Environmental and Earth Sciences

Abstract: Accurate and reliable rainfall data is crucial for climate sensitive applications in Kenya. This study evaluates the performance of nine satellite rainfall estimates (SREs) datasets. The study aims to identify the most suitable dataset for various applications in the country. The datasets include Africa Rainfall Climatology (ARC2), NOAA’s Rainfall Estimation Version 2 (RFEv2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Integrated Multi-satellitE Retrievals for GPM (IMERG), Climate Prediction Center Morphing Technique (CMORPH), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Hazards Infrared Precipitation (CHIRP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP). The datasets are assessed based on their spatial and temporal accuracy against ground-based observations in Kenya. The daily rainfall station data is obtained from the Kenya Meteorological Department which maintains an updated archive of quality-controlled observed rainfall datasets across the country. Metrics are evaluated on how well satellite estimates capture ground-based observed rainfall. The metrics used include correlation coefficient (r), root mean square error, mean error, mean absolute error, bias, probability of detection (POD), false alarm ratio (FAR), and Heidke Skill Score (HSS). Results indicate that the RFEv2 is the best-performing satellite rainfall estimate dataset with r=0.58, HSS=0.64 and FAR=0.26. Therefore, it can be applied especially in cases of scarce data. In terms of performance, RFEv2 is followed closely by CMORPH with r=0.63, HSS=0.64 and FAR=0.32.

Abstract: Double tropopauses are recurrent features of the extratropical upper-troposphere–lower-stratosphere (UTLS), but their climatology and long-term evolution over the Mediterranean region remain poorly characterized. Here, ERA5 pressure-level data for 1979–2025 are used to investigate the seasonal distribution, vertical structure, trends, and geographical redistribution of double-tropopause events over the Mediterranean sector. Lapse-rate tropopauses were identified using a discrete pressure-level implementation of the WMO criteria. The analysis focuses on double-tropopause profiles with the first lapse-rate tropopause at or above 8 km (DT–UTLS). To improve the validation of high second-tropopause cases, the temperature and geopotential profiles used for lapse-rate tropopause detection were extended up to 10 hPa, allowing a more complete evaluation of the WMO 2-km layer above LRT2 on the available ERA5 pressure-level grid. DT–UTLS occurrence shows a marked seasonal structure, with winter maxima over the southern part of the domain and a relative northward displacement in summer. The detected events exhibit a layered vertical structure, with a lower first lapse-rate tropopause and a second tropopause several kilometres higher. Long-term changes are spatially heterogeneous and seasonally asymmetric. The most robust frequency signal is a summertime decline over the north-eastern part of the study domain, centred near the Black Sea–northern Anatolia sector and the adjacent eastern/northern Mediterranean margin, accompanied by a westward redistribution of the JJA occurrence regime. In DJF, the geographical distribution shifts significantly southward. A positive winter trend in the subtropical belt is obtained with the standard DT–UTLS definition, but its statistical significance weakens under a stricter exact-2-km sensitivity test and is therefore interpreted with caution. Detrended associations with upper-level circulation and event-conditioned vertical-structure diagnostics provide dynamical context for the observed variability, but are not interpreted as causal attribution. These results identify Mediterranean DT–UTLS occurrence as a useful indicator of the seasonally evolving UTLS transition zone and its long-term regional redistribution.

Abstract: Given its large range and spatial footprint, radar remote sensing has been met with increased interest in the wind energy community. Paired radars can yield complete wind speed and direction measurements over this large spatial footprint, an invaluable asset for wind farm operators; this was also the main goal of the Texas Tech University X-band radars installed for the American Wake Experiment (AWAKEN). However, care must be taken in how these measurements are quality-controlled and understood. This study examines the data filtering algorithms used in AWAKEN, and how their refinement can depict of atmospheric flows around wind farms differently. The first filtering method used allowed some non-atmospheric measurements to remain while also expunging other, higher-quality measurements, leading to a high bias in radar-estimated wind speeds. Closer inspection of the algorithm and its output led to the discovery of biological tracers (e.g., birds, bats, and insects) influencing wind speed fields, and to the development of a new algorithm that more effectively removed low-quality and non-atmospheric data and preserved high-quality data. Comparisons between radar data processed with the new algorithm and a ground-based lidar in the radars’ overlapping scanning areas showed reduction in the previous high bias, demonstrating the sensitivity of radar measurements to processing techniques.

