Meteorological Applications最新文献

Power utilities are increasingly emphasizing the need for high-resolution reanalysis datasets to develop resilience plans for protecting and managing infrastructure against extreme weather events. In response, Ricerca Sul Sistema Energetico (RSE) S.p.A. created the new MEteorological Reanalysis Italian DAtaset (MERIDA) High-RESolution (HRES) reanalysis, a 4-km resolution dataset with explicit convection specifically designed for Italy. This dataset, publicly available from 1986 to the present, has been evaluated and compared with the previously developed MERIDA reanalysis dataset (7-km resolution over Italy) and ERA5, the global reanalysis driver. The validation is conducted across different scales (i.e., from climatology to single extreme events) and for multiple variables (i.e., 2-meter temperature, daily total precipitation, and 10-meter wind speed). Specific cases, such as a convective storm in July 2016 in northern Italy near Bergamo and the more synoptically driven Vaia storm in October 2018, are analyzed to illustrate the dataset's potential in capturing precipitation and wind extremes. Additionally, the Arbus wildfire event in Sardinia is examined to showcase a multivariable application for assessing fire weather hazards. Through performance maps and statistical analyses, the ability of MERIDA HRES to represent both long-term statistics and extreme events is highlighted. Despite a consistent cold temperature bias across Italy, with higher peaks over mountainous regions, the performance of precipitation and wind outperforms that of both MERIDA and ERA5 in all analyzed cases. These findings demonstrate the significant potential of this product for multiple applications in Italy.

Accurate wind speed forecasts are essential for optimizing the efficiency of wind energy operations. Currently, there is limited research on nationwide assessment of predictive performance in multiple numerical weather prediction (NWP) models for wind speed at turbine hub height over China, especially concerning wind ramp events. Utilizing observed measurements from 262 wind farms, this study evaluated the performance of five NWP models in forecasting the mean state and spatiotemporal variations of wind speed as well as wind ramps. The results indicated that the European Center for Medium-Range Weather Forecast Integrated Forecasting System (ECMWF–IFS) performed the best in forecasting climatological wind speed with a temporal correlation coefficient (TCC) of 0.74 and root mean square error (RMSE) of 2.34 m s⁻¹. Although not widely utilized in China, the model from Meteo-France (MF–ARPEGE) showed promising potential for wind energy forecasting with a TCC of 0.72 and RMSE of 2.45 m s⁻¹. In terms of temporal variations of wind speed, all the models could reasonably predict the seasonal variations of wind speed, whereas only three NWP models were able to capture the characteristics of the observed diurnal variation. An error decomposition analysis further revealed that the primary source of predicted error for wind speed was the sequence error component (SEQU), indicating the model errors were mainly attributed from the temporal inconsistency between forecasts and observations. Furthermore, the occurrences of wind ramps were generally underestimated by NWP models, while this shortcoming can be partly overcome by improving the time resolution of NWP models.

This study systematically evaluated global tropical cyclone (TC) activity in a new global atmospheric reanalysis dataset named the “40-year Global Reanalysis” (CRA40) against the best track data. For comparison, four state-of-the-art reanalyses—ERA5, JRA55, CFSR, and MERRA2—were also assessed. The results showed that there is a general underestimation of global TC genesis frequency and intensity in both CRA40 and other reanalyses. A detailed investigation of spatial distribution, seasonality, interannual variation, and long-term trend for TC genesis frequency, as well as pressure–wind relationship for TC intensity, revealed similarities and differences among these reanalyses datasets. Overall, CRA40 does not exhibit clear advantages over other reanalyses in these aspects, but its biases are also not more pronounced. However, regarding TC translation speed, CRA40 outpeforms other reanalyses, evident by its high level of consistency with the observation in the zonal average pattern, meridional distribution at peak latitudes, and interannual variation, suggesting its reasonable capability in capturing large-scale atmospheric characteristics. Our findings indicate that the use of CRA40 is appropriate for conducting TC-related studies, within the scope of its limitations.

High-frequency measurements obtained at two micrometeorological towers are investigated for a rare northward surging gust front that impacted the Amazon Tall Tower Observatory (ATTO), in central Amazon. The gust front originated from a decaying mesoscale convective system (MCS) during the morning hours of 27 December 2021 near Manaus, Amazonas state, northern Brazil, and surged north-eastward towards the ATTO site. Large temperature drops and vigorous, persistent winds were observed at the towers which lasted for over 4 h despite the gust front being detached from its parent, decaying MCS. More importantly, the gust front was responsible for drastic increases of CO₂ concentrations throughout the tower depths, which suggests that the gust front winds horizontally advected CO₂-rich air from a source upstream from the ATTO site. The CO₂-rich outflow is hypothesized to originate from downward transport and/or biomass burning from forest fires in southeastern Amazon, both ideas that are supported by large increases of aerosol concentrations measured at ATTO following the gust front passage. Our results stress the need for further investigations addressing the role played by mesoscale convective circulations in the redistribution of trace gases and aerosols in the Amazon.

