicability of the method to other heights of the down-valley wind (2 and 30 m) and to summer conditions is also demonstrated. This allowed a reconstruction of the climatology of the thermally driven down-valley wind that demonstrates that the wind exists throughout the year and is strongly linked to nighttime duration. This threshold technique will make it possible to forecast the subgrid-scale down-valley wind from operational numerical weather coarse-grid simulations by means of statistical downscaling.
Abstract
A simple relation to diagnose the existence of a thermally driven down-valley wind in a shallow (100 m deep) and narrow (1–2 km wide) valley based on routine weather measurements has been determined. The relation is based on a method that has been derived from a forecast verification principle. It consists of optimizing a threshold of permanently measured quantities to nowcast the thermally driven Cadarache (southeastern France) down-valley wind. Three parameters permanently observed at a 110-m-high tower have been examined: the potential temperature difference between the heights of 110 and 2 m, the wind speed at 110 m, and a bulk Richardson number. The thresholds are optimized using the wind observations obtained within the valley during the Katabatic Winds and Stability over Cadarache for the Dispersion of Effluents (KASCADE) field experiment, which was conducted in the winter of 2013. The highest predictability of the down-valley wind at the height of 10 m (correct nowcasting ratio of 0.90) was found for the potential temperature difference at a threshold value of 2.6 K. The applicability of the method to other heights of the down-valley wind (2 and 30 m) and to summer conditions is also demonstrated. This allowed a reconstruction of the climatology of the thermally driven down-valley wind that demonstrates that the wind exists throughout the year and is strongly linked to nighttime duration. This threshold technique will make it possible to forecast the subgrid-scale down-valley wind from operational numerical weather coarse-grid simulations by means of statistical downscaling.
Abstract
Airborne measurements are currently used for computing turbulence fluxes of heat and momentum. The method generally used is the eddy correlation technique, which requires sophisticated equipments to calculate the absolute velocities of the air. We used the well-known inertial dissipation method to calculate the turbulent fluxes of heat and momentum from low-level airborne measurements This only requires knowledge of inertial subrange characteristics of velocity and scalars. The method was validated by comparing dissipation fluxes with those computed by the eddy correlation method. The agreement between the two is very good, particularly for heat fluxes. Last, it is shown how the turbulent kinetic energy dissipation rate can be easily calculated, using a single measurement (the attack angle by example), and therefore how turbulent fluxes can be simply calculated from low level airborne measurements.
Abstract
Airborne measurements are currently used for computing turbulence fluxes of heat and momentum. The method generally used is the eddy correlation technique, which requires sophisticated equipments to calculate the absolute velocities of the air. We used the well-known inertial dissipation method to calculate the turbulent fluxes of heat and momentum from low-level airborne measurements This only requires knowledge of inertial subrange characteristics of velocity and scalars. The method was validated by comparing dissipation fluxes with those computed by the eddy correlation method. The agreement between the two is very good, particularly for heat fluxes. Last, it is shown how the turbulent kinetic energy dissipation rate can be easily calculated, using a single measurement (the attack angle by example), and therefore how turbulent fluxes can be simply calculated from low level airborne measurements.
Abstract
We hereby present a new method with which to nowcast a thermally driven, downvalley wind using an artificial neural network (ANN) based on remote observations. The method allows the retrieval of wind speed and direction. The ANN was trained and evaluated using a 3-month winter-period dataset of routine weather observations made in and above the valley. The targeted valley winds feature two main directions (91% of the total dataset) that are aligned with the valley axis. They result from downward momentum transport, channeling mechanisms, and thermally driven flows. A selection procedure of the most pertinent ANN input variables, among the routine observations, highlighted three key variables: a potential temperature difference between the top and the bottom of the valley and the two wind components above the valley. These variables are directly related to the mechanisms that generate the valley winds. The performance of the ANN method improves on an earlier-proposed nowcasting method, based solely on a vertical temperature difference, as well as a multilinear regression model. The assessment of the wind speed and direction indicates good performance (i.e., wind speed bias of −0.28 m s−1 and 84% of calculated directions stray from observations by less than 45°). Major sources of error are due to the misrepresentation of cross-valley winds and very light winds. The validated method was then successfully applied to a 1-yr period with a similar performance. Potentially, this method could be used to downscale valley wind characteristics for unresolved valleys in mesoscale simulations.
Abstract
We hereby present a new method with which to nowcast a thermally driven, downvalley wind using an artificial neural network (ANN) based on remote observations. The method allows the retrieval of wind speed and direction. The ANN was trained and evaluated using a 3-month winter-period dataset of routine weather observations made in and above the valley. The targeted valley winds feature two main directions (91% of the total dataset) that are aligned with the valley axis. They result from downward momentum transport, channeling mechanisms, and thermally driven flows. A selection procedure of the most pertinent ANN input variables, among the routine observations, highlighted three key variables: a potential temperature difference between the top and the bottom of the valley and the two wind components above the valley. These variables are directly related to the mechanisms that generate the valley winds. The performance of the ANN method improves on an earlier-proposed nowcasting method, based solely on a vertical temperature difference, as well as a multilinear regression model. The assessment of the wind speed and direction indicates good performance (i.e., wind speed bias of −0.28 m s−1 and 84% of calculated directions stray from observations by less than 45°). Major sources of error are due to the misrepresentation of cross-valley winds and very light winds. The validated method was then successfully applied to a 1-yr period with a similar performance. Potentially, this method could be used to downscale valley wind characteristics for unresolved valleys in mesoscale simulations.