Abstract: A lattice Boltzmann model (LBM) was developed for thermal radiation transfer in atmospheric aerosol medium. The basic principle and computation procedure of LBM of radiation transfer in aerosol medium were described in this article. A new normalization method for the aerosol scattering phase function was described following the recent literature. The LBM results were validated by comparing the outputs from a Monte Carlo model (MCM) with same boundary conditions and aerosol property parameters. Two cases of validation with beam and diffused incoming radiation boundaries were simulated with both LBM and MCM. The spheric participation medium was assumed in which a Henyey-Greenstein Mie scattering phase function was used. The third LBM result was also compared with the MCM results in the situation of partial cloudy and clear atmospheric media in the domain. All three validations cases indicated that the LBM gives accurate results compared with the traditional MCM in atmospheric aerosol media, the root square difference between the LBM and MCM results were less than 0.03 for normalized unit incoming radiative intensity. The LBM was successfully implemented on a GPU to speed up the computation and reduce energy consumption. The test results indicated that CPU/GPU computation time ratio was around 260 for a 5013 grids computational domain, indicating a significant GPU computational advantage of the LBM for radiation transfer in aerosol medium.

Abstract: Simulation of the surface energy balance (SEB) is essential for land–atmosphere coupling and weather–climate prediction, yet land surface models remain uncertain in turbulent and ground heat fluxes. We evaluated CoLM2024 with Land Cover Type (LCT) and Plant Community (PC) schemes and ECLand v1.0 against energy-balance-corrected observations from 80 PLUMBER2 towers spanning 11 land cover types. Observations and simulations were decomposed at 30-min, daily, and monthly scales. All experiments reproduced net radiation well, whereas ground heat flux was poorly simulated over forests and wetlands because of excessive daytime amplitude, indicating limitations in canopy–soil heat partitioning and soil heat storage. ECLand achieved the best latent heat flux performance through smaller systematic errors. PC reduced unsystematic errors, but this benefit was offset by a large negative growing-season bias. Sensible heat flux performance depended on vegetation: PC performed best over evergreen needleleaf and mixed forests, while ECLand was superior over broadleaf forests and had the lowest unsystematic errors, although its implicit coupling damped variability. Performance was scale dependent: ECLand was favored at 30 min, PC was competitive for daily forest sensible heat correlations, and no consistent ranking emerged monthly. Complex canopy schemes therefore require robust formulations, improved heat-storage processes, and better parameter calibration.

Abstract: Before Cyclone Senyar made landfall on Sumatra in November 2025 it drew moisture from across the South China Sea, Gulf of Thailand and Indian Ocean. The substantial damage its heavy rainfall caused on the NE and W coasts of Sumatra urges us to rethink the relationships between climate, forests, hydrology, land use, human presence, and vulnerability; the simple ‘deforestation causes floods’ narrative is no longer adequate to guide a building-back-better strategy for the areas affected. We reviewed key concepts and framing of the links between ocean temperature, atmospheric moisture transport (‘rivers in the sky’), rainfall extremes, saturation of the existing buffers, river flow and flooding as they account for the space-time pattern of Senyar effects with its multiple landfalls. Atmospheric roughness (slowing down sky rivers and unloading precipitation), surface infiltration and water retention in the soil profile, and mid- and downstream flow delays due to water retention depend on land cover beyond what a simple forest—nonforest terminology can represent. Rather than indiscriminate tree planting efforts, future adaptation and disaster avoidance efforts should balance the reduction of human exposure through effective land use planning and efforts to reduce hazard by restoring and managing vegetation cover and drainage systems.

