Meteorological Applications最新文献

River floods in the mountainous regions of the Ukrainian Carpathians are a natural hazard that often leads to significant destruction and substantial economic damage to the region. The key driver of flooding is typically heavy rainfall, which results from certain patterns in regional atmospheric circulation. We studied the atmospheric circulation regimes over Ukraine for the period 1948–2021 using the modified Jenkinson–Collison classification. Circulation types associated with airflows from the western quarter are the most frequent throughout the year. However, seasonality in circulation patterns related to the dynamics of regional atmospheric centers of action is also well expressed. The linear trends in the frequency of circulation types are found statistically significant for meridional processes associated with advection from the north or south. Circulation types according to the Jenkinson–Collison classification, as well as the Niedźwiedź regional synoptic classification, were applied to cases of extreme floods in the river basins of the Ukrainian Carpathians to identify features of the pressure field leading to the formation of heavy precipitation. During the study period, 10 flood events, characterized by extremely high or historically significant water levels, were selected. Both pluvial floods in summer and mixed floods in winter were considered. In cases of the warm period, the circulation types with airflows directed towards the mountain range from the east or north are observed, and floods formed in the Ciscarpathia. In the cold period, circulation types with airflows from the western quarter increased precipitation and river discharge in Transcarpathia. 45% of observed circulation types belonged to the cyclonic group; however, the relative position of baric systems in other types also ensured the convergence of atmospheric moisture into the flood area.

Accurate classification of Land Use and Land Cover (LULC) is crucial in Remote-Sensing (RS) and satellite imaging to understand Earth's surface attributes. However, existing methods often face challenges in effectively extracting and categorizing complex spatial patterns from satellite imagery. The evolution of deep learning techniques has offered promising advancements in this domain, yet further enhancements are needed to achieve optimal performance. This study introduces a novel deep learning-based spatial pattern modeling technique designed to address these challenges. The proposed method leverages the Inception-V3 model to extract detailed features from the EuroSAT dataset comprising 27,000 images across 10 LULC classifications. By fine-tuning hyperparameters and conducting rigorous training-validation experiments, the model achieves notable performance metrics: an accuracy of 0.9943 and a validation accuracy of 0.9850, with corresponding losses of 0.0184 and 0.0566. This approach represents a significant advancement over traditional methods, offering enhanced accuracy and efficiency in LULC classification, thereby facilitating more informed decision-making in environmental monitoring and spatial analysis.

With the recent rise of artificial intelligence (AI), data-driven global weather forecasting models have demonstrated superior performance compared to state-of-the-art physics-based global models across various weather elements. This work reports on tropical cyclone (TC) simulations using a hybrid weather modeling system that harnesses the advantages of both AI-based and physics-based models. The system utilizes AI-based global models, Pangu-Weather and AIFS, to drive the atmospheric model within a regional atmosphere–ocean-wave coupled model (abbreviated as UWIN-CM). It preserves skillful TC track forecasting from the global AI models while gaining the benefits of predicting fine-scale details contributed by the high-resolution UWIN-CM model. The performances in forecasting seven TCs that necessitated the issuance of TC warning signals in Hong Kong in 2024 are studied. Results show that the AI-model-driven UWIN-CM can achieve a reduction in track error by 34% compared to the UWIN-CM driven by IFS. The track error is reduced to a level comparable to that of the AI models themselves. In terms of intensity, the AI-model-driven UWIN-CM also gives a reduction in intensity error by 20% compared to the UWIN-CM driven by IFS, and very significantly improves the intensity forecast provided by the AI global models. Other forecasting aspects, such as genesis, rapid intensification, and wind structure of TCs, are also investigated. The AI-model-driven results generally outperform those driven by IFS in these aspects. This work demonstrates that AI-based global models and high-resolution physics-based regional models can complement each other to achieve more accurate TC forecasts.

