Search Results (727)

Atmospheric temperature and humidity profiles are essential for numerical weather prediction and severe weather monitoring. To effectively utilize data from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4 satellite, this study proposes a retrieval method based on a radial basis function (RBF) neural network, which integrates numerical model background profiles with GIIRS simulated radiance errors to construct a mapping from these two inputs to background profile errors. A channel selection strategy is developed using correlations between background errors and radiance errors to identify channels sensitive to temperature and humidity variations at different pressure levels. Experiments are conducted using data from land stations in Zhejiang Province, China, from August to December 2024, including 829 clear-sky and 2109 cloudy profiles. Under clear-sky conditions, the method reduces temperature and humidity root mean square error (RMSE) by approximately 39% and 22.3% compared to background profiles. Under cloudy conditions, despite severe radiation interference, RMSE reductions of 38.5% for temperature and 15.3% for humidity are achieved, with notable improvements below 900 hPa and above 750 hPa for humidity. Compared with the multilayer perceptron (MLP) method, RBF shows superior performance under all test conditions, especially in cloudy-sky humidity retrieval. The proposed approach provides an effective, physically constrained framework for operational GIIRS data application in temperature and humidity retrieval. Full article

Achieving a balance between accuracy and computational efficiency in solar energy flux estimation models remains a key challenge in atmospheric radiative transfer research. Given the high computational cost of spectral models, a widely used simplification strategy consists of parameterizing atmospheric spectral transmittances through wavelength-averaging formulations. This study introduces a Clear-Sky Multivariable (CSMV) broadband parametric model derived from the Leckner spectral model for estimating the three components of solar irradiance under clear-sky conditions: direct normal irradiance (DNI), diffuse irradiance (Gd), and global irradiance (G). The model development follows a two-stage procedure. First, discrete broadband transmittances are obtained by applying an independent spectral integration scheme to the transmittances of the source spectral model. In the second stage, these discrete values are fitted with analytical functions expressed solely in terms of atmospheric state parameters, yielding wavelength-independent broadband formulations. While the overall development framework follows a classical parameterization approach, the calculation of the diffuse component introduces a novel way of estimating the fraction of aerosol scattering directed toward the ground. The model was tested against data collected from eight radiometric stations distributed across six continents and benchmarked against two well-established reference models. Overall, the results indicate a high level of accuracy and demonstrate the practical applicability of the model. Full article

Against the backdrop of accelerated terrestrial hydrological cycling and the increasing concurrence of drought-heatwave compound extremes under global warming, regional land-atmosphere coupling has emerged as a central mechanism shaping climate feedbacks and trajectories of ecosystem carbon uptake. However, prior studies spanning climatic regimes, observational scales, and data sources have often yielded contradictory conclusions. Here, we challenge these fragmented perspectives by constructing an integrated LST-SM-ET-GPP chain that jointly represents land surface temperature, soil moisture, evapotranspiration, and gross primary productivity, thereby linking water availability, surface energy balance, and plant physiological processes within a unified framework. We synthesize a conceptual diagnostic roadmap for interpreting land-atmosphere coupling across observations and models. When ecosystems operate in humid, energy-limited environments, radiative and advective controls should be prioritized to diagnose system forcing. By contrast, as the system becomes water-depleted, attribution must shift to a nonlinear regime transition framework governed by a critical soil moisture threshold. This threshold mechanism implies that, once the system enters the moisture-limited regime, even modest declines in soil moisture can trigger a rapid weakening of evaporative cooling, substantially amplifying LST anomalies and strongly suppressing GPP. The competitive regulation of stomatal conductance by atmospheric demand (vapor pressure deficit, VPD) and terrestrial supply (rootzone soil moisture) further explains why the “dominant” controlling factor can dynamically reverse across hydrothermal states, timescales, and stages of extreme-event evolution. Notably, the steady-state coupling assumption may break down under flux “flooring” during extreme drought, or when structural buffering such as deep root water uptake is present, delineating strict applicability bounds for existing diagnostic frameworks. Finally, current assessments remain constrained by multiple uncertainties, particularly the lack of ET partitioning constraints, representativeness biases arising from clear-sky observations and sampling-depth limitations, and systematic errors in Earth system model simulations during the warm season. Full article