Abstract: Wind shear coefficients (WSCs) are fundamental parameters for wind resource assessment, wind turbine design, and offshore wind farm operation. However, the characteristics of WSCs in the offshore waters of Zhuanghe have not yet been systematically investigated. In this study, two years of in-situ observational data (2015-2017) were analyzed to characterize the temporal variability of WSCs under different atmospheric stability conditions. The results indicate that the atmospheric boundary layer in the study area is dominated by stable and unstable conditions, exhibiting a distinct U-shaped annual distribution. The diurnal evolution of atmospheric stability varies considerably among months and is strongly associated with both wind speed and wind direction. Under low wind speed conditions, stable and unstable atmospheres occur with comparable frequencies. As wind speed increases, the occurrence of neutral conditions increases steadily, and the atmosphere becomes predominantly neutral when wind speed exceeds 18.5 m s^-1. Northerly winds are generally associated with unstable atmospheric conditions, whereas southerly winds are primarily associated with stable conditions. The WSC ranges from 0.01 to 0.32 and exhibits pronounced seasonal and monthly variability. Under neutral and unstable atmospheric conditions, the diurnal variation of WSC is relatively consistent across different measurement heights. In contrast, under stable conditions, significant vertical differences in WSC are observed throughout most of the year, except in March and April. These findings improve the understanding of offshore wind shear characteristics under different atmospheric stability regimes and provide valuable guidance for offshore wind resource assessment, wind profile modeling, and the design, operation, and optimization of offshore wind farms in the Zhuanghe offshore region.

Abstract: Global Navigation Satellite System (GNSS)-derived precipitable water vapour (PWV) has become an important source of atmospheric moisture information for severe weather monitoring. While previous studies have primarily relied on static GNSS-derived PWV metrics, comparatively little attention has been given to dynamic metrics capturing the temporal evolution of atmospheric moisture. This study evaluates the relationships of dynamic and static GNSS-derived PWV metrics with heavy rainfall characteristics in Cyprus using 30 events recorded between 2020 and 2026. GNSS-PWV observations from the CLOUDWATER network and collocated rainfall measurements from the Cyprus Department of Meteorology were analysed. The pre-rainfall PWV growth rate (ΔPWV/Δt) and peak PWV were compared using correlation and regression analyses. All events exhibited a distinct increase in PWV before rainfall onset, with peak PWV typically occurring immediately before or shortly after precipitation began. The PWV growth rate showed a stronger relationship with peak rainfall intensity (R = 0.73) than peak PWV (R = 0.58) and remained the only significant predictor in multiple regression analysis. Neither metric was significantly related to total rainfall accumulation or rainfall timing. These findings show that the dynamic PWV metric provide a more informative characterization of heavy rainfall intensity than the static PWV metric alone.

Abstract: Cold-pool structure controls the confinement and transport environment relevant to surface wintertime ozone in the mountainous Uinta Basin, Utah, USA, but point surface values alone do not establish that structure. We evaluated deterministic responses in Weather Research and Forecasting (WRF) simulations of the 2 February 2013 episode to the supplied Global Forecast System (GFS) or North American Model (NAM) driving analysis, nest feedback, boundary-layer and surface-layer treatment, slope-aware radiation, and static-terrain source. Across a common five-hour sample, GFS-driven members were warmer at the evaluated surface sites, approximately 3.6 K less stable through 50–500 m above ground level, and 0.8–1.0 m s−1 faster in fixed-layer transport wind than feedback-matched NAM members. GFS-driven members also retained a smaller footprint-mean, vertically integrated heat deficit under all seven definitions, although every simulation gained heat deficit over the retained window. The repeated separation is therefore a driving-product state response rather than demonstrated faster cold-pool loss. Controlled feedback and physics responses were smaller and metric dependent, while bounded terrain and fine-grid process diagnostics showed spatial structure that the station sample could miss without establishing terrain superiority, resolution benefit, basin export, or inversion depth. The study provides a meteorological result with implications for subsequent air-chemistry modelling; it does not contain a chemistry calculation or identify the initial-versus-lateral-boundary contribution to the driving-product response.

Abstract: We propose an ‘Invisible Bridge’ framework that links in situ measurements to remote sensing observations of atmospheric pollutants via atmospheric chemical transformations. An exploratory multivariate workflow, non-negative matrix factorization (NMF, used here as a PMF-type decomposition), principal component analysis (PCA) and k-means clustering identified three robust atmospheric regimes. One factor is a primary circulation signal (high levels of NOx and VOCs linked to traffic), another is a magnification/resuspension regime, and a third is a secondary ‘regenerated’ regime dominated by O3, SO2 and secondary PM. Compositions based on remote sensing data regimes show that the secondary/regenerated regime represents the crucial chemical and vertical link between surface emissions and satellite measurements integrated in the column, thus improving the interpretation of satellite observations in terms of surface exposure. The results show that chemical regeneration and regime context strongly modulate surface-column consistency, which has implications for the interpretation of satellite air quality indicators considering regime.

