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In this study, single and aggregated forecasters of half-hourly global horizontal irradiance are assessed. The models are the standard persistent model and four newly proposed static, dynamic, moving average, and amplified persistent models. These sub-forecasters are aggregated using equal, annual optimal, and monthly optimal weights. A particle swarm optimizer was used to find those weights. Measured data, obtained from two desert sites for the years 2015–2018, was used for fitting and training the different models, while the data of the year 2019 was used to test their prediction capabilities. For the single forecasters, the dynamic model is the most accurate, followed by the static and average models. When the aggregated model of annual optimal weights was tested, the three contributing forecasters were the dynamic, average, and amplified models. The dynamic forecaster held the largest weight due to its prediction superiority during overcast and partially cloudy days. When monthly optimal weights were used, all forecasters contributed, and the dynamic model held the largest weight during winter but not in the summer when the clear sky condition is dominant. The aggregated model was the most precise, with relative mean square errors lower than 15.0% and coefficients of determination higher than 98.8%.

© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.Meteorological drought analyses were conducted for Eastern Mediterranean, Seyhan, Ceyhan, and Asi Basins located in the south of Turkey. The gauged data of total precipitation, average temperature, relative humidity, number of sunny days, and number of overcast days, all in monthly steps, obtained from 35 meteorological stations were used as drought indicators. Aggregate drought index (ADI) was used as drought index calculator. Meteorological data series were arranged in accordance with the hydrological years starting on October 1st and ending on September 30th. The ADI results were organized in order to see the variation of the ADI values of the stations from year to year. ADI thresholds were determined individually for all stations. The ADI results obtained by using the principal component analysis (PCA) were presented in form of drought maps by using the inverse distance weighting (IDW) method. The regional drought frequency analysis by the L-moments method was performed to determine extreme ADI values corresponding to generally referred to specific average return periods. Using both the growth curves yielded by the most suitable probability distributions and the regression equations for the means, the extreme ADI values against these average return periods were computed and presented in tabular forms. According to the outcome of the ADI results, it can be concluded that a severe drought does not prevail over the study area as a whole, but some stations experience extreme droughts locally from time to time because of excessive irrigation water demand.

High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar irradiance provides a pivotal solution to ensure optimal use of the produced energy and reduce its uncertainty. This study proposes a hybrid convolutional neural network and Multilayer perceptron (CNN–MLP) model to forecast the global irradiance 15 min ahead. The model uses images from a hemispherical sky imager, time series of GHI, and weather variables collected from a ground meteorological station in Morocco. The evaluation of the proposed model under clear, mixed, and overcast days shows that the proposed model performs better than the persistence model. The root mean square error (RMSE) varies between 13.05 W/m2 and 49.16 W/m2 for CNN–MLP and between 45.76 W/m2 and 114.19 W/m2 for persistence. The coefficient of determination (R2) varies between 0.99 and 0.94 for the MLP–CNN and between 0.98 and 0.79 for persistence. The results show that the proposed model could be an appropriate choice for short-term forecasting even under cloudy conditions.

A crucial variable to evaluate thermal comfort is the mean radiant temperature (Tmrt). In this paper we evaluate the performance of the 150 mm black globe thermometer to provide reliable Tmrt values for outdoor settings in Singapore. Accurate Tmrt values are calculated by the method of integral radiation measurements. Based on these, the mean convection coefficient of the black globe has been re-calibrated. Results show an improvement in the estimation of Tmrt with the new coefficient in comparison with the default version suggested in ISO7726:1998. Increasing the averaging periods of the measured variables improved the performance of the derived mean convective coefficients to estimate Tmrt. During clear skies day and for 10-min averaged data, RMSE for Tmrt reduce to 3.9 °C (7.4 °C for ISO7726:1998 coefficient) with an overestimation on high incoming solar radiation periods and an underestimation during the morning and evening (low solar elevation). During overcast dry conditions an underestimation of Tmrt is also expected which is higher in the rain/wet periods. The mean convective coefficient presented in this work can provide improved estimations of Tmrt relevant for outdoor thermal comfort studies in hot and humid tropical climates like Singapore.

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.

In this article, we measured and estimated the outputs of car-mounted PV panels under actual driving conditions and different effective shading angles. On a sunny day, the performance ratio ( PR ) decreased to 0.99, despite the decrease in module temperature caused by the increased wind speed when driving. The PR decreased because of the effect of partial shading over the PV module. Public infrastructure could have led to dynamic partial shading passing across the PV module at high speeds. Furthermore, the effective shading angle being close to the sun's altitude could also have led to static partial shading. In contrast, on an overcast day the PR value was almost the same ( $>$ 1) under both driving and parking conditions; this was because of the low module temperature and the spectrum effect.

Imagine that we traveled into the future where more than 50% of electricity production is generated by wind and solar. Will you wonder how the grid will operate in an overcast, windless, extremely cold/hot day, where half of its generation fleet may become unavailable?

