Meteorological Applications最新文献

An increasing body of evidence indicates that publics want more probabilistic information included in their weather forecasts. However, more guidance on incorporating probability information into weather risk communication is needed. The National Hurricane Center (NHC) recently developed prototype forecast graphics that include probabilistic values of intensity at landfall when landfall is possible. The goal of this research was to develop those prototypes into a forecast product that expresses technical uncertainty in an intensity forecast in a manner that is understandable and effective to various publics. In Study 1, an online survey among Florida residents was conducted. Quantitative analysis of the survey data showed few significant differences between the prototypes and the currently operational forecast track graphic, commonly referred to as the cone of uncertainty (COU). Analysis of the responses to open-ended questions in the survey and feedback from focus group participants consisting of NHC partners working in hurricane-prone areas guided revisions to improve the prototypes. In Study 2, the modified prototypes produced an improvement in understanding of certain aspects of the intensity forecast. Promisingly, most people surveyed preferred the additional probabilistic information in the prototypes to the status quo COU message. In fact, nearly 90% of respondents indicated that they preferred at least some percentage values in their weather forecasts as opposed to forecasts with words only. This suggests that further development of a probabilistic landfall intensity product might be warranted.

The availability of precipitation data from in situ stations faces various challenges including quality, temporal resolution, irregular spatial distribution, and scarcity in many regions. This is particularly true for the West Bank. Hence, the need to identify alternatives sources is a priority as high quality precipitation estimates are essential for accurate hydrological applications. This study assesses the reliability of four satellite precipitation products (IMERG Final Run, PDIR-Now, CCS-CDR, CMORPH) against 442 in situ rainfall stations across Israel (354) and Palestine (88). These four satellite products, with spatial resolutions ranging from 4 to 10 km, were evaluated at the daily timescale to maximize the number of in situ stations available. The analysis reveals that IMERG outperforms the other products, with a mean $��R^2�$ of 0.33 and a Probability of Detection (POD) of 0.7, without any adjustments. The study also examined the influence of elevation on satellite performance, noting that while IMERG consistently excels in most indices, PDIR has lower Mean Absolute Errors at lower elevations. The results highlight a disparity in performance between the Israeli and Palestinian in situ stations. Overall, IMERG emerges as the most reliable satellite-based estimate for the Levant region, proving effective across different elevations, climatic zones, and rainfall intensities.

Windstorms are a significant natural hazard in Europe and Norway, and while many national meteorological agencies issue warnings for severe storm events, studies estimating their impacts are rare. It has been hypothesized that forecasting storm damages could help stakeholders make better informed decisions in the event of a storm. Using 41 years of daily municipality-level historical Norwegian insurance loss data and high resolution wind speed data from the Norwegian hindcast (NORA3), we propose a novel conceptual framework for probabilistic storm damage forecasting and we test it on the Norwegian Meteorological Institute's MetCoOp Ensemble Prediction System (MEPS). The damage forecasting is performed in two steps: first, a color-coded warning system that issues warnings based on the municipality-level probabilities of the event being a medium, high, or extreme loss event, and second, forecasting damages in monetary terms using damage functions. The color-coded warning system is implemented at the municipality level and the gridded wind speeds are weighted with population density to account for local exposure. The monetary damages are estimated on a county level using four different damage functions. The damage-informed color-coded warning system shows promising results in comparison with a more traditional wind-informed return period-based warning system, demonstrating the ability to forecast the spatial patterns of losses across different loss categories. The county-specific recorded damages lie within the range of the ensemble of damage forecasts 70% of the time for storms not used in the fitting of the damage functions. However, the proposed color-coded warning for damage forecasting is not free from false alarms but is suited to act as a decision help for skilled users.

