Search Results (155)

A globally applicable and fully automated simulation method based on satellite-derived Earth Observation (EO) data and global circulation models was developed and validated. Inputs to the simulation are DSM/DTM layers, surface roughness layer, forest canopy layer, and single-level point data from the European Centre for Medium-Range Weather Forecasts fifth-generation ECMWF reanalysis (ECMWF ERA5, a global circulation model produced by the Copernicus Climate Change Service (C3S)). High-resolution roughness length maps are produced by deep learning from optical satellite data. Velocity fields are predicted by fluid dynamics simulations in OpenFOAM using the IDDES turbulence model, a 3D resolved tree canopy implemented as isotropic momentum sinks, and a corrector step based on sub-grid-scale dynamic downscaling of ERA5 data. No calibration data from wind measurements close to the target are necessary to achieve results accurate enough for site assessments and wind park planning. The presented method is suitable for the prediction of average wind speeds and average power densities in complex terrain with high ruggedness indices for WEC (wind energy converter) installations closer to the ground and at hub heights of typical large-scale WECs. Full article

Upper troposphere (UT) humidity records are crucial for climate studies. To maximize temporal representativeness and enhance the lidar signal, pseudo-monthly averaging—limited to nighttime measurement—is applied, yielding water vapor mixing ratio (WVMR) profiles up to 16 km. This study evaluates 11 years (2013–2023) of WVMR profiles from a UV Raman lidar (Li1200) at Réunion Island, comparing them with MLS-Aura satellite retrievals, ERA5 reanalysis data, and GRUAN-processed M10 radiosondes. The results reveal a systematic dry shift in MLS of up to 30% above 12 km, particularly during the wet season. The lidar exhibits a slight downward shift in WVMR, approximately 5% lower than ERA5 throughout the UT, with the largest deviations occurring above 14 km and greater variability during the wet season. Calibration-related challenges during the dry season result in lidar WVMR profiles that are up to 10% drier than ERA5. Additionally, comparisons with GRUAN-processed radiosondes show a substantial dry shift relative to the lidar, exceeding 30% above 12 km. We investigate the effect of GNSS-based lidar calibration by applying an alternative calibration method, which produces higher WVMR values. This reveals a dry shift in ERA5 relative to the lidar, increasing with altitude in the UT up to 25%. These measurements contribute to the global effort to monitor and validate tropical and subtropical upper tropospheric humidity. Full article

We analyze the evolution of atmospheric and surface physical properties over the region of the Earth observed by the Meteosat Second Generation (MSG) satellites during the period 2005–2024. Long-term changes are detected in the observed radiances, with a decrease in the solar domain (−1.3%) and an increase in the thermal infrared domain (+0.4%), consistent with trends reported by independent broadband radiometers such as CERES. The outgoing solar radiance (OSR) exhibits a marked decline, which we associate with a reduction in low-level cloud cover within the nominal Meteosat field of view (MFoV) centered at 0° longitude. Changes in atmospheric CO₂ concentration also contribute to the observed radiative imbalance at the top of the atmosphere (TOA). Instrument calibration stability and inter-satellite homogenization across the MSG series are explicitly addressed, enabling the detection of robust interdecadal signals. By subdividing the MFoV into 60 regional sectors, we characterize spatial variations in cloud amount at low and high atmospheric levels and relate these changes to regional TOA radiative imbalances and concurrent variations in Atlantic sea surface temperature (SSTs). The spectral information provided by SEVIRI allows a more detailed attribution of radiative changes than broadband observations alone from other instruments. In particular, radiances measured in the atmospheric split-window region near 11 µm are shown to be sensitive to variations in low-tropospheric humidity, which exhibits a widespread decadal-scale increase. The results indicate a close coupling between cloud-cover changes, radiative fluxes, and SST evolution on the recent interdecadal time scale. The observed decrease in low-level total cloud cover is independently in line with ECMWF ERA5 reanalysis data. These findings highlight the value of long, stable geostationary observations for investigating atmosphere–ocean interactions and their role in regional climate variability. Full article

