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The variable brightness of the night sky affects plants as well as animals and humans. However, knowledge about this variability is still insufficient. Outstanding questions regarding how significant the influence of the moon, clouds, and artificial lighting remain. To be able to make statements about these effects, measurements over a long period of time are necessary. Fraunhofer IOSB performs such measurements in the 380–780 nm photopic visual and 800–1700 nm shortwave infrared spectral range. As the latter is only marginally affected by artificial lighting, a comparison of the two bands deepens insight into the influence of artificial lighting. First analyses show that the moon is, as expected, the dominant light source in the night sky, especially during a full moon. Illuminance values up to 200 mlx and irradiance values up to 600 µW/m2 were measured in the visible and infrared respectively. The influence of clouds is more complicated. The measured intensities depend, among other things, on cloud cover and cloud altitude. When the night sky is overcast, the measured intensities can drop as low as 0.5 mlx and 0.5 µW/m2, respectively. These small values were measured during rainfall. The influence of artificial illumination is difficult to estimate, as intensities in the shortwave infrared decrease with increasing cloud cover, but increase in the visual.

We experimentally quantified the impact of cloud fraction and cloud type on the heating rate (HR) of black and brown carbon (HRBC and HRBrC). In particular, we examined in more detail the cloud effect on the HR detected in a previous study (Ferrero et al., 2018). High-time-resolution measurements of the aerosol absorption coefficient at multiple wavelengths were coupled with spectral measurements of the direct, diffuse and surface reflected irradiance and with lidar–ceilometer data during a field campaign in Milan, Po Valley (Italy). The experimental set-up allowed for a direct determination of the total HR (and its speciation: HRBC and HRBrC) in all-sky conditions (from clear-sky conditions to cloudy). The highest total HR values were found in the middle of winter (1.43 ± 0.05 K d−1), and the lowest were in spring (0.54 ± 0.02 K d−1). Overall, the HRBrC accounted for 13.7 ± 0.2 % of the total HR, with the BrC being characterized by an absorption Ångström exponent (AAE) of 3.49 ± 0.01. To investigate the role of clouds, sky conditions were classified in terms of cloudiness (fraction of the sky covered by clouds: oktas) and cloud type (stratus, St; cumulus, Cu; stratocumulus, Sc; altostratus, As; altocumulus, Ac; cirrus, Ci; and cirrocumulus–cirrostratus, Cc–Cs). During the campaign, clear-sky conditions were present 23 % of the time, with the remaining time (77 %) being characterized by cloudy conditions. The average cloudiness was 3.58 ± 0.04 oktas (highest in February at 4.56 ± 0.07 oktas and lowest in November at 2.91 ± 0.06 oktas). St clouds were mostly responsible for overcast conditions (7–8 oktas, frequency of 87 % and 96 %); Sc clouds dominated the intermediate cloudiness conditions (5–6 oktas, frequency of 47 % and 66 %); and the transition from Cc–Cs to Sc determined moderate cloudiness (3–4 oktas); finally, low cloudiness (1–2 oktas) was mostly dominated by Ci and Cu (frequency of 59 % and 40 %, respectively). HR measurements showed a constant decrease with increasing cloudiness of the atmosphere, enabling us to quantify for the first time the bias (in %) of the aerosol HR introduced by the simplified assumption of clear-sky conditions in radiative-transfer model calculations. Our results showed that the HR of light-absorbing aerosol was ∼ 20 %–30 % lower in low cloudiness (1–2 oktas) and up to 80 % lower in completely overcast conditions (i.e. 7–8 oktas) compared to clear-sky ones. This means that, in the simplified assumption of clear-sky conditions, the HR of light-absorbing aerosol can be largely overestimated (by 50 % in low cloudiness, 1–2 oktas, and up to 500 % in completely overcast conditions, 7–8 oktas). The impact of different cloud types on the HR was also investigated. Cirrus clouds were found to have a modest impact, decreasing the HRBC and HRBrC by −5 % at most. Cumulus clouds decreased the HRBC and HRBrC by −31 ± 12 % and −26 ± 7 %, respectively; cirrocumulus–cirrostratus clouds decreased the HRBC and HRBrC by −60 ± 8 % and −54 ± 4 %, which was comparable to the impact of altocumulus (−60 ± 6 % and −46 ± 4 %). A higher impact on the HRBC and HRBrC suppression was found for stratocumulus (−63 ± 6 % and −58 ± 4 %, respectively) and altostratus (−78 ± 5 % and −73 ± 4 %, respectively). The highest impact was associated with stratus, suppressing the HRBC and HRBrC by −85 ± 5 % and −83 ± 3 %, respectively. The presence of clouds caused a decrease of both the HRBC and HRBrC (normalized to the absorption coefficient of the respective species) of −11.8 ± 1.2 % and −12.6 ± 1.4 % per okta. This study highlights the need to take into account the role of both cloudiness and different cloud types when estimating the HR caused by both BC and BrC and in turn decrease the uncertainties associated with the quantification of their impact on the climate.

In situ remote sensing reflectance (Rrs) measured using the above-water approach usually suffers from residual reflected skylight (Δ), and thus its applicability is limited. In this study, we proposed a simple method, which only required the in situ Rrs spectrum itself as input, to further remove the Δ effects in the in situ Rrs spectrum. The performance of the proposed method was evaluated using both simulation data and in situ Rrs spectra measured using a radiance sensor with a black tube (Δ-free in situ Rrs). The results showed that the proposed method outperformed other existing methods and can be applied to various types of waters. For example, from the two simulation experiments, it was seen that the proposed method can provide accurate Rrs spectra with a mean absolute percentage error (MAPE) = 6% even if they are measured under various sky conditions and wind speeds (using Hydrolight) and can estimate accurate Δ values with MAPE = 5% (using a simple bio-optical model). From a comparison analysis using Δ-free in situ Rrs data, the proposed method also showed the best performance with the smallest MAPE of 45% (MAPE ranged from 52% to 260% for other existing methods). In addition, the proposed method can improve the quality of Rrs spectra collected under various sky conditions (clear, scattered clouds and overcast), with an increased proportion of higher quality assurance scores. Two showcases indicate that the Δ corrections should be carried out for all above water approach-measured Rrs spectra before they are applied to develop water quality estimation algorithms or to evaluate the performances of atmospheric correction algorithms.

Road weather conditions are closed-related to the transportation safety and traffic capacity. With the development of road surveillance systems, weather conditions could be recognized from video. However, it is hard to be detected by machine. To address it, a deeply supervised convolution neural network (DS-CNN) is designed and trained on a self-established dataset. The traffic image dataset includes five groups labeled with “sunny”, “overcast”, “rainy”, “snowy” and “foggy”. Each group has manually labeled and selected more than 2500 images. The DS-CNN, can achieve the precision rate of 0.9681 and the recall rate of 0.9681. This practical weather detection method has been built in the surveillance system covers five freeways. The experimental result used in practice shows much worse detection results at first with disturbance of difference scenarios, worn camera, transmission failure and so on. With further improvement of DS-CNN, we found that it was much more effective than hand-crafted features in this task, and a deeper neural architecture could derive more powerful features. Moreover, the results show that dense weather information has more details in a small scale. In order to fast report the regional weather detection result, a designed visualization method is also proposed in spatiotemporal dimension to fuse with currently-used system. The high accuracy and fast detection speed with friendly visualization would lead to more precise traffic management and prompt the road weather traffic control to more intelligence levels.