Knowledge of ultraviolet radiation (UVA) at the Earth’s surface is of great importance in different technological and scientific applications. In this study, UVA (315–400 nm) and global solar radiation (G, 400–1100 nm) data from Riyadh (Central Saudi Arabia) for the period between 2015 and April 2020 are used to characterize the UVA at different cloudiness levels. The sky was classified into several categories according to the clearness index. For all sky conditions, the mean values of UVA, G, and the ratio UVA/G were 11.58 ± 6.1 W/m2, 549.35 ± 264.55 W/m2, and 0.021 ± 0.003, respectively. While the mean UVA and G values under totally overcast skies were 5.51 ± 2.7 W/m2 and 267.90120.44 W/m2, respectively, the UVA was 21.63 ± 2.14 W/m2 and G was 933.19 ± 39.94 under clear skies. For partly cloudy skies, the amount of UVA and G increases as the sky gets clearer. The annual mean value of UVA decreased gradually from 2016 to 2018. In comparison with the mean value of UVA in 2016, the mean values of UVA were down about 7% and 18% in 2017 and 2018, respectively. This may be due to the heavy dust storms that occurred in those two years. The monthly and hourly variations in UVA and G are investigated and discussed. The distribution of the monthly values of UVA and G exhibits high symmetry. The UVA and G are at the maximum in summer and the minimum in winter. The amount of radiation appears similar in spring and summer and in autumn and winter. This may be explained by the seasonal symmetry of the summer and winter solstices. The hourly variations in the average UVA and G attain their minimum values in the early morning (06:00 local time) and reach their maximum values at around midday. Finally, several empirical models relating ultraviolet (UV), with solar global radiation (G), and the clearness index (Kt) under all sky conditions are established. The results reveal that the proposed empirical models accurately predict hourly values.

This study investigated the effect solar incidence angles and irradiance intensity on two commercially available semi-transparent organic photovoltaics panels. The results of electrical measurements taken for the panels at different solar incidence angles and in sunny and overcast conditions is presented. This data is compared to diurnal data of the same panels on a frame at 30° tilt facing south. It was found that the panels behaved very differently under different solar incidence angles and irradiance conditions. Panel A had high absorption from the underside of the panel, whereas panel B did not. Panel A efficiency and fill factor values were highest under lower irradiance and panel B efficiency was highest under higher irradiance. In sunny conditions, panel A efficiencies and fill factors increased with increasing solar incidence angles, indicating a higher absorption of the panel in lower irradiance. Panel B fill factor reduced, efficiency remained constant and % transmittance reduced with increased solar incidence angles, indicating reflected irradiance increased with increasing θ. Diurnal measurements showed that for both panels, efficiency and fill factor were higher in the morning compared to the afternoon with the same incident irradiance levels, indicating an overnight recovery phenomenon.

Robotic apple harvesting has received much research attention in the past few years due to growing shortage and rising cost in labor. One key enabling technology towards automated harvesting is accurate and robust apple detection, which poses great challenges as a result of the complex orchard environment that involves varying lighting conditions and foliage/branch occlusions. This letter reports on the development of a novel deep learning-based apple detection framework named Suppression Mask R-CNN. Specifically, we first collect a comprehensive apple orchard dataset for "Gala" and "Blondee" apples, using a color camera, under different lighting conditions (overcast and front lighting vs. back lighting). We then develop a novel suppression Mask R-CNN for apple detection, in which a suppression branch is added to the standard Mask R-CNN to suppress non-apple features generated by the original network. Comprehensive evaluations are performed, which show that the developed suppression Mask R-CNN network outperforms state-of-the-art models with a higher F1-score of 0.905 and a detection time of 0.25 second per frame on a standard desktop computer.

Trees in northern latitude ecosystems are projected to experience increasing drought stress as a result of rising air temperatures and changes in precipitation patterns in northern latitude ecosystems. However, most drought-related studies on high-latitude boreal forests (>50 degrees N) have been conducted in North America, with few studies quantifying the response in European and Eurasian boreal forests. Here, we tested how daily whole-tree transpiration (Q, Liters day(-1)) and Q normalized for mean daytime vapor pressure deficit (Q(DZ), Liters day(-1) kPa(-1)) were affected by the historic 2018 drought in Europe. More specifically, we examined how tree species, size, and topographic position affected drought response in high-latitude mature boreal forest trees. We monitored 30 Pinus sylvestris (pine) and 30 Picea abies (spruce) trees distributed across a topographic gradient in northern Sweden. In general, pine showed a greater Q(DZ) control compared to spruce during periods of severe drought (standardized precipitation-evapotranspiration index: SPEI

This study is based on the fifteen standard sky models proposed by the International Commission of Illumination (CIE) and explores the sky luminance distribution throughout the severe cold regions of China. The experiment analysis is based on 22,829 sets of effective luminance data from January 2019 to December 2019. The Tregenza method and the normalization ratio method are used to analyze changes in the sky environment throughout the year in Northeast China. The results indicate that the standard sky models proposed by the CIE can represent the basic characteristics of the sky over a whole year in Harbin, but the frequency of each sky type is clearly different. Type V.4 (CIE standard clear sky, low illuminance probability) has a frequency exceeding 22% over the year, and is the most common type of sky in Harbin, while type VI.6 (white blue turned sky with broad solar corona) has the lowest frequency type. The use of these two methods indicates that the frequency of clear sky in Harbin over the year exceeds 50%, and this mainly occurs in spring, autumn and winter. The probability of overcast and partly cloudy skies is over 20%, and in summer an overcast sky is more probable. When comparing against different solar elevations, it is found that for solar elevations below 50°, V.4 is the most common sky type with a probability above 15%, whilst for solar elevations above 50°, type I.1 is the most frequent.

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.