With non-rainfall water (NRW), principally dew and fog, serving as an important water source, especially in arid and semiarid regions, factors that may increase the NRW yield may have important hydrological and ecological consequences. On the other hand, dew and fog may also have hazardous effect on inorganic and human-made materials that may undergo corrosion and/or degradation. It has long been noted that dew and fog are affected by neighboring objects, the effect of which was, however, only barely explored. Hypothesizing that it may principally be linked to the sky view factor (SVF) (determining, in turn, substrate temperature and heat flow) and, therefore, to the angle that is formed between the collecting substrate and the height of the neighboring objects, a set of square boxes (30 × 30 or 60 × 60 cm) was constructed. The boxes had variable heights, forming angles of 15°, 30°, 45°, 60°, and 75° between 6 × 6 × 0.1 cm cloth attached to a substratum (10 × 10 × 0.2 cm glass plate overlying 10 × 10 × 0.5 cm plywood) at the center of each box and the top walls of the box. NRW that accumulated at the cloths was compared with cloths placed in the open, serving as control. Another set served to measure the plate temperatures. A clear decrease in NRW, with an angle corresponding to a third-degree polynomial equation, was found (r² = 0.998). Taking 0.1 mm as the threshold for vapor condensation (dew), and taking the average maximal NRW as measured for two years in the Negev (0.20 mm), angles of ≥45° will suffice to impair condensation. However, with the projected decrease in NRW with global warming, even angles of ≥30° may impair condensation in 1–2 decades. While it may decrease the dew amounts and subsequently negatively affect the vegetation in forest clearings and wadis or canyons, it may decrease the exposure of construction materials to corrosion and/or degradation, thus exerting a positive effect on construction materials in urban settings. Full article

In this study, a post-processing algorithm was developed to mitigate the over-detection tendency of the Geo-KOMPSAT-2A fog detection algorithm (GK2A_FDA) by integrating surface observations, facilitated by the recent availability of high-resolution gridded surface analysis data. The method was optimized for six sub-algorithms (inland/coastal × daytime/nighttime/twilight) using an interpretable decision tree model with data from 2021 to 2023. The $RH$ (relative humidity) and $\mathsf{\Delta }FTs$ (clear-sky background minus fog-top brightness temperature) step defines detection boundaries in a two-dimensional decision space using joint false alarm-to-hit ratio and hit count distributions to effectively remove false-alarm-dominated regions with minimal impact on the probability of detection (POD). The post-processing steps were sequenced according to independently quantified accuracy gains ($RH$ and $\mathsf{\Delta }FTs$ >> $T_a$ > wind speed > solar zenith angle), with thresholds conservatively derived and seasonally optimized for South Korea. Following post-processing, the POD decreased only slightly (0.08–0.27%), while the false alarm ratio (FAR) and bias were reduced by 5.13–13.68% and 16.13–52.61%, respectively. Improvements were more pronounced during drier seasons than wet seasons; however, the residual high daytime bias (3.348–5.319) indicated the need for further GK2A_FDA refinement. This study demonstrated that integrating satellite and surface observations could effectively address the limitations of satellite-based fog detection. Full article