[EN]Unmanned Aerial Vehicles (UAVs) offer great flexibility in acquiring images in inaccessible study areas, which are then processed with stereo-matching techniques through Structure-from-Motion (SfM) algorithms. This procedure allows generating high spatial resolution 3D point clouds. The high accuracy of these 3D models allows the production of detailed snow depth distribution maps through the comparison of point clouds from different dates. In this way, UAVs allow monitoring of remote areas that were not achievable previously. The large number of works evaluating this novel technique has not, to date, conducted a systematic evaluation of concurrent snowpack observations with different UAV devices. Taking into account this, and also bearing in mind that potential users of this technique may be interested in exploiting ready-to-use commercial devices, we conducted an evaluation of the snow depth distribution maps with different commercial UAVs. During the 2018-19 snow season, two multi-rotors (Parrot Anafi and DJI Mavic Pro2) and one fixed-wing device (SenseFly eBee plus) were used on three different dates over a small test area (5 ha) within Izas Experimental Catchment in the Central Pyrenees. Simultaneously, snowpack distribution was retrieved with a Terrestrial Laser Scanner (TLS, RIEGL LPM-321) and was considered as ground truth. Three different georeferencing methods (Ground Control Points, ICP algorithm over snow-free areas and RTK-GPS positioning) were tested, showing equivalent performances under optimum illumination conditions. Additionally, for the three acquisition dates, both multi-rotors were flown at two distinct altitudes (50 and 75 m) to evaluate impact on the obtained snow depth maps. The evaluation with the TLS showed an equivalent performance of the two multi-rotors, with mean RMSE below 0.23 m and maximum volume deviations of less than 5%. Flying altitudes did not show significant differences in the obtained maps. These results were obtained under contrasted snow surface characteristics. This study reveals that under good illumination conditions and in relatively small areas, affordable commercial UAVs provide reliable estimations of snow distribution compared to more sophisticated and expensive close-range remote sensing techniques. Results obtained under overcast skies were poor, demonstrating that UAV observations require clear-sky conditions and acquisitions around noon to guarantee a homogenous illumination of the study area. This work was supported by the research projects of the Spanish Ministry of Economy and Competitiveness projects "El papel de la nieve en la hidrologia de la peninsula iberica y su respuesta a procesos de cambio global-CGL2017-82216-R" and the JPI-Climate co-funded call of the European Commission and INDECIS and CROSSDRO which are part of ERA4CS, and ERA-NET. Authors do not have any conflict of interest.). J. Revuelto is supported by a "Juan de la Cierva Incorporacion" postdoctoral fellow of the Spanish Ministry of Science, Innovation and Universities (Grant IJC2018-036260-I). I. Vidaller is supported by the Grant FPU18/04978 and is studying in the PhD program in the University of Zaragoza (Earth Science Department).

The Increased industrial subsector in Indonesia each year have some negative impact on health in Indonesia. Exhaust emissions of industrial activities is one of the sources of air pollution that occurs continuously. Exhaust gas quality monitoring needs to be done regularly to control the level of air pollution as required under KepMenLH 13/1995. During the determination of the opacity of the black smoke chimney industry performed manually using Ringlemann. Results opasitasbergantung on the observer on the ground so that the level of accuracy obtained is still less opacity akurat. Paper is about the development of imaging technology for monitoring parameters of black smoke in the exhaust stack opacity industri.Metode measurement used in this monitoring is a method Ringlemann. Segmentation algorithm used in this research is using thresholding. The result of the automatic segmentation using thresholding sample data has a high degree of accuracy, but still dependent on weather conditions. If you do smoke segmentation when the sky is clear, the level of accuracy of the segmentation is very high, but it is different when overcast or cloudy weather conditions. Results from this study is the obtainment function to be able to determine the level of black smoke opacity parameter. Searches made through a logarithmic function. Determination of the proper function is determined by looking at the highest value of Rଶ. Different sky conditions lead to different functions, so the algorithm functions to determine the parameters of black smoke opacity made in some circumstances under the conditions of the sky.

An operating temperature of the photovoltaic (PV) module greatly affects performance and its lifetime. Therefore, it is essential to evaluate operating temperature of the photovoltaic module in different weather conditions and how it affects its performance. The primary objective of this paper is to present a dynamic thermo-electric model for determining the temperature and output power of the photovoltaic module. The presented model is validated with field measurement at the Institute of Energy Technology, Faculty of Energy Technology, University of Maribor, Slovenia. The presented model was compared with other models in different weather conditions, such as clear, cloudy and overcast. The evaluation was performed for the operating temperature and output power of the photovoltaic module using Root-Mean-Square-Error (RMSE) and Mean-Absolute-Error (MAE). The average RMSE and MAE values are 1.75 °C and 1.14 °C for the thermal part and 20.34 W and 10.97 W for the electrical part.

Red-headed buntings (Emberiza bruniceps) perform long-distance migrations within their southerly overwintering grounds and breeding areas in the northern hemisphere. Long-distance migration demands essential orientation mechanisms. The earth’s magnetic field, celestial cues, and memorization of geographical cues en route provide birds with compass knowledge during migration. Birds were tested during spring migration for orientation under natural clear skies, simulated overcast skies at natural day length and temperature, simulated overcast at 22 °C and 38 °C temperatures, and in the deflected (−120°) magnetic field. Under clear skies, the red-headed buntings were oriented NNW (north–northwest), simulated overcast testing resulted in a northerly mean direction at local temperatures as well as at 22 °C and 38 °C. The buntings reacted strongly in favor of the rotated magnetic field under the simulated overcast sky, demonstrating the use of a magnetic compass for migrating in a specific direction.

Atmospheric temperature and humidity retrievals from ground-based microwave remote sensing are useful in a variety of meteorological and environmental applications. Though the influence of clouds is usually considered in current retrieval algorithms, the resulting temperature and humidity estimates are still biased high in overcast conditions compared to radiosonde observations. Therefore, there is a need to improve the quality of retrievals in cloudy conditions. This paper presents an approach to make brightness temperature (TB) correction for cloud influence before the data can be used in the inversion of vertical profiles of atmospheric temperature and humidity. A three-channel method is proposed to make cloud parameter estimation, i.e., of the total 22 channels of the ground-based radiometer, three are adopted to set up a relationship between cloud parameters and brightness temperatures, so that the observations from the three channels can be used to estimate cloud thickness and water content and complete the cloud correction for the rest of the channels used in the retrieval. Based on two years of data from the atmosphere in Beijing, a comparison of the retrievals with radiosonde observations (RAOB) shows: (1) the temperature retrievals from this study have a higher correlation with RAOB and are notably better than in the vendor-provided LV2. The bias of the temperature retrievals from this study is close to zero at all heights, and the RMSE is greatly reduced from &gt, 5 °C to &lt, 2 °C in the layer, from about 1.5 km up to 5 km. The temperature retrievals from this study have higher correlation with RAOB data compared to the vendor-provided LV2, especially at and above a 2 km height. (2) The bias of the water vapor density profile from this study is near to zero, while the LV2 has a positive bias as large as 4 g/m3. The RMSE of the water vapor density profile from this study is &lt, 2 g/m3, while the RMSE for LV2 is as large as 10 g/m3. That is, both the bias and RMSE from this study are evidently less than the LV2, with a greater improvement in the lower troposphere below 5 km. Correlation with RAOB is improved even more for the water vapor density. The correlation of the retrievals from this study increases to one within the boundary layer, but the correlation of LV2 with RAOB is only 0.8 at 0.5 km height, 0.7 at 1 km, and even less than 0.5 at 2 km. (3) A parameter named the Cloud Impact index, determined by cloud water concentration and cloud thickness, together with the cloud base height, has been defined to show that both BIAS and RMSE of “high-CI subsample” are larger than those of the “low-CI subsample”, indicating that high-CI cloud has a higher impact on the retrievals and the correction for cloud influence is more necessary.