The retrieval of aerosol parameters from passive satellite instruments in cloudy scenes is challenging, partly because clouds and cloud-related processes may significantly modify aerosol optical depth (AOD) and particle size, a problem that is further compounded by 3D radiative processes. Recent advances in retrieval algorithms such as the “color ratio” method, which utilizes the measurements at a shorter (470 nm) and a longer (860 nm) wavelength, have demonstrated the simultaneous derivation of AOD and cloud optical depth (COD) for scenes in which absorbing aerosols are found to overlay low-level cloud decks. This study shows simultaneous retrievals of above-cloud aerosol optical depth (ACAOD) and aerosol-corrected cloud optical depth (COD) from airborne measurements of cloud-reflected and sky radiances using the color ratio method. These airborne measurements were taken over marine stratocumulus clouds with NASA's Cloud Absorption Radiometer (CAR) during the SAFARI 2000 field campaign offshore of Namibia. The ACAOD is partitioned between the AOD below-aircraft (AOD_cloudtop) and above-aircraft AOD (AOD_sky). The results show good agreement between AOD_sky and sun-photometer measurements of the above-aircraft AOD. The results also show that the use of aircraft-based sun-photometer measurements to validate satellite retrievals of the ACAOD is complicated by the lack of information on AOD below aircraft. Specifically, the CAR-retrieved AOD_cloudtop captures this “missing” aerosol layer caught between the aircraft and cloud top, which is required to quantify above-cloud aerosol loading and effectively validate satellite retrievals. In addition, the study finds a strong anticorrelation between the AOD_cloudtop and COD for cases in which COD < 10 and a weaker anticorrelation for COD > 10, which may be associated with the uncertainties in the color ratio method at lower AODs and CODs. The influence of 3D radiative effects on the retrievals is examined, and the results show that at cloud troughs, 3D effects increase retrieved ACAOD by about 3 %–11 % and retrieved COD by about 25 %. The results show that the color ratio method has little sensitivity to 3D effects at overcast stratocumulus cloud decks. These results demonstrate a novel airborne measurement approach for assessing satellite retrievals of aerosols above clouds, thereby filling a major gap in global aerosol observations.

Retrieving surface properties from airborne hyperspectral imagery requires the use of an atmospheric correction model to compensate for atmospheric scattering and absorption. In this study, a solar spectral irradiance monitor (SSIM) from the University of Colorado Boulder was flown on a Twin Otter aircraft with the National Ecological Observatory Network’s (NEON) imaging spectrometer. Upwelling and downwelling irradiance observations from the SSIM were used as boundary conditions for the radiative transfer model used to atmospherically correct NEON imaging spectrometer data. Using simultaneous irradiance observations as boundary conditions removed the need to model the entire atmospheric column so that atmospheric correction required modeling only the atmosphere below the aircraft. For overcast conditions, incorporating SSIM observations into the atmospheric correction process reduced root-mean-square (rms) error in retrieved surface reflectance by up to 57% compared with a standard approach. In addition, upwelling irradiance measurements were used to produce an observation-based estimate of the adjacency effect. Under cloud-free conditions, this correction reduced the rms error of surface reflectance retrievals by up to 27% compared with retrievals that ignored adjacency effects.

Increased concentration of airborne particulate matter (PM) in the atmosphere alters the degree of polarization of skylight which is used by honeybees for navigation during their foraging trips. However, little has empirically shown whether poor air quality indeed affects foraging performance (foraging trip duration) of honeybee. Here, we show apparent increases in the average duration of honeybee foraging during and after a heavy air pollution event compared with that of the pre‐event period. The average foraging duration of honeybees during the event increased by 32 min compared with the pre‐event conditions, indicating that 71% more time was spent on foraging. Moreover, the average foraging duration measured after the event did not recover to its pre‐event level. We further investigated whether an optical property (Depolarization Ratio, DR) of dominant PM in the atmosphere and level of air pollution (fine PM mass concentration) affect foraging trip duration. The result demonstrates the DR and fine PM mass concentration have significant effects on honeybee foraging trip duration. Foraging trip duration increases with decreasing DR while it increases with increasing fine PM mass concentration. In addition, the effects of fine PM mass concentration are synergistic with overcast skies. Our study implies that poor air quality could pose a new threat to bee foraging., The average foraging duration of honeybees during the event increased by 32 min compared with the pre‐event conditions, indicating that 71% more time is spent on foraging.

Crime analysis is important for social security management. With the advance of crowd sensing techniques, abundant multisource crowd sensed data could be used for crime analysis. The occurrence of crimes usually has some patterns in terms of temporal and spatial aspects. Investigating the spatio-temporal correlation of crimes could provide more useful cues for crime analysis and help discover underlying crime patterns. In this paper, we conduct a spatio-temporal study to understand urban crimes leveraging multisource crowd sensed data, including crime data, meteorological data, POI distribution, and taxi trips. Specifically, we first present monthly temporal trends and spatial distribution of crimes. We then investigate the spatio-temporal correlation using meteorological data (e.g., weather conditions and air temperature) and POI distribution and taxi trips. It is found that taxi trips and air temperature have a strong correlation with the crime, and some POI categories have a valuable correlation with the crime, e.g., College & University. We also find that Overcast days would witness more crime than other weather conditions.

I was riding my bike on a late Sunday morning of Memorial Day weekend 2020, sensing that the world was coming back to life. The sun was burning through the overcast of the earlier morning, lighting...