This study proposes an advanced short-term Direct Normal Irradiance (DNI) prediction model for Concentrated Solar Power (CSP) systems, integrating a convolutional neural network (CNN) with a fitted clear-sky DNI model. Leveraging all-sky images and historical DNI data, the model precisely identifies cloud motion patterns through dense optical flow analysis and forecasts DNI using a targeted region-of-interest (ROI) approach. When maximum cloud pixel velocity falls below 5 pixels per minute, the clear-sky DNI model or persistence model directly applies; for higher-velocity conditions, the CNN predicts the clear-sky index to dynamically adjust the forecast. Experimental validation across diverse weather conditions demonstrates superior accuracy, achieving significantly lower normalized Mean Absolute Errors (nMAEs) and normalized Root Mean Squared Errors (nRMSEs) for various forecast horizons under cloudy skies compared to recent state-of-the-art deep learning approaches. This work delivers a robust solution for preventing thermal shock in the receiver and improving the CSP operational stability. Full article

In recent times, increasing attention has been devoted to the investigation of atmospheric processes through remote sensing in order to improve our understanding of climate dynamics and atmospheric physics. This requires accurate simulation of the spectra emitted by the Earth, from which atmospheric composition and thermodynamic conditions can be retrieved. The FORUM mission focuses on observations of the Earth’s outgoing radiation in the far-infrared spectral region, which has been only sparsely explored due to observational challenges, despite its significant contribution to the characterization of atmospheric processes. As part of the mission activities, dedicated simulations of the measurements expected from the FORUM instrument are required. Different models and codes can be employed for this purpose. Fast radiative transfer models, such as SIGMA-FORUM, efficiently simulate all-sky conditions, whereas detailed line-by-line models, such as KLIMA, have generally been limited to clear-sky applications. In this context, SIGMA-FORUM, an all-sky fast radiative transfer model operating in the 10–2760 cm⁻¹ spectral range and KLIMA, a FORTRAN-based line-by-line algorithm extensively validated under clear-sky conditions, are used to simulate FORUM radiances in both clear and cloudy atmospheres. This study extends the comparison between SIGMA-IASI/F2N and KLIMA to cloudy-sky scenarios by incorporating cloud optical properties into KLIMA using the same parametrization approach adopted in SIGMA-FORUM version 2.4. By combining complementary modeling approaches, this work enables KLIMA to simulate atmospheric radiances under all-sky conditions, thereby broadening its applicability. Full article

Adaptive façade systems are increasingly used to mitigate glare in daylit spaces and improve visual comfort while supporting sustainable daylight utilisation and reduced reliance on electric lighting in buildings. However, their performance is often evaluated using illuminance-based metrics or uncalibrated simulations, limiting the reliability of glare assessment. This study proposes a calibrated experimental–simulation framework for evaluating glare reduction achieved by a kinetic horizontal shading system (KSS) under real daylight conditions. The approach integrates reduced-scale physical measurements with Radiance-based simulations using a digitally reconstructed twin of the experimental setup. Two geometrically identical test chambers positioned side-by-side—a static reference chamber and a kinetic chamber equipped with six adaptive fins (0.63 m real-scale depth)—were investigated using a 1:20 scale mock-up. Internal illuminance measurements were normalised between chambers, and a sky-scaling procedure was applied to calibrate simulated sky luminance distributions against measured data on an hourly basis, enabling photometrically validated HDR renderings for glare evaluation. Glare performance was analysed for three representative clear-sky days during periods of maximum solar exposure (11:00–17:00) under late-summer conditions at approximately 51° N latitude in Wrocław, Poland. Visual comfort was assessed using Daylight Glare Probability (DGP), Daylight Glare Index (DGI), and veiling luminance (L_veil). The kinetic shading system reduced mean DGP from 0.57 to 0.35 (−38%) and peak glare values by nearly half compared with the static configuration, while veiling luminance decreased by 73%, indicating substantial improvement in physiological visual comfort. These results demonstrate that adaptive fin movement effectively suppresses both perceptual and physiological glare during critical daylight hours. The proposed calibrated experimental–simulation workflow provides a robust and transferable methodology for evaluating the glare performance of adaptive façade systems and supports sustainable daylight management by enabling high daylight availability while maintaining acceptable glare levels in buildings. Full article

