ABSTRACT Accurate wind prediction is essential for forecasting the transport and decay of wake vortices that might cause an adverse impact to light aircraft operations. This paper proposes an innovative framework for deterministic and probabilistic wind nowcasting, designed to be integrated into a Wake Vortex Warning System for Vertiports (WVWS‐V) near major airports. The study presents and tests different deterministic nowcasting models. The superior approach employs a hybrid concept combining complex Continuous Wavelet Transform (CWT), two‐dimensional Convolutional Neural Networks (2D‐CNN), Long Short‐Term Memory (LSTM) networks, and Light Gradient Boosting Machine (LGBM), enabling it to capture both the general patterns and abrupt fluctuations of wind occurring intermittently and repeatedly. The proposed hybrid model achieved a Mean Absolute Error (MAE) of 0.75 m/s for wind speed and 9.1° for wind direction over a 20‐min prediction horizon, outperforming all other evaluated models including the statistical persistence prediction method serving as baseline. The probabilistic nowcasting establishing 95% confidence intervals of wind speed and direction utilizes a multivariate‐Mixture Density Network (MDN) with weighted Mahalanobis distance. The MDN‐based model provides a narrower probability range than the statistical persistence baseline model, with average improvements by 0.91 m/s for wind speed and 12.05° for wind direction. The comparison of the statistical persistence baseline and other machine learning architectures demonstrates that the proposed models achieve superior performance, enabling the WVWS‐V to ensure safe and reliable light aircraft operations at vertiports.

ABSTRACT Tornadoes are characterized by short duration and small spatial scales, while conventional observational data exhibits inadequate spatiotemporal resolution for detailed analysis, presenting a significant challenge to mechanistic studies. In this study, the June 23, 2016, EF4 Funing tornado was simulated using the WRF model driven by ERA5 reanalysis data, by employing hectometer‐scale grid spacing and optimized numerical schemes. Based on the simulation results, the structural characteristics of the occurrence and development of the tornado were analyzed. The three‐dimensional dynamics of the tornado vortex and the intensification of its rotation were further investigated through diagnostic equations. Results show that the Funing tornado occurred under the typical circulation situation of the Meiyu period, and the hook‐shaped echoes and other features of the individual tornado were successfully reproduced on the 111 m grid. The cyclonic circulation driven by gust fronts and cold surges played a crucial role, revealing that the main energy for the formation and development of tornadoes originated from the lower troposphere. In addition, during the genesis and evolution of tornadoes, there were two tornado‐like vortices (TLV), accompanied by an increase in the vertical acceleration of low‐level small‐scale airflows and an enhancement of near‐surface vortices. The mature TLV exhibited the typical characteristics of sinking in the middle and rising in the periphery. The stretching term of the vorticity equation played a dominant role in the intensity variation of the TLV near the ground, while the tilting term affected the vertical structure formation of the tornado vortex at upper levels. Together, they drove the tornado from its inception to maturity. These results have significant implications for better understanding and analysis of tornadoes.

ABSTRACT Arid and Semi‐Arid Lands have witnessed a surge in extreme climatic events with devastating environmental and livelihood effects. Understanding the dynamics of these extreme events, including drought, is essential for anticipatory action among resource‐dependent communities. This study utilised Earth Observatory Systems and Google Earth Engine to analyse 24 years of Normalised Difference Drought Index trends in the Narok West landscape of Kenya across six timeframes (2000, 2005, 2010, 2015, 2020, and 2024). It revealed that the Normalised Difference Drought Index ranged from −0.489 (April 2000) to 0.469 (August 2005). Additionally, it established that during June–July–August dry seasons, there was an increase in the proportionate area under severe drought from 11% in 2000 to 24% in 2024 (average 19.17%, SD: 8.43%); and a decrease in the proportionate area under non‐drought (good conditions) from 57.5% in 2000 to 40.5% in 2024 (average 40.5%, SD: 7.43%) respectively. Temporal increase in drought events was observed to be increasing from 2015, with extremes witnessed in 2020. Moreover, we established that season dry season rainfall averages 147.2 mm (95% CI: 100.7–193.8) and is decreasing at a rate of 1.25 mm annually. It is anticipated that the frequency and severity of drought across the landscape might increase due to weather variability, predominantly attributed to climate change. The increase could have a detrimental effect on water quality and quantity, public and ecosystem health, mental health and wellness, peace and protection, and rangeland ecology. Our study contributes to the body of research on future drought scenarios, which could assist with methodological and empirical studies and corrective actions. To adapt to and manage the effects of changing climate, these scenarios necessitate interdisciplinary community and landscape strategies, including the need for communities to develop a comprehensive understanding of the impacts of climate change and plan for the sustainable management of water resources.

ABSTRACT Tropical cyclone (TC) Doksuri (2023) exhibited a sudden northward turn over the northeastern part of the South China Sea (SCS). However, most global models failed to capture such track change. The US National Centers for Environmental Prediction Final Analysis (FNL) data and The International Grand Global Ensemble (TIGGE) data were therefore used to study the underlying mechanisms for the sudden track change and the factors leading to the track forecast errors of different global models so as to give some insight for the forecasters in predicting such TC track change and global model developers in modifying the model physics. The non‐linear advection of the vorticity of the asymmetric winds associated with the monsoon trough over the SCS and that of the symmetric wind of the TC resulted in the sudden northward turn of the TC track. However, the strength and the eastward extension of the monsoon trough were underpredicted, leading to a westward‐moving track without a sharp northward turn. On the contrary, if the strength of the monsoon trough was overpredicted, the environmental steering was over‐altered, resulting in an early northward turn. The intensity and outer wind structure of the TC also played important roles in the monsoonal interaction and thus the track forecast errors.

