In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 90 stations making high-quality measurements of atmospheric composition that began official operations in 1991 after 5 years of planning. Apart from sonde measurements, all measurements in the network are performed by ground-based remote-sensing techniques. Originally named the Network for the Detection of Stratospheric Change (NDSC), the name of the network was changed to NDACC in 2005 to better reflect the expanded scope of its measurements. The primary goal of NDACC is to establish long-term databases for detecting changes and trends in the chemical and physical state of the atmosphere (mesosphere, stratosphere, and troposphere) and to assess the coupling of such changes with climate and air quality. NDACC's origins, station locations, organizational structure, and data archiving are described. NDACC is structured around categories of ground-based observational techniques (sonde, lidar, microwave radiometers, Fourier-transform infrared, UV-visible DOAS (differential optical absorption spectroscopy)-type, and Dobson–Brewer spectrometers, as well as spectral UV radiometers), timely cross-cutting themes (ozone, water vapour, measurement strategies, cross-network data integration), satellite measurement systems, and theory and analyses. Participation in NDACC requires compliance with strict measurement and data protocols to ensure that the network data are of high and consistent quality. To widen its scope, NDACC has established formal collaborative agreements with eight other cooperating networks and Global Atmosphere Watch (GAW). A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.

We present a new model for the calculation of the diffuse fraction of the global solar irradiance for solar system simulations. The importance of an accurate estimation of the horizontal diffuse irradiance is highlighted by findings that an inaccurately calculated diffuse irradiance can lead to significant over- or underestimations in the annual energy yield of a photovoltaic (PV) system by as much as 8%. Our model utilizes a time series of global irradiance in one-minute resolution and geographical information as input. The model is validated by measurement data of 28 geographically and climatologically diverse locations worldwide with one year of one-minute data each, taken from the Baseline Surface Radiation Network (BSRN). We show that on average the mean absolute deviation of the modelled and the measured diffuse irradiance is reduced from about 12% to about 6% compared to three reference models. The maximum deviation is less than 20%. In more than 80% of the test cases, the deviation is smaller 10%. The root mean squared error (RMSE) of the calculated diffuse fractions is reduced by about 18%.

In this case study we compare cloud fractional cover measured by radiometers on polar satellites (AVHRR) and on one geostationary satellite (SEVIRI) to ground-based manual (SYNOP) and automated observations by a cloud camera (Hemispherical Sky Imager, HSI). These observations took place in Hannover, Germany, and in Lauder, New Zealand, over time frames of 3 and 2 months, respectively. Daily mean comparisons between satellite derivations and the ground-based HSI found the deviation to be 6 ± 14% for AVHRR and 8 ± 16% for SEVIRI, which can be considered satisfactory. AVHRR's instantaneous differences are smaller (2 ± 22%) than instantaneous SEVIRI cloud fraction estimates (8 ± 29%) when compared to HSI due to resolution and scenery effect issues. All spaceborne observations show a very good skill in detecting completely overcast skies (cloud cover ≥ 6 oktas) with probabilities between 92 and 94% and false alarm rates between 21 and 29% for AVHRR and SEVIRI in Hannover, Germany. In the case of a clear sky (cloud cover lower than 3 oktas) we find good skill with detection probabilities between 72 and 76%. We find poor skill, however, whenever broken clouds occur (probability of detection is 32% for AVHRR and 12% for SEVIRI in Hannover, Germany). In order to better understand these discrepancies we analyze the influence of algorithm features on the satellite-based data. We find that the differences between SEVIRI and HSI cloud fractional cover (CFC) decrease (from a bias of 8 to almost 0%) with decreasing number of spatially averaged pixels and decreasing index which determines the cloud coverage in each "cloud-contaminated" pixel of the binary map. We conclude that window size and index need to be adjusted in order to improve instantaneous SEVIRI and AVHRR estimates. Due to its automated operation and its spatial, temporal and spectral resolution, we recommend as well that more automated ground-based instruments in the form of cloud cameras should be installed as they cover larger areas of the sky than other automated ground-based instruments. These cameras could be an essential supplement to SYNOP observation as they cover the same spectral wavelengths as the human eye.

This work assesses the performance of five transposition models that estimate the global and diffuse solar irradiance on tilted planes based on the global horizontal irradiance. The modelled tilted irradiance values are compared to measured one-minute values from pyranometers and silicon sensors tilted at different angles at Hannover (Germany) and NREL (Golden, CO, USA). It can be recognized that the deviations of the model of Liu and Jordan, Klucher and Perez from the measurements increases as the tilt angle increases and as the sensors are oriented away from the south direction, where they receive lower direct radiation than south-oriented surfaces. Accordingly, the vertical E, W and N planes show the highest deviation. Best results are found by the models from Hay and Davies and Reindl, when horizontal pyranometer measurements and a constant albedo value of 0.2 are used. The relative root mean squared difference (rRMSD) of the anisotropic models does not exceed 11% for south orientation and low inclination angles (β = 10–60°), but reaches up to 28.9% at vertical planes. For sunny locations such as Golden, the Perez model provides the best estimates of global tilted irradiance for south-facing surfaces. The relative mean absolute difference (rMAD) of the Perez model at NREL ranges from 4.2% for 40° tilt to 8.7% for 90° tilt angle, when horizontal pyranometer measurements and a measured albedo value are used; the use of measured albedo values instead of a constant value of 0.2 leads to a reduction of the deviation to 3.9% and 6.0%, respectively. The use of higher albedo values leads to a significant increase of rMAD. We also investigated the uncertainty resulting from using horizontal pyranometer measurements, in combination with constant albedo values, to estimate the incident irradiance on tilted photovoltaic (PV) modules. We found that these uncertainties are small or negligible.