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Highly accurate nighttime cloud detection in satellite imagery is challenging due to the absence of visible to near-infrared (0.38–3 μm, VNI) data, which is critical for distinguishing clouds from other ground features. Fortunately, Machine learning (ML) techniques can more effectively leverage the limited wavelength information and show high-accuracy cloud detection based on vast sample volume. However, accurately distinguishing cloud pixels solely through thermal infrared bands (8–14 μm, TIR) is challenging, and acquiring numerous, high-quality and representative samples of nighttime images for ML proves to be unattainable in practice. Given the thermal infrared radiation transmission process and the fact that daytime and nighttime have the same source of radiance in TIR, we propose a sample generation idea that uses daytime images to provide samples of nighttime cloud detection, which is different from the traditional sample construction methods (e.g., manually label, simulation and transfer learning method), and can obtain samples effectively. Based on this idea, nighttime cloud detection experiments were carried out for MODIS, GF-5 (02) and Himawari-8 satellites, respectively. The results were validated by the Lidar cloud product and manual labels and show that our nighttime cloud detection result has higher accuracy than MYD35 (78.17%) and the ML model trained by nighttime manual labels (75.86%). The accuracy of the three sensors is 82.19%, 88.71%, and 79.34%, respectively. Moreover, we validated and discussed the performance of our algorithm on various surface types (vegetation, urban, barren and water). The results revealed that the accuracy of the three sensors over barren was found to be poor and varied with surface types but overall high. Our study can provide a novel perspective on nighttime cloud detection of muti-spectral satellite imagery.

Accurate cloud detection is critical for quantitative applications of satellite-based advanced imager observations, yet nighttime cloud detection presents challenges due to the lack of visible and near-infrared spectral information. Nighttime cloud detection using infrared (IR)-only information needs to be improved. Based on a collocated dataset from Fengyun-3D Medium Resolution Spectral Imager (FY-3D MERSI) Level 1 data and CALIPSO CALIOP lidar Level 2 product, this study proposes a novel framework leveraging Light Gradient-Boosting Machine (LGBM), integrated with grey level co-occurrence matrix (GLCM) features extracted from IR bands, to enhance nighttime cloud detection capabilities. The LGBM model with GLCM features demonstrates significant improvements, achieving an overall accuracy (OA) exceeding 85% and an F1-Score (F1) of nearly 0.9 when validated with an independent CALIOP lidar Level 2 product. Compared to the threshold-based algorithm that has been used operationally, the proposed algorithm exhibits superior and more stable performance across varying solar zenith angles, surface types, and cloud altitudes. Notably, the method produced over 82% OA over the cryosphere surface. Furthermore, compared to LGBM models without GLCM inputs, the enhanced model effectively mitigates the thermal stripe effect of MERSI L1 data, yielding more accurate cloud masks. Further evaluation with collocated MODIS-Aqua cloud mask product indicates that the proposed algorithm delivers more precise cloud detection (OA: 90.30%, F1: 0.9397) compared to that of the MODIS product (OA: 84.66%, F1: 0.9006). This IR-alone algorithm advancement offers a reliable tool for nighttime cloud detection, significantly enhancing the quantitative applications of satellite imager observations.

A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are collocated with active footprints. This nighttime spectral radiance matching (NSRM) method is tested using measurements from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud layer heights are estimated up to 400 km on both sides of the ground track and reconstructed with the dead zone setting for an approximate evaluation of the reliability. By mimicking off-nadir pixels with a dead zone around pixels along the ground track, reconstruction of nadir profiles shows that, at 200 km from the ground track, the cloud top height (CTH) and the cloud base height (CBH) reconstructed by the NSRM method are within 1.49 km and 1.81 km of the original measurements, respectively. The constructed cloud structure is utilized for cloud classification in the nighttime. The same method is applied to the daytime measurements for comparison with collocated MODIS classification based on the International Satellite Cloud Climatology Project (ISCCP) standard. The comparison of eight cloud types over the expanded distance shows good agreement in general.

Comparing cloud cover (CC) products from different satellites with the same ground-based CC dataset provides information on the similarities or differences of values among satellite products. For this reason, 42-month CC products from Moderate Resolution Imaging Spectrometer’s (MODIS) Collection 6.1 daily cloud cover products (MOD06_L2, MYD06_L2, MOD08_D3, and MYD08_D3) and Himawari-8 are compared with the ground-based camera datasets. The comparison shows that CC from MODIS differs from ground measurement CC by as much as 57% over Chiba, Japan, when low CC is observed by the camera. This indicates MODIS’s ability to capture high-level clouds that are not effectively seen from the ground. When the camera detects high CC, an indication of the presence of low-level clouds, CC from MODIS is relatively higher than the CC from the camera. In the case of Himawari-8 data, when the camera observes low CC, this difference is around 0.7%. This result indicates that high-level clouds are not effectively observed, but the Himawari-8 data correlates well with camera observations. When the camera observes high CC, Himawari-8-derived CC is lower by around 10% than CC from the camera. These results show the potential of continuous observations of nighttime clouds using the camera to provide a dataset that can be used for intercomparison among nighttime satellite CC products.