Abstract
Since the early 1990s, Météo-France has used an automatic system combining three numerical models to simulate meteorological parameters, snow cover stratification, and avalanche risk at various altitudes, aspects, and slopes for a number of mountainous regions in France. Given the lack of sufficient directly observed long-term snow data, this “SAFRAN”–Crocus–“MEPRA” (SCM) model chain, usually applied to operational avalanche forecasting, has been used to carry out and validate retrospective snow and weather climate analyses for the 1958–2002 period. The SAFRAN 2-m air temperature and precipitation climatology shows that the climate of the French Alps is temperate and is mainly determined by atmospheric westerly flow conditions. Vertical profiles of temperature and precipitation averaged over the whole period for altitudes up to 3000 m MSL show a relatively linear variation with altitude for different mountain areas with no constraint of that kind imposed by the analysis scheme itself. Over the observation period 1958–2002, the overall trend corresponds to an increase in the annual near-surface air temperature of about 1°C. However, variations are large at different altitudes and for different seasons and regions. This significantly positive trend is most obvious in the 1500–2000-m MSL altitude range, especially in the northwest regions, and exhibits a significant relationship with the North Atlantic Oscillation index over long periods. Precipitation data are diverse, making it hard to identify clear trends within the high year-to-year variability.
Abstract
Since the early 1990s, Météo-France has used an automatic system combining three numerical models to simulate meteorological parameters, snow cover stratification, and avalanche risk at various altitudes, aspects, and slopes for a number of mountainous regions in France. Given the lack of sufficient directly observed long-term snow data, this “SAFRAN”–Crocus–“MEPRA” (SCM) model chain, usually applied to operational avalanche forecasting, has been used to carry out and validate retrospective snow and weather climate analyses for the 1958–2002 period. The SAFRAN 2-m air temperature and precipitation climatology shows that the climate of the French Alps is temperate and is mainly determined by atmospheric westerly flow conditions. Vertical profiles of temperature and precipitation averaged over the whole period for altitudes up to 3000 m MSL show a relatively linear variation with altitude for different mountain areas with no constraint of that kind imposed by the analysis scheme itself. Over the observation period 1958–2002, the overall trend corresponds to an increase in the annual near-surface air temperature of about 1°C. However, variations are large at different altitudes and for different seasons and regions. This significantly positive trend is most obvious in the 1500–2000-m MSL altitude range, especially in the northwest regions, and exhibits a significant relationship with the North Atlantic Oscillation index over long periods. Precipitation data are diverse, making it hard to identify clear trends within the high year-to-year variability.
Abstract
Since the early 1990s, Météo-France has used an automatic system combining three numerical models to simulate meteorological parameters, snow cover stratigraphy, and avalanche risk at various altitudes, aspects, and slopes for a number of mountainous regions (massifs) in the French Alps and the Pyrenees. This Système d’Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN)–Crocus–Modèle Expert de Prévision du Risque d’Avalanche (MEPRA) model chain (SCM), usually applied to operational daily avalanche forecasting, is here used for retrospective snow and climate analysis. For this study, the SCM chain used both meteorological observations and guess fields mainly issued from the newly reanalyzed atmospheric model 40-yr ECMWF Re-Analysis (ERA-40) data and ran on an hourly basis over a period starting in the winter of 1958/59 until recent past winters. Snow observations were finally used for validation, and the results presented here concern only the main climatic features of the alpine modeled snowfields at different spatial and temporal scales. The main results obtained confirm the very significant spatial and temporal variability of the modeled snowfields with regard to certain key parameters such as those describing ground coverage or snow depth. Snow patterns in the French Alps are characterized by a marked declining gradient from the northwestern foothills to the southeastern interior regions. This applies mainly to both depths and durations, which exhibit a maximal latitudinal variation at 1500 m of about 60 days, decreasing strongly with the altitude. Enhanced at low elevations, snow depth shows a mainly negative temporal variation over the study period, especially in the north and during late winters, while the south exhibits more smoothed features. The number of days with snow on the ground shows also a significant general signal of decrease at low and midelevation, but this signal is weaker in the south than in the north and less visible at high elevation. Even if a statistically significant test cannot be performed for all elevations and areas, the temporal decrease is present in all the studied quantities. Concerning snow duration, this general decrease can also be interpreted as a sharp variation of the mean values at the end of the 1980s, inducing a step effect in its time series rather than a constant negative temporal trend. The results have also been interpreted in terms of potential for a viable ski industry, especially in the southern areas, and for different changing climatic conditions. Presently, French downhill ski resorts are economically viable from a range of about 1200 m MSL in the northern foothills to 2000 m in the south, but future prospects are uncertain. In addition, no clear and direct relationship between the North Atlantic Oscillation (NAO) or the ENSO indexes and the studied snow parameters could be established in this study.
