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Mesoscale numerical weather prediction models are frequently utilized for wind speed analysis and forecasting in the planning and support of wind power generation. However, high computational costs only allow for routine use up to a kilometer scale, which is sometimes too coarse to support onshore wind power generation in areas with complex orography. To address this, an algorithm was developed in southern Italy to downscale the wind fields output using the weather research and forecasting (WRF) model for the first 250 m above ground level. The algorithm is based on artificial neural networks (ANNs) and uses the WRF model outputs on a 1.2 km regular grid, and the land surface height and orientation on a 240 m regular grid to downscale wind fields to a 240 m regular grid. To train the ANNs, a WRF simulation dataset in large eddy simulation (LES) mode was developed. Particular attention was paid to defining the ANN architectures and analyzing inputs to mitigate overfitting risk while maintaining manageable computation costs. The evaluation of outcomes conducted using independent test datasets from WRF-LES simulations reveals that the wind speed root-mean-square difference (RMSD) is 0.5 m/s over land and 0.2 m/s over the sea surface, respectively, at a spatial resolution of approximately 800 m. These figures are lower than the RMSD values of 1.6 m/s over land and 1.0 m/s over the sea surface, accompanied by a spatial resolution of 1.8 km, which were obtained through comparison with the spline interpolation method. more...

A neural network (NN) approach is proposed to combine future infrared (IASI-NG) and microwave (MWS) observations to retrieve cloud liquid and ice water path. The methodology is applied to simulated IASI-NG and MWS observations in the period January–October 2019. IASI-NG and MWS observations are simulated globally at synoptic hours (00:00, 06:00, 12:00, 18:00 UTC) and on a regular spatial grid (0.125° × 0.125°) from ECMWF 5-generation reanalysis (ERA5). The state-of-the-art σ-IASI and RTTOV radiative transfer codes are used to simulate IASI-NG and MWS observations, respectively, from the earth's state vector given by ERA5. A principal component analysis of the simulated IASI-NG observations is performed. Accordingly, a NN is developed to retrieve cloud liquid and ice water path from a combination of 24 MWS channels and 30 IASI-NG PCs. Validation indicates that this combination results in liquid and ice water path retrievals with overall accuracy of 1.85 10−2 kg/m2 and 1.18 10−2 kg/m2, respectively, and 0.97 correlation with respect to reference values. The root-mean-square error (RMSE) for CLWP results in about 30% of the mean value (5.91 10−2 kg/m2) and 22% of the variability (1-sigma). Similarly, the RMSE for CIWP results in about 41% of the mean value (2.91 10−2 kg/m2) and 22% of the variability. Two more NN are developed, retrieving cloud liquid and ice water path from microwave observations only (24 MWS channels) and infrared observations only (30 IASI-NG PCs), demonstrating quantitatively the advantage of using the combination of infrared and microwave observations with respect to either one alone. more...