Climate variability in the North Atlantic influences processes such as hurricane activity and droughts. Global model simulations have identified aerosol–cloud interactions (ACIs) as an important driver of sea surface temperature variability via surface aerosol forcing. However, ACIs are a major cause of uncertainty in climate forcing; therefore, caution is needed in interpreting the results from coarse-resolution, highly parameterized global models. Here, we separate and quantify the components of the surface shortwave effective radiative forcing (ERF) due to aerosol in the atmosphere-only version of the UK Earth System Model (UKESM1) and evaluate the cloud properties and their radiative effects against observations. We focus on a northern region of the North Atlantic (NA) where stratocumulus clouds dominate (denoted the northern NA region) and a southern region where trade cumulus and broken stratocumulus dominate (southern NA region). Aerosol forcing was diagnosed using a pair of simulations in which the meteorology is approximately fixed via nudging to analysis; one simulation has pre-industrial (PI) and one has present-day (PD) aerosol emissions. This model does not include aerosol effects within the convective parameterization (but aerosol does affect the clouds associated with detrainment) and so it should be noted that the representation of aerosol forcing for convection is incomplete. Contributions to the surface ERF from changes in cloud fraction (fc), in-cloud liquid water path (LWPic) and droplet number concentration (Nd) were quantified. Over the northern NA region, increases in Nd and LWPic dominate the forcing. This is likely because the already-high fc there reduces the chances of further large increases in fc and allows cloud brightening to act over a larger region. Over the southern NA region, increases in fc dominate due to the suppression of rain by the additional aerosols. Aerosol-driven increases in macrophysical cloud properties (LWPic and fc) will rely on the response of the boundary layer parameterization, along with input from the cloud microphysics scheme, which are highly uncertain processes. Model grid boxes with low-altitude clouds present in both the PI and PD dominate the forcing in both regions. In the northern NA, the brightening of completely overcast low cloud scenes (100 % cloud cover, likely stratocumulus) contributes the most, whereas in the southern NA the creation of clouds with fc of around 20 % from clear skies in the PI was the largest single contributor, suggesting that trade cumulus clouds are created in response to increases in aerosol. The creation of near-overcast clouds was also important there. The correct spatial pattern, coverage and properties of clouds are important for determining the magnitude of aerosol forcing, so we also assess the realism of the modelled PD clouds against satellite observations. We find that the model reproduces the spatial pattern of all the observed cloud variables well but that there are biases. The shortwave top-of-the-atmosphere (SWTOA) flux is overestimated by 5.8 % in the northern NA region and 1.7 % in the southern NA, which we attribute mainly to positive biases in low-altitude fc. Nd is too low by −20.6 % in the northern NA and too high by 21.5 % in the southern NA but does not contribute greatly to the main SWTOA biases. Cloudy-sky liquid water path mainly shows biases north of Scandinavia that reach between 50 % and 100 % and dominate the SWTOA bias in that region. The large contribution to aerosol forcing in the UKESM1 model from highly uncertain macrophysical adjustments suggests that further targeted observations are needed to assess rain formation processes, how they depend on aerosols and the model response to precipitation in order to reduce uncertainty in climate projections.

Sky conditions, and the corresponding luminance and radiance distribution patterns are essential to daylighting, energy and thermal environmental studies. The CIE Standard Skies define the overcast, partly cloudy, and clear sky conditions intuitively by rigorous luminance distributions, which are, however, usually determined by sophisticated and uncommon measurements (e.g. radiation in multiple vertical directions). This study proposes a simple approach to identifying the hourly sky conditions from the global horizontal irradiance (EHG) that is most widely accessible in weather stations with basic radiation measurement facilities. The sophisticated measurements are, therefore, avoided for simple applications. The partly cloudy sky, especially, is identified by the notable disparity of its EHG measurement from the theoretical cloudless condition. The proposed approach interprets the ISO/CIE Standard Sky conditions and their diffuse luminance and radiance distributions successfully. From the datasets at two sites with different climates, the approach correctly identifies 7% more sky conditions than a previous work when the direct beam and diffuse radiation measurements were not accessible. The model can thus be essential to solar energy studies for places with ground-based solar radiation measurements only.

The improvement of cloud modelling for global and regional climate and weather studies requires comprehensive information on many cloud parameters. This information is delivered by remote observations of clouds from ground-based and space-borne platforms using different methods and processing algorithms. Cloud liquid water path (LWP) is one of the main obtained quantities. Previously, the measurements of LWP by the SEVIRI and AVHRR satellite instruments provided the evidences of the systematic differences between LWP values over land and water areas in Northern Europe. An attempt is made to detect such differences by means of ground-based microwave observations performed near the coastline of the Gulf of Finland in the vicinity of St. Petersburg, Russia. The microwave radiometer RPG HATPRO located 2.5 km from the coastline is functioning in the angular scanning mode and is probing the air portions over land (at elevation angle 90°) and over water area (at 7 elevation angles in the range 4.8°–30°). The problem of the LWP horizontal gradient detection is examined in the measurement domain: the brightness temperatures of the microwave radiation measured at different elevation angles in the 31.4 GHz and 22.24 GHz spectral channels are analysed and compared with the corresponding values which were calculated under the assumption of horizontal homogeneity of the atmosphere. Several specific cases, selected on the basis of the analysis of the satellite observations by the SEVIRI instrument were considered in detail including: clear-sky conditions, the presence of clouds over the radiometer and at the same time the absence of clouds over the Gulf of Finland, and overcast conditions over the radiometer and over the opposite shore of the Gulf of Finland. The influence of the land-sea LWP difference on the brightness temperature values in the 31.4 GHz spectral channel has been demonstrated and the following features have been detected: (1) an interfering systematic signal is present in the 31.4 GHz channel which can attributed to the humidity horizontal gradient; (2) clouds over the opposite shore of the Gulf of Finland mask the LWP gradient effect. Preliminary results of the retrieval of LWP over water by statistical regression method are presented. These monthly averaged results are compared to the corresponding values derived from the satellite observations by the SEVIRI instrument. The agreement between satellite and ground-based results is very good for warm season in terms of temporal behaviour if systematic difference is neglected.

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.

