Meteorological Applications最新文献

The present study delineates the relative performance of 3D-Var and 4D-Var data assimilation (DA) techniques in the regional NCUM-R model to simulate three heavy rainfall events (HREs) over the Indian region. Four numerical experiments for three extreme rainfall cases were conducted by assimilating different combinations of observations from surface, aircraft, upper-air and satellite-derived Atmospheric Motion Vectors (AMVs) using 3D-Var and 4D-Var techniques. These experiments generated initial conditions (ICs) for the NCUM-R forecast model to simulate HREs. Key atmospheric variables, such as wind speed and direction, vertically integrated moisture transport (VIMT: kg.m⁻¹.s⁻¹), vertical profiles of relative humidity and temperature as well as various stability indices are analysed during the HREs. Forecast verification was performed using statistical skill scores and object-based methods from the METplus tool, comparing NCUM-R output against GPM rainfall data. The results demonstrate that the 4D-Var technique improves simulation accuracy compared to 3D-Var, particularly when assimilating satellite wind data. Incorporating satellite-derived AMVs improved the representation of rainfall intensity and spatial patterns, as well as other atmospheric variables. It is found that rainfall for Case-01, the VIMT was notably high along the eastern coast of India and southwest of BoB, with the 4DVS simulation better capturing moisture transport patterns compared to 3DVS and 3DV. The SWEAT index ranged from 205 to 250 J·kg⁻¹ in the morning, rising to 250–300 J·kg⁻¹ by noon, indicating increasing convective instability. On 18 March 2023 (Day-1), the K-index exceeded 30, signalling scattered thunderstorms, consistent with the IMD's reports of isolated to scattered rainfall on 19th and 20th March 2023. Similarly, it is found that satellite wind assimilation improved the statistical skill scores in predicting heavy precipitation in all three cases. Overall, the study suggested that the performance of the NCUM-R model integrated with the 4D-Var technique improved the model's forecast skill in the simulation of HREs.

In this paper, we present a novel approach for forecasting weather variables that are not currently available in many state-of-the-art AI models. A variable not found in most models is the 100-m wind speed, which is commonly used in the energy sector to predict the power generated by wind turbines. We trained a convolutional neural network model on 12 years of ERA5 data to instantaneously predict the 100-m wind speed based on a subset of variables found in the ECMWF-AIFS forecast. We evaluated our model with 2020 ERA5 data and achieved an average 100-m wind speed RMSE of 0.18 m/s, outperforming the wind profile power law method with an RMSE of 0.63 m/s. Using the AIFS output as input to our trained model, we generated 10-day 100-m wind speed forecasts without requiring autoregressive steps, significantly reducing computational costs. We compared our predictions with the ECMWF-IFS forecast using the ECMWF analysis as ‘ground truth’ and showed greater accuracy at longer lead times. Additionally, we produced power generation forecasts for onshore and offshore wind farms across the United Kingdom, with improvements over the IFS after a lead time of 3 days. We also showed that our model exhibits spatial and temporal coherence between local predictions and discussed the common limitation of over-smoothing in the predictions of AI models.

Cold surges can significantly affect maritime transportation safety, owing to the strong wind, significant temperature drop, as well as dense fog. Therefore, it is crucial to make an accurate prediction of meteorological phenomenon in the maritime regions during cold surges. The present study evaluates the performance of planetary boundary layer (PBL) and land surface schemes in Weather Research and Forecasting (WRF) model, specifically for the wind (wind speed and direction) and fog (temperature, dew point temperature, and relative humidity), during two cold surge events that occurred in November 2022, in the Bohai Bay Area, China. To make a thorough investigation of those complex meteorological processes, the WRF model was configured over Bohai Bay with a high spatial resolution of 2 km in the horizontal direction, and results were verified using three accessible meteorological stations around the Shandong Peninsula. Our studies demonstrate that the WRF tends to perform better in strong winds than in weak ones, particularly in the simulation of wind direction. Besides, Mellor–Yamada Nakanishi Niino Level 2.5 (MYNN2.5) and Yonsei University Scheme (YSU) PBL schemes demonstrate superior performance in simulating wind speed and sea fog, respectively, compared with the Noah-MP scheme. Unified Noah demonstrates superior performance in dew point temperature and humidity compared with both Noah-MP and 5-layer thermal diffusion schemes, whereas Noah-MP excels in temperature performance. Finally, we utilize the optimal results produced by the WRF model and integrate them with the risk thresholds for ship navigation. This allows us to visualize the spatiotemporal distribution of risks associated with strong winds and fog during navigation in the Bohai Bay area. The abovementioned findings are supposed to be helpful for make more accurate weather forecast of strong wind and dense fog in future cold surge events, from the viewpoint of a safe maritime transportation.