The power fluctuation of large-scale photovoltaic (PV) clusters is significantly affected by cloud movement. Aiming at the engineering reality that meteorological observation data are generally lacking for most power stations in wide-area PV clusters, as well as the problem that existing models overfit second-order high-frequency noise such as microscopic cloud deformation, this paper proposes a disturbance reconstruction and smoothing effect quantification method for PV clusters focusing on the first-order dominant meteorological component. First, a clear-sky model is introduced as a deterministic trend filter to extract the purely random disturbance sequence that induces grid-connection risks from the measured output power. Second, the dimensionality reduction modeling concept of “macro-advection dominance and microscopic deformation filtering” is established: the PV cluster is finely partitioned by fusing Dynamic Time Warping (DTW) and geographical distance, and a cross-space inversion of the macro-cloud velocity vector is realized, driven by pure power data using the Time-Lagged Cross-Correlation (TLCC) algorithm, thus constructing a disturbance power generation model that accounts for the phase misalignment of power output. Independent verification based on measured data in Jilin Province shows that the 95% confidence interval of the power reconstructed only by the first-order advection characteristics can cover 90.2% of the measured fluctuations, and the reconstruction error of the fluctuation standard deviation—an indicator that determines the system reserve demand—is merely 5.9%. This verifies that the macro-cloud displacement is the absolute dominant factor governing the extreme fluctuations of PV clusters. Finally, a normalized Smoothing Factor (SF) characterizing the “reserve capacity release ratio” is constructed, and combined with its statistical indicators, it is used to quantitatively evaluate the smoothing benefits provided by different spatial layout schemes. Under data-constrained conditions, the method proposed in this paper verifies the engineering rationality that microscopic meteorological noise can be safely neglected at the macro-PV cluster scale, providing a reliable quantitative basis for the safe grid expansion and peak-shaving energy storage capacity sizing of high-proportion PV bases. Full article

An eighteen-year record of in situ surface meteorology and computed bulk air–sea fluxes of heat, freshwater, and momentum from an ocean site windward of the Hawaiian Islands is presented. Observations were logged every minute. The one-minute, one-hour, and one-day time series statistics are presented. The daily-averaged time series provide an overview of this trade wind site, with mean wind of 6.8 m s⁻¹ toward the west–southwest, mean ocean heat gain of 23.2 W m⁻², and freshwater loss of 1.2 m yr⁻¹. Energetic variability was found at the higher sampling rates, evidenced by spectral peaks in solar insolation and sea-level pressure and by striking transient signals including short-lived insolation values higher than clear-sky values, short periods with air warmer than the sea surface, and by series of downdrafts of dry air. At longer periods, the presence of moist air accompanying low winds and sunny skies enhanced ocean heating. Winter events with dry air and wind, resulting in large latent and net heat loss, led to ocean cooling. Signals of two hurricanes, Darby and Douglas, were recorded. Normalized by their duration, short-lived events have the potential to make significant contributions to the heat, freshwater, and mechanical energy exchanges. Full article

The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS. Full article

Urban heat islands (UHIs) increase the cooling load and reduce the performance of rooftop photovoltaic (PV) systems; thus, the co-benefits of integrating bio-solar green roofs require quantification and real-world demonstration to encourage the uptake of this technology. Consequently, this study compares the thermal and electrical performances of four simultaneously installed roof assemblies, namely conventional roof (CR), green roof (GR), photovoltaic roof (pCR), and bio-solar green roof (pGR), under clear-sky summer periods in Lahore, Pakistan. The experiment equipped the same insulated test cells with meteorological, thermal, moisture, and PV power gauging to collect data every 1 min; standardized layers were built, and the PV tilt was set to 22°. The results show that pGR always performs better compared with other roof assemblies: the temperature on the outer surface is lower, the diurnal amplitude is the most reduced (ΔDF ≈ +19% vs. CR), the thermal response is the most delayed (ΔTL ≈ −21%), and TPI improves by 6.5–7%. All of these results indicate a new, field-validated synergy between evapotranspiration and PV shading/ventilation that could translate into practical value through reduced peak cooling loads (demand control), lower day-to-day cooling energy, and incremental PV gains. These are critical factors for achieving positive techno-economic outcomes in hot, sunny cities, with the aim of realizing UHI mitigation and resilient building energy systems. Full article