Wheat is a crucial staple worldwide, serving both human and animal needs. In Iran, where climate conditions vary widely, wheat farming faces significant challenges, especially in areas facing freezing winters and unfavorable temperatures during reproductive stages. Unfortunately, existing models often fail to account these extreme and specific climate conditions, leading to inaccurate predictions, notably in cold areas. To address this issue, wheat dryland farming (WDF) model was evaluated in predicting dryland winter wheat yields in five distinct areas including Shahrekord, Borujen, Koohrang, Farsan, Lordegan, and Ardal in Chahar-Mahal and Bakhtiari province, Iran. The results showed that changes in precipitation and temperature significantly impacted dryland wheat production. While higher precipitation generally associates with higher yields, this relationship is not always straightforward due to factors like unfavorable precipitation patterns and types (i.e., rainfall or snow). Likewise, unfavorable temperatures, particularly during crucial growth stages and winter freezes, pose significant challenges to wheat growth and yield modeling. The WDF model's performance was evaluated across various temperature conditions in the study area, and it was more accurate in regions with certain minimum and maximum temperature values above thresholds. However, the model performance was poor in colder areas, where freezing temperatures were occurred in winter duration (Shahrekord, Borujen, Koohrang, and Farsan). In order to improve the model's accuracy, a correction factor based on the minimum and maximum air temperatures was incorporated in the model. The findings emphasized the importance of considering both precipitation and temperature dynamics when modeling winter wheat yields, especially in regions with diverse climates. By refining models like WDF, agricultural planners can better forecast the yield fluctuations and address the impacts of climate variability on food security in Iran and similar regions worldwide.

Producing quantitative volcanic ash forecasts is challenging due to multiple sources of uncertainty. Careful consideration of this uncertainty is required to produce timely and robust hazard warnings. Structural uncertainty occurs when a model fails to produce accurate forecasts, despite good knowledge of the eruption source parameters, meteorological conditions and suitable parameterizations of transport and deposition processes. This uncertainty is frequently overlooked in forecasting practices. Using a Lagrangian particle dispersion model, simulations with varied output spatial resolution, temporal averaging period and particle release rate are performed to quantify the impact of these structural choices. This experiment reveals that, for the 2019 Raikoke eruption, structural choices give measurements of peak ash concentration spanning an order of magnitude, significantly impacting decision-relevant thresholds used in aviation flight planning. Conversely, along-flight dosage estimates exhibit less sensitivity to structural choices, suggesting it is a more robust metric to use in flight planning. Uncertainty can be reduced by eliminating structural choices that do not result in a favourable level of agreement with a high-resolution reference simulation. Reliable forecasts require output spatial resolution $��\le �$ 80 km, temporal averaging periods $��\le �$ 3 h and particle release rates $��\ge �$ 5000 particles/h. This suggests that simulations with relatively small numbers of particles could be used to produce a large ensemble of simulations without significant loss of accuracy. Comparison with previous Raikoke simulations indicates that the uncertainty associated with these constrained structural choices is smaller than those associated with satellite constrained eruption source parameter and internal model parameter uncertainties. Thus, given suitable structural choices, other epistemic sources of uncertainty are likely to dominate. This insight is useful for the design of ensemble methodologies which are required to enable a shift from deterministic to probabilistic forecasting. The results are applicable to other long-range dispersion problems and to Eulerian dispersion models.

Rainfall associated with landfalling tropical cyclones (TCs) along the Northern Vietnam coast is examined to develop an asymmetric parametric TC-induced rainfall model starting from the axisymmetric Rain-Climatology and Persistence (R-CLIPER) model. We recalibrated the R-CLIPER model (original R-CLIPER denoted as NHC) against observed rainfall patterns of 14 landfalling TCs from 2001 to 2021 in the Northern Vietnam coast, while relaxing the model's underlying linear relationships. The recalibrated R-CLIPER (denoted as Fit-Ax), still axisymmetric, suggests that some parameters are better correlated with the normalized maximum wind speed using logarithmic and exponential relationships. Fit-Ax reduces the 12-hr total rainfall overall root-mean-square errors (RMSEs) and Bias magnitudes in the before- and after-landfall periods from NHC for the entire 500-km TC domain. We further redistribute the Fit-Ax rainfall intensity across the four quadrants with respect to the TC forward motion to account for the observed large asymmetry in quadrant rainfall (version denoted as Fit-As). The vertical wind shear (VWS) and landfall (before or after) are considered in this redistribution. Fit-As generally outperforms Fit-Ax and NHC in reproducing the observed rainfall distribution for the 14 TCs. At the quadrant level, both Fit-Ax and Fit-As show significant improvement in Bias over NHC. Fit-As is further better overall in RMSE and Skill when weighted by quadrant rainfall volume. In pattern matching, Fit-As produces the best grid-averaged Pearson correlation coefficients for 11 TCs. In addition, its equitable threat scores (ETSs) are best beyond the 20-mm rainfall threshold, with the maximum of 0.299 at the 90-mm rainfall threshold. Thus, our locally fitted asymmetric rainfall model demonstrates improved capability in reproducing the historical TC-induced rainfall along the Northern Vietnam coast.