ABSTRACT Some commercial radiosondes use heating‐type humidity sensors to prevent condensation and improve response time during soundings. However, the heating process affects the temperature and relative humidity (RH) in the ground facilities where they are tested. In this study, we conduct a test of the humidity sensor in a commercial radiosonde (Vaisala RS41) to assess its RH measurements and response time. An upper air simulator (UAS) is used to control the air ventilation speed to 5 m s −1 and adjust the ventilation direction to 0°, 45°, and 90° relative to the boom plane, thereby inducing convective cooling relevant to sounding conditions for testing heated humidity sensors. The temperature and RH ranges covered by our tests were −67°C to +20°C and 10% rh to 90% rh, respectively. Results indicate that the temperature measured in the test cell by a calibrated reference thermometer aligns with the temperature measured by the RS41 temperature sensor within their respective uncertainties. The mean difference in RH between the UAS and three RS41 units is less than 3.1% rh, with a maximum standard deviation of 2% rh. Furthermore, the response time of the RS41 humidity sensors during water sorption and desorption was measured. The response curves are fitted using a double exponential function with two time constants (short and long ones). As the test temperature decreases, both time constants increase. The response curves are formulated for their reconstruction and subsequently time‐lag correction. The results of this work can contribute to enhance the traceability of radiosonde RH measurements to the International System of Units.

ABSTRACT Characterizing inter‐instrument variability of sensors is crucial to assessing uncertainties in observational campaigns, networks, and for data assimilation. Here, we co‐locate six high signal‐to‐noise ratio Vaisala CL61 lidar‐ceilometers for a period of 10 days to quantify instrument‐related differences in several observed variables: profiles of attenuated backscatter, its components (parallel‐ and cross‐polarized backscatter) and the volume linear depolarisation ratio (), as well as derived cloud variables and mixed‐layer height. Analysing intervals between 5 and 60 min, median absolute differences between sensors (AD) and percentiles (e.g., AD) are used to quantify instrument related uncertainties. For backscatter and , we differentiate between conditions with rain, clear sky, and clouds. Here we address instrument precision rather than accuracy, with instrument accuracy assumed. The detected agreement between instruments suggests a distributed measurement network should be capable of providing context for interpretation of spatial differences. If instruments measure accurately, it is possible to resolve spatial differences (e.g., urban–rural) for attenuated backscatter, derived cloud variables and layer heights. However, differences exist and vary with signal‐to‐noise ratio and atmospheric conditions. The AD inter‐sensor results for 15 min intervals for total cloud‐cover fraction (excluding clear sky and fully overcast conditions) is 1.9%, and for cloud base height 7.3 m. Agreement of all cloud variables is better for boundary layer clouds (when first cloud layer 4 km agl) than for all five cloud layers recorded by the sensor firmware. The 15 min mixed‐layer height AD is 0 m and the AD 21.5 m. We show that instrument precipitation flags are in good agreement, but do not link closely with ground‐level rainfall observations, hence an alternative algorithm is proposed. We provide quality control recommendations for data processing to improve inter‐instrument agreement of cloud variables and mixed‐layer height.

ABSTRACTSmall islands such as the Galápagos Islands are highly vulnerable to changes in water availability, affecting ecosystems and communities. Understanding temporal precipitation variability is crucial but challenging due to limited ground‐based observations. This study evaluates five global precipitation products (satellite, reanalysis and multi‐source products) at a monthly scale, complementing conventional assessment against ground‐based observations with the analysis of three climatic time‐series components: seasonality, anomalies, and trends, which capture distinct aspects of long‐term precipitation variability relevant to climate applications. The analysis focuses on Santa Cruz and San Cristóbal Islands, where long‐term ground data are available, and includes a spatial comparison of global products across the entire archipelago. Results showed that reanalysis and multi‐source products (ERA5‐Land, MSWEP, MSWX) generally outperformed satellite‐based products (CHIRPS, PERSIANN‐CCS‐CDR). For example, in the cool lowlands, reanalysis and multi‐source products achieved correlation values between 0.81 and 0.94, bias ranging from −0.52% to −40.3%, and probability of detection between 0.76 and 0.96. These products showed high and medium agreement with ground data in precipitation seasonality, anomalies, and trend detection. In contrast, satellite‐based products revealed lower correlation values between 0.52 and 0.86, a higher underestimation bias (−10.86% to −75.43%), a lower probability of detection (0.22–0.32), and only medium or no agreement with ground data in precipitation anomalies and trends, with no agreement in seasonality. All global precipitation products exhibited significant limitations in representing precipitation seasonality in the highlands. The component‐based assessment complements conventional evaluation, offering deeper insight into how errors are distributed over time. This integrated approach supports a more informed selection of precipitation products for climate analysis and water resource management in data‐scarce island regions like Galápagos.