Meteorological Applications最新文献

Weather regimes describe the large-scale atmospheric circulation in the mid-latitudes in terms of a few circulation states that modulate regional surface weather conditions on time scales of multiple days to a few weeks. This low-dimensional representation of weather has proven useful for the study of large-scale dynamics, climate trends, flow-dependent predictability, and as proxies for applied medium- to extended-range forecasting in the energy sector, for example. Previous studies have often focused on the mean surface weather associated with a regime, with only a few commenting quantitatively on intra-regime variability. In this paper, we comprehensively quantify variability of daily surface weather within regimes and show that it cannot be ignored as mean-composite approaches can be misleading. Signal-to-noise metrics highlight regime configurations that provide windows of predictive opportunity, where surface dynamics are well controlled by the large-scale regime. We discuss in detail wintertime temperature and wind speed regime anomalies for four selected countries (Spain, Norway, Germany, and the United Kingdom) and show that in each case there is impactful intra-regime variability that can be explained by different subtypes and life cycle stages of a regime. This nuance can be captured by continuous regime indices, allowing a refined application of weather regimes on the pan-European scale. This relatively simple guidance on regime interpretation and operational use comes without the need to change the underlying regime framework. An accompanying interactive archive, documenting intra-regime variability in national-scale, energy-relevant variables, supports immediate practical application of our regime analysis for all European countries.

Urban sensor deserts call for a densification of weather sensor networks to provide climate information to local residents and decision-makers. We evaluated the usability of low-cost long-range communication weather sensors for urban microclimate studies. Focusing on the east of London, next to the Olympic Park, from the 20^th of June 2024 to the 21^st of June 2025, we showed that low-cost weather sensors can inform about existing climate differences between local heterogeneous urban environments. We found that the Olympic Park was cooler than surrounding neighbourhoods throughout the year and that greater differences were observed during the summer. Studied districts located further from the Olympic Park were warmer than the closest ones by 0.21°C on average. They were also hotter than the Olympic Park by 0.53°C on average, going up to 0.87°C during summer. This highlighted the benefits brought by parks in providing cooling to local populations. Districts with a greater presence of water bodies also experienced cooler conditions during the day and warmer during the night than their built-up counter parts. During winter and spring, several days had lower daily maxima than the local park in these districts with a higher proportion of water bodies, with cooling reaching down to ~2°C and with about 50% of winter days observing cooling of ~0.5°C. Data from low-cost weather sensors should be carefully interpreted during cold seasons and during daytime hours due to the low accuracy of these sensors. Only ~30%, ~10%, and ~50% of daily average differences to the Olympic Park fell outside of the range of uncertainty in autumn, winter, and spring, respectively. The yearly and seasonal temperature differences compared to the Olympic Park are, however, not caused by sensor errors. Observation of more complicated phenomena, like urban heat advection, remains challenging at local scales.

This study investigated the climatology, trends, and variability of precipitation, reference evapotranspiration (ET_o), and climatic water balance (CWB) in Ethiopia and its 12 basins from 1980 to 2021. Mean annual rainfall was 773 mm, with significant regional variations, while the mean annual ET_o was 958 mm. Kiremt (June–September) received the highest rainfall (393 mm), and Belg (February–May) exhibited high ET_o. The annual mean CWB was −185 mm, with only four basins showing a positive CWB. Spatially, western Ethiopia experienced higher rainfall, while the northeast had higher ET_o. Temporally, both annual rainfall (2.01 mm/year) and ET_o (0.40 mm/year) significantly increased nationally, with regional variations. Rainfall variability was highest in the Bega (October–January) season (CV = 45.5%) and lowest in Kiremt (CV = 21.9%). CWB showed the highest variability. Years with moderate to extreme dry and wet conditions were identified through standardized rainfall anomaly analysis. These hydroclimatic patterns and their changes have significant implications for forestry development in Ethiopia, necessitating region-specific strategies. Positive rainfall trends in western and southern basins offer opportunities for faster tree growth, while decreasing rainfall and negative CWB in the northeast pose challenges requiring drought-tolerant species and water conservation. The increasing ET_o and high interannual rainfall variability further emphasize the need for careful species selection and resilient forestry management practices across Ethiopia.

Data-driven weather models have shown the potential to match the accuracy of state-of-the-art numerical weather predictions (NWPs). However, existing data-driven forecasting models still have limitations in operational applications. For example, most of them are predominantly trained via fifth-generation climate reanalysis data (ERA5). However, in actual forecasting operations, the models are usually initiated by analysis fields instead of reanalysis data; this leads to a mismatch between the training data used by machine learning (ML) forecasting models and the actual operational data. To address this issue, we attempt to fine-tune the data-driven model with the initiation fields in operation. This study first develops a fine-tuned Pangu Weather Model (PGW) by integrating forecasting system (IFS) analysis data from 2021 to 2022 and conducts a comprehensive evaluation of its performance. By comparing the fine-tuned version (PGW_O) with the public version (PGW_P) against IFS models with different resolutions (IFS_L at 0.25° and IFS_H at 0.1°), this research highlights advancements in data-driven forecasting methodologies. The models are tested on data from South China, a region with dense meteorological observation networks, over a three-month period, encompassing a detailed case study of Tropical Cyclone Haikui (2023). The findings show that with the forecast activity (FA) level comparable to PGW_P, PGW_O significantly reduces the root mean square error (RMSE) and mean error (ME) across upper atmospheric variables and demonstrates superior accuracy in predicting surface elements. The operational relevance of these models is evaluated through both ERA5 reanalysis and surface observations, revealing that fine-tuning with IFS data enhances PGW compatibility and forecasting precision, particularly for severe weather events.