Abstract: Ionospheric response to a geomagnetic storm depends on several factors. Strength of a storm, commencement type, type of the solar origin of a geomagnetic storm (e.g., coronal mass ejections or high-speed solar wind streams) and observational site location are among them. In this work we present the results of a statistical analysis of eighty moderate to major geomagnetic storms that took place during the declining phase of the 24th solar cycle, from 2015 to 2019. We carried out an analysis on the ionospheric response to these storms using the total electron content (TEC) data obtained from three geodetic receivers located in Portugal: Lisbon (Continental Portugal), Furnas (Azores) and Funchal (Madeira). Two of the receivers, at Lisbon and Azores, are located at about the same latitude (~39ºN) while the third receiver, at Madeira, is positioned to the south (~33ºN). The receivers are also distributed in longitude from 9ºW to 25ºW. Statistical analysis of the observed TEC variations allowed detection of specific patterns in the ionospheric response to storms with different characteristics. Special attention was given to the longitudinal and latitudinal (dis)similarities in the ionospheric variations, observed at the three studied locations.

Abstract: Deterministic AI weather and ocean models are far more computationally efficient and achieve lower mean-square error than state-of-the-art operational forecasts, yet they are widely criticized for violating physical constraints, smoothing small scales, and underestimating extremes. We offer a unified Markov-process explanation: MSE-trained deterministic models learn the conditional mean E[x_{t+Δt} | x_t] rather than the full transition law P(x_{t+Δt} | x_t). This mean-field limit links low RMSE to poor conservation, spectral dissipation, and weak extremes. We support the theory with a finite-state Markov experiment and a stochastically perturbed Kelvin-Helmholtz fluid experiment using matched U-Net backbones. From an information-entropy perspective, deterministic forecasts are low-entropy and overconfident relative to atmospheric and oceanic variability, whereas diffusion models sample from conditional transition distributions and better match intrinsic complexity. Distributional generative forecasting may therefore be better suited to future AI prediction, reconstruction, and bias correction.

Abstract: The threat of lightning to human society is increasingly severe, and the characteristics of the resulting casualties, along with their underlying mechanisms, have long been key topics in natural disaster research.Based on lightning disaster compilation data from 2000 to 2023, lightning monitoring data from 2009 to 2020, and multi-source data including soil resistivity, elevation and topographic relief in Zhaoqing, this paper statistically analyzes the distribution characteristics of lightning disasters in various districts of Zhaoqing from the perspectives of temporal occurrence, spatial distribution, casualties, disaster environments, hazard factors, and hazard-pregnant environmental characteristics. The results indicate that: (1) Lightning disasters have shown a decreasing trend since 2000.From 2000 to 2023, a total of 777 lightning disasters occurred in Zhaoqing, causing direct economic losses of approximately 22.6131 million yuan. (2) The monthly distribution of lightning disasters in Zhaoqing peaks mainly from April to September. The Fengkai-Huaiji area and the Sihui-Gaoyao area are high-incidence zones for lightning casualty accidents. (3) Farmlands, areas near buildings, and the interiors of buildings are high-risk environments for lightning casualties in Zhaoqing. Disasters occurring inside buildings result in the highest number of casualties. Lightning casualty accidents are primarily concentrated in rural areas or associated with agricultural activities. (4) Sihui, Duanzhou, and Dinghu rank top three in cloud-to-ground (CG) lightning density in the city. In terms of casualty density per unit area, Duanzhou, Dinghu, and Deqing are the highest. (5) The distribution characteristics of topographic relief and elevation show obvious commonalities, with high-value areas concentrated in the central, mid-western parts of Zhaoqing, and northern Huaiji, while low-value areas are mainly in the southeastern part of Zhaoqing.

