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Applications of weather radar data to complex orography are manifold, as are the problems. The difficulties start with the choice of suitable locations for the radar sites and their construction, which often involves long transport routes and harsh weather conditions. The next challenge is the 24/7 operation and maintenance of the remote, unmanned mountain stations, with high demands on the availability and stability of the hardware. The data processing and product generation also require solutions that have been specifically designed and optimised in a mountainous region. The reflection and shielding of the beam by the mountains, in particular, pose great challenges. This review article discusses the main problems and sources of error and presents solutions for the application of weather radar technology in complex orography. The review is focused on operational radars and practical applications, such as nowcasting and the automatic warning of thunderstorms, heavy rainfall, hail, flash floods and debris flows. The presented material is based, to a great extent, on experience collected by the authors in the Swiss Alps. The results show that, in spite of the major difficulties that emerge in mountainous regions, weather radar data have an important value for many practical quantitative applications.

Quantitative Precipitation Estimates (QPEs) from the Integrated Multisatellite Retrievals for GPM (IMERG) provide crucial information about the spatio-temporal distribution of precipitation in semiarid regions with complex orography, such as Catalonia (NE Spain). The network of automatic weather stations of the Meteorological Service of Catalonia is used to assess the performance of three IMERG products (Early, Late and Final) at different time scales, ranging from yearly to sub-daily periods. The analysis at a half-hourly scale also considered three different orographic features (valley, flat and ridgetop), diverse climatic conditions (BSk, Csa, Cf and Df) and five categories related to rainfall intensity (light, moderate, intense, very intense and torrential). While IMERG_E and IMERG_L overestimate precipitation, IMERG_F reduces the error at all temporal scales. However, the calibration to which a Final run is subjected causes underestimation regardless in some areas, such as the Pyrenees mountains. The proportion of false alarms is a problem for IMERG, especially during the summer, mainly associated with the detection of false precipitation in the form of light rainfall. At sub-daily scales, IMERG showed high bias and very low correlation values, indicating the remaining challenge for satellite sensors to estimate precipitation at high temporal resolution. This behaviour was more evident in flat areas and cold semi-arid climates, wherein overestimates of more than 30% were found. In contrast, rainfall classified as very heavy and torrential showed significant underestimates, higher than 80%, reflecting the inability of IMERG to detect extreme sub-daily precipitation events.

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows us to estimate direct, diffuse, and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterization techniques and allows the generation of high-quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data, and its resolution is two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

This study examined the effect of atmospheric, topographic, and Bidirectional Reflectance Distribution Function (BRDF) corrections of Sentinel-2 images implemented in Google Earth Engine (GEE) for use in land cover classification. The study was carried out in an area of complex orography in northern Spain and made use of the Spanish National Forest Inventory plots and other systematically located plots to cover non-forest classes. A total of 2991 photo-interpreted ground plots and 15 Sentinel-2 images, acquired in summer at a spatial resolution of 10–20 m per pixel, were used for this purpose. The overall goal was to determine the optimal level of image correction in GEE for subsequent use in time series analysis of images for accurate forest cover classification. Particular attention was given to the classification of cover by the major commercial forest species: Eucalyptus globulus, Eucalyptus nitens, Pinus pinaster, and Pinus radiata. The Second Simulation of the Satellite Signal in the Solar Spectrum (Py6S) algorithm, used for atmospheric correction, provided the best compromise between execution time and image size, in comparison with other algorithms such as Sentinel-2 Level 2A Processor (Sen2Cor) and Sensor Invariant Atmospheric Correction (SIAC). To correct the topographic effect, we tested the modified Sun-canopy-sensor topographic correction (SCS + C) algorithm with digital elevation models (DEMs) of three different spatial resolutions (90, 30, and 10 m per pixel). The combination of Py6S, the SCS + C algorithm and the high-spatial resolution DEM (10 m per pixel) yielded the greatest precision, which demonstrated the need to match the pixel size of the image and the spatial resolution of the DEM used for topographic correction. We used the Ross-Thick/Li-Sparse-Reciprocal BRDF to correct the variation in reflectivity captured by the sensor. The BRDF corrections did not significantly improve the accuracy of the land cover classification with the Sentinel-2 images acquired in summer; however, we retained this correction for subsequent time series analysis of the images, as we expected it to be of much greater importance in images with larger solar incidence angles. Our final proposed dataset, with image correction for atmospheric (Py6S), topographic (SCS + C), and BRDF (Ross-Thick/Li-Sparse-Reciprocal BRDF) effects and a DEM of spatial resolution 10 m per pixel, yielded better goodness-of-fit statistics than other datasets available in the GEE catalogue. The Sentinel-2 images currently available in GEE are therefore not the most accurate for constructing land cover classification maps in areas with complex orography, such as northern Spain.