Rain-fed crop yields are heavily influenced by seasonal rainfall patterns and temperature, particularly during vegetative and reproductive growth stages. This study was conducted to investigate the effects of rainfall distribution indices (monthly, seasonal, and annual) on rain-fed wheat and barley yields using polynomial regression analysis across six different locations with varying elevations in Chaharmahal and Bakhtiari province, Iran. Additionally, the economic feasibility of rain-fed wheat and barley in all locations was evaluated. Results showed that the monthly rainfall distribution index could not accurately predict wheat/barley yield, where elevation exceeds 2000 m and the average annual minimum temperature is below 4°C (such as in Koohrang, Borujen, Shahrekord, and Farsan). Conversely, the monthly rainfall distribution index was able to predict the wheat/barley yield with high accuracy (R² > 0.75) in locations with lower elevation and higher average annual minimum temperature (such as Lordegan and Ardal). Compared to seasonal rainfall indices, annual rainfall indices showed weaker predictive accuracy in all locations. Furthermore, a significant relationship (p-value < 0.0001) with a high coefficient of determination (R² > 0.80) was found between spring rainfall index, spring minimum temperature, and wheat/barley yield in all locations. Therefore, incorporating minimum mean air temperature with the spring rainfall index is recommended for yield prediction for all locations. Economic analysis revealed that the internal return rates in Borujen, Farsan, Lordegan and Ardal exceeded the bank interest rate (14%), indicating that cultivating wheat and barley in these four locations was profitable and economic. Moreover, an exponential relationship between the average annual temperature and internal return rate was also established, offering a useful tool for farmers and planners to estimate the internal return rate based on only the average annual temperature.

Climate change poses significant challenges to agricultural production, particularly in Central Africa, where the livelihoods of millions depend on key crops such as maize, groundnut, soybean, and rice. The potential effects of climate projections on agricultural yields are significant, as variations in temperature, rainfall, humidity, and soil moisture can lead to substantial changes in crop performance. The research aims to model and predict crop yields based on these meteorological variables by utilizing machine learning models, including Gaussian process and Random forest. The findings demonstrate that regional agricultural production differences may arise from future climatic conditions. The random forest model aligned more closely with observed values, achieving better average accuracies depending on the season. The performance of the machine learning models is closely tied to the specific crops and countries within the study region. Furthermore, the insights gained can greatly benefit political decision-makers and stakeholders in developing targeted adaptation plans and policies.

Vertical mixing in the planetary boundary layer greatly influences thunderstorm activity. The sensitivity of two local (MYJ and MYNN) and one non-local (YSU) PBL schemes with a combination of Single Layer Urban Canopy Model (SLUCM) of the Weather Research and Forecasting (WRF) model is studied at 2 km horizontal resolution for the evolution of thunderstorms. Twelve thunderstorms over four cities in the eastern Indian region are identified during 2016–2021. Results highlighted that the YSU scheme performs better with a rainfall absolute percentage of error of 27%, while the MYJ and MYNN exhibited comparatively higher errors of 31% and 38%, respectively, within a 50 km area from the city center. The mean timing error of initiation and mature stage against GPM rainfall is 0–1 h in the YSU scheme and 0.5–2 h for both MYJ and MYNN. The lead–lag correlation (0.6 at 00 h) and quantitative rain rate verification also confirm the better performance of YSU. Surface (2 m) and atmospheric dynamical and thermodynamic profiles are replicated well with lower errors in YSU, except for 10 m wind speed. Diagnostic analysis indicates that higher frictional velocities and turbulent kinetic energy in YSU resemble the higher vertical mixing, leading to an unstable atmosphere with stronger updrafts. These PBL characteristics are relatively weaker in MYJ and MYNN as well as the stability indices. Overall, the better performance of the YSU scheme can be attributed to the better transport of surface characteristics, including turbulent fluxes and moisture, to the upper levels in an unstable atmosphere with strong vertical velocities. Further, results highlight that the simulation of urban thunderstorms improved with urban physics when compared with no-urban simulations. Thus, this study emphasizes the role of PBL along with urban physics in steering the dynamics of urban thunderstorms.

The Advanced Baseline Imager (ABI) onboard the geostationary satellite GOES-18, launched on March 14, 2022, and re-designated as GOES-West in January 2023, has been providing data to the National Centre for Medium Range Weather Forecasting (NCMRWF) since its operational commencement. This study endeavors to develop and evaluate GOES-18 satellite radiance data assimilation within the Global Data Assimilation System (GDAS) at NCMRWF, specifically on simulating Pacific hurricanes impacting the Western United States. The study includes two main components: (1) developing and assessing the reliability of the GOES-18 radiance observation assimilation capability in the NCMRWF Global Forecasting System (NGFS), and (2) simulating and analyzing the catastrophic Category-4 hurricane Hilary, which caused severe damage and heavy rain in the Western United States and Mexico. A month-long analysis of data reveals that GOES-18 provides a substantially larger number of observations compared to GOES-16, with a more significant proportion of observations being accepted during the assimilation cycle. Error metrics (e.g., spread, standard deviation, RMSE) were estimated for background fields without bias correction, with BC, and analysis with BC compared to observations. The results indicate a significant reduction in RMSE (~50%) in the analysis, thereby establishing a positive signature for the assimilation of GOES-18 observations. This study further investigates the efficacy of assimilating GOES-18 data in simulating hurricane Hilary using the NGFS with a focus on evaluating potential improvements in track, intensity, and inner core structure of the system.