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  • American Meteorological Society  (4)
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  • 1
    Publication Date: 2016-03-28
    Description: In the winter of 2012/13, the Katabatic Winds and Stability over Cadarache for the Dispersion of Effluents (KASCADE) observational campaign was carried out in southeastern France to characterize the wind and thermodynamic structure of the (stable) planetary boundary layer (PBL). Data were collected with two micrometeorological towers, a sodar, a tethered balloon, and radiosoundings. Here, this dataset is used to evaluate the representation of the boundary layer in the Weather Research and Forecasting (WRF) Model. In general, it is found that diurnal temperature range (DTR) is largely underestimated, there is a strong negative bias in both longwave radiation components, and evapotranspiration is overestimated. An illustrative case is subjected to a thorough model-physics evaluation. First, five PBL parameterization schemes and two land surface schemes are employed. A marginal sensitivity to PBL parameterization is found, and the sophisticated Noah land surface model represents the extremes in skin temperature better than does a more simple thermal diffusion scheme. In a second stage, sensitivity tests for land surface–atmosphere coupling (through parameterization of z0h/z0m), initial soil moisture content, and radiation parameterization were performed. Relatively strong surface coupling and low soil moisture content result in a larger sensible heat flux, deeper PBL, and larger DTR. The larger sensible heat flux is not supported by the observations, however. It turns out that, for the selected case, a combination of subsidence and warm-air advection is not accurately simulated, but this inaccuracy cannot fully explain the discrepancies found in the WRF simulations. The results of the sensitivity analysis reiterate the important role of initial soil moisture values.
    Print ISSN: 1558-8424
    Electronic ISSN: 1558-8432
    Topics: Geography , Physics
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  • 2
    Publication Date: 2016-07-01
    Description: 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.
    Print ISSN: 1558-8424
    Electronic ISSN: 1558-8432
    Topics: Geography , Physics
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  • 3
    Publication Date: 2019-04-24
    Description: 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.
    Print ISSN: 1558-8424
    Electronic ISSN: 1558-8432
    Topics: Geography , Physics
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  • 4
    Publication Date: 2021-10-08
    Description: This study improves surface wind predictions in an unresolved valley using an artificial neural network (ANN). Forecasting winds in complex terrain with a mesoscale model is challenging. This study assesses the quality of 3-km wind forecasts by the Weather Research and Forecasting (WRF) model and the potential of post-processing by an ANN within the 1-2 km wide Cadarache Valley in southeast France. Operational wind forecasts for 110m above ground level and the near-surface vertical potential temperature gradient with a lead time of 24-48h were used as ANN input. Observed horizontal wind components at 10m within the valley were used as targets during ANN training. We use the Directional ACCuracy (DACC45, wind direction error ≤ 45°) and mean absolute error to evaluate the WRF direct model output and the ANN results. By post-processing, the score for DACC45 improves from 56% in the WRF direct model output to 79% after applying the ANN. Furthermore, the ANN performed well during the day and night, but poorly during the morning and afternoon transitions. The ANN improves the DACC45 at 10m even for poor WRF forecasts (direction bias ≥ 45°) from 42% to 72%. A shorter lead time and finer grid spacing (1 km) showed negligible impact which suggests that a 3 km grid spacing and a 24-48h lead time is effective and relatively cheap to apply. We find that WRF performs well in near-neutral conditions and poorly in other atmospheric stability conditions. The ANN post-treatment consistently improves the wind forecast for all stability classes to a DACC45 of about 80%. The study demonstrates the ability to improve Cadarache valley wind forecasts using an ANN as post-processing for WRF daily forecasts.
    Print ISSN: 0882-8156
    Electronic ISSN: 1520-0434
    Topics: Geography , Physics
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