The prosperous implementation of Building Integrated Photovoltaics (BIPV), as well as Building Attached Photovoltaics (BAPV), needs an accurate and detailed assessment of the potential of solar irradiation and electricity production of various commercialised technologies in different orientations on the outer skins of the building. This article presents a dataset for the solar incident radiation and electricity production of PV systems in the north and south orientations in a dense urban area (in the northern hemisphere). The solar incident radiation and the electricity production of two back-to-back PV panels with a ten-centimetre gap for one year are monitored and logged as primary data sources. Using Microsoft Excel, both panels’ efficiency is also presented as a secondary source of data. The implemented PV panels are composed of polycrystalline silicon cells with an efficiency of 16.9%. The results depicted that the actual efficiency of the south-facing panel (13%–15%) is always closer to the standard efficiency of the panel compared to the actual efficiency of the north-facing panel (8%–12%). Moreover, although the efficiency of the south-facing panel on sunny days of the year is almost constant, the efficiency of the north-facing panel decreases significantly in winter. This phenomenon might be linked to the spectral response of the polycrystalline silicon cells and different incident solar radiation spectrum on the panels. While the monitored data cover the radiation and system electricity production in various air conditions, the analysis is mainly conducted for sunny days, and more investigation is needed to analyse the system performance in other weather conditions (like cloudy and overcast skies). The presented database could be used to analyse the performance of polycrystalline silicon PV panels and their operational efficiency in a dense urban area and for different orientations.

The business model of building-integrated photovoltaics (BIPV) is developing expeditiously and BIPV will soon be recognised as a building envelope material for the entire building skins, among other alternatives such as brick, wood, stone, metals, etc. This paper investigates the effect of climate on the solar radiation components on building skins and BIPV materials in the northern hemisphere. The selected cities are Stavanger in Norway, Bern in Switzerland, Rome in Italy, and Dubai in the UAE. The study showed that for all the studied climates, the average incident radiation on the entire building skins is slightly more than the average incident radiation on the east or west facades, regardless of the orientations of the building facades. Furthermore, the correlation between solar radiation components and different BIPV technologies is discussed in this paper. It is also found that when it comes to the efficiency of different BIPV cells, the impact of the climate on some of the BIPV technologies (such as DSC and OSC) is much more significant than others (such as c-Si, mc-Si and CIGS). The evidence from this study suggests that in climates with higher diffuse radiation-or with more overcast days per year-the contribution of IR radiation decreases. Therefore, the efficiency of BIPV materials that their spectral responses are dependent on the IR radiation (like Si and CIGS) in such a climate would drop down meaningfully. On the other hand, the DSC and OSC solar cells could be a good option for cloudy climates since they have more stable performance, even in such a climate. Although, their efficiency compared to other BIPV materials such as Si-based BIPV solar cells is still significantly less thus far.

Although the representation of unresolved clouds in radiation schemes of coarse-resolution weather and climate models has progressed noticeably over the past years, a lot of room remains for improvement, as the current picture is by no means complete. The main objective of the present study is to advance the cloud-radiation interaction parameterization, focusing on the issues related to model misrepresentation of cloud horizontal inhomogeneity. This subject is addressed with the state-of-the-art Tripleclouds radiative solver, the fundamental feature of which is the inclusion of the optically thicker and thinner cloud fraction, where the thicker is associated with the presence of convective updraft elements. The research challenge is to optimally set the pair of cloud condensates characterizing the two cloudy regions and the corresponding geometrical split of layer cloudiness. A diverse cloud field data set was collected for the analysis, comprising case studies of stratocumulus, cirrus and cumulonimbus. The primary goal is to assess the validity of global cloud variability estimate along with various condensate distribution assumptions. More sophisticated parameterizations are subsequently explored, optimizing the treatment of overcast as well as extremely heterogeneous cloudiness. The radiative diagnostics including atmospheric heating rate and net surface flux are consistently studied using the Tripleclouds method, evaluated against a three-dimensional radiation computation. The performance of Tripleclouds mostly significantly surpasses the conventional calculation on horizontally homogeneous cloudiness. The effect of horizontal photon transport is further quantified. The overall conclusions are intrinsically different for each particular cloud type, encouraging endeavors to enhance the use of cloud regime dependent methodologies in next-generation atmospheric models. This study highlighting the Tripleclouds potential for three essential cloud types signifies the need for more research examining a broader spectrum of cloud morphologies.

In the near future, wind and solar generation are projected to play an increasingly important role in Europe's energy sector. With such fast-growing renewable energy development, the presence of simultaneous calm wind and overcast conditions could cause significant shortfalls in production with potentially serious consequences for system operators. Such events are sometimes dubbed “Dunkelflaute” events and have occurred several times in recent history. The capabilities of contemporary mesoscale models to reliably simulate and/or forecast a Dunkelflaute event are not known in the literature. In this paper, a Dunkelflaute event near the coast of Belgium is simulated utilizing the Weather Research and Forecasting (WRF) model. Comprehensive validation using measured power production data and diverse sets of meteorological data (e.g., floating lidars, radiosondes, and weather stations) indicates the potential of WRF to reproduce and forecast the boundary layer evolution during the event. Extensive sensitivity experiments with respect to grid-size, wind farm parameterization, and forcing datasets provide further insights on the reliability of the WRF model in capturing the Dunkelflaute event.

Assessing the development of wildfire scars during a period of consecutive active fires and smoke overcast is a challenge. The study was conducted during nine months when Israel experienced massive pyro-terrorism attacks of more than 1100 fires from the Gaza Strip. The current project strives at developing and using an advanced Earth observation approach for accurate post-fire spatial and temporal assessment shortly after the event ends while eliminating the influence of biomass burning smoke on the ground signal. For fulfilling this goal, the Aerosol-Free Vegetation Index (AFRI), which has a meaningful advantage in penetrating an opaque atmosphere influenced by biomass burning smoke, was used. On top of it, under clear sky conditions, the AFRI closely resembles the widely used Normalized Difference Vegetation Index (NDVI), and it retains the same level of index values under smoke. The relative differenced AFRI (RdAFRI) set of algorithms was implemented at the same procedure commonly used with the Relative differenced Normalized Burn Ratio (RdBRN). The algorithm was applied to 24 Sentinel-2 Level-2A images throughout the study period. While validating with ground observations, the RdAFRI-based algorithms produced an overall accuracy of 90%. Furthermore, the RdAFRI maps were smoother than the equivalent RdNBR, with noise levels two orders of magnitude lower than the latter. Consequently, applying the RdAFRI, it is possible to distinguish among four severity categories. However, due to different cloud cover on the two consecutive dates, an automatic determination of a threshold level was not possible. Therefore, two threshold levels were considered through visual inspection and manually assigned to each imaging date. The novel procedure enables calculating the spatio-temporal dynamics of the fire scars along with the statistics of the burned vegetation species within the study area.

