Meteorological Applications最新文献

Under the background of climate change, extreme weather events are apparently becoming more frequent. In Hong Kong, a record-breaking ‘Black’ Rainstorm on 7–8 September 2023 brought widespread flooding and caused landslides, paralysing the entire community. To enhance the community's response and resilience in coping with extreme weather, the Hong Kong Observatory has developed the Flood Risk Assessment System (FRAS), which embraces an impact-based forecasting method and a risk-based warning strategy. The main inputs are real-time rainfall data from rain gauges and the in-house developed probabilistic rainfall nowcast. The process from rain falling through the air to flooding observed on the ground is complicated, involving many non-meteorological and random factors. As a result, the corresponding impact assessment is highly non-trivial. The key technique adopted by FRAS is the use of a district-scale ‘rainfall-flooding impact’ statistical model, developed through in-depth study of historical flood reports and rainfall records. The risk-based warning strategy is designed largely based on the risk matrix recommended by the World Meteorological Organization. The performance of FRAS has been optimised in accordance with users' operational needs under the premises of a high safety margin and early alert. FRAS was launched in May 2024 for trial by government departments/bureaux, operating continuously in real-time and offering automatic flood risk assessment for all districts every minute during rainy seasons. This paper briefly presents the design, key techniques, and warning products of FRAS. Its performance as an early warning service is also examined through objective verification results and user feedback.

Typhoon-induced rainfall can trigger floods and landslides that pose significant hazards in Zhejiang. However, accurately forecasting its magnitude remains a significant challenge for global Numerical Weather Prediction (NWP) models, particularly in mountainous regions where complex orographic effects play a critical role. Using forecast data from three global NWP models (ECMWF, NCEP, and CMA-GFS) and observed station data from 2021 to 2024, this study revealed that these models consistently underestimated heavy rainfall in high-altitude areas, associated with their limited resolution in representing terrain-induced amplification. To address this, the terrain correction method proposed by Xu et al. (2019) was applied to the models to improve the estimation of typhoon-induced orographic rainfall in Zhejiang. Significant improvements in forecast skill were evidenced by increased Threat Scores (TS) and Probabilities of Detection (POD). In the ECMWF model, TS for rainstorms (≥ 50 mm·day⁻¹) increased from 0.33 to 0.35, while POD rose from 0.54 to 0.68. Larger gains were observed for heavy downpours (≥ 250 mm·day⁻¹), with TS rising from 0.02 to 0.08 and POD from near zero to 0.34. Similar improvements were found in the NCEP and CMA-GFS models. This study also discussed the limitations of terrain correction and identified two scenarios in which it underperformed. One occurred when the original forecast overestimated rainfall due to excessive moisture flux convergence, sometimes further amplifying errors (e.g., the forecast initialized at 00:00 UTC 24 July 2021). The other involved spatial displacement of the predicted rainfall field due to typhoon track errors, resulting in poor alignment with observations (e.g., 00:00 UTC 12 September 2021). Despite these limitations, the terrain correction notably improved forecasting skills, as indicated by TS and POD metrics, thereby enhancing local preparedness against typhoon-induced heavy rainfall and helping mitigate the risks of flooding and other related hazards in Zhejiang.

In this study, the indicative effect of the steering flow and the ventilation flow on the motion of tropical cyclones (TCs) is studied when the TCs are nearshore or landfalling. Results show that the steering flow derived with a radius of 3°–5° has smaller directional deviation and better directional indication effect for nearshore or landfalling TCs compared with traditional calculations with a radius of 5°–7° over the ocean, with an average deviation of about 25°–26° for a radius of 3°–5° and about 37°–40° for a radius of 5°–7°. Moreover, ventilation flow with a radius of 3° has a further better directional indication effect and smaller directional deviation, with the average deviation of about 18°–23° for nearshore or landfalling TCs, which is further improved compared with that of the steering flow. Although the directional deviation of steering flow and ventilation flow both increase as TCs approach the mainland, the closer to the land, the weaker the TC or the slower the TC motion, the more the guiding effect of ventilation flow improves compared to steering flow. Therefore, the ventilation flow is more suitable as an indicator for the TC path than steering flow when TCs approach the mainland or make landfall. Statistical analysis shows that the translation speed and the intensity of TCs contribute most to the asymmetry of TCs, which has a high relationship with the ventilation flow. In addition, the high asymmetry of TCs usually occurs when TCs move into regions with large vorticity at low level, low temperature at high- or low-level, large zonal wind at high level, and large deep environmental vertical wind shear.

Lightning is one of the most hazardous natural phenomena, causing significant damage to life and property. In India, lightning activity peaks during pre-monsoon and monsoon seasons. In 2022, 36% of the deaths from natural disasters were attributed to lightning. Accurate forecasting is critical for preparedness and mitigation, but complex convection processes often lead to spatial mismatches in forecasts. Spatial verification methods offer valuable insights into the accuracy of modeling systems. This study evaluates the performance of a high-resolution (4 km) regional model, operational at the National Centre for Medium Range Weather Forecasting (NCMRWF), that is, NCUMR (NCMRWF Regional Model), in predicting lightning strikes over India during pre-monsoon and monsoon seasons from 2021 to 2024. The Method for Object-Based Diagnostic Evaluation (MODE) was applied to assess the model's ability to predict lightning-prone regions. The primary objectives of this study are (i) to analyze the performance of the NCUMR model in predicting regions affected by lightning and (ii) to determine whether MODE can be used as an effective tool for forecasting lightning-prone areas. Results demonstrate that the NCUMR model is capable of forecasting the spatial structure and distribution of lightning events with reasonable accuracy up to 3 days in advance. On Day 1, more than 88% of lightning objects for thresholds above 5 strikes/day show boundary overlap with observations, with centroid distances for 50% of matched objects remaining below 55 km. For Day 2 lead time, 83%–85% of objects show boundary overlap. On Day 3, although displacement errors increase slightly, over 85% of objects still exhibit zero boundary distance at lower thresholds, and centroid distances remain within 1°–1.5°. For all lead times, 75% of the forecasted objects have area ratios exceeding 0.7, and complexity ratios consistently above 0.7, indicating good structural agreement. While intensity is generally under-forecasted, 90^th percentile intensity ratios exceed 0.5 in most cases. The model performs better for lower thresholds and shows improved object correspondence during the monsoon season compared to pre-monsoon. These results confirm the utility of object-based verification using MODE in capturing spatial aspects of lightning forecasts and highlight its potential application for real-time impact-based forecasting and early warning systems.

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.

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.