Aircraft observations derived from Mode-Select Enhanced Surveillance (Mode-S EHS) reports are a valuable, high temporo-spatial resolution, source of upper-air information that can be assimilated into numerical weather prediction models. At present temperature and wind Mode-S EHS observations are assimilated into the Met Office's convection-permitting model, the UKV. These observations are obtained from two different sources, an inhouse set of receivers and via the European Meteorological Aircraft Derived Data Centre (EMADDC). Currently, Mode-S EHS data are assimilated using the same observation error standard deviation profiles as AMDAR data; however, differing observation processing is anticipated to result in differing error profiles for the Met Office and EMADDC data and for the AMDAR data. Therefore, we estimate new observation error statistics, including error correlations for the two types of Mode-S EHS data. We also consider the impact of the different aircraft data on the UKV analysis. We find that the observation error standard deviation profiles for wind and temperature are dependent on observation type and season and differ from the current profiles used in the assimilation. Additionally, the Mode-S EHS observation errors have a considerable spatial correlation that increases with height and is much longer than the spatial thinning distance. The estimated observation influence shows that Mode-S EHS data are not optimally assimilated, and that the use of updated, observation-type specific, error profiles is expected to improve the assimilation. The assimilation may be further optimized by modifying the observation thinning distance or including the correlated observation errors in the assimilation.

This paper investigates the relationship between seasonal precipitation over Iran and low-level moisture, in terms of vertically integrated specific humidity (VISH) from the surface to 850 hPa. The VISH is calculated from ERA5 data for the domain (10°N–60°N, 15°E–80°E), and the precipitation is calculated from 50 stations across Iran, both for the period 1968–2023. Canonical correlation analysis (CCA) is applied to examine the spatial–temporal relationship between seasonal averages of moisture and precipitation during January–March (JFM), April–Jun (AMJ), and October–December (OND). VISH and precipitation are considered as the simultaneous predictor and predictand fields in the CCA, respectively. The CCA time series are correlated to global sea surface temperatures to assess the connections to large-scale, potentially predictable modes of variability. The CCA spatial patterns indicate that there is a strong relationship between low-level moisture and seasonal precipitation, with VISH over the Persian Gulf, Oman Sea, Arabian Sea, and Red Sea positively correlated with precipitation over most areas of Iran, while VISH over the Caspian Sea and Black is negatively correlated. Generally, these relationships are notably low over northwestern areas of Iran and the coastal regions of the Caspian Sea and the prediction skill of CCA remains limited over these regions. In OND, the leading CCA time series exhibits the well-known connection to the El Niño–Southern Oscillation (ENSO). However, the highest CCA skill is found for JFM precipitation, which does not exhibit an ENSO connection, and so may present an additional source of skill.

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), as two state-of-the-art approaches, were applied to improve the prediction skills of 24-h accumulated precipitation over East Asia with lead days of 1–7 days. The multimodel ensemble precipitation probabilistic forecast experiments were constructed using ensemble forecasts from multiple ensemble prediction systems, revealing that the standard BMA (s-BMA) and the standard EMOS (s-EMOS) outperformed the raw ensemble forecasts. In comparison with the raw ensembles, the improvement by the s-BMA model increases as lead days increase, while the s-EMOS model consistently enhances prediction accuracy by around 30% for all lead days. Overall, the s-EMOS model demonstrates superior performance compared with the s-BMA model, which struggles with forecasting heavy daily precipitation exceeding 25 mm. Accordingly, the hierarchical BMA (h-BMA) model is introduced in this study, designed for different precipitation classifications. Compared with the s-BMA model, the h-BMA model notably improves the probabilistic forecast skill for all precipitation thresholds throughout East Asia, particularly for heavy precipitation events. Moreover, the h-BMA model also improves the forecast reliability across various precipitation thresholds. A hierarchical EMOS (h-EMOS) model is also developed to validate the benefits of the precipitation classifications and further improves the forecast accuracy as expected. The prediction probability density functions of the hierarchical models are much sharper and more concentrated than those of the standard models. In general, the improvement in precipitation probabilistic forecast skill of the h-BMA model relative to the s-BMA model surpasses that of the h-EMOS model compared with the s-EMOS model.