Intensive agriculture in semi-arid watersheds is considered a threat to global water security; however, the hydro-agronomic mechanisms that control diffuse pollution sources are often insufficiently characterized at the watershed scale. This study evaluates the hydrological response and nitrate leaching dynamics in the Upper Aguascalientes watershed by implementing the SWAT model, forced with meteorological data and calibrated using runoff derived from ERA5 reanalysis. Methodologically, the Potential Nitrate Leaching Risk Index (IRP_N) was formulated and coupled to the hydrological results. The comparative analysis shows that ERA captures the temporal dynamics of the HRUs, although it tends to significantly overestimate runoff volumes. The basin exhibits a marked scale-dependent duality, with the upper zone operating under a Hortonian regime, while the lower basin exhibits attenuation at the basin scale due to spatial integration and distributed storage processes. The $\left(IR{P}_N\right)$ analysis demonstrates a critical disconnect between fertilization rates (>1300 kg N·ha⁻¹) and crop absorption capacity, turning excess nitrogen into a rapid transport vector during runoff events. Finally, the results underscore the need to complement water management and infrastructure strategies with technical training programs and regulatory frameworks that promote modern agricultural practices aligned with the system’s retention capacity. Full article

To address the suboptimal radiometric calibration accuracy observed in specific beam codes of the GaoFen-3 (GF-3) series satellite for sea surface wind speed (SSWS) retrieval, this study introduces a calibration constant correction method based on the geophysical model function (GMF). This approach enables high-precision SSWS retrieval from GF-3B data. Conventional SAR-based SSWS retrieval models typically rely on pointwise mapping relationships, which overlook the spatial characteristics inherent in dynamic sea surface wind fields. To overcome this limitation, this study proposes an attention-guided dual-branch feature-fusion network (ADBFF-NET). The first branch, implemented as a backpropagation neural network (BPNN), learns nonlinear mappings between the normalized radar cross-section (NRCS, σ⁰), incidence angle, azimuth look direction, and wind vectors (speed and direction). The second branch, designed as a residual convolutional neural network, extracts spatial features of wind fields. An attention mechanism fuses the outputs of both branches, thereby enhancing retrieval accuracy. Experiments conducted with GF-3 series satellite data were validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5), Advanced Scatterometer (ASCAT) wind fields, and altimeter-derived wind speeds. The results indicate that the SSWS retrieved from GF-3B SAR data using the corrected calibration constants achieve a root mean square error (RMSE) of 1 m/s against ERA5 wind speeds, representing an approximately 40% reduction compared with the RMSE obtained using the original calibration constant. Furthermore, compared to ERA5 and ASCAT data, the RMSE of the wind speeds retrieved by the ADBFF-NET model reaches 1.17 m/s and 1.03 m/s, respectively. Full article

Understanding the coupling between hydrological dynamics and carbon sequestration is critical for predicting hemiboreal forest resilience to climate extremes. This study investigates water–carbon interactions in the Järvselja forest (Estonia) through a multi-objective hybrid modeling framework. We integrated long-term (2014–2025) eddy covariance flux measurements and daily meteorological data with a coupled architecture combining the process-based GR4J-Cemaneige model and a Long Short-Term Memory (LSTM) network. To validate the physical consistency of the deep learning component, we employed Support Vector Regression (SVR) diagnostic probes to map LSTM internal cell states against ERA5 soil moisture reanalysis data and in situ water table measurements. The combined LSTM + GR4J-Cemaneige model outperformed standalone approaches in the calibrated Reola catchment (NSE = 0.887), so by assuming hydrological similarity the hybrid model was regionalized to the streamflow ungauged Kalli basin. An in silico interpretability probe validated that the LSTM implicitly encoded physically meaningful soil moisture dynamics ($r>0.9$) without explicit training data. The analysis revealed that the 2018 heatwave triggered a synchronous collapse in water availability and carbon uptake, shifting the ecosystem from a robust sink to a net source. A significant legacy effect was observed, with carbon sequestration capacity lagging behind hydrological recovery for two years. The results of this paper substantiate the influence of climate warming on hemiboreal forests, demonstrating its implications for soil hydrology and the availability of water to sustain photosynthesis. Full article