The Philippines is frequently affected by tropical cyclones (TCs). Among the TC-associated hazards, rainfall can lead to cascading impacts such as floods and landslides. A robust and computationally inexpensive TC rainfall forecasting method is critical in disaster preparation and risk reduction efforts. We used machine learning (ML) to develop a TC rainfall forecast model from parameters such as TC track and locale-specific characteristics. Specifically, a self-organizing map (SOM) was utilized to cluster the TC tracks, which were then fed into a random forest (RF) regression model that used TC position, intensity, translational speed, and other parameters to predict accumulated TC rainfall. The resulting artificial intelligence (AI)-based TC rainfall model was initially assessed against ground rainfall observations for calibration. Then, the model was evaluated for its prediction skill. Model interpretability of the RF model revealed insights into how the input parameters influence the model response. The RF model determined that distance to TC has the most influence on the variability of the accumulated TC rainfall, followed by TC duration, latitude of land grid, and the type of TC track as clustered by the SOM. The model produced similar rainfall distributions to calibrated satellite rainfall observations. It was able to produce rain predictions well and is particularly skillful in predicting intense rainfall events in comparison with the other statistical or dynamical weather models (i.e., WRF model). The predictive ability of the RF model, together with its low computational power requirement, makes it a potential tool to augment TC rainfall forecasting in the Philippines.

Increasing the proportion of energy generation from renewables is a necessary step towards reducing greenhouse gas emissions. However, renewable energy sources such as wind and solar are highly weather sensitive, leading to a challenge when balancing energy demand and renewable energy production. Identifying periods of high shortfall, here defined as when electricity demand substantially exceeds renewable production, and understanding how these periods are affected by weather is therefore critical. We use a previously constructed energy dataset derived from reanalysis data for a fixed electricity system to analyse the link between weather regimes and periods of high shortfall during the winter for 28 European countries. Building on previous work and following similar studies, we provide both a subcontinental and country-specific perspective. For each country, we identify days with critical energy conditions, specifically high-energy demand, low wind and solar generation, and high-energy shortfall. We show that high shortfall is more driven by demand than by production in countries with colder climates or less installed wind capacity, and is more driven by production than by demand in countries with warmer climates or more installed wind capacity. Of the six weather regimes considered here, only a subset is found to favour the occurrence of high shortfall days. This subset affects much of Europe, causing simultaneous shortfall days across multiple countries. Furthermore, if multiple countries experience shortfall days, neighbouring countries are more likely to experience shortfall days. Motivated by this result, we examine the hypothetical impact the coldest European winter of the 20th century, 1962/1963, would have had on the present-day energy system. We found that persistent blocking conditions associated with that winter, if they occurred today, would lead to higher demand and shortfall across Europe during most of the winter and would be extreme in this respect compared to other winters.

Ventilation of cities by local cold-air flows is an important measure in urban heat island mitigation and climate-resilient urban planning. We introduce a cold-air connectivity analysis to identify relevant cold-air formation areas as well as urban quarters ventilated by cold-air flows. The nocturnal cold-air flow trajectories are calculated from numerical model simulations using the single-layer cold-air drainage model KLAM_21 and the newly developed trajectory calculator KLATra. The German city of Freiburg im Breisgau is chosen to demonstrate the cold-air connectivity analysis based on trajectories calculated for two 3-hourly periods during an idealised night. Hydrological catchment boundaries and land use define eight rural cold-air formation areas as starting points for forward trajectories, whereas administrative urban district boundaries and land use data are used to define five built-up quarters potentially prone to overheating as starting points for cold-air backward trajectories. A rate of connectivity is calculated from the ratio of trajectories connecting cold-air formation areas with overheated urban quarters to the total number of trajectories. The analysis reveals the potential of cold-air formation areas to ventilate single or multiple urban quarters at connectivity rates up to 82%. The connectivity analysis therefore supports identification and assessment of the relevance of specific cold-air formation areas for urban heat island mitigation and may serve as a valuable planning tool and data basis for objective decision making.