Abstract
Since the early 1990s, Météo-France has used an automatic system combining three numerical models to simulate meteorological parameters, snow cover stratigraphy, and avalanche risk at various altitudes, aspects, and slopes for a number of mountainous regions (massifs) in the French Alps and the Pyrenees. This Système d’Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN)–Crocus–Modèle Expert de Prévision du Risque d’Avalanche (MEPRA) model chain (SCM), usually applied to operational daily avalanche forecasting, is here used for retrospective snow and climate analysis. For this study, the SCM chain used both meteorological observations and guess fields mainly issued from the newly reanalyzed atmospheric model 40-yr ECMWF Re-Analysis (ERA-40) data and ran on an hourly basis over a period starting in the winter of 1958/59 until recent past winters. Snow observations were finally used for validation, and the results presented here concern only the main climatic features of the alpine modeled snowfields at different spatial and temporal scales. The main results obtained confirm the very significant spatial and temporal variability of the modeled snowfields with regard to certain key parameters such as those describing ground coverage or snow depth. Snow patterns in the French Alps are characterized by a marked declining gradient from the northwestern foothills to the southeastern interior regions. This applies mainly to both depths and durations, which exhibit a maximal latitudinal variation at 1500 m of about 60 days, decreasing strongly with the altitude. Enhanced at low elevations, snow depth shows a mainly negative temporal variation over the study period, especially in the north and during late winters, while the south exhibits more smoothed features. The number of days with snow on the ground shows also a significant general signal of decrease at low and midelevation, but this signal is weaker in the south than in the north and less visible at high elevation. Even if a statistically significant test cannot be performed for all elevations and areas, the temporal decrease is present in all the studied quantities. Concerning snow duration, this general decrease can also be interpreted as a sharp variation of the mean values at the end of the 1980s, inducing a step effect in its time series rather than a constant negative temporal trend. The results have also been interpreted in terms of potential for a viable ski industry, especially in the southern areas, and for different changing climatic conditions. Presently, French downhill ski resorts are economically viable from a range of about 1200 m MSL in the northern foothills to 2000 m in the south, but future prospects are uncertain. In addition, no clear and direct relationship between the North Atlantic Oscillation (NAO) or the ENSO indexes and the studied snow parameters could be established in this study.
BOREAL—A Fixed-Wing Unmanned Aerial System for the Measurement of Wind and Turbulence in the Atmospheric Boundary LayerAbstract
An instrumentation package for wind and turbulence observations in the atmospheric boundary layer on an unmanned aerial vehicle (UAV) called BOREAL has been developed. BOREAL is a fixed-wing UAV built by BOREAL company, which weighs up to 25 kg (5 kg of payload) and has a wingspan of 4.2 m. With a light payload and optimal weather conditions, it has a flight endurance of 9 h. The instrumental payload was designed in order to measure every parameter required for the computation of the three wind components, at a rate of 100 s−1, which is fast enough to capture turbulence fluctuations: a GPS–inertial measurement unit (IMU) platform measures the three components of the groundspeed a well as the attitude angles; the airplane nose has been replaced by a five-hole probe in order to measure the angles of attack and sideslip, according to the so-called radome technique. This probe was calibrated using computational fluid dynamics (CFD) simulations and wind tunnel tests. The remaining instruments are a Pitot tube for static and dynamic pressure measurement and temperature/humidity sensors in dedicated housings. The optimal airspeed at which the vibrations are significantly reduced to an acceptable level was defined from qualification flights. With appropriate flight patterns, the reliability of the mean wind estimates, through self-consistency and comparison with observations performed at 60 m on an instrumented tower could be assessed. Promising first observations of turbulence up to frequencies around 10 Hz and corresponding to a spatial resolution to the order of 3 m are hereby presented.
Abstract
An instrumentation package for wind and turbulence observations in the atmospheric boundary layer on an unmanned aerial vehicle (UAV) called BOREAL has been developed. BOREAL is a fixed-wing UAV built by BOREAL company, which weighs up to 25 kg (5 kg of payload) and has a wingspan of 4.2 m. With a light payload and optimal weather conditions, it has a flight endurance of 9 h. The instrumental payload was designed in order to measure every parameter required for the computation of the three wind components, at a rate of 100 s−1, which is fast enough to capture turbulence fluctuations: a GPS–inertial measurement unit (IMU) platform measures the three components of the groundspeed a well as the attitude angles; the airplane nose has been replaced by a five-hole probe in order to measure the angles of attack and sideslip, according to the so-called radome technique. This probe was calibrated using computational fluid dynamics (CFD) simulations and wind tunnel tests. The remaining instruments are a Pitot tube for static and dynamic pressure measurement and temperature/humidity sensors in dedicated housings. The optimal airspeed at which the vibrations are significantly reduced to an acceptable level was defined from qualification flights. With appropriate flight patterns, the reliability of the mean wind estimates, through self-consistency and comparison with observations performed at 60 m on an instrumented tower could be assessed. Promising first observations of turbulence up to frequencies around 10 Hz and corresponding to a spatial resolution to the order of 3 m are hereby presented.