This study aims to optimize the estimation of reference evapotranspiration (ETo) in data-scarce regions by integrating ERA5-Land reanalysis data with machine learning (ML) models. Daily meteorological data from 33 stations across Turkey’s diverse climate zones (1981–2010) were utilized to train and validate three ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM). The methodology involved rigorous quality control of ground-based observations, spatial correlation of ERA5-Land grids to station locations, and performance evaluation under various data-limited scenarios. Results indicate that while ERA5-Land provides highly accurate solar radiation (R_s) and temperature (T) data, variables like wind speed (U₂) and relative humidity (RH) exhibit systematic biases. Among the used models, XGBoost demonstrated superior performance (R² = 0.95, RMSE = 0.43 mm day⁻¹, and MAE = 0.30 mm day⁻¹) and computational efficiency. This study provides a robust, regionally calibrated framework that corrects reanalysis biases using ML, offering a reliable alternative for ETo estimation in areas where local measurements are insufficient for sustainable water management. Full article

Wildfire occurrence in arid and semiarid landscapes is increasingly driven by shifts in climatic and biophysical conditions, yet its dynamics remain poorly understood in the mountainous environments of western Saudi Arabia. This study modeled wildfire probabilities across the Aseer, Al Baha, Makkah Al-Mukarramah, and Jazan regions via multisource Earth observation datasets from 2012–2025. Active fire detections from VIIRS were integrated with ERA5-Land reanalysis variables, vegetation indices, and Copernicus DEM GLO30 topography. A random forest classifier was trained and validated via stratified sampling and cross-validation to predict monthly burn probabilities. Calibration, reliability assessment, and independent temporal validation confirmed strong model performance (AUC-ROC = 0.96; Brier = 0.03). Climatic dryness (dew-point deficit), vegetation structure (LAI_lv), and surface soil moisture emerged as dominant predictors, underscoring the coupling between energy balance and fuel desiccation. Temporal trend analyses (Kendall’s τ and Sen’s slope) revealed the gradual intensification of fire probability during the dry-to-transition seasons (February–April and September–November), with Aseer showing the most persistent risk. These findings establish a scalable framework for wildfire early warning and landscape management in arid ecosystems under accelerating climatic stress. Full article

Soil moisture (SM) is a key variable regulating land–atmosphere energy exchange, hydrological processes, and ecosystem functioning. Though important, there are still unresolved problems in accurate SM monitoring and the practical application and validation of existing methods. In this review, we integrate mechanistic classification and applicability and constraint discussions to develop a coherent understanding of current SM monitoring approaches. Within this framework, in situ measurements, optical and thermal infrared methods, active and passive microwave remote sensing (RS) techniques, and model-based simulations are compared, and publicly accessible SM dataset products are comparatively analyzed in terms of product characteristics and application limitations. Different from other published reviews, this study covers a large scope of SM monitoring methods varying from in situ observation to RS inversion, and classifies them based on their mechanisms, thereby constructing a complete comparative framework for SM research. Moreover, three types of open-access SM dataset products are investigated, optical and microwave RS products, model simulation and data fusion products, and reanalysis dataset products, and evaluated according to their resolution, depth, applicability, advantages, and limitations. By doing so, it is concluded that in situ observations remain essential for calibration and validation but are spatially limited. Optical and thermal infrared methods are restricted by atmospheric conditions and a shallow penetration depth, while microwave techniques exhibit varying performances under different vegetation and soil conditions. Existing datasets differ significantly in resolution, consistency, and coverage, making no single product universally applicable. Future research should focus on multi-source and spatiotemporal data fusions, the integration of machine learning with physical mechanisms, enhancement for cross-sensor consistency, the establishment of standardized uncertainty evaluation frameworks, and the refinement of high-order RTMs and parameterization. Full article