In recent years, there has been a rise in human activities in oceanic areas, making the land–atmosphere interactions over islands a major scientific concern on a global scale. Examining the observation data from offshore areas enables a more comprehensive understanding of the turbulent fluxes in offshore atmospheric environments, patterns of momentum, energy and material exchange between the atmosphere and underlying surface in an oceanic boundary layer, and development of a heterogeneous atmospheric boundary layer. The related findings will assist in developing theoretical models and parameterization schemes to simulate the influence of heterogeneous surfaces on land–atmosphere interactions on the South China Sea Islands. Existing studies on the turbulent fluxes over the South China Sea Islands were mainly conducted on the Nansha Islands, whereas studies on the waters of the South China Sea are scarce. In this study, we used 10 Hz high-frequency turbulence measurements to calculate the latent and sensible heat fluxes over the South China Sea Islands using the eddy correlation method. These findings were then compared with data from the Dunhuang Gobi, Ordos desert, and Xilingol grassland regions in inland China, along with the observed net radiation and surface heat fluxes. The findings indicate that the energy fluxes over the South China Sea in summer exhibit prominent diurnal variations. The magnitude of either latent or soil heat flux is low, and the net radiation is predominantly transformed into sensible heat flux, which warms the atmosphere. Furthermore, the daily variation curves of sensible and latent heat fluxes are influenced by intermittent turbulence on the islands and reefs, resulting in a less smooth pattern compared with soil heat flux. Although the South China Sea Islands have small land areas and are surrounded by the sea, the land–atmosphere interactions over the underlying surface of this region are similar to those over the underlying surface of grasslands in inland China during summer. The daily mean sensible heat flux on the islands is higher than that in an inland area, and the time lag in its response to sunrise is longer than that in inland areas by approximately 1 h. The overall energy balance ratio is approximately 0.75, c which is in line with the average level, but an energy balance residual of approximately 25% still exists. Furthermore, extreme weather conditions, such as typhoons, can disrupt the diurnal variations of sensible and latent heat fluxes, and the cyclical patterns are subsequently restored.

High spatio-temporal variability is a characteristic of extreme rainfall. In mountainous regions like the Tropical Andes, where intricate orography and mesoscale atmospheric dynamics greatly impact rainfall systems, this particularly holds for mountain areas like the Tropical Andes. Thus, the absence of operational rainfall monitoring networks with high spatio-temporal resolution has imposed difficulties for a proper analysis of extreme rainfall events in the Ecuadorian Andes. Nowhere, we present our improved knowledge on rainfall extremes based on newly available rainfall radar data of this region. In our study we employ a clustering approach to identify types of extreme rainfall events and analyze their spatio-temporal characteristics. Based on 3 years of data obtained from an X-band scanning weather radar data, the study was conducted in the southern Ecuadorian Tropical Andes at 4450 m a.s.l. By applying a rainfall threshold, 67 extreme rainfall events were selected. The rainfall characteristics of each extreme rainfall event, such as the amount of rain, its duration, its hour, and month of occurrence were determined and used as input variables of a k-means clustering analysis to group the events into different classes. The result revealed three main classes of extreme rainfall events. The first class is characterized by highest rain intensity and lowest duration. The second class is characterized by its month of occurrence, during the first 5 months of the year. The third class showed lowest rain intensity and highest duration mainly occurred at higher elevations. The typology of events advances our understanding of the spatio-temporal characteristics of extreme rainfall in the Tropical Andes.

In recent years, Eastern Africa has been severely impacted by extreme climate events such as droughts and flooding. In a region where people's livelihoods are heavily dependent on climate conditions, extreme hydrometeorological events can exacerbate existing vulnerabilities. For example, suppressed rainfall during the March to May 2019 rainy season led to substantial food insecurity. In order to enhance preparedness against forecasted extreme events, it is critical to assess rainfall predictions and their known drivers in forecast models. In this study, we take a case study approach and evaluate drivers during March to May seasons of 2018, 2019 and 2020. We use observations, reanalysis and predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) to identify and evaluate rainfall drivers. Extreme rainfall during March to May 2018 and 2020 was associated with an active Madden–Julian Oscillation (MJO) in Phases 1–4, or/and a tropical cyclone to the east of Madagascar. On the other hand, the dry 2019 March to May MAM season, which included a delayed rainfall onset, was associated with tropical cyclones to the west of Madagascar. In general, whilst ECMWF forecasts correctly capture temporal variations in anomalous rainfall, they generally underestimate rainfall intensities. Further analysis shows that underestimated rainfall is linked to a weak forecasted MJO and errors in the location and intensity of tropical cyclones. Taking a case study approach motivates further study to determine the best application of our understanding of rainfall drivers. Communicated effectively, knowledge of rainfall drivers and forecast uncertainty will inform preparedness actions and reduce climate-driven social and economic consequences.