In situ profiles and fixed-altitude time series of all four components of net radiation were obtained at Ny-Ålesund, Svalbard (78.9° N, 11.9° E), in the period May 04–21, 2015. Measurements were performed using adapted high-quality instrumentation classified as “secondary standard” carried by a tethered balloon system. Balloon-lifted measurements of albedo under clear-sky conditions demonstrate the local dependence on altitude and on the surface inhomogeneity of this parameter over coastal terrain of Ny-Ålesund. Depending on the surface composition within the sensor’s footprint near the coastline, the albedo over predominantly snow-covered surfaces was found to decrease to 0.548 and 0.452 at 494 m and 881 m altitude compared with 0.731 and 0.788 measured with near-surface references, respectively. Albedo profiles show an all-sky maximum at 150 m above surface level due to local surface inhomogeneity, and an averaged vertical change rate of − 0.040/100 up to 750 m aboveground level (clear sky) and − 0.034/100 m (overcast). Profiling of arctic low-level clouds reveals distinct vertical gradients in all radiative fluxes but longwave upward at the cloud top. Observed radiative cooling at the top of a partly dissolving stratus cloud with heating rates of − 40.4 to − 62.1 Kd−1 in subsequent observations is exemplified., Bundesrepublik Deutschland, Deutscher Wetterdienst, vertreten durch den Vorstand, Deutsche Meteorologische Bibliothek (4242)

We estimated the Korea Shortwave Radiation Budget (KSRB) providing new insights into the Korea climate system. Monthly averaged clear-sky, overcast-sky, and all-sky flux measurements with cloud amounts from 2000 to 2015 were used to assess the impacts of cloud on the KSRB. A deterministic model for shortwave radiation transfer was utilized with climatological data to compute the monthly mean of KSRB at the surface, in the atmosphere and at the top-of-atmosphere (TOA). For 10 Korean stations ranging from 33.5°N to 37.7°N, the daily mean of incoming solar flux at TOA is 345.2 Wm−2. 45.5% of that is transmitted to the surface, 25.0% is absorbed in the atmosphere and 29.5% is reflected directly back to space by the cloud, atmosphere and ground surface under all-sky conditions. Under clear- and overcast-sky conditions, 63.2% and 24.9% are transmitted to the surface, 17.8% and 37.1% are absorbed in the atmosphere, whereas 19.0% and 38.0% are reflected to space, respectively, showing a remarkably invariant value with respect to locations. Clouds in all- (or overcast-) sky atmosphere diminish surface solar irradiances (SSI) from 218.1 Wm−2 to 156.9 Wm−2 (or 85.8 Wm−2) and enhance atmospheric absorptions (AA) from 61.5 Wm−2 to 86.3 Wm−2 (or 128.2 Wm−2). Clouds also enhance the reflected irradiances (RI) at the TOA from 65.6 Wm−2 to 102.0 Wm−2 (or 131.2 Wm−2) for all- (or overcast-) skies. As a result, the all- (or overcast-) sky shortwave (SW) cloud forcing (CF) is −61.2 Wm−2 (or −132.3 Wm−2) at the surface, AA is 24.8 Wm−2 (or 66.7 Wm−2) in the atmosphere and RI is 36.4 Wm−2 (or 65.6 Wm−2) at the TOA, respectively. Consequently, it is found that the values for SW-CF at the surface is 1.7 times and 2.0 times greater than that at the TOA in the KSRB under all- and overcast-sky conditions, respectively. We have also compared the KSRB with the global shortwave radiation budget (GSRB) and discussed on the shortwave radiation budget.

The Tibetan Plateau (TP) is one of the research hot spots in the climate change research due to its unique geographical location and high altitude. Downward longwave radiation (DLR), as a key component in the surface energy budget, has practical implications for radiation budget and climate change. A couple of attempts have been made to parametrize DLR over the TP based on hourly or daily measurements and crude clear-sky discrimination methods. This study uses 1 min shortwave and longwave radiation measurements at three stations over the TP to parametrize DLR during summer months. Three independent methods are used to discriminate clear sky from clouds based on 1 min radiation and lidar measurements. This guarantees the strict selection of clear-sky samples that is fundamental for the parametrization of clear-sky DLR. A total of 11 clear-sky and 4 cloudy DLR parametrizations are examined and locally calibrated. Compared to previous studies, DLR parametrizations here are shown to be characterized by smaller root-mean-square errors (RMSEs) and higher coefficients of determination (R2). Clear-sky DLR can be estimated from the best parametrization with a RMSE of 3.8 W m−2 and R2>0.98. Systematic overestimation of clear-sky DLR by the locally calibrated parametrization in one previous study is found to be approximately 25 W m−2 (10 %), which is very likely due to potential residual cloud contamination on previous clear-sky DLR parametrization. The cloud base height under overcast conditions is shown to play an important role in cloudy DLR parametrization, which is considered in the locally calibrated parametrization over the TP for the first time. Further studies on DLR parametrization during nighttime and in seasons except summer are required for our better understanding of the role of DLR in climate change.

Weather forecasting is an emerging domain that predicts the weather condition at a particular location at a particular time. Weather forecasting is considered as the most sensitive research field which facing a lot of real-time issues such as inaccurate prediction, lack of handling in huge data volume and inadequate in technology advancement. In this paper, we propose the SPRINT algorithm which is works with the principle of the decision tree. The experimental work is carried out with climate dataset and applied on WEKA tool. Based on the climate parameters such as Outlook, Temperature, Humidity, and Windy the data is classified into sunny, overcast and rainy. From the obtained result the weather is predicted, to prove the proposed methods of proficiency in accuracy level. Performance comparison is done with the existing method navie Bayes, both results are plotted on the graph. The outcome proves SPRINT algorithm is efficient and accurate in predicting the weather conditions.