Satellite altimeters provide global observations of significant wave height (SWH, in m), yet buoy-based validation is affected by representativeness errors and sampling mismatches. This study develops a consistent framework for validating and scaling HY-2B SWH that integrates nearest-point spatiotemporal collocation, sea-state-binned diagnostics, three complementary calibration schemes (bias correction, ordinary least-squares (OLS) linear regression scaling, and machine-learning residual correction), and Extended Triple Collocation (ETC) for sensor-independent uncertainty estimates. The dataset includes HY-2B SWH, National Data Buoy Center (NDBC) buoy records, seven buoys in the Taiwan Strait, and the sea surface significant wave height (VHM0, in m) from the Copernicus Marine Environment Monitoring Service (CMEMS) Global Wave Reanalysis. Sensitivity tests show that tightening the collocation radius from 100 to 25 km reduces scatter (RMSE/STD) while preserving near-zero bias; correlations remain ≥0.97 for 25–50 km but degrade at larger windows, underscoring representativeness effects. Error metrics increase monotonically with sea state, whereas mean biases remain small. ETC applied to HY-2B, NDBC, and CMEMS yields random error standard deviations of 0.158, 0.147, and 0.179 m, respectively, with squared correlation coefficients (${\rho }^2$) of approximately $0.96$–$0.98$ for all systems. Scaling experiments reveal a data-quality-dependent behavior: for NDBC matchups, HY-2B already agrees closely with buoys (e.g., RMSE ≈ 0.24 m), and additional scaling brings no benefit; for the Taiwan Strait buoys, all three schemes improve agreement (RMSE ≈ 0.41 m; correlation ≈ 0.95), with the residual machine-learning model providing the largest reduction in random error. The results support a practical protocol for HY-2B SWH validation: a 30 min/25–50 km window, modest outlier screening, and selective use of linear or residual corrections depending on buoy network and environment. Full article

Estimating global radiation (GR) is crucial for assessing solar energy potential, understanding surface energy balance, and forecasting agricultural production. However, several regions require additional monitoring and sparse sensor networks. The ERA5-ECMWF reanalysis is a viable alternative for estimating meteorological elements in unmonitored areas. This study aimed to train an artificial neural network (ANN) model to estimate GR based on ERA5 data and map its distribution in the study area. We utilized GR data from 32 automatic weather stations of the Brazilian National Institute of Meteorology in Mato Grosso, Brazil, for model training. The model input consisted of ERA5 air temperature, precipitation data, and top-of-atmosphere solar radiation (R₀) calculated from the latitude and day of the year. The calibrated model demonstrated high accuracy, with Nash–Sutcliffe and Kling–Gupta efficiency indices exceeding 0.99. This enabled the generation of historical time series and maps of GR spatial distribution in the study area. The results demonstrate that—as input for the ANN—ERA5 data enables precise and accurate estimation of GR distribution, even in locations without meteorological stations. Full article

Accurately understanding the impact of Significant Wave Height (SWH) on mariculture productivity is crucial for developing a sustainable blue economy and mitigating the effects of increasing marine extreme events under climate change. However, a significant research gap exists in macroscale empirical tools capable of quantifying the complex, non-linear, and spatially non-stationary relationships between SWH and mariculture yield. Addressing this, our study focused on the Bohai and Yellow Seas, a critical mariculture region in China. We developed five novel SWH indices (LSDI, MSDI, HSDI, RSI, NDSI) to statistically link SWH with the Unit Area Yield (UAY) using buoy-calibrated ERA5 reanalysis data and regional fishery statistics. Geographically Weighted Regression (GWR) was further employed to uncover the spatial heterogeneity of this relationship. Results demonstrated that the Normalized Difference SWH Index (NDSI) most effectively captured the SWH-UAY relationship (r = 0.61, R² = 0.37), as its non-linear form integrates the positive effects of low SWH conditions and the negative effects of high SWH conditions. GWR analysis revealed significant spatial non-stationarity, with the SWH impact on yield being stronger in the eastern and southern open waters of the Yellow Sea and weaker in the northern semi-enclosed Bohai Sea. The index framework and spatial analysis method developed in this study provide a transferable tool for quantifying the impact of physical oceanographic processes on mariculture productivity at a macro scale, which can offer a scientific basis for climate-resilient mariculture zoning and adaptive management. Full article

Reliable precipitation inputs are essential for hydrological modeling, yet global precipitation products often exhibit substantial discrepancies that introduce significant uncertainties into streamflow simulations and projections. In this study, we assessed the relative contribution of precipitation dataset uncertainty to discharge simulations and projections, in comparison with uncertainties from model structure, model parameters, and climate projections, in the Liujiang catchment, southwest China. Three widely used satellite-based products (CHIRPS, PERSIANN, and IMERG) and one reanalysis dataset (ERA5) were combined with three hydrological models of varying structural complexity to simulate streamflow. Using an ANOVA-based variance decomposition framework, we quantified the contributions of different uncertainty sources under both historical and future climate conditions. Results showed that precipitation input uncertainty dominates discharge simulations during the calibration period, contributing over 60% of total variance particularly at high flows, while interactions among precipitation, model structure, and parameters govern low-flow simulations. Under future climate scenarios, climate projection uncertainty overwhelmingly dominates discharge predictions with 50–80% of uncertainty contribution, yet precipitation products still contribute significantly across time scales. The compensation of precipitation biases by hydrological models can cause parameter values to deviate from their true physical meaning. This deviation may further amplify the differences in discharge projections driven by different precipitation products under future climate conditions and increase the overall uncertainty of streamflow projections. Overall, this study introduced an integrated approach to simultaneously assess precipitation uncertainty across flow regimes and future climate scenarios. These results emphasized the necessity of using ensemble approaches that incorporate multiple precipitation products in hydrological forecasting and impact studies, particularly in data-scarce regions reliant on global datasets. Full article