Abstract: Based on daily precipitation observations from six national meteorological stations in the Zhangye region from 1960 to 2023, this study examines the spatiotemporal evolution and possible driving factors of extreme precipitation using four ETCCDI-recommended indices (SDII, R10mm, R95p, and RX1day). Trends were evaluated using linear regression and the Mann–Kendall test, with Sen's slope estimation. Results reveal significant (p < 0.05) increasing trends in both frequency and intensity of extreme precipitation, with a shift from low-intensity, low-frequency to high-intensity, high-variability modes. Temporally, all indices exhibited a step-like surge around 2000, entering a period of high-level oscillation, with extreme characteristics amplified during strong El Niño years. Spatially, a distinct "higher in the south, lower in the north" pattern prevails among the six stations, with the southern Qilian Mountains as the primary contributor and central plains showing a bimodal distribution, reflecting joint modulation by westerly troughs and local strong convection. The intensification is linked to enhanced atmospheric water vapor, northward penetration of the East Asian summer monsoon, and topographically forced lifting. While alleviating drought stress, this trend substantially elevates the risk of flash floods and debris flows in mountainous areas.

Abstract: Earth System Models (ESMs) and General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) still exhibit substantial biases over West Africa, particularly for daily precipitation extremes. This study evaluates raw CMIP6 simulations and simulations bias-corrected with the Quantile Delta Mapping (QDM) and CDF-transform (CDF-t) methods, for mean precipitation and two extreme indices: consecutive dry days (CDD) and total precipitation from very wet days (R95pTOT). Three observational datasets (CHIRPS, TAMSAT, MSWEP) are used over 1983-2012 for the West African Monsoon season (JJAS), with projections for 2071-2100 under SSP5-8.5. Raw simulations reproduce the main precipitation gradients but show considerable biases and inter-model spread, especially over the Guinean coast and Sahel. Both methods considerably improve performance, increasing spatial correlations and bringing normalized standard deviations closer to unity. CDF-t performs slightly better for mean precipitation and CDD, with comparable performance for R95pTOT; no single method is optimal for all indices. Projections indicate wetter conditions over the central and eastern Sahel, longer dry spells over the western Sahel, and a widespread intensification of extreme rainfall. Based on the Wilcoxon signed-rank test, this intensification is significant mainly at the ensemble level, while CDD changes show the weakest significance.

Abstract: Airborne microplastics (AMPs) are increasingly recognized as an emerging air pollutant. However, observational data remains scarce in Southeast Asia. This study provides the first observations of AMPs in Phnom Penh, Cambodia, using µFTIR-ATR imaging. Number concentration, morphology, polymer composition, aerodynamic size distribution, Feret diameter, and surface aging characteristics were investigated together with meteorological parameters, gaseous pollutants, water-soluble ionic tracers, and HYSPLIT backward trajectories for source attribution. AMPs were dominated by polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), with 52% classified as fragments and 82% having Feret diameters smaller than 30 µm. AMP concentrations ranged from 0.55 to 1.27 MP m^-3 in TSP (mean: 0.97 ± 0.30 MP m^-3, n = 134) and from 0.23 to 0.49 MP m^-3 in PM2.5 (mean: 0.33 ± 0.10 MP m^-3, n = 46). Carbonyl and hydroxyl indexes indicate that PE and PP were relatively fresh and in low-to-moderate photo-oxidative aging states. Correlation analysis further indicates that PE and PP were mainly associated with local waste fragmentation, whereas PET was linked to precipitation scavenging and resuspension processes. In addition, HYSPLIT backward trajectories suggest regional transport of AMPs by the Southwest Monsoon from marine source regions, including the Indian Ocean, Arabian Sea, and Andaman Sea. These findings provide the first baseline dataset for AMP pollution in Phnom Penh, Cambodia and highlight the combined importance of local emissions and regional atmospheric transport in Southeast Asia.

Abstract: This study introduces the AERONET Analyzer Tool (AAT), a specialized Python-based suite designed to streamline aerosol research via the AERONET network. The software facilitates the download, data cleaning, and processing of photometric datasets. Key functionalities include the generation of variable climatologies and the application of diverse aerosol classification frameworks based on established literature. To demonstrate the performance and practical utility of the newly developed software, an interannual study of aerosol optical properties was carried out at the San Miguel de Tucumán station (Tucuman_UNT, Argentina). This investigation, spanning the period from 2023 to 2025, provides a detailed look into the regional atmospheric conditions. Through this software, the Aerosol Optical Depth (AOD₄₄₀) and the Ångström Exponent (AE_440-870) can be evaluated for aerosol classification using thresholds defined by different authors, enabling the user to select the framework best suited to their region. As a practical application of some of the software's functionalities, an interannual climatological characterization of aerosol optical properties was conducted at the cited station during the 2023–2025 period. Using the AAT, seasonal variations of the AOD₄₄₀ and the AE_440-870 were assessed, revealing a marked seasonality conditioned by the local piedmont topography.