In the passive house system in cold regions today, the strategies focus on reducing energy loss by enhancing the thermal insulation performance of the building’s envelope. Yet, under the specific background, the application of passive house evaluation system lacks emphasis on the significance of solar heat gain. In this paper, two independent passive houses in cold areas in China were selected as research objects, which had almost the same geographic locations, building shapes, and floor plans, through simulation and on-site measurement to discuss the influence of solar heat gain on non-heating sunny and overcast winter days on the indoor thermal environment. The result shows that the house which had a weaker envelope in thermal insulation performance but more solar heat gain performed better than the one which had a stronger envelope but less solar heat gain. It indicates that in the passive house system in cold areas in China, solar heat gain is of great significance and has equal status with the thermal insulation performance of envelopes, which has significant reference value for the early stages of passive house designing and the evaluation criteria.

This study examines the performance of the estimated solar radiation components obtained via the Meteorological Radiation Model, satellite-based data sets (CAMS, PVGIS-CMSAF-SARAH) and reanalysis (PVGIS-ERA5) against ground measurements taken with the Sunshine-Pyranometer at Methoni station, Greece. MRM shows satisfactory simulations for the global solar irradiation (R2 = 0.97, RMSE = 11.5%, MBE = −2.5%) at 15-min time-intervals, while for the diffuse larger biases are found (R2 = 0.57, RMSE = 45%). Solar irradiation estimates via CAMS at 15-min intervals reveal RMSE values of 19.5%, 38% and 28% for the global, diffuse and direct radiations, respectively. Biases are progressively reduced for hourly, daily and monthly data sets. PVGIS databases simulate the global irradiance reasonably well (R2 = 0.82–0.92), exhibiting high uncertainties for the diffuse (R2 = 0.39–0.49) and direct (R2 = 0.75–0.87), regarding instantaneous measurements. Simulations under clear-sky conditions of all components are found to be significantly improved, from both MRM, satellite-based retrievals and reanalysis. Overcast and partially cloudy skies result in large uncertainties, especially for the diffuse and direct irradiations, since the satellite sensors may detect clouds at time intervals of unobstructed Sun disk by clouds. In addition, broken bright clouds near to the Sun's disk may increase significantly the measured diffuse irradiance, leading to large biases in the simulations from both MRM and satellite databases.

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.

Actual evapotranspiration (AET) can be studied and estimated using remote-sensing-based methods at multiple spatial and temporal scales. Reflectance and Land surface temperature are essential in these methods. However optical and thermal sensors fail to provide these data under overcast conditions and this creates gap in the AET product. Besides, there is a necessity of the AET method that requires less data and estimates AET with better accuracy. In this regard, AET was estimated for all-sky conditions using the vegetation coefficient (VI-Kv) method utilizing microwave, thermal, and optical data. Essential reference evapotranspiration (ET0) under cloudy conditions was estimated using LST-based Penman–Monteith temperature (PMT) and Hargreaves–Samani equations. Furthermore, LST predicted using the microwave polarization difference index (PLST) and LST of moderate resolution imaging spectroradiometer (MODIS) cloud product (MLST) were evaluated with in-situ air temperature (Ta) under cloudy sky conditions. Results revealed that the PLST correlated better with Ta than MLST with correlation coefficient ( r ) values of 0.71 and 0.81 for day and night times, respectively. Hence, PLST-based solar radiation (Rs) estimation yielded better accuracy with observed Rs with r and root mean square error values of 0.864 and 0.07 for Berambadi station under cloudy conditions, respectively. PMT-based ET0 values corresponded well with the observed ET0 under cloudy sky condition during this study. In addition, AET estimated using the VI-Kv method was compared with the simple two-source energy balance (TSEB) method under clear sky conditions. It was found that the improved VI-Kv method performed better than the TSEB method and could also fairly estimate AET even under cloudy sky conditions.

This study analyzed the surface radiation characteristics and evaluated downward cloud radiative forcing (CRF) in southern Xinjiang during summer 2019. Surface radiation fluxes show prominent day-to-day and diurnal variations, with the maximums of 915.6, 921.2, and 121.9 Wm−2, respectively, for total, direct, and diffuse shortwave radiations under cloudless conditions. Cloud layer or dust layer could significantly impede the incident solar irradiance and moderately reduce the emission of longwave radiation to space, and hence bring a strong downward negative radiative impact. A rigorous four-step screening algorithm was applied to effectively identify the clear-sky periods based on 1-min total and diffuse shortwave irradiances. The clear-sky shortwave (SW) radiation is empirically fitted using a power law equation and cosine of solar zenith angle, and the clear-sky longwave (LW) radiation is calculated via the average diurnal cycles of all datasets of detected 1-min LW radiation under cloudless conditions. The 24-h daily mean CRFs of SW are largely negative (− 204.1 to − 3.3 Wm−2), and corresponding LW are moderately positive (− 1.5 to + 70.4 Wm−2), which coincide with the overcast conditions or large cloud cover amounts. The overall averages of SW and LW CRFs and net cloud effect are − 69.7 ± 62.3, + 29.5 ± 22.2, and − 40.1 ± 42.1 Wm−2, respectively, implying that clouds have a net cooling effect on the regional climate. Our results are consistent with the summer mean values in northern China, but less than those in southern China, and greater than those of the global mean values. Such negative cloud radiative forcing would partly counteract the global warming induced by greenhouse gases and modulate the Earth’s radiation budget balance.