The Huanjing-2A/2B (HJ-2A/2B) satellites are China’s next-generation environmental monitoring satellites, equipped with four visible light wide-swath charge-coupled device (CCD) sensors. These sensors enable the acquisition of 16-m multispectral imagery (16m-MSI) with a swath width of 800 km through field-of-view stitching. However, traditional vicarious calibration techniques are limited by their calibration frequency, making them insufficient for continuous monitoring requirements. To address this challenge, the present study proposes a spectral-angle difference correction-based cross-calibration approach, using the Landsat 8/9 Operational Land Imager (OLI) as the reference sensor to calibrate the HJ-2A/2B CCD sensors. This method improves both radiometric accuracy and temporal frequency. The study utilizes cloud-free image pairs of HJ-2A/2B CCD and Landsat 8/9 OLI, acquired simultaneously at the Dunhuang and Golmud calibration sites between 2021 and 2024, in combination with atmospheric parameters from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset and historical ground-measured spectral reflectance data for cross-calibration. The methodology includes spatial matching and resampling of the image pairs, along with the identification of radiometrically stable homogeneous regions. To account for sensor viewing geometry differences, an observation-angle linear correction model is introduced. Spectral band adjustment factors (SBAFs) are also applied to correct for discrepancies in spectral response functions (SRFs) across sensors. Experimental results demonstrate that the cross-calibration coefficients differ by less than 10% compared to vicarious calibration results from the China Centre for Resources Satellite Data and Application (CRESDA). Additionally, using Sentinel-2 MSI as the reference sensor, the cross-calibration coefficients were independently validated through cross-validation. The results indicate that the radiometrically corrected HJ-2A/2B 16m-MSI CCD data, based on these coefficients, exhibit improved radiometric consistency with Sentinel-2 MSI observations. Further analysis shows that the cross-calibration method significantly enhances radiometric consistency across the HJ-2A/2B 16m-MSI CCD sensors, with radiometric response differences between CCD1 and CCD4 maintained below 3%. Error analysis quantifies the impact of atmospheric parameters and surface reflectance on calibration accuracy, with total uncertainty calculated. The proposed spectral-angle correction-based cross-calibration method not only improves calibration accuracy but also offers reliable technical support for long-term radiometric performance monitoring of the HJ-2A/2B 16m-MSI CCD sensors. Full article

Deep learning models for atmospheric pattern recognition require spatially consistent training labels that align precisely with input meteorological fields. This study introduces an automatic cold front detection method using the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) at 850 hPa, specifically designed to generate physically consistent labels for machine learning applications. The approach combines the Thermal Front Parameter (TFP) with temperature advection (AdvT), applying optimized thresholds (TFP < 5 × 10⁻¹¹ K m⁻²; AdvT < −1 × 10⁻⁴ K s⁻¹), morphological filtering, and polynomial smoothing. Comparison against 1426 manual charts from 2009 revealed systematic spatial displacement, with mean offsets of ~502 km. Although pixel-level overlap was low, with Intersection over Union (IoU) = 0.013 and Dice coefficient (Dice) = 0.034, spatial concordance exceeded 99%, confirming both methods identify the same synoptic systems. The automatic method detects 58% more fronts over the South Atlantic and 44% fewer over the Andes compared to manual charts. Seasonal variability shows maximum activity in austral winter (31.3%) and minimum in summer (20.1%). This is the first automatic front detection system calibrated for South America that maintains direct correspondence between training labels and reanalysis input fields, addressing the spatial misalignment problem that limits deep learning applications in atmospheric sciences. Full article