Small islands such as the Galápagos Islands are highly vulnerable to changes in water availability, affecting ecosystems and communities. Understanding temporal precipitation variability is crucial but challenging due to limited ground-based observations. This study evaluates five global precipitation products (satellite, reanalysis and multi-source products) at a monthly scale, complementing conventional assessment against ground-based observations with the analysis of three climatic time-series components: seasonality, anomalies, and trends, which capture distinct aspects of long-term precipitation variability relevant to climate applications. The analysis focuses on Santa Cruz and San Cristóbal Islands, where long-term ground data are available, and includes a spatial comparison of global products across the entire archipelago. Results showed that reanalysis and multi-source products (ERA5-Land, MSWEP, MSWX) generally outperformed satellite-based products (CHIRPS, PERSIANN-CCS-CDR). For example, in the cool lowlands, reanalysis and multi-source products achieved correlation values between 0.81 and 0.94, bias ranging from −0.52% to −40.3%, and probability of detection between 0.76 and 0.96. These products showed high and medium agreement with ground data in precipitation seasonality, anomalies, and trend detection. In contrast, satellite-based products revealed lower correlation values between 0.52 and 0.86, a higher underestimation bias (−10.86% to −75.43%), a lower probability of detection (0.22–0.32), and only medium or no agreement with ground data in precipitation anomalies and trends, with no agreement in seasonality. All global precipitation products exhibited significant limitations in representing precipitation seasonality in the highlands. The component-based assessment complements conventional evaluation, offering deeper insight into how errors are distributed over time. This integrated approach supports a more informed selection of precipitation products for climate analysis and water resource management in data-scarce island regions like Galápagos.

Weather predictability varies between tropical and middle latitudes: rotational effects enable forecasts on moderate spatial scales up to 10 days in middle latitudes, while longer term predictions are less reliable; in contrast, tropical weather is challenging to predict at short lead times, but seasonal forecasts are more accurate due to the influence of larger-scale oscillations, such as slowly varying oceanic surface conditions. This behaviour has been demonstrated in previous studies, but has yet to be focused on in detail, despite its importance to the development of forecasting systems in Tropical regions. This study systematically evaluates precipitation in weather prediction models across both regions using the fractions skill score, evaluating performance at progressively longer lead times and averaging scales, and compares the results with an evaluation based on upper air error kinetic energy. The results confirm that the prediction systems perform better on smaller scales and shorter lead times at middle latitudes and on larger scales and longer lead times at tropical latitudes. A “crossover” in performance is seen at forecast lead times of 5–7 days, a result that appears to be consistent across a range of model resolutions, and occurs both when specifically comparing European and African domains and when comparing whole latitude bands. This differential pattern of model skill even occurs for machine learning-based forecast models, suggesting that it is a fundamental property of the atmosphere rather than an effect of the construction of currently used operational forecasting systems. These findings highlight the need for different forecasting methodologies in tropical regions to address the lack of short-term predictability and leverage long-term statistical predictability.

Singh, V., G. Tiwari, A. Singh, et al. 2025. “Tropical Cyclones Across Global Basins: Dynamics, Tracking Algorithms, Forecasting, and Emerging Scientometric Research Trends.” Meteorological Applications 32, no. 3: e70067. 10.1002/met.70067.

The abbreviation “(ARB)” should be added after “Arabian Sea” in the Abstract section. The revised sentence should read as follows:

We apologize for these errors.

This paper assesses dynamical models to construct monthly (January through December for lead times of 0.5–2.5 months) and seasonal (January–March [JFM], April–June [AMJ], July–September [JAS], and October–December [OND] for lead times of 1.5–3.5 months) forecasting of drought based on the standardized precipitation evapotranspiration index (SPEI) over Iran. The air temperature (minimum, maximum, and mean) and precipitation data, as the components of SPEI, are forecasted using six North American Multi-Model Ensemble (NMME) and European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS51 as well as their ensemble multi-model mean (MMM) for a common period from 1991 to 2021. These forecast data are interpolated to stations using inverse distance weighting, and then the SPEI is computed for each model. The observed SPEI is calculated for 67 synoptic stations across Iran. The SPEI forecast skill of the MMM surpasses that of individual models. Additionally, MMM demonstrates improved forecast skill during wet and cold months (November–March) compared to dry and warm months (June–September). There is a statistically significant Pearson correlation coefficient between observed and forecast JFM SPEI in most areas of the study area for lead times of 1.5, 2.5, and 3.5 months at a 5% significance level. Moreover, the SPEI forecast is significant in most areas for JFM, AMJ, and OND for the 1.5-month lead time. The canonical correlation analysis is employed to investigate the relationship between observed global sea surface temperature anomalies (SSTA) and seasonal SPEI to achieve insights into the source of drought predictability in Iran, as well as how the skill of the MMM forecasts is affected by SSTA. The spatial pattern root mean square error of the MMM forecasts and SSTA is similar. The canonical correlation coefficient between SSTA and observed SPEI is stronger than in JFM, indicating that MMM exhibits promising potential for SPEI forecasts.