In recent years, the availability of crowd-sourced weather measurements has increased substantially. Yet, despite offering an insight into the weather where people live, these measurements are not currently being utilized by public weather services in the operational objective verification of forecasts. Here, we explore the use of crowd-sourced temperature observations from the Weather Observations Website (WOW) to verify and compare the performance of the Met Office's replacement post-processing system, known as IMPROVER, against the old system. It is found that, even after quality control, the WOW data still has up to five times the number of sites compared to the official surface network. The overall errors are marginally worse than using the official network; for example, the Mean Absolute Error is approximately 0.2 K larger for IMPROVER verified with WOW over SYNOP sites. However, 95% of the errors at all quality-controlled WOW sites are less than or equal to 2.5 K, and 70% of the errors are less than or equal to 1 K, indicating a good level of consistency with the forecasts. The sensitivity of the results to quality control depends on the choice of error metric. Finally, given the degree of consistency, quantity, and location of good-quality WOW data, it is recommended that crowd-sourced data continue to be used as an operational verification truth source in conjunction with the official surface network.

Saffron could be produced under rain-fed conditions, but the required conditions are not well known. To determine these conditions, crop growth models can be used. The modified SYEM model for rain-fed saffron was calibrated and validated. Then, it was used to predict the rain-fed saffron production in different saffron production areas. Comparison of the measured and predicted values of crop parameters showed that in modeling the saffron crop, it is essential to consider the age of the field; the density of corm at the beginning of each growing season should be included in the model. The saffron yield (SY) values were predicted by the validated model for important saffron cultivation areas in Iran under rain-fed conditions with the use of plastic mulch (PM) and pre-flowering irrigation (PFI) in 3 years with high, low, and mean rainfall depth. In general, in rain-fed conditions, soil texture, time, depth, and frequency of rainfall are very important in saffron growth and SY. The use of PM and PFI increased the SY by 1.5 and 3.0 times, respectively, compared to not using them. The use of PM and in-furrow planting, in areas with light soil texture and low annual rainfall (< 200 mm), has a greater effect on increasing the SY. In areas with medium to heavy soil texture and high annual rainfall, the use of PM increased the SY at rainfall depths below 300 mm. In general, the use of PFI in all areas with any annual rainfall depth is necessary due to softening the soil surface at the beginning of the growing season after the summer dormancy period. Depending on the soil texture, the PFI value should raise the soil water content in the saffron root zone to the soil field capacity.

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.

Jeddah, the second-largest city in the Kingdom of Saudi Arabia, experienced an unprecedented 220 mm of rainfall on November 24, 2022. This extreme rainfall, which was four times the climatological monthly mean rainfall for November, resulted in severe flooding and significant damage to infrastructure. This study investigates the underlying physical mechanisms contributing to this extreme event and its predictability using in situ and satellite observations and numerical modeling. Our analysis reveals the event initially developed as a frontal system over the northwest regions of the Red Sea through interactions between cold air from mid-latitudes and warm air from the southeast. It reached Jeddah at 0600 UTC, November 24, accompanied by strong surface convergence, which is typical of winter rainfall in Jeddah. The system was further fueled by persistent moisture intrusion from the Mediterranean and the southern Red Sea, driven by the southeast movement of the Arabian Anticyclone. We evaluated the predictive capability of the Weather Research and Forecasting (WRF) model to forecast this extreme event at different lead times, utilizing a cloud-resolving 1-km configuration. The WRF model, driven by the National Centers for Environmental Prediction operational Global Forecasts, successfully reproduced the extreme rainfall event up to 5 days in advance. Even at a 5-day lead time, the model captured the storm's movement from northwest to southeast and the qualitative spatial distribution of rainfall, consistent with satellite observations and radar reflectivity. Additionally, the predicted distribution of total precipitable water vapor aligned closely with Meteosat brightness temperatures. This demonstrates that the high predictive skill of the WRF model is due to its high-resolution configuration, careful selection of the domain, and physical parameterizations. By addressing both the physical mechanisms and the model's performance, this work provides valuable insights into extreme rainfall forecasting and highlights the potential for mitigating the impacts of such extreme events in the Jeddah region.