[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 NASA Clouds and the Earth’s Radiant Energy System (CERES) project has provided the climate community more than a 20-year record of top of the atmosphere (TOA) reflected shortwave (SW) and emitted longwave (LW) fluxes. The fluxes are used to monitor the Earth’s energy balance as well as for climate model validation and cloud feedback studies. One of the largest uncertainties in climate models is the response of clouds in feedback studies. Reducing this uncertainty requires a more stringent validation of model-generated fluxes by cloud-type. Rather than relying on radiative transfer model generated fluxes from observed cloud-type retrievals, the CERES FluxByCloudTyp (FBCT) product relies on MODIS empirical narrowband to broadband relationships to convert the cloudy portions of the CERES footprints into fluxes. The overcast footprint and sub-footprint cloud layer fluxes are then stratified by 7 pressure layers and 6 optical depth bins and temporally averaged into daily and monthly regional cloud-type fluxes. The CERES project analyzed a total of 19 MODIS channels for use in the next generation FBCT product narrowband to broadband conversion. This was accomplished by comparing the narrowband to broadband RMS errors from all possible 5-channel combinations. The SW and LW optimized channels were selected by also considering analogous geostationary, MODIS and VIIRS imager channels as well as channel combinations that provided the lowest RMS errors across all surface types. The optimized channel combinations will be tested in the FBCT algorithm to investigate any narrowband to broadband dependencies as a function of cloud fraction, effective pressure, optical depth, PW, solar and view angle, surface type.

The radiative closure methodologies to obtain Cloud Optical Depth (COD) from Remote Sensing techniques have traditionally relied on one-dimensional (1D) assumptions. These assumptions might be far away from the radiation transport over a realistic three-dimensional (3D) atmosphere, especially in cloudy conditions, as the natural inhomogeneities of clouds are not conveniently represented and treated in 1D models. The differences between the 1D and 3D approaches manifests in the 3D effects: a) the plane-parallel albedo bias and, b) the horizontal transport effect. The plane-parallel albedo bias is usually addressed by means of the Independent Pixel approximation (IPA), that considers each pixel radiatively independent from the others. Nevertheless, the IPA neglects the horizontal transport, entailing bias in the retrievals. In this work, we use the advantages of 3D radiative transfer (RT) to analyze COD and parameterize the 3D biases in terms of the plane-parallel approach. Detailed 3D RT simulations using MYSTIC are performed over two Highly Resolved Large Eddy Simulations cloud fields of known optical thickness. The output radiance is analyzed by a 1D IPA inversion retrieval based on a radiative closure to obtain the COD. The comparison between the retrieved COD fields for diverse illumination conditions and the real COD allow us to study the 3D effects separately and evaluate the retrieval. Our results show radiation enhancement in cloud edges depending on solar, viewing and cloud geometries, that induces a COD underestimation. The 1D approach works well for overcast conditions and underestimates the COD in broken clouds scenarios.

Satellite-based visible and near-infrared imaging of the Earth’s surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using hyper-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion of the panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and operational applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment, as well as disturbance, change, hazard, and disaster detection.

Unmanned aerial vehicles with hyperspectral sensors present new opportunities for acquiring imagery in overcast conditions. Under such conditions, the empirical line method, with targets of known reflectance can be used for atmospheric compensation. Therefore, understanding changes in spectral response from lab, to field and UAV conditions is important. We assess four standard reference targets (2%-50% reflectance) that were initially characterized in the laboratory. Then, we assess their reflectance derived from field spectroscopy measurements and UAV hyperspectral imagery under diffuse illumination field conditions. In-scattering from surrounding vegetation and high attenuation of the SWIR were found in field-based spectra. Resampled laboratory spectra show good correspondence with UAV HSI spectra of the panels., IGARSS 2021: 2021 IEEE International Geoscience and Remote Sensing Symposium, July 11-16, 2021, Brussels, Belgium

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.

For any particular location glazing transmission varies with season and time of day. Thus, glazing transmission angular behaviour is more crucial than single glazing transmittance value for building energy simulation and design. In this work, the spectral behaviour of the dye-sensitized solar cell (DSSC) glazing with three different transparencies are studied. Transmittance of the devices are measured after 2 years to understand the effects of device stability on DSSC glazing applications. The solar factor for the devices is calculated for different light incident angles for a whole year at a particular location. The correlation between clearness index and DSSC transmittance is also studied. Finally, glare analysis is performed for all the devices on a sunny day, intermittent day and overcast day, and is also compared with double glazing. It is found that the 37% transparent DSSC glazing leads to a greater reduction in disturbing glare by 21% compared to double glazing on a clear sunny day. All the above results suggest that DSSC glazings could be productively used for fenestration integration in buildings.

At Ny-Alesund, Svalbard, Arctic, two distinct states were seen in downward longwave radiation (LD) during winter 2015/16. After the cold state with the temperature around −10 to −20 °C, a warm state with plus temperature appeared and LD increased from about 170 to 320–330 W m−2. This abrupt and large increase of LD was due to the change of cloud condition, from clear to overcast, which was examined from the cloud radar data taken during GRENE Arctic Research Project. However, only the cloud change could not explain this large change of LD. During the warm state, the atmospheric circulation patterns changed, distortion of tropospheric polar vortex appeared with a high pressure ridge (blocking high), and strong cyclone and intrusion of warm and moist air from the Atlantic Ocean together with longwave radiation from clouds influenced the warming at Svalbard. Thus, the discussions of two Arctic winter states in LD was much effective than those in net longwave. This intrusion of warm-moist air from the lower latitude was one of the major processes contributing to the Arctic amplification, just as also proposed using GCM by Yoshimori et al. (2017). The paper provided the actual observational evidence of this warming process.

