Meteorological Applications最新文献

This study investigates the accuracy of GLDAS-Noah-2.1 daily minimum soil temperature (DMST) at 0–10 cm (shallow) and 40–100 cm (deeper) depths in Khorasan Razavi, Iran, using 5 years (2008–2012) of data from 13 synoptic stations. Initial evaluations using statistical metrics revealed significant discrepancies, with GLDAS tending to overestimate DMST. The initial raw GLDAS data showed a considerable systematic error, with a bias ranging from 1.96°C to 6.0°C in the shallow profile and from 0.58°C to 6.62°C in the deeper soil profile. On average, the shallow layer performed poorly, yielding an RMSE of 4.82°C and an average bias of 3.85°C, while the deeper layer showed an average RMSE of 2.72°C and a bias of 2.14°C. To mitigate these biases, a K-nearest neighbors (KNN) supervised machine learning algorithm was employed and optimized through grid search. The KNN model dramatically enhanced performance for both layers. For the shallow depth, the average RMSE was reduced to 2.56°C (a ~47% reduction), and the average bias was reduced to 0.06°C. For the deeper layer, the average RMSE was reduced to 1.30°C (a ~52% reduction), and the average bias was reduced to 0.00°C. Furthermore, the Nash–Sutcliffe efficiency (NSE) improved from an initial average of 0.75 and 0.86–0.92 and 0.97 for the shallow and deeper layers, respectively. Post-correction, the model achieved a “Very Good” performance rating for all stations, with the average percent bias (Pbias) falling to 0.37% (shallow) and −0.08% (deeper). The results underscore the efficacy of machine learning-based bias correction in enhancing the reliability of GLDAS datasets for regional climate and agricultural applications.

Tropical cyclone (TC) Doksuri (2023) exhibited a sudden northward turn over the northeastern part of the South China Sea (SCS). However, most global models failed to capture such track change. The US National Centers for Environmental Prediction Final Analysis (FNL) data and The International Grand Global Ensemble (TIGGE) data were therefore used to study the underlying mechanisms for the sudden track change and the factors leading to the track forecast errors of different global models so as to give some insight for the forecasters in predicting such TC track change and global model developers in modifying the model physics. The non-linear advection of the vorticity of the asymmetric winds associated with the monsoon trough over the SCS and that of the symmetric wind of the TC resulted in the sudden northward turn of the TC track. However, the strength and the eastward extension of the monsoon trough were underpredicted, leading to a westward-moving track without a sharp northward turn. On the contrary, if the strength of the monsoon trough was overpredicted, the environmental steering was over-altered, resulting in an early northward turn. The intensity and outer wind structure of the TC also played important roles in the monsoonal interaction and thus the track forecast errors.

Dry intrusions are coherent airstreams that originate from the upper troposphere, or lower stratosphere, and descend towards the surface where they can influence the dynamics of mid-latitude weather. Notably, the occurrence of these airflows with atmospheric fronts and extratropical cyclones can exacerbate their impacts, leading to increased precipitation and stronger surface winds. Therefore, it is of interest to understand how dry intrusions may respond to our changing climate. Traditional identification methods with Lagrangian trajectory analysis, however, cannot always be applied to climate projection data due to the computational cost of this approach and a lack of necessary available data from climate models. This research explores the alternative application of image segmentation concepts to build a machine learning classification model for dry intrusion outflows in the North Atlantic. A U-Net convolutional neural network (CNN) is trained to predict the presence of dry intrusion objects from ERA5 atmospheric data, including temperature and relative humidity. With a catalogue of labelled dry intrusion objects calculated from trajectory analysis as predictands, the ability of this CNN to identify individual dry intrusion footprints, capture their size and shape, and recreate long-term climatologies is evaluated with Matthew's correlation coefficient and intersection over union. Compared with a multiple logistic regression model, the CNN outperforms across all metrics and compares more favourably with the target data. However, the CNN struggles to predict small dry intrusion signatures and limitations are encountered outside of the spatial training domain in a high-impact case study. Despite this, these results provide proof-of-concept for an alternative way to identify dry intrusion outflows that uses less data, is fast and easy to implement, and could be utilised to study the possible futures of dry intrusions and extreme mid-latitude weather.

Near-surface observations can suffer from significant representativeness errors, especially for Numerical Weather Prediction (NWP) at lower resolution in global applications. Therefore, in Data Assimilation (DA), many operational centers have long been reluctant to assimilate them (e.g., the European Center for Medium-range Weather Forecast, ECMWF, started assimilating all 6-h screen-level temperature reports only in 2024). For forecast verification, some studies advocate that we should not rely on them and use only verification against our own near-surface analyses. At Environment and Climate Change Canada (ECCC), both temperature and humidity observations from SYNOPs have been assimilated in our global NWP system for more than two decades and, in June 2024, METARs have been added following some positive impacts found only when comparing forecasts against near-surface observations. To shed light on the impact of the assimilation of screen-level observations, in this study we present an evaluation of the impact of removing the assimilation of all screen-level temperature and humidity observations using various verification references: the NWP forecasts were evaluated against radiosondes and surface observations, independent (ECMWF) analysis, our own analysis and surface analysis. Results show that, despite the lack of a proper estimation of representativeness errors in the DA approach, the assimilation of screen-level temperature and humidity leads to forecast improvements that can be detected from the verification against independent measurement sources, here radiosondes and ECMWF upper-air analyses. Verification against own analyses, for both upper-air and screen-level variables, led instead to opposite and misleading conclusions. In fact, the removal of assimilated screen-level temperature and humidity measurements renders the NWP forecast more similar to the own analysis, therefore leading to better scores but detachment from the observed world.

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.