Local-Scale Valley Wind Retrieval Using an Artificial Neural Network Applied to Routine Weather Observations

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.

Valley Winds at the Local Scale: Correcting Routine Weather Forecast Using Artificial Neural Networks

“…The aim of this study is to build an ANN, using simulated variables (from operational WRF forecasts outputs) as predictors and observations as ground truth (temporary observations used to train the ANN). This study advances the nowcasting work of Duine et al [31] and Dupuy et al [22] by using a combined ANN and numerical simulation technique to forecast local winds by correcting mesoscale simulations.…”

“…The comparison of wind forecasts is based on the metrics described in Dupuy et al [22]. Wind direction forecast evaluation is based on (i) the DACC (Direction Accuracy [46]) metric, which represents the proportion of horizontal winds which do not depart by more than 45°from observations, and (ii) the PC (Proportion Correct) metric, which indicates the proportion of values correctly classified in different wind sectors defined from the observed wind rose to represent the main wind regimes.…”

“…Atmosphere 2021, 1, 0 5 of 20 stable conditions and katabatic flows that have been extensively documented during the KASCADE 2013 campaign [10]. The reader is referred to Dupuy et al [22] for a detailed description of these classic weather conditions over Cadarache.…”

“…Gardner and Dorling [21] presented an overview of their utilization in atmospheric science. ANNs can be used to nowcast some meteorological parameters [22], develop parameterizations used in numerical weather prediction models [23,24], and even improve the representation of subgrid scale processes [25,26]. ANNs have already been used to postprocess wind speed forecasts [27,28].…”

“…They found that, among the permanent measurements available, a valley-scale vertical temperature gradient is a good indicator of the presence of down-valley winds. Dupuy et al [22] improved the method using an ANN. Moreover, a comparison with a widely used postprocessing technique, a multi-linear regression, demonstrated that ANNs are appropriate to calculate the wind in the Cadarache valley.…”