A knowledge of how light varies within gaps is fundamental to understanding variation in survival and growth of regenerating trees. Simple indices based on a small number of instantaneous measurements by portable sensors or cameras are widely used for this purpose. But the effectiveness of these indices for estimating growing season transmittance is influenced by stand characteristics and local weather conditions. This study was conducted in two small (50 m × 150 m) clearings in a mixed stand of the interior cedar hemlock zone of southern British Columbia to evaluate several commonly used light estimation techniques, including: (1) hemispherical photography; (2) LAI-2000 plant canopy analyzer; and, (3) midday measurements of % photosynthetic photon flux density (hourly average PPFD) under sunny and overcast skies in a strip-shelterwood system. While diffuse indices from hemispherical methods (LAI-2000 and photograph) performed poorly, their direct and total indices (photograph) gave lower variability (lower rmse) and stronger correlations (higher r2) with growing season transmittance. Sky conditions under which light measurements were recorded by photosynthetically active radiation (PAR) sensors influenced the relationships between PAR sensor indices and growing season transmittance. For example, measuring light around midday on a sunny day provided stronger correlation with growing season transmittance than overcast day measurements. Moreover, rather than using one midday light measurement on a sunny or an overcast day, an average of two measurements increased r2 and reduced bias while measuring light three times on a sunny day (morning, noon and afternoon) gave the highest r2 and the lowest variability. Several indices also correlated strongly with monthly light levels, measured from May – September. Based on the finding that indices that accounted for direct beam contribution gave the best outcomes, we suggest that canopy conditions (degree of heterogeneity), stand orientation and gap sizes should be considered in selecting an instantaneous light index for studies in high latitude forests.

A new physically based method to estimate hemispheric-directional reflectance factor (HDRF) from lightweight multispectral cameras that have a downwelling irradiance sensor is presented. It combines radiometry with photogrammetric computer vision to derive geometrically and radiometrically accurate data purely from the images, without requiring reflectance targets or any other additional information apart from the imagery. The sky sensor orientation is initially computed using photogrammetric computer vision and revised with a non-linear regression comprising radiometric and photogrammetry-derived information. It works for both clear sky and overcast conditions. A ground-based test acquisition of a Spectralon target observed from different viewing directions and with different sun positions using a typical multispectral sensor configuration for clear sky and overcast showed that both the overall value and the directionality of the reflectance factor as reported in the literature were well retrieved. An RMSE of 3% for clear sky and up to 5 for overcast sky was observed.

The use of luminescent solar concentrators (LSCs) is considered a promising option to impart energy harvesting capability to buildings with minimal alternation to the building structure. To fill the gap between fundamental research and practical application, this study investigated the performance (power conversion efficiency, ηLSC and power concentration ratio, C) of the LSCs of different sizes under different light conditions including simulated sunlight condition, outdoor sunny, overcast and raining conditions, and indoor condition employing four different types of light bulbs (i.e., incandescent, halogen, fluorescent and light-emitting diode). It was observed that the LSCs exhibited higher one-sun performance under outdoor sunny condition than under simulated sunlight. Under overcast and raining conditions, ηLSC decreased significantly with the increase of the device size. Under indoor incandescent and halogen light conditions, the LSCs exhibited low ηLSC due to the low percentage of the visible portion in the light spectrum. Surprisingly high ηLSC boosted by 50% compared to the outdoor sunny condition was obtained in the LSCs under fluorescent and LED light conditions. This research suggested very promising application of the LSCs in indoor light energy recycling, which has not been proposed before.

The overcast, partly cloudy and clear sky conditions can determine the diffuse sky radiance and luminance distributions over the sky dome, and thus are crucial for evaluating the solar energy and daylight on and through building envelopes. It is preferable to properly identify the sky condition and determining its diffuse radiance and luminance distribution patterns by the readily available data. In this work, we propose a new approach to identifying the sky conditions, especially the cloudy and clear skies mainly by the horizontal illuminance fluctuation frequency. Illuminance on ground level may fluctuate at high frequency under cloudy skies due to the broken cloud, while vary smoothly under clear skies with few cloud blockages. For input data, the proposed approach needs the horizontal global illuminance that can be readily accessible for many places instead of the “uncommon” measurements on vertical planes or in sky zenith. The fluctuation frequency factor we propose can reduce the misclassification rate for daily and half-day representative sky conditions by 5.7% and 11.5%, respectively, compared to the classifications using the clearness index only.

We present results from 3D radiative-hydrodynamical simulations of HD 209458b with a fully coupled treatment of clouds using the EddySed code, critically, including cloud radiative feedback via absorption and scattering. We demonstrate that the thermal and optical structure of the simulated atmosphere is markedly different, for the majority of our simulations, when including cloud radiative effects, suggesting this important mechanism can not be neglected. Additionally, we further demonstrate that the cloud structure is sensitive to not only the cloud sedimentation efficiency (termed $f_{\textrm{sed}}$ in EddySed), but also the temperature-pressure profile of the deeper atmosphere. We briefly discuss the large difference between the resolved cloud structures of this work, adopting a phase-equilibrium and parameterised cloud model, and our previous work incorporating a cloud microphysical model, although a fairer comparison where, for example, the same list of constituent condensates is included in both treatments, is reserved for a future work. Our results underline the importance of further study into the potential condensate size distributions and vertical structures, as both strongly influence the radiative impact of clouds on the atmosphere. Finally, we present synthetic observations from our simulations reporting an improved match, over our previous cloud-free simulations, to the observed transmission, HST WFC3 emission and 4.5 $\mu$m Spitzer phase curve of HD 209458b. Additionally, we find all our cloudy simulations have an apparent albedo consistent with observations., Comment: 26 pages, accepted for publication in MNRAS