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Abstract. The automatic and non-supervised detection of the planetary boundary layer height (z PBL ) by means of lidar measurements was widely investigated during the last several years. Despite considerable advances, the experimental detection still presents difficulties such as advected aerosol layers coupled to the planetary boundary layer (PBL) which usually produces an overestimation of the z PBL . To improve the detection of the z PBL in these complex atmospheric situations, we present a new algorithm, called POLARIS (PBL height estimation based on lidar depolarisation). PO-LARIS applies the wavelet covariance transform (WCT) to the range-corrected signal (RCS) and to the perpendicularto-parallel signal ratio (δ) profiles. Different candidates for z PBL are chosen and the selection is done based on the WCT applied to the RCS and δ. We use two ChArMEx (Chemistry-Aerosol Mediterranean Experiment) campaigns with lidar and microwave radiometer (MWR) measurements, conducted in 2012 and 2013, for the POLARIS' adjustment and validation. POLARIS improves the z PBL detection compared to previous methods based on lidar measurements, especially when an aerosol layer is coupled to the PBL. We also compare the z PBL provided by the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model with respect to the z PBL determined with POLARIS and the MWR under Saharan dust events. WRF underestimates the z PBL during daytime but agrees with the MWR during night-time. The z PBL provided by WRF shows a better temporal evolution compared to the MWR during daytime than during night-time.

Solar radiation plays a key role in the atmospheric system but its distribution throughout the atmosphere and at the surface is still very uncertain in atmospheric models, and further assessment is required. In this study, the shortwave downward total solar radiation flux (SWD) predicted by the Weather Research and Forecasting (WRF) Model at the surface is validated over Spain for a 10-yr period based on observations of a network of 52 radiometric stations. In addition to the traditional pointwise validation of modeled data, an original spatially continuous evaluation of the SWD bias is also conducted using a principal component analysis. Overall, WRF overestimates the mean observed SWD by 28.9 W m−2, while the bias of ERA-Interim, which provides initial and boundary conditions to WRF, is only 15.0 W m−2. An important part of the WRF SWD bias seems to be related to a very low cumulus cloud amount in the model and, possibly, a misrepresentation of the radiative impact of this type of cloud.

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A methodology, aimed to be fully operational, for automatic cloud classification based on the synergetic use of a sky camera and a ceilometer is presented. The random forest machine learning algorithm was used to train the classifier with 19 input features: 12 extracted from the sky camera images and 7 from the ceilometer. The method was developed and tested based on a set of 717 images collected at the radiometric stations of the Univ. of Jaén (Spain). Up to nine different types of clouds (plus clear sky) were considered (clear sky, cumulus, stratocumulus, nimbostratus, altocumulus, altostratus, stratus, cirrocumulus, cirrostratus, and cirrus) plus an additional category multicloud, aiming to account for the frequent cases in which the sky is covered by several cloud types. A total of eight experiments was conducted by (1) excluding/including the ceilometer information, (2) including/excluding the multicloud category, and (3) using six or nine different cloud types, aside from the clear‐sky and multicloud category. The method provided accuracies ranging from 45% to 78%, being highly dependent on the use of the ceilometer information. This information showed to be particularly relevant for accurately classifying “cumuliform” clouds and to account for the multicloud category. In this regard, the camera information alone was found to be not suitable to deal with this category. Finally, while the use of the ceilometer provided an overall superior performance, some limitations were found, mainly related to the classification of clouds with similar cloud base height and geometric thickness.

The ability of six microphysical parameterizations included in the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model to represent various macroscopic cloud characteristics at multiple spatial and temporal resolutions is investigated. In particular, the model prediction skills of cloud occurrence, cloud base height, and cloud cover are assessed. When it is possible, the results are provided separately for low‐, middle‐, and high‐level clouds. The microphysical parameterizations assessed are WRF single‐moment six‐class, Thompson, Milbrandt‐Yau, Morrison, Stony Brook University, and National Severe Storms Laboratory double moment. The evaluated macroscopic cloud properties are determined based on the model cloud fractions. Two cloud fraction approaches, namely, a binary cloud fraction and a continuous cloud fraction, are investigated. Model cloud cover is determined by overlapping the vertically distributed cloud fractions following three different strategies. The evaluation is conducted based on observations gathered with a ceilometer and a sky camera located in Jaén (southern Spain). The results prove that the reliability of the WRF model mostly depends on the considered cloud parameter, cloud level, and spatiotemporal resolution. In our test bed, it is found that WRF model tends to (i) overpredict the occurrence of high‐level clouds irrespectively of the spatial resolution, (ii) underestimate the cloud base height, and (iii) overestimate the cloud cover. Overall, the best cloud estimates are found for finer spatial resolutions (1.3 and 4 km with slight differences between them) and coarser temporal resolutions. The roles of the parameterization choice of the microphysics scheme and the cloud overlapping strategy are, in general, less relevant.

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