Abstract: Greek olive groves are subject to multiple thermal, water, biotic, and extreme-weather pressures, yet national-scale maps of their combined historical exposure remain limited. The present study quantified climate risk for olive cultivation across Greece using twenty agroclimatic indicators grouped into four thematic categories. Using hourly ERA5-Land data (1995–2024) and quality-controlled ESWD reports (2014–2024), each indicator rec-orded how often predefined adverse thresholds were met at the grid-cell level during the reference period. We integrated the resulting layers using a weighted multi-criteria deci-sion analysis, with lethal frost applied as a separate constraint, to produce a composite spatial distribution of climate risk and district-level summaries linked to CORINE Land Cover 2018, olive-grove class (2.2.3). Composite risk was spatially heterogeneous: cold and frost recurrence predominated in northern and upland areas, whereas water-related indicators occurred most persistently in southern and island districts, including eastern Crete. Most of the mapped olive-grove area fell into intermediate composite classes ra-ther than at the extremes of the score range. Comparison with a recent nationwide olive suitability assessment showed agreement in major western and southern producing dis-tricts, but also contrasting patterns where high suitability coincided with elevated recur-rence-based risk. The resulting products provide a national historical baseline for cli-mate-risk recurrence in Greek olive groves, offer a spatial basis for regionally targeted adaptation planning, and demonstrate the applicability of an AI-Assisted geospatial framework for reproducible national-scale climate-risk assessment of perennial crops.

Abstract: The planetary boundary layer height (PBLH) represents an essential parameter of the climate system, which controls the exchanges of momentum, heat, moisture, and pollutants between the terrestrial surface and the free atmosphere. In this study, the spatiotemporal variability of the PBLH in Romania during the warm season (May–September) was investigated utilizing the ERA5 reanalysis data for the 1989-2022 period. To identify the dominant modes of variability, the Empirical Orthogonal Function (EOF) analysis was applied, conducted separately for the daily, daytime, and nighttime fields of the PBLH. The results highlight the fact that the first EOF mode explains more than half of the total PBLH variability and is associated with the variability of the large-scale atmospheric circulation, represented by the North Atlantic Oscillation (NAO). The subsequent EOF modes reflect the influence of regional thermodynamic processes, heatwaves, atmospheric moisture, and the meridional circulation modulated by the Carpathian topography. The findings indicate that the evolution of the planetary boundary layer in Romania is determined by the interaction between the hemispheric-scale atmospheric circulation, regional thermodynamic processes, and the topographic effects associated with the Carpathian arc. These results contribute to the understanding of the mechanisms that control PBLH variability in Southeastern Europe and provide useful information for climatological and air quality studies.

Abstract: The utilisation of microwave radiances is crucial for enhancing the precision of weather forecasts. Despite existing uncertainties over land and ice-covered surfaces, recent advances have enhanced their use. This study examines the impact of assuming either Lambertian or specular surface reflection on the simulation of brightness temperatures for surface-sensitive, clear-sky AMSU-A microwave radiances across land and snow-covered areas. It represents the preliminary work before to run a full assimilation and forecast impact study. Using the high-resolution HARMONIE-AROME regional modelling system, experiments were conducted to retrieve and analyse the retrieved emissivity in different conditions/seasons. The emissivity was also used as input to the radiative transfer model to simulate brightness temperatures at surface-sensitive sounding channels. The results show that the Lambertian assumption produces higher variability in dynamic surface emissivity, while the specular approach yields smaller and more consistent deviations. During winter, specular reflection shows higher first-guess departures (e.g. observations minus simulations) to surface-sensitive sounding observations, whereas in summer it performs better over land surfaces. Over snow-covered regions, the use of the Lambertian reflection to simulate the brightness temperature gives smaller mean errors for AMSU-A channels 4 (52.8 GHz) and 5 (53.59 GHz). These findings encourage us to further investigate the implementation of a parameter that accounts for the Lambertian component of surface reflection when simulating brightness temperature in high-resolution limited-area models.