Meteorological Applications最新文献

This paper describes a new seamless blended multi-model ensemble configuration of an existing probabilistic medium- to extended-range weather pattern forecasting tool (called Decider) run operationally at the Met Office. In its initial configuration, the tool calculated and presented probabilistic weather pattern forecast information for five individual ensemble forecasting systems, which varied in terms of their number of ensemble members, horizontal resolution, update frequencies and forecast lead time. This resulted in multiple forecasts for the same validity time which varied in terms of forecast skill depending on the lead time in question. This presented challenges for end-users (e.g., operational meteorologists) in terms of knowing which forecast output is best to use and at which lead time, as well as knowing what to do in situations where forecasts varied between ensembles. To get around these challenges, a new seamless blended multi-model ensemble configuration has been implemented operationally, comprising of output from five separate ensembles, and provides a single best forecast from day one out to day 45. Objective verification for a set of eight weather pattern groups covering forecasts initialized over a 6-year period (2017–2022) shows that the seamless blended multi-model ensemble forecasts are at least as good as, if not better than the best performing individual model.

Dynamical seasonal forecast models are improving with time but tend to underestimate the amplitude of atmospheric circulation variability and to have lower skill in predicting summer variability than in winter. Here, we construct Nonlinear AutoRegressive Moving Average models with eXogenous inputs (NARMAX) to develop the analysis of drivers of North Atlantic atmospheric circulation and jet-stream variability, focusing on the East Atlantic (EA) and Scandinavian (SCA) patterns as well as the North Atlantic Oscillation (NAO) index. New time series of these indices are developed from empirical orthogonal function (EOF) analysis. Geopotential height data from the ERA5 reanalysis are used to generate the EOFs. Sets of predictors with known associations with these drivers are developed and used to formulate a sliding-window NARMAX model. This model demonstrates a high degree of predictive accuracy, as indicated by its average correlation coefficients over the testing period (2006–2021): 0.78 for NAO, 0.83 for EA and 0.68 for SCA. In comparison, the SEAS5 and GloSea5 dynamical forecast models exhibit lower correlations with observed circulation changes: for NAO, the correlation coefficients are 0.51 for SEAS5 and 0.34 for GloSea5, for EA they are 0.15 and 0.09, respectively, and for SCA, they are 0.28 and 0.24, respectively. Comparison of NARMAX predictions with forecasts and hindcasts from the SEAS5 and GloSea5 models highlights areas where NARMAX can be used to help improve seasonal forecast skill and inform the development of dynamical models, especially in the case of summer.

Biomass burning (BB) as an important atmospheric carbon source has significant environmental and climatic influence. The frequent extreme BB cases in recent years have raised extensive concerns, yet the latest changes in BB emission on a global scale are not fully understood. Here, we systematically quantify the changes in BB carbon emission for 1999–2022 by fire types and on different scales based on the Global Fire Emissions Database with small fires (GFED4s) dataset. We find contrasting trends of savanna and boreal forest fires persistent over the study period, shaping the variation of global total BB carbon emission. The receding savanna fire drives a declining global BB carbon emission at −8 Tg C year⁻¹ (−0.4% year⁻¹) for 1999–2022, while an upturn of global carbon emission (5 Tg C year⁻¹, 0.3% year⁻¹) occurs in the recent decadal period (2008–2022) due to intensified boreal forest fires. The burned area decouples from carbon emission in terms of the changing tendency, as exhibited by the decreasing global burned area after 2008. Regionally, the fire carbon emission enhancement over the past 15 years (2008–2022) mainly comes from the boreal forests in northwestern North America, northeastern Siberia, and parts of the savanna area, all of which coincide with local climate change toward higher fire proneness. This study reveals a climate-driven aggravation of the BB carbon emission, especially in high-latitude boreal forests, and calls for attention to its potential impacts and effective fire management strategies.

Recurving tropical cyclones (TCs) can sometimes produce tornado outbreaks, while some TCs with similar tracks and intensities may produce none of tornado, which makes it challenging to assess tornado risk within recurving TCs. This study investigates two recurving TCs, Typhoon Yagi (2018) and Typhoon In-Fa (2021), that made landfall in eastern China. Despite the similar recurving tracks and intensities, Yagi produced 11 tornadoes while In-Fa produced none. Results show that both TCs were characterized by similar large-scale conditions that were dynamically favourable for tornadoes during the recurvature process. The non-tornadic In-Fa even featured a higher shear and helicity environment in its northeast sector than did the tornado-productive Yagi. The greatest difference between Yagi and In-Fa is the thermodynamic instability owing to the different lower–middle-tropospheric lapse rates that are attributable to the differences in air trajectories at low levels. In-Fa featured marginal instability due to the cooler air at low levels because almost all of the air parcels came from the Pacific Ocean while most air parcels for Yagi came from the warm land. The cooler low-level air tends to create higher relative humidity in In-Fa's interior and thus leads to widespread precipitation which in turn also contributes to the low-level cooling. The different air trajectories are demonstrated related to the TC's translation speed, size and synoptic characteristics days before TC's landfall. Numerical simulations suggest that the upward motions within the widespread precipitation regions of In-Fa are overall weaker than those of Yagi due to the limited instability in the former. These findings suggest that even though two TCs were characterized by similar tracks, intensities and large-scale forcings, their different low-level air pathways may have significant influence on priming the mesoscale environment for supercell or tornado formation.

Quantiles of precipitation are widely used in ensemble forecast systems. At present, the common practice is to provide precipitation amounts corresponding to different quantiles to users directly, which will make it difficult for users to extract reliable forecast information