Eelgrass beds are critical in coastal ecosystems and can be useful as a measure of nearshore ecosystem health. Population declines have been seen around the world, including in Atlantic Canada. Restoration has the potential to aid the eelgrass population. Traditionally, field-level protocols would be used to monitor restoration; however, using unmanned aerial vehicles (UAVs) would be faster, more cost-efficient, and produce images with higher spatial resolution. This project used RGB UAV imagery and data acquired over five sites with eelgrass beds in the northern part of the Shediac Bay (New Brunswick, Canada). The images were mosaicked using Pix4Dmapper and PCI Geomatica. Each RGB mosaic was tested for the separability of four different classes (eelgrass bed, deep water channels, sand floor, and mud floor), and training areas were created for each class. The Maximum-likelihood classifier was then applied to each mosaic for creating a map of the five sites. With an average and overall accuracy higher than 98% and a Kappa coefficient higher than 0.97, the Pix4D RGB mosaic was superior to the PCI Geomatica RGB mosaic with an average accuracy of 89%, an overall accuracy of 87%, and a Kappa coefficient of 0.83. This study indicates that mapping eelgrass beds with UAV RGB imagery is possible, but that the mosaicking step is critical. However, some factors need to be considered for creating a better map, such as acquiring the images during overcast conditions to reduce the difference in sun illumination, and the effects of glint or cloud shadow on the images.

Construction schedule delays could also be explained by means of the late completion of construction add comparison of designed schedule of project. If the explanations of the development projects schedule delay are found then the project schedule delay in construction may be reduced. During this study, factors of schedule delay were selected after the past review of literature then arranged them in primary sets. On following the survey outcomes, the highest causative groups and factors that require more consideration of delays in kokan region were explained. Satej R. Desai | A. T. Pawar "Investigating the Effectiveness of Construction Practices in Delayed Residential Projects in Gadhinglaj City through Time and Cost Perspective" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30530.pdf Paper Url :https://www.ijtsrd.com/engineering/civil-engineering/30530/investigating-the-effectiveness-of-construction-practices-in-delayed-residential-projects-in-gadhinglaj-city-through-time-and-cost-perspective/satej-r-desai

Temperature measurements of InterMET Inc. aluminum-coated iMET-XQ sensors were tested in an outdoor setting under a variety of solar radiation and wind speed conditions. Twelve unshielded sensors were mounted side-by-side on the tower of a South Alabama Mesonet weather station next to a reference sensor on the tower. The iMET-XQ temperatures were most precise and accurate in solar radiation values that were close to zero, regardless of wind speed. Under overcast conditions, wind speeds of 2 m s&minus, 1 were sufficient to obtain precise and accurate temperature measurements. During the day-time, aspiration of wind speeds higher than or equal to 3 m s&minus, 1 is sufficient. An iMET-XQ was placed in a radiation shield next to the tower reference sensor to test the need for a radiation shield. A second iMET-XQ was placed unshielded on the tower. The iMET-XQ sensors with aluminum coating do not need to be shielded, but they do need to be aspirated. It is recommended that, when taking temperature measurements using unmanned aerial vehicles (UAV) with iMET-XQ sensors, the UAV either fly at 3 m s&minus, 1, be embedded in winds of those speeds, or to use the propeller wash of the UAV to aspirate the sensors.

Squeeze overcasting has emerged as an attractive option for casting of Al alloys in terms of mechanical properties. The attainment of the desired magnitude of these properties is challenging in overcasting due to the involvement of a number of process parameters. In this study, the effects of insert preheat temperature (TI) along with pouring temperature (TP), and squeeze pressure (PS) on the mechanical properties of squeeze overcast AA2026-AA2026 joint were investigated. Experimental results revealed that the squeeze pressure is the most prominent factor affecting the ultimate tensile strength (UTS) while micro-hardness (MH) is significantly influenced by the pouring temperature. Maximum values of UTS (315 MPa) and MH (130 HV) were achieved at aPSof 120 MPa,TPof 780 °C, andTIof 250 °C. Energy dispersive X-ray (EDX) analysis witnessed thatTIhas also a significant role in determining the quality of bond between the substrate and the melt. Scanning electron microscopy (SEM) depicts that the morphology of the fractured surface has a sound influence on both selected responses. Both the strength and hardness are noticed better if the fractured surface possesses the flat-faced morphology. Furthermore, an empirical regression model was developed using response surface methodology (RSM) design and validated through eight confirmatory experiments. RSM integrated multi-objective optimization genetic algorithm (MO-GA) was deployed to optimize the UTS and MH. The comparative results obtained from RSM and MO-GA demonstrated that the deviation in experimental and predicted values is less than 5%.

A microgrid consists of electrical generation sources, energy storage assets, loads, and the ability to function independently, or connect and share power with other electrical grids. Thefocus of this work is on the behavior of a microgrid, with both diesel generator and photovoltaic resources, whose heating or cooling loads are influenced by local meteorological conditions. Themicrogrid's fuel consumption and energy storage requirement were then examined as a function of the atmospheric conditions used by its energy management strategy (EMS). A fuel-optimal EMS, able to exploit meteorological forecasts, was developed and evaluated using a hybrid microgrid simulation. Weather forecast update periods ranged from 15 min to 24 h. Four representative meteorological sky classifications (clear, partly cloudy, overcast, or monsoon) were considered. Forall four sky classifications, fuel consumption and energy storage requirements increased linearly with the increasing weather forecast interval. Larger forecast intervals lead to degraded weather forecasts, requiring more frequent charging/discharging of the energy storage, increasing both the fuel consumption and energy storage design requirements. The significant contributions of this work include the optimal EMS and an approach for quantifying the meteorological forecast effects on fuel consumption and energy storage requirements on microgrid performance. The findings of this study indicate that the forecast interval used by the EMS affected both fuel consumption and energy storage requirements, and that the sensitivity of these effects depended on the 24-hour sky conditions.

Because semiconductors absorb wavelengths dependent on the light absorption coefficient, photovoltaic (PV) energy output is affected by the solar spectrum. Therefore, it is necessary to consider the solar spectrum for highly accurate PV output estimation. Bird&rsquo, s model has been used as a general spectral model. However, atmospheric parameters such as aerosol optical depth and precipitable water have a constant value in the model that only applies to clear days. In this study, atmospheric parameters were extracted using the Bird&rsquo, s spectrum model from the measured global spectrum and the seasonal fluctuation of atmospheric parameters was examined. We propose an overcast spectrum model and calculate the all-weather solar spectrum from clear to overcast sky through linear combination. Three types of PV modules (fixed Si, two-axis tracking Si, and fixed InGaP/GaAs/InGaAs triple-junction solar cells) were installed at the University of Miyazaki. The estimated performance ratio (PR), which takes into account incident angle and spectral variations, was consistent with the measured PR. Finally, the energy yield of various PVs installed across Japan was successfully estimated.