Heat waves harm human health and adversely impact the natural environment and society, especially in urban regions. Understanding the differences between heat waves in urban agglomerations and their driving mechanisms is essential for sustainable development. In this study, we investigate the spatiotemporal distribution of summertime heat waves and their association with sea surface temperature modes in two of China's most densely populated urban areas: the Beijing–Tianjin–Hebei (BTH) and the Yangtze River Economic Belt (YREB). The results indicate an increase in the frequency of heat waves for BTH and YREB by 0.02 times a⁻¹ and 0.1 times a⁻¹ and duration by 0.09d a⁻¹ and 0.48d a⁻¹, respectively. Regarding spatial distribution, the duration and frequency of BTH heat waves gradually decreased from northeast to southwest. In contrast, the heat waves in YREB were concentrated in the upper and parts of the lower reaches. The Atlantic Multidecadal Oscillation significantly influences heat waves in both the BTH and YREB regions. Nevertheless, the Pacific Decadal Oscillation, Indian Ocean Basin-Wide Index, and Cold-tongue ENSO Index primarily impact heat waves in the YREB region, with limited influence observed in the BTH region. This study provides a scientific basis for accurately identifying heat waves and understanding their changes, assisting decision-makers in formulating mitigation, adaptation strategies, and disaster prevention policies related to heat-induced consequences.

Precipitation, essential for the water cycle and key to surface runoff and groundwater, causes floods and droughts when unevenly distributed. Understanding the variations in precipitation across China is vital for managing water resources and preventing weather-related disasters. In this study, we analyzed the spatial–temporal variations in rainfall amounts and the number of rainy days across different levels in China using daily precipitation data during 1960–2019. We found a nonsignificant increase in annual total precipitation (ATP), but a significant decline in the number of days with ATP during this period. This shift suggests that precipitation is becoming more concentrated in fewer days, potentially due to an increase in the frequency of heavy rain (25 ≤ p < 50 mm/day, L₃), rainstorm (50 ≤ p < 100 mm/day, L₄), and heavy rainstorm (p > 100 mm/day, L₅). The amount and frequency of precipitation in light rain (0.1 ≤ p < 10 mm/day, L₁) and moderate rain (10 ≤ p < 25 mm/day, L₂) exhibited a decreasing trend during this period, whereas the patterns for L₃, L₄, and L₅ demonstrated an increasing trend. Notably, the decrease in the number of days with L₁ and L₂ precipitation was relatively minor compared with the substantial increase in the number of days experiencing L₃, L₄, and L₅ precipitation. Despite L₁ precipitation making up only 24.9% of China's ATP, it accounts for 78.6% of total precipitation days. This underscores the important role played by L₁ precipitation events in determining the overall frequency of precipitation occurrences in China. Significant regional disparities are observed in both precipitation amounts and the number of precipitation days across different precipitation levels. Furthermore, large-scale climate indices have consistently affected China's precipitation patterns since 1960, impacting not just the current year but possibly extending into the subsequent year.

Increasing global temperatures have triggered several environmental and ecological challenges. Recurring droughts across the globe are an adverse consequence of global warming. In this research, a new drought forecasting index—the Multimodal Forecastable Standardized Precipitation Evapotranspiration Index (MFSPEI)—has been suggested using projections from multiple climate models. The MFSPEI methodology is primarily based on the first component of the Forecastable Component Analysis (FCA) and the Standardized Precipitation Evapotranspiration Index (SPEI). For application purposes, the time series data of SPEI from 10 climatic models endorsed by the Coupled Model Intercomparison Project phase 6 (CMIP-6) at 50 random locations over the region of the Tibetan Plateau (TP) have been considered. The outcomes show that the first component of FCA captures a sufficient amount of variation while maintaining high forecastability in all the selected grid points and the chosen prominent timescales of drought monitoring indices. To assess the predictive performance of the proposed index (MFSPEI), comparison matrices of artificial neural network (ANN) models were identified. During the training and testing phases, the forecast efficiency of the developed indicator (MFSPEI) proved superior to that of the individual SPEI. The numerical assessment indicates that the deviations and difficulties in interpreting SPEI data from individual climate models can be addressed more effectively with the proposed indicator. Therefore, MFSPEI effectively reinforces drought predictions for drought preparedness and management in the context of multiple climate model projections.