Meteorological Applications最新文献

The article presents results from a computationally low-cost regional numerical weather prediction chain based on the Weather Research and Forecasting (WRF) model and its data assimilation (DA) suite WRFDA. Experiments with 24-h forecasts were performed twice daily (at 00 and 12 UTC) over a domain encompassing the European Alps and their surroundings with a 3.5 km grid spacing. The assimilation of surface observations with the 3D-Var algorithm improves near-surface temperature and humidity forecasts compared to control runs without assimilation. The forecast skill for near-surface variables is evaluated using independent surface observations. In the first six forecast hours, it is generally better in the assimilation experiments than in the control ones, with a mean error reduction of 0.26 K for temperature and 0.13 g kg⁻¹ for specific humidity in the 00 UTC runs, and of 0.12 K for temperature and 0.18 g kg⁻¹ for specific humidity in the 12 UTC runs. The assimilation reduces the standard deviation of the errors by a factor between 7% and 10% both for temperature and specific humidity. Verification with radiosonde measurements shows that assimilating surface observations increases the mean error in temperature and humidity forecasts within the planetary boundary layer (PBL), relative to the control. We show that the vertical structure of the adjustments to the model state resulting from DA (the analysis increments) is such that model biases are reduced near the surface but amplified higher up in the PBL. Finally, the assimilation of surface observations has a different impact on surface temperature forecasts in mountainous regions compared to adjacent plains. The error reduction is substantially higher in the plains than in the mountains, which likely depends on the inappropriate spreading of information along terrain-following model levels by the static covariances in 3D-Var. The relative accuracy of surface temperature forecasts in these two regions has a diurnal variability, with larger mean errors in the mountains during the day and in the plains at night.

Validating high-resolution weather and climate models is challenged by insufficient spatial and temporal resolution of meteorological observations, particularly for the precipitation in complex terrain. Traditional datasets, which rely on sparse official weather stations and gridded datasets, often lack the spatio-temporal resolution needed for accurate localized studies. This study serves as a first step in investigating the potential of including Personal Weather Stations (PWSs) in the validation of high-resolution regional climate models. We performed a quality control on PWS data, flagging approximately 13% and retaining around 450 stations in Western Norway. Compared to 124 official meteorological stations (MET stations), PWSs provided significantly improved spatial coverage, especially in densely populated areas, revealing spatial variability often missed by MET stations and traditional gridded datasets. We validated simulations from the Weather Research and Forecasting (WRF) regional climate model using the combined PWS and MET observational dataset for two cases: multiple frontal passages in November 2022 and a record-breaking convective burst in August 2023, which were sparsely captured by official MET stations. Although biases existed in the WRF dataset, the incorporation of PWSs in the observational dataset revealed a more nuanced precipitation pattern and provided enhanced spatial validation opportunities. In conclusion, PWS networks significantly enhance observational coverage, aiding high-resolution model validation and opportunities for improved local precipitation understanding. As the number of PWSs grows, refined quality control measures will further solidify their role in meteorological research and emergency preparedness, particularly for localized extreme weather events. This integration is vital for advancing climate science and improving community resilience to weather-related challenges.

A test bench has been developed allowing to simulate air flow in a 20 L cylindrical stainless-steel chamber under conditions of the stratosphere and the troposphere: pressure from about 500 to 30 hPa, air temperature from 293 to 223 K and air flow velocity of about 5 m/s. Humidity of the air flow is controlled in the range of frost temperature from 253 to 193 K with accuracy better than 0.3 K for a frost temperature of 198 K. Specifically designed to test a newly developed frost point hygrometer, this facility may as well be used for testing instruments with suitable dimensions especially those operating with sounding balloons.

Mitigating against the impacts of catastrophic flooding requires funding for the communities at risk, ahead of an event. Simulation library flood forecasting systems are being deployed for forecast-based financing (FbF) applications. The FbF trigger is usually automated and relies on the accuracy of the flood inundation forecast, which can lead to missed events that were forecast below the trigger threshold. However, earth observation data from satellite-based synthetic aperture radar (SAR) sensors can reliably detect most large flooding events. A new data assimilation framework is presented to update the flood map selection from a simulation library system using SAR data, taking account of observation uncertainties. The method is tested on flooding in Pakistan, 2022. The Indus River in the Sindh province was not forecast to reach flood levels, which resulted in no selection of the flood maps and no triggering of the FbF scheme. Following observation assimilation, the flood map selection could be triggered in four out of five sub-catchments tested, with the exception occurring in a dense urban area due to the simulation library flood map accuracy here. Thus, the analysis flood map has potential to be used to trigger a secondary finance scheme during a flood event and avoid missed financing opportunities for humanitarian action.

In order to investigate the impact of increasing the vertical resolution of the initial field on the 12–24 h forecasts of the TRAMS (Tropical Regional Atmosphere Model System) model, this study conducted numerical experiments focusing on a typical coastal warm sector rainfall event that occurred in the South China. The findings indicate that increasing the vertical resolution of the initial field led to improved simulation of coastal convection during the 0–12 h period. However, spurious convection was observed over the sea surface and continued to intensify in the 12–24 h period. Subsequent analysis revealed that the spurious convection is primarily associated with the hydrostatic adjustment of initial potential temperature in the TRAMS model. The hydrostatic adjustment leads to a reduction in the stability of the initial temperature stratification in the lower layers of the model, particularly when the number of vertical layers in the initial field increased from 17 to 32. A noticeable spurious unstable layer emerged between 0–200 m over the sea surface, triggering false convection. Further investigation revealed that the area where this unstable stratification occurs over the sea is situated below the height of the lowest level of the input analysis field (1000 hPa), indicating that the spurious disturbances are caused by an unreasonable vertical extrapolation process. Therefore, the findings of this study indicate that the extrapolation calculations using cubic splines in the initialization module of the TRAMS model introduce significant errors. Moreover, these errors increase with the enhancement of the vertical resolution of the initial field, which limits the improvement in model forecasting that could be achieved by increasing the vertical resolution of the initial field.

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.