Cloud-induced atmospheric extinction and occlusion significantly affect the effectiveness and quality of telescope observations. Real-time cloud-cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. Visual inspection is currently the primary approach for analyzing cloud distribution at ground-based astronomical sites. However, the main disadvantages of manual observation methods are human subjectivity, heavy workloads, and poor real-time performance. Therefore, a real-time automatic cloud image classification method is desperately needed. This paper presents a novel cloud identification method named the PSO+XGBoost model, which combines eXtreme Gradient Boosting (XGBoost) with particle-swarm optimization (PSO). The entire cloud image is divided into 37 sub-regions to identify the distribution of the clouds more precisely. Nineteen features, including the sky background, star density, lighting conditions, and subregion grayscale values, are extracted. The experimental results have shown that the overall classification accuracy is 96.91%, and our model can outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

A consistently processed annual global nighttime lights time series (2012–2019) was produced using monthly cloud-free radiance averages made from low light imaging day/night band (DNB) data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The processing steps are modified from the original methods developed to produce annual nighttime lights products from nightly data. Only two years of VIIRS nighttime lights (VNL) were produced with the V.1 methods: 2015 and 2016. Here we report on methods used to produce a V.2 VNL time series from the monthly averages with filtering to remove extraneous features such as biomass burning, aurora, and background. In this case, outlier removal is achieved with a twelve-month median, which discards high and low radiance outliers, thus isolating the background to a narrow range of radiances under 1 nW/cm2/sr. Background areas with no detectable lighting are further isolated using a statistical measure of texture, 3 × 3 data range (DR). The DR threshold for zeroing out background rises as the number of cloud-free observations falls. The V.2 method extends the temporal leverage in the noise filtering by developing the DR threshold from a multiyear maximum DR and a multiyear percent cloud-free grid. Additional noise filtering is achieved by zeroing out grid cells that have low average radiances (<0.6 nW/cm2/sr) and detection in only one or two years out of eight. The spatial extent and average radiance levels are compared for the V.1 and V.2 2015 VNL. For the vast majority of grid cells, the average radiances are nearly the same in the two products. However, the V.2 product has more areas of dim lighting detected. The key advantages of the V.2 time series include consistent processing and threshold levels across all years, thus optimizing the set for change detection analyses.

Ascension Island (8∘ S, 14.5∘ W) is located at the northwestern edge of the south Atlantic stratocumulus deck, with most clouds in August characterized by surface observers as “stratocumulus and cumulus with bases at different levels”, and secondarily as “cumulus of limited vertical extent” and occurring within a typically decoupled boundary layer. Field measurements have previously shown that the highest amounts of sunlight-absorbing smoke occur annually within the marine boundary layer during August. On more smoke-free days, the diurnal cycle in cloudiness includes a nighttime maximum in cloud liquid water path and rain, an afternoon cloud minimum, and a secondary late-afternoon increase in cumulus and rain. The afternoon low-cloud minimum is more pronounced on days with a smokier boundary layer. The cloud liquid water paths are also reduced throughout most of the diurnal cycle when more smoke is present, with the difference from cleaner conditions most pronounced at night. Precipitation is infrequent. An exception is the mid-morning, when the boundary layer deepens and liquid water paths increase. The data support a view that a radiatively enhanced decoupling persisting throughout the night is key to understanding the changes in the cloud diurnal cycle when the boundary layer is smokier. Under these conditions, the nighttime stratiform cloud layer does not always recouple to the sub-cloud layer, and the decoupling maintains more moisture within the sub-cloud layer. After the sun rises, enhanced shortwave absorption in a smokier boundary layer can drive a vertical ascent that momentarily couples the sub-cloud layer to the cloud layer, deepening the boundary layer and ventilating moisture throughout, a process that may also be aided by a shift to smaller droplets. After noon, shortwave absorption within smokier boundary layers again reduces the upper-level stratiform cloud and the sub-cloud relative humidity, discouraging further cumulus development and again strengthening a decoupling that lasts longer into the night. The novel diurnal mechanism provides a new challenge for cloud models to emulate. The lower free troposphere above cloud is more likely to be cooler, when boundary layer smoke is present, and lower free-tropospheric winds are stronger and more northeasterly, with both (meteorological) influences supporting further smoke entrainment into the boundary layer from above.