The present study explores the cloud microphysics (MPs) impact on the simulation of two convective rainfall events (CREs) over the complex topography of Andes mountains, using the Weather Research and Forecasting- Advanced Research (WRF-ARW) model. The events occurred on December 29 2015 (CRE1) and January 7 2016 (CRE2). Six microphysical parameterizations (MPPs) (Thompson, WSM6, Morrison, Goddard, Milbrandt and Lin) were tested, which had been previously applied in complex orography areas. The one-way nesting technique was applied to four domains, with horizontal resolutions of 18, 6, and 3 km for the outer ones, in which cumulus and MP parameterizations were applied, while for the innermost domain, with a resolution of 0.75 km, only MP parameterization was used. It was integrated for 36 h with National Centers for Environmental Prediction (NCEP Final Operational Global Analysis (NFL) initial conditions at 00:00 UTC (Coordinated Universal Time). The simulations were verified using Geostationary Operational Environmental Satellites (GOES) brightness temperature, Ka band cloud radar, and surface meteorology variables observed at the Huancayo Observatory. All the MPPs detected the surface temperature signature of the CREs, but for CRE2, it was underestimated during its lifetime in its vicinity, matching well after the simulated event. For CRE1, all the schemes gave good estimations of 24 h precipitation, but for CRE2, Goddard and Milbrandt underestimated the 24 h precipitation in the inner domain. The Morrison and Lin configurations reproduced the general dynamics of the development of cloud systems for the two case studies. The vertical profiles of the hydrometeors simulated by different schemes showed significant differences. The best performance of the Morrison scheme for both case studies may be related to its ability to simulate the role of graupel in precipitation formation. The analysis of the maximum reflectivity field, cloud top distribution, and vertical structure of the simulated cloud field also shows that the Morrison parameterization reproduced the convective systems consistently with observations.

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.

Climate Monitoring Satellite Application Facility (CM SAF) surface solar irradiance (SSI) products were compared with ground-based observations over the Piedmont region (north-western Italy) for the period 1990–2016. These products were SARAH-2.1 (Surface Solar Radiation DataSet—Heliosat version 2.1) and CLARA-A2 (Cloud, Albedo and Surface Radiation dataset version A2). The aim was to contribute to the discussion on the representativeness of satellite SSI data including a focus on high-elevation areas. The comparison between SSI averages shows that for low OCI (orographic complexity index) stations, satellite series have higher values than corresponding ground-based observations, whereas for high OCI stations, SSI values for satellite records are mainly lower than for ground stations. The comparison between SSI anomalies highlights that satellite records have an excellent performance in capturing SSI day-to-day variability of ground-based low OCI stations. In contrast, for high OCI stations, the agreement is much lower, due to the higher uncertainty in both satellite and ground-based records. Finally, if the temporal trends are considered, average low-elevation ground-based SSI observations show a positive trend, whereas satellite records do not highlight significant trends. Focusing on high-elevation stations, the observed trends for ground-based and satellite records are more similar with the only exception of summer. This divergence seems to be due to the relevant role of atmospheric aerosols on SSI trends.

In recent years, the study of numerical weather prediction (NWP) in complex orographic areas has attracted a great deal of attention. Complex orography plays an important role in the occurrence and development of extreme rainfall events. In this study, the Yin–He Global Spectrum Model (YHGSM) was used, and the wave number truncation method was employed to decompose the orographic data to different resolutions. The obtained orographic data with different resolutions were used to simulate the extreme rainfall in Zhengzhou, Henan Province, China, to discuss the degree of influence and mechanism of the different orographic resolutions on the extreme rainfall. The results show that the simulation results of the YHGSM with high-resolution orography are better than those of the low-resolution orography in terms of the rainfall intensity and range. When the rainfall intensity is higher, the results of the low-resolution orography simulated the rainfall range of big heavy rainfall better. The orography mainly affected the rainfall by affecting the velocity of the updraft, but it had a limited influence on the maximum height that the updraft could reach. A strong updraft is one of the key factors leading to extreme rainfall in Henan Province. When the orographic resolution changes, the sensitivity of the vertical velocity of the updraft to the orographic resolution is the greatest, the sensitivity of the upper-air divergence and low-level vorticity to the orographic resolution is lower than that of the vertical velocity. In conclusion, the high-resolution orography is helpful in improving the model’s prediction of extreme rainfall, and when predicting extreme rainfall in complex orographic areas, forecasters may need to artificially increase rainfall based on model results.

This study employs machine learning algorithms to construct Multi Model Ensembles (MMEs) based on Regional Climate Models (RCMs) within the Esca River basin in the Pyrenees. RCMs are ranked comprehensively based on their performance in simulating precipitation (pr), minimum temperature (tmin), and maximum temperature (tmax), revealing variability across seasons and influenced by the General Circulation Model (GCM) driving each RCM. The top-ranked approach is used to determine the optimal number of RCMs for MME construction, resulting in the selection of seven RCMs. Analysis of MME results demonstrates significant improvements in precipitation on both annual and seasonal scales, while temperature-related enhancements are more subtle at the seasonal level. The effectiveness of the ML–MME technique is highlighted by its impact on hydrological representation using a Temez model, yielding outcomes comparable to climate observations and surpassing results from Simple Ensemble Means (SEMs). The methodology is extended to climate projections under the RCP8.5 scenario, generating more realistic information for precipitation, temperature, and streamflow compared to SEM, thus reducing uncertainty and aiding informed decision-making in hydrological modeling at the basin scale. This study underscores the potential of ML–MME techniques in advancing climate projection accuracy and enhancing the reliability of data for basin-scale impact analyses.