Sky classification is a complex problem, due in part to such abstract conceptual definitions as clear, intermediate, and overcast, as well as other intermediate ranges. The CIE (Commission Internationale de L’Éclairage) Standard classification offers a solution to this problem, although its application requires data on the luminance distribution of the whole sky that are less commonly available. A benchmarking and classification system of ten meteorological indices is introduced in this study to classify the sky types from overcast to clear. The indices can be calculated from measurements of global, diffuse, and direct irradiance that are widely available from meteorological ground stations. The classification system uses confusion matrices, a machine-learning tool that generates a visual display of the results of supervised-learning algorithms. The CIE Standard skies classification, applied to half hourly sky-scanner measurements in Burgos (Spain), over the period June 2016 - May 2017, is used in this study as a baseline reference for a comparative review of the results from the meteorological indices and their results. They are classified by four performance ratings: Accuracy, Jaccard, Cohen, and Matthews, which feature both classification similarity and the randomness of any agreement. All meteorological indices yielded a high average degree of accuracy - close to 80% - in a detailed review of their classification. Neverthless, the results suggested that Perez’s Clearness Index based on global, diffuse and direct radiation measurements offered the most precise classification of the skies, followed closely by the Klucher Clearness Index and the Perraudeau Nebulosity Index., Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (ORDEN EDU/667/2019) and the Spanish Ministry of Science, Innovation & Universities under the I + D + i state programme “Challenges Research Projects” (Ref. RTI2018-098900-B-I00)

Using almost 10 years of observations of clouds and aerosols from the US Southern Great Plains (SGP) atmospheric observatory and the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) in China, the impact of aerosols on single-layer overcast clouds over continental land for different regimes were investigated. Atmospheric conditions at the two sites were first compared in an attempt to isolate the influence of aerosols on cloud properties from dynamic and thermodynamic influences. Cloud types and amounts are similar at the two sites. The dominant aerosol types at the SGP and SACOL sites are sulphate and dust, respectively, with greater aerosol optical depths (AODs) and absorption at the SACOL site. Aerosol first indirect effect (FIE) ranges from 0.021 to 0.152 and from −0.078 to 0.047 at the SGP and SACOL sites, respectively, when using the AOD below cloud base as CCN proxy. Although differences exist, the influence of meteorological conditions on the FIE at the two sites is consistent. FIEs are easily detected under descending motion and dry condition. The FIE at the SGP site is larger than that at the SACOL site, which suggests that the cloud albedo effect is more sensitive under relatively cleaner atmospheric conditions and the dominating aerosol at the SACOL site has less hygroscopicity. The radiative forcing of the FIE over the SGP site is −3.2 W m−2 for each 0.05 increment in FIE. Cloud durations generally prolong as aerosol loading increases, which is consistent with the hypothesis of the aerosol second indirect effect. The negative relationship between cloud duration time and aerosol loading when aerosol loading reaches a large value further might suggest a semidirect effect.

The weather is arguably one of the most common topics in daily conversation. However, it is also a well-known factor in psychiatric problems worldwide such as depression, where particularly elders and females are prone to the condition. Previous studies have mostly focused on the association between long-term climate/season change and depressive symptoms to test the belief that people feel depressed when the weather is bad. Yet, little is known about the relationship between intraday weather conditions and depressive symptoms. This study aimed to decompose this association and explore age and gender differences in depressive symptoms’ report under different intraday weather conditions. We analyzed data with a total sample size of 20,987 individuals aged 15–96 years obtained via the China Labor-force Dynamic Survey (CLDS) project in 2016. The surveyors filled in the intraday weather conditions (i.e., sunny, cloudy, overcast, light rain, moderate rain, and heavy rain) in the questionnaire with depression being self-reported using the Center for Epidemiologic Studies Depression (CES-D) Scale. In the survey, 17.3% [95% confidence interval (CI) = 16.8%, 17.8%] individuals were assessed to have depressive symptoms (CES-D scores >15). Overcast day was the peak weather condition for depressive symptoms, while gender and age differences of depressive symptoms were biggest in the weather condition of moderate rain. Multivariate logistic regression revealed that female and middle-old age individuals were more likely to report having depressive symptoms, and people under overcast day were more likely to report having depressive symptoms compared to under sunny day [odds ratio (OR) = 1.25, 95%CI = 1.11, 1.39]. This intraday weather conditions-depression link was significant in males and middle-old age individuals but not in females and youngsters.

In the coming decades, both wind and solar power production will be playing increasingly important roles in Europe’s energy economy. It is absolutely essential that power grids are resilient against any unusual weather phenomena. One such meteorological phenomenon, “Dunkelflaute”, is causing serious concern to the renewable energy industry, which is primarily characterized by calm winds and overcast conditions. For example, a Dunkelflaute event happened in the Netherlands on 30th April 2018 leading to a significant shortfall in renewable energy generation requiring emergency intervention by the system operator. By analyzing this case, this paper investigates the performance of a state-of-the-art mesoscale model, Weather Research and Forecasting (WRF), in forecasting a Dunkelflaute event. Multiple WRF simulations are driven using real-time Global Forecast System (GFS) operational data over a range of prediction horizons. For comparison, a benchmark run is carried out using ERA5 reanalysis data as boundary conditions. Through validation using a variety of measured data covering onshore and offshore areas, wind speed is shown to be more predictable than cloud-cover in this particular case study.

The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area within the tropical Andes. We propose a model based on Markov chains for forecasting the hourly solar irradiance for one day ahead. The Markov model was compared against estimates produced by different configurations of the weather research forecasting model (WRF). Our assessment showed that for the period considered, the average availability of the solar resource was of 5 PSH (peak sun hours), corresponding to an average daily radiation of ~5 kWh/m2. This shows that Medellín, Colombia, has a substantial availability of the solar resource that can be a complementary source of energy during the dry season periods. In the case of the Markov model, the estimates exhibited typical root mean squared errors between ~80 W/m2 and ~170 W/m2 (~50%−~110%) under overcast conditions, and ~57 W/m2 to ~171 W/m2 (~16%−~38%) for clear sky conditions. In general, the proposed model had a performance comparable with the WRF model, while presenting a computationally inexpensive alternative to forecast hourly solar radiation one day in advance. The Markov model is presented as an alternative to estimate time series that can be used in energy markets by agents and power-system operators to deal with the uncertainty of solar power plants.