Satellite observations of tropical maritime convection indicate an afternoon maximum in anvil cloud fraction that cannot be explained by the diurnal cycle of deep convection peaking at night. We use idealized cloud-resolving model simulations of single anvil cloud evolution pathways, initialized at different times of the day, to show that tropical anvil clouds formed during the day are more widespread and longer lasting than those formed at night. This diurnal difference is caused by shortwave radiative heating, which lofts and spreads anvil clouds via a mesoscale circulation that is largely absent at night, when a different, longwave-driven circulation dominates. The nighttime circulation entrains dry environmental air that erodes cloud top and shortens anvil lifetime. Increased ice nucleation in more turbulent nighttime conditions supported by the longwave cloud-top cooling and cloud-base heating dipole cannot compensate for the effect of diurnal shortwave radiative heating. Radiative–convective equilibrium simulations with a realistic diurnal cycle of insolation confirm the crucial role of shortwave heating in lofting and sustaining anvil clouds. The shortwave-driven mesoscale ascent leads to daytime anvils with larger ice crystal size, number concentration, and water content at cloud top than their nighttime counterparts.

Diurnal cycle of cloud (DCC), referring to the diurnal variation of cloud macro- and micro-physical properties, thus largely determining the strength of net cloud radiative forcing (CRF), is a critical feature of clouds’ variation and is important for weather and climate evolutions. Nevertheless, neither the DCC vertical structures and their links to meteorology are well understood, nor the DCCs for different cloud type are accurately represented in current climate models. With unique orbit of the international space station, Cloud-Aerosol Transport System (CATS) lidar onboard the international space station (ISS) can sample cloud profiles at different local times and provide DCC vertical structures. In this study, we analyzed 2-year CATS data and found that the amplitude of diurnal cycle is significantly correlated with the mean frequency of occurrence. High clouds and oceanic low clouds have strong vertical development during nighttime, and continental low clouds tend to develop in daytime. These DCC features can impact the strength and the direction of CRF. Overall, large cloud cover and amplitude can amplify net cloud cooling effects, and high cloud nighttime (18:00 PM–06:00 AM) occurrence frequency can strengthen the cloud warming effects. To explain the DCC phenomenon, the instantaneous links between cloud vertical structure and lower-tropospheric stability (LTS), vertical velocity and cold point temperature (CPT) are discussed individually to show the evidence of their controls on cloud properties from tropics to midlatitude. Our results confirm that tropical water clouds and cirrus are more affected by LTS and CPT, respectively. Towards midlatitude from tropics, vertical velocity gradually plays a more important role in cloud development and dissipation. According to the diurnal cycles of these factors, temperature and static stability have the largest daily amplitude in the boundary layer of tropics and subtropics, which can explain the diurnal cycle of relative humidity and low clouds evolution, whereas vertical velocity has the largest daily amplitude in midlatitude, which is more related to the diurnal cycle of relative humidity and clouds in upper level of troposphere.

Aerosol–cloud interactions are explored using 1 km simulations of a case study of predominantly closed-cell SE Pacific stratocumulus clouds. The simulations include realistic meteorology along with newly implemented cloud microphysics and sub-grid cloud schemes. The model was critically assessed against observations of liquid water path (LWP), broadband fluxes, cloud fraction (fc), droplet number concentrations (Nd), thermodynamic profiles, and radar reflectivities.Aerosol loading sensitivity tests showed that at low aerosol loadings, changes to aerosol affected shortwave fluxes equally through changes to cloud macrophysical characteristics (LWP, fc) and cloud albedo changes due solely to Nd changes. However, at high aerosol loadings, only the Nd albedo change was important. Evidence was also provided to show that a treatment of sub-grid clouds is as important as order of magnitude changes in aerosol loading for the accurate simulation of stratocumulus at this grid resolution.Overall, the control model demonstrated a credible ability to reproduce observations, suggesting that many of the important physical processes for accurately simulating these clouds are represented within the model and giving some confidence in the predictions of the model concerning stratocumulus and the impact of aerosol. For example, the control run was able to reproduce the shape and magnitude of the observed diurnal cycle of domain mean LWP to within  ∼  10 g m−2 for the nighttime, but with an overestimate for the daytime of up to 30 g m−2. The latter was attributed to the uniform aerosol fields imposed on the model, which meant that the model failed to include the low-Nd mode that was observed further offshore, preventing the LWP removal through precipitation that likely occurred in reality. The boundary layer was too low by around 260 m, which was attributed to the driving global model analysis. The shapes and sizes of the observed bands of clouds and open-cell-like regions of low areal cloud cover were qualitatively captured. The daytime fc frequency distribution was reproduced to within Δfc = 0.04 for fc >  ∼ 0.7 as was the domain mean nighttime fc (at a single time) to within Δfc = 0.02. Frequency distributions of shortwave top-of-the-atmosphere (TOA) fluxes from the satellite were well represented by the model, with only a slight underestimate of the mean by 15 %; this was attributed to near–shore aerosol concentrations that were too low for the particular times of the satellite overpasses. TOA long-wave flux distributions were close to those from the satellite with agreement of the mean value to within 0.4 %. From comparisons of Nd distributions to those from the satellite, it was found that the Nd mode from the model agreed with the higher of the two observed modes to within  ∼  15 %.