Radiance data from ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station), which is the first of a planned virtual constellation of wide-swath ultra-high-resolution thermal satellites, were used to test the concept of on-orbit cross-calibration based on the Global Space-based Inter-Calibration System (GSICS) with the Infrared Atmospheric Sounding Interferometer (IASI) as the reference. Validation of the results was performed using comparisons of corrected ECOSTRESS radiances with strictly independent data from IASI and the Cross-Track Infrared Sounder (CrIS) and with RTTOV radiative transfer simulations of clear-sky observations in iQuam (the NOAA in situ sea surface temperature quality monitor database). ECOSTRESS has known brightness temperature biases in ECOSTRESS Collections 1 and 2, and the biases of Collection 2 are expected to remain in Collection 3 because it retains the Collection 2 radiance calibrations. Our approach reduced both the brightness temperature bias and the temperature dependence of the bias in both Collections 1 and 2 by one to two orders of magnitude. The necessary radiance correction coefficients are provided. The results support the proof-of-concept on-orbit cross-calibration method based on GSICS. Full article

Building-integrated photovoltaics (BIPVs) can substantially increase renewable electricity utilization in buildings under China’s “dual-carbon” targets. Yet, fixed photovoltaic (FPV) windows cannot respond to seasonal and diurnal variations in solar altitude and azimuth, limiting their ability to jointly optimize power generation, shading, and solar heat gains. This study proposes a shading-type sun-tracking photovoltaic (STPV) window for south-facing residential glazing and evaluates its annual performance for a detached house in Hangzhou (hot-summer and cold-winter climate). Representative clear-sky field measurements were combined with annual EnergyPlus simulations to quantify PV yield, radiation regulation, and impacts on air-conditioning (HVAC) and lighting electricity use. STPV windows deliver an additional annual PV gain of ~336 kWh relative to FPV windows, mainly during transition seasons and around summer noon. Using the no-shading case as the baseline (4967 kWh/year), FPV windows reduce total electricity use to 4010 kWh (−957 kWh), while STPV windows further reduce it to 3281 kWh (−1686 kWh), providing an extra −729 kWh versus FPV. Accounting for PV generation, the annual net electricity demand decreases from 2929 kWh (FPV) to 1864 kWh (STPV), i.e., −1065 kWh (36.4%). These results highlight the synergy of tracking-enabled generation enhancement and cooling-load reduction for façade PV in Hangzhou-like climates. Full article

We present a supervised learning method to estimate two local descriptors of time-series dynamics, the mean-reversion rate $\theta$ and a heavy-tail estimate $\alpha$, from short windows of data. These parameters summarize recovery behavior and tail heaviness and are useful for interpreting stochastic signals in sensing applications. The method is trained on synthetic, dimensionless Ornstein–Uhlenbeck processes with $\alpha$-stable noise, ensuring robustness for non-Gaussian and heavy-tailed inputs. Gradient-boosted tree models (CatBoost) map window-level statistical features to discrete $\alpha$ and $\theta$ categories with high accuracy and predominantly adjacent-class confusion. Using the same trained models, we analyze daily financial returns, daily sunspot numbers, and NASA POWER climate fields for Austria. The method detects changes in local dynamics, including shifts in the financial tail structure after 2010, weaker and more irregular solar cycles after 2005, and a redistribution in clear-sky shortwave irradiance around 2000. Because it relies only on short windows and requires no domain-specific tuning, the framework provides a compact diagnostic tool for signal processing, supporting the characterization of local variability, detection of regime changes, and decision making in settings where long-term stationarity is not guaranteed. Full article