This work presents synergistic satellite, airborne and surface based observations of a Pocket of Open Cells (POC) in the remote south-east Atlantic. The observations were obtained over and upwind of Ascension Island during the CLouds and Aerosol Radiative Impacts and Forcing (CLARIFY) and the Layered Smoke Interacting with Clouds (LASIC) field experiments. A novel aspect of this case-study is that an extensive free-tropospheric biomass burning aerosol plume that had been transported from the African continent was observed to be in contact with the boundary layer inversion over the POC and the surrounding closed cellular cloud regime. The in-situ measurements show marked contrasts in the boundary layer thermodynamic structure, cloud properties, precipitation and aerosol conditions between the open cells and surrounding overcast cloud field. The data demonstrate that the overlying biomass burning aerosol was mixing down into the boundary layer in the stratocumulus cloud downwind of the POC, with elevated carbon monoxide, black carbon mass loadings and accumulation mode aerosol concentrations measured beneath the trade-wind inversion. The stratocumulus cloud in this region was moderately polluted and exhibited very little precipitation falling below cloud base. A rapid transition to actively precipitating cumulus clouds and detrained stratiform remnants in the form of thin quiescent veil clouds was observed across the boundary into and deep within the POC. The sub-cloud layer in the POC was much cleaner than that in the stratocumulus region. The clouds in the POC formed within an ultra-clean layer (accumulation mode aerosol concentrations ~ few cm−3) in the upper region of the boundary layer, that was likely to have been formed via efficient collision-coalescence and sedimentation processes. Enhanced Aitken mode aerosol concentrations were also observed intermittently in this ultra-clean layer, suggesting that new particle formation was taking place. Across the boundary layer inversion and immediately above the ultra-clean layer, accumulation mode aerosol concentrations were ~ 1000 cm−3. Importantly, the airmass in the POC showed no evidence of elevated carbon monoxide over and above typical background conditions at this location and time of year. As carbon monoxide is a good tracer for biomass burning aerosol that is not readily removed by cloud processing and precipitation, it demonstrates that the open cellular convection in the POC is not able to entrain large quantities of the free-tropospheric aerosol that was sitting directly on top of the boundary layer inversion. This suggests that the structure of the mesoscale cellular convection may play an important role in regulating the transport of aerosol from the free-troposphere down into the marine boundary layer. We then develop a climatology of open cellular cloud conditions in the south-east Atlantic from 19 years of September MODIS Terra imagery. This shows that the maxima in open cell frequency (> 0.25) occurs far offshore and in a region where subsiding biomass burning aerosol plumes may often come into contact with the underlying boundary layer cloud. If the results from the observational case-study applied more broadly, then the apparent low susceptibility of open cells to free-tropospheric intrusions of additional cloud condensation nuclei could have some important consequences for aerosol-cloud interactions in the region.

The climatic relevance of aerosol–cloud interactions depends on the sensitivity of the radiative effect of clouds to cloud droplet number N, and liquid water path LWP. We derive the dependence of cloud fraction CF, cloud albedo AC, and the relative cloud radiative effect rCRE=CF⋅AC on N and LWP from 159 large-eddy simulations of nocturnal stratocumulus. These simulations vary in their initial conditions for temperature, moisture, boundary-layer height, and aerosol concentration but share boundary conditions for surface fluxes and subsidence. Our approach is based on Gaussian-process emulation, a statistical technique related to machine learning. We succeed in building emulators that accurately predict simulated values of CF, AC, and rCRE for given values of N and LWP. Emulator-derived susceptibilities ∂ln⁡rCRE/∂ln⁡N and ∂ln⁡rCRE/∂ln⁡LWP cover the nondrizzling, fully overcast regime as well as the drizzling regime with broken cloud cover. Theoretical results, which are limited to the nondrizzling regime, are reproduced. The susceptibility ∂ln⁡rCRE/∂ln⁡N captures the strong sensitivity of the cloud radiative effect to cloud fraction, while the susceptibility ∂ln⁡rCRE/∂ln⁡LWP describes the influence of cloud amount on cloud albedo irrespective of cloud fraction. Our emulation-based approach provides a powerful tool for summarizing complex data in a simple framework that captures the sensitivities of cloud-field properties over a wide range of states.

The aim of this work is to investigate whether snow albedo seasonality and trend under all sky conditions at Johnsons Glacier (Livingston Island, Antarctica) can be tracked using the Moderate Resolution Imaging Spectroradiometer (MODIS) snow albedo daily product MOD10A1. The time span is from December 2006 to February 2015. As the MOD10A1 snow albedo product has never been used in Antarctica before, we also assess the performance for the MOD10A1 cloud mask. The motivation for this work is the need for a description of snow albedo under all sky conditions (including overcast days) using satellite data with mid-spatial resolution. In-situ albedo was filtered with a 5-day windowed moving average, while the MOD10A1 data were filtered using a maximum filter. Both in-situ and MOD10A1 data follow an exponential decay during the melting season, with a maximum decay of 0.049/0.094 day&minus, 1 (in-situ/MOD10A1) for the 2006&ndash, 2007 season and a minimum of 0.016/0.016 day&minus, 1 for the 2009&ndash, 2010 season. The duration of the decay varies from 85 days (2007&ndash, 2008) to 167 days (2013&ndash, 2014). Regarding the albedo trend, both data sets exhibit a slight increase of albedo, which may be explained by an increase of snowfall along with a decrease of snowmelt in the study area. Annual albedo increases of 0.2% and 0.7% are obtained for in-situ and MOD10A1 data, respectively, which amount to respective increases of 2% and 6% in the period 2006&ndash, 2015. We conclude that MOD10A1 can be used to characterize snow albedo seasonality and trend on Livingston Island when filtered with a maximum filter.

The role of clouds in recent Arctic warming is not fully understood, including their effects on the shortwave radiation and the surface energy budget. To investigate relevant small-scale processes in detail, an intensive field campaign was conducted during early summer in the central Arctic during the Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL) drifting ice floe station. During this campaign, the small-scale spatiotemporal variability of global irradiance was observed for the first time on an ice floe with a dense network of autonomous pyranometers. 15 stations were deployed covering an area of 0.83 km × 1.3 km from June 4–16, 2017. This unique, open-access dataset is described here, and an analysis of the spatiotemporal variability deduced from this dataset is presented for different synoptic conditions. Based on additional observations, 5 typical sky conditions were identified and used to determine the values of the mean and variance of atmospheric global transmittance for these conditions. Overcast conditions were observed 39.6 % of the time predominantly during the first week, with an overall mean transmittance of 0.47. The second-most frequent conditions corresponded to multi-layer clouds (32.4 %) which prevailed in particular during the second week, with a mean transmittance of 0.43. Broken clouds had a mean transmittance of 0.61 and a frequency of occurrence of 22.1 %. Finally, the least frequent sky conditions were thin clouds and cloudless conditions, which both had a mean transmittance of 0.76, and occurrence frequencies of 3.5 % and 2.4 %, respectively. For overcast conditions, lower global irradiance was observed for stations closer to the ice edge, likely attributable to the low surface albedo of dark open water, and a resulting reduction of multiple reflections between the surface and cloud base. Using a wavelet-based multi-resolution analysis, power spectra of the time-series of atmospheric transmittance were compared for single-station and spatially averaged observations, and for different sky conditions. It is shown that both the absolute magnitude and the scale-dependence of variability contains characteristic features for the different sky conditions.

We present a neural network that predicts HDR outdoor illumination from a single LDR image. At the heart of our work is a method to accurately learn HDR lighting from LDR panoramas under any weather condition. We achieve this by training another CNN (on a combination of synthetic and real images) to take as input an LDR panorama, and regress the parameters of the Lalonde-Matthews outdoor illumination model. This model is trained such that it a) reconstructs the appearance of the sky, and b) renders the appearance of objects lit by this illumination. We use this network to label a large-scale dataset of LDR panoramas with lighting parameters and use them to train our single image outdoor lighting estimation network. We demonstrate, via extensive experiments, that both our panorama and single image networks outperform the state of the art, and unlike prior work, are able to handle weather conditions ranging from fully sunny to overcast skies., 8 pages, CVPR 19. Project page: http://lvsn.github.io/allweather

Clouds are a key modulator of the Earth energy budget at the top of the atmosphere and at the surface. While the cloud top height is operationally retrieved with global coverage, only few methods have been proposed to determine cloud base height (zbase) from satellite measurements. This study presents a new approach to retrieve cloud base heights using the Multi-angle Imaging SpectroRadiometer (MISR) on the Terra satellite. It can be applied if some cloud gaps occur within the chosen distance of typically 10 km. The MISR cloud base height (MIBase) algorithm then determines zbase from the ensemble of all MISR cloud top heights retrieved at a 1.1 km horizontal resolution in this area. MIBase is first calibrated using 1 year of ceilometer data from more than 1500 sites within the continental United States of America. The 15th percentile of the cloud top height distribution within a circular area of 10 km radius provides the best agreement with the ground-based data. The thorough evaluation of the MIBase product zbase with further ceilometer data yields a correlation coefficient of about 0.66, demonstrating the feasibility of this approach to retrieve zbase. The impacts of the cloud scene structure and macrophysical cloud properties are discussed. For a 3-year period, the median zbase is generated globally on a 0.25∘ × 0.25∘ grid. Even though overcast cloud scenes and high clouds are excluded from the statistics, the median zbase retrievals yield plausible results, in particular over ocean as well as for seasonal differences. The potential of the full 16 years of MISR data is demonstrated for the southeast Pacific, revealing interannual variability in zbase in accordance with reanalysis data. The global cloud base data for the 3-year period (2007–2009) are available at https://doi.org/10.5880/CRC1211DB.19.

In this study, Himawari-8 Advanced Himawari Imager (AHI) longwave channel data that is sensitive to clouds and absorption gas were used to improve the accuracy of the algorithm used to calculate outgoing longwave radiation (OLR) at the top of the atmosphere. A radiative transfer model with a variety of atmospheric conditions was run using Garand vertical profile data as input data. The results of the simulation showed that changes in AHI channels 8, 12, 15, and 16, which were used to calculate OLR, were sensitive to changes in cloud characteristics (cloud optical thickness and cloud height) and absorption gases (water vapor, O3, CO2, aerosol optical thickness) in the atmosphere. When compared to long-term analysis OLR data from 2017, as recorded by the Cloud and Earth’s Radiant Energy System (CERES), the OLR calculated in this study had an annual mean bias of 2.28 Wm−2 and a root mean square error (RMSE) of 11.03 Wm−2. The new calculation method mitigated the problem of overestimations in OLR in mostly cloudy and overcast regions and underestimated OLR in cloud-free desert regions. It is also an improvement over the result from the existing OLR calculation algorithm, which uses window and water vapor channels.

Observations from the new Japanese geostationary satellite Himawari-8 permit quasi-real-time estimation of global shortwave radiation at an unprecedented temporal resolution. However, accurate comparisons with ground-truthing observations are essential to assess their uncertainty. In this study, we evaluated the Himawari-8 global radiation product AMATERASS using observations recorded at four SKYNET stations in Japan and, for certain analyses, from the surface network of the Japanese Meteorological Agency in 2016. We found that the spatiotemporal variability of the satellite estimates was smaller than that of the ground observations; variability decreased with increases in the time step and spatial domain. Cloud variability was the main source of uncertainty in the satellite radiation estimates, followed by direct effects caused by aerosols and bright albedo. Under all-sky conditions, good agreement was found between satellite and ground-based data, with a mean bias in the range of 20–30 W m−2 (i.e., AMATERASS overestimated ground observations) and a root mean square error (RMSE) of approximately 70–80 W m−2. However, results depended on the time step used in the validation exercise, on the spatial domain, and on the different climatological regions. In particular, the validation performed at 2.5 min showed largest deviations and RMSE values ranging from about 110 W m−2 for the mainland to a maximum of 150 W m−2 in the subtropical region. We also detected a limited overestimation in the number of clear-sky episodes, particularly at the pixel level. Overall, satellite-based estimates were higher under overcast conditions, whereas frequent episodes of cloud-induced enhanced surface radiation (i.e., measured radiation was greater than expected clear-sky radiation) tended to reduce this difference. Finally, the total mean bias was approximately 10–15 W m−2 under clear-sky conditions, mainly because of overall instantaneous direct aerosol forcing efficiency in the range of 120–150 W m−2 per unit of aerosol optical depth (AOD). A seasonal anticorrelation between AOD and global radiation differences was evident at all stations and was also observed within the diurnal cycle.

Artificial skyglow, the fraction of artificial light at night that is emitted upwards from Earth and subsequently scattered back within the atmosphere, depends on atmospheric conditions but also on the ground albedo. One effect that has not gained much attention so far is the amplification of skyglow by snow, particularly in combination with clouds. Snow, however, has a very high albedo and can become important when the direct upward emission is reduced when using shielded luminaires. In this work, first results of skyglow amplification by fresh snow and clouds measured with all-sky photometry in a suburban area are presented. Amplification factors for the zenith luminance of 188 for snow and clouds in combination and 33 for snow alone were found at this site. The maximum zenith luminance of nearly 250 mcd/m&sup2, measured with snow and clouds is a factor of 1000 higher than the commonly used clear sky reference of 0.25 mcd/m&sup2, Compared with our darkest zenith luminance of 0.07 mcd/m&sup2, measured for overcast conditions in a very remote area, this leads to an overall amplification factor of ca. 3500. Horizontal illuminance measurements show values of up to 0.79 lx, exceeding maximum possible full-moon illuminance levels by more than a factor of two. Additional measurements near the Arctic Circle for clear and overcast conditions are presented and strategies for further studies are discussed. We propose the term &ldquo, snowglow&rdquo, to describe the amplification of skyglow by snow with and without clouds.

To facilitate the construction of a satellite-derived 2 m air temperature (T2 m) product for the snow- and ice-covered regions in the Arctic, observations from weather stations are used to quantify the relationship between the T2 m and skin temperature (Tskin). Multiyear data records of simultaneous Tskin and T2 m from 29 different in situ sites have been analysed for five regions, covering the lower and upper ablation zone and the accumulation zone of the Greenland Ice Sheet (GrIS), sea ice in the Arctic Ocean, and seasonal snow-covered land in northern Alaska. The diurnal and seasonal temperature variabilities and the impacts from clouds and wind on the T2 m–Tskin differences are quantified. Tskin is often (85 % of the time, all sites weighted equally) lower than T2 m, with the largest differences occurring when the temperatures are well below 0 ∘C or when the surface is melting. Considering all regions, T2 m is on average 0.65–2.65 ∘C higher than Tskin, with the largest differences for the lower ablation area and smallest differences for the seasonal snow-covered sites. A negative net surface radiation balance generally cools the surface with respect to the atmosphere, resulting in a surface-driven surface air temperature inversion. However, Tskin and T2 m are often highly correlated, and the two temperatures can be almost identical ( ∘C difference), with the smallest T2–Tskin differences around noon and early afternoon during spring, autumn and summer during non-melting conditions. In general, the inversion strength increases with decreasing wind speeds, but for the sites on the GrIS the maximum inversion occurs at wind speeds of about 5 m s−1 due to the katabatic winds. Clouds tend to reduce the vertical temperature gradient, by warming the surface, resulting in a mean overcast T2 m–Tskin difference ranging from −0.08 to 1.63 ∘C, with the largest differences for the sites in the low-ablation zone and the smallest differences for the seasonal snow-covered sites. To assess the effect of using cloud-limited infrared satellite observations, the influence of clouds on temporally averaged Tskin has been studied by comparing averaged clear-sky Tskin with averaged all-sky Tskin. To this end, we test three different temporal averaging windows: 24 h, 72 h and 1 month. The largest clear-sky biases are generally found when 1-month averages are used and the smallest clear-sky biases are found for 24 h. In most cases, all-sky averages are warmer than clear-sky averages, with the smallest bias during summer when the Tskin range is smallest.

Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive-microwave (MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25° resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product.This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR-based LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

Light pipes offer a passive way to bring daylight inside deep buildings, such as agricultural buildings. However, the lack of reliable performance predictability methods for light pipes represents a major obstacle preventing their widespread use. This paper evaluates a simulation approach for performance prediction and identifies key light pipe design parameters affecting their daylight transmission performance. The study was carried out through continuous monitoring of daylight in two full-scale, identical pig stables fitted with two light pipe systems, Solatube® and Velux®. The experiment included three continuously measuring sensors in each stable and an outdoor sensor during 2013 and 2014. A forward raytracing tool, TracePro®, was used for illuminance prediction and parametric simulations. The simulation results for overcast skies indicated discrepancies between the simulated and average measurement results below 30% in all cases. The discrepancies for clear skies were somewhat higher, i.e., below 30% for 67% of the cases. The higher discrepancies with clear skies were due to the overestimation of absolute sunlight levels and absence of an advanced and detailed optical characterization of the dome collector’s surface. The parametric results have shown that light pipes’ performance is better during summer time, in sunny climates, at low to mid-latitudes, which provides higher solar altitudes than during winter and cloudy climates at high latitudes. Methods to improve the luminous transmittance for low solar altitudes occurring in Scandinavia include: bending or tilting the pipe, increasing the aspect ratio, improving the pipe specular reflectance, tilting the collector to the south, and using optical redirecting system in the collector. (Less)

UAS become a very valuable tool for coastal morphology. Not only for mapping but also for change detection and a better understanding of processes along and across the shore. This contribution investigates the possibilities of UAS to determine the water depth in clear shallow waters by means of the so called "photo bathymetry". From the results of several test flights it became clear that three factors influence the ability and the accuracy of bathymetric sea floor measurements. Firstly, weather conditions. Sunny weather is not always good. Due to the high image resolution the sunlight gets focussed even in very small waves causing moving patterns on shallow grounds with high reflection properties, such as sand. This effect invisible under overcast weather conditions. Waves, may also introduce problems and mismatches. Secondly the quality and the accuracy of the georeferencing with SFM algorithms. As multi image key point matching will not work over water, the proposed approach will only work for projects closely to the coastline with enough control on the land. Thirdly the software used and the intensity of post processing and filtering. Refraction correction and the final interpolation of the point cloud into a DTM are the last steps. If everything is done appropriately, accuracies in the bathymetry in the range of 10 – 50 cm, depending on the water depth are possible.

The marine boundary layer (MBL) structure is important to the marine low cloud processes, and the exchange of heat, momentum, and moisture between oceans and the low atmosphere. This study examines the MBL structure over the eastern Pacific region and further explores the controlling factors of MBL structure over the global oceans with a new 4-year satellite-based data set. The MBL top (boundary layer height, BLH) and the mixing layer height (MLH) were identified using the MBL aerosol lidar backscattering from the CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations). Results showed that the MBL is generally decoupled with MLH ∕ BLH ratio ranging from ∼ 0.5 to ∼ 0.8 over the eastern Pacific Ocean region. The MBL decoupling magnitude is mainly controlled by estimated inversion strength (EIS), which in turn controls the cloud top entrainment process. The systematic differences between drizzling and non-drizzling stratocumulus tops also show dependence on EIS. This may be related to the meso-scale circulations or gravity wave in the MBL. Further analysis indicates that the MBL shows a similar decoupled structure for clear-sky and cumulus-cloud-topped conditions, but is better mixed under stratiform cloud breakup and overcast conditions.

Daylighting is recognized as an important and useful strategy in the design of energy efficient buildings. Daylight is still the best source of light for good colour rendering and visual comfort. In this study, a new model of global luminous efficacy over a horizontal surface is proposed. A comparative study of eighteen classic models is presented, to obtain global horizontal illuminance, using both, the original formulation and new formulae with local adaptations, in order to determine the most suitable models for the conditions in Burgos (Spain). With this aim in mind, the selected models consisted of six models developed for all sky conditions, five models for clear sky conditions, three for partly cloudy sky and four for modelling overcast sky conditions. These eighteen models were also compared with the proposed model using experimental global illuminance measurements for different sky conditions. It was shown that the proposed model behaved in a better way than most of the classic models selected from the literature; both for all sky conditions and for particular sky conditions (clear, partly cloudy and overcast). The proposed model was therefore generally applicable, with no need to employ a different model for each particular sky condition., Spanish Government (Ministerio de Economía y Competitividad) (ENE2014-54601-R)

In the absence of ground reflected radiation measured data, an average albedo value of 0.2, which describes the reflective properties of the bare ground is generally used. The variation of albedo based on different foreground surfaces and under different atmospheric conditions is an area which is under research. This paper presents an experimental investigation of albedos of different foreground materials that can be used for photovoltaic (PV) applications. Overcast skies are predominant in northerly locations and will have a high frequency of low solar altitudes, and thus ground reflection is an important contributor towards total solar energy gain. The foreground surfaces include common materials: Grass, sand, and cement slabs, and some non-conventional materials: White pebbles, white boards, white tiles, and aluminium foil. The impact of factors, such as ageing, solar elevation, rain, and cloud cover (sky conditions) is analysed to determine the changes in albedos of these materials. Each material was observed to have individual performance characteristics under these factors. It was found that the non-conventional materials were least prone to weather-related changes and have higher albedo values as compared to the conventional materials, and also have good potential to replace the conventional materials for any given PV application.

Previous studies have suggested that shading by riparian vegetation may reduce maximum water temperatures and provide refugia for temperature-sensitive aquatic organisms. Longitudinal cooling gradients have been observed during the daytime for stream reaches shaded by coniferous trees downstream of clear cuts or deciduous woodland downstream of open moorland. However, little is known about the energy exchange processes that drive such gradients, especially in semi-natural woodland contexts without confounding cool groundwater inflows. To address this gap, this study quantified and modelled variability in stream temperature and heat fluxes along an upland reach of the Girnock Burn (a tributary of the Aberdeenshire Dee, Scotland) where riparian land use transitions from open moorland to semi-natural, predominantly deciduous woodland. Observations were made along a 1050 m reach using a spatially distributed network of 10 water temperature data loggers, 3 automatic weather stations and 211 hemispherical photographs that were used to estimate incoming solar radiation. These data parameterised a high-resolution energy flux model incorporating flow routing, which predicted spatio-temporal variability in stream temperature. Variability in stream temperature was controlled largely by energy fluxes at the water-column–atmosphere interface. Net energy gains occurred along the reach, predominantly during daylight hours, and heat exchange across the bed–water-column interface accounted for 1 h. Temperature gradients were not generated by cooling of stream water but rather by a combination of reduced rates of heating in the woodland reach and advection of cooler (overnight and early morning) water from the upstream moorland catchment. Longitudinal thermal gradients were indistinct at night and on days when net radiation gains were low (under overcast skies), thus when changes in net energy gains or losses did not vary significantly in space and time, and heat advected into the reach was reasonably consistent. The findings of the study and the modelling approach employed are useful tools for assessing optimal planting strategies for mitigating against ecologically damaging stream temperature maxima.