It is often argued that numerical weather prediction models remain deficient in forecasting specific weather features and that such deficiencies contribute significantly to overall forecast errors. To clarify these claims, we quantify how cyclones, fronts, upper tropospheric jets, moisture transport axes (MTAs), and cold-air outbreaks (CAOs) contribute to short-term (12-h) forecast errors and biases in the ERA5 reanalysis dataset from 1979 to 2022. Employing a feature-based attribution method, we evaluate errors globally, focusing particularly on temperature, moisture, and wind fields, and examine regional and seasonal variations during winter (DJF) and summer (JJA). The presence of weather features is generally associated with increased forecast errors (RMSEs) compared to feature-free conditions. RMSEs are especially pronounced for moisture fields in conjunction with fronts and MTAs, where errors in total column water vapor can be twice as large. Cyclone-related errors are more pronounced in the low-level wind field. During CAOs, on the other hand, errors are reduced. In terms of systematic biases, wind speeds and moisture are underestimated along western boundary currents, together with insufficient moisture transport along MTAs. Wintertime temperature biases over the Northern Hemisphere oceans have stronger associations with fronts and MTAs than those over the Southern Hemisphere oceans. A persistence analysis confirms that for some features and specific variables, forecasts yield less added value relative to non-feature conditions. Cyclones are the most notable example, where forecasts provide less added value in most cases. In contrast, jets and CAOs are features where forecasts consistently add more added value. The identified feature-based error diagnostics can aid targeted efforts to improve numerical weather prediction systems.

Power spectra are evaluated for a range of ensemble systems run at the European Centre for Medium-Range Weather Forecasts (ECMWF). These spectra allow us to chart and compare the spatial–temporal evolution of ensemble spread and error, and to evaluate the impact of model and observational changes. We investigate whether differences between spread and error indicate issues of reliability or other deficiencies. In agreement with previous studies, for ensembles made with the physics-based model, extratropical variances (of 250 hPa geopotential height) saturate quickly at small scales, while planetary scale errors are far from saturated at day 10. At intermediate lead-times, forecasts are over-dispersive at synoptic scales. Tropical errors (for 200 hPa velocity potential) grow most rapidly over the first day, but are not fully saturated even by day 40. Tropical differences between spread and error at scales below 500 km are thought to reflect a need for more observations of tropical (divergent) winds, rather than a lack of reliability. Forecast variances in a “near perfect twin” ensemble suggest there is the potential to improve predictive skill by 5 days. Error variances highlight the substantial observational and modeling developments required to ensure that such forecasts are reliable. The impact of a recent system upgrade (which includes a change to the formulation of model uncertainty) and results from an experiment where additional radio occultation observations are assimilated, demonstrate that progress can be made when developments are focused on synoptic scale uncertainty and error-growth. Power spectra for two prototype data-driven ensembles show similar spatial–temporal evolution at large scales to that of the physics-based model; one has better overall reliability, and the other has reduced error. At smaller scales, the prototypes display a tendency for small-scale forecast variance and error to increase with lead-time beyond their theoretical limits. With the speed and breadth of ensemble development, these results illustrate the potential utility of power spectra diagnostics for comparing and developing ensemble systems.

As meteorological organisations transition to high-resolution ensemble-based forecasting, they risk leaving behind downstream users who rely on deterministic data: a need that may arise from the inability to process large volumes of data or difficulty integrating probabilistic information into decision-making processes. Proposed solutions for such users typically involve providing the control (unperturbed) member of the ensemble or deriving a forecast through the independent treatment of variables (such as the median). However, relying solely on the control member undermines the benefits of ensemble forecasting, while univariate approaches can result in forecasts that lack physical consistency across variables. To address this, we propose a novel method to select ‘most-likely’ ensemble realisations, combining techniques from pre-existing ensemble post-processing methods. For a given location, we construct a timeseries of ‘most-likely values’ for variables of interest by extracting the mode from multivariate probability density distributions created at each timestep. We then select the ensemble member most similar to this timeseries using clustering techniques. Since the chosen realisation is a complete forecast from an individual model run, this allows us to deliver a spot forecast for that location that maintains physical consistency across all variables, including those not directly analysed. As a demonstration, we apply this method to output from the Met Office convective-scale ensemble MOGREPS-UK at 240 locations across the Met Office synoptic observation network, focusing on near-surface air temperature and windspeed. We find that the chosen member performs comparably to the control member at short lead times, but is able to outperform the control member at longer lead times. This is an important finding as it demonstrates an alternative to the control member for users who require physically consistent spot forecasts, utilising the additional information available in the ensemble. In addition to improving forecast accuracy, this method also offers the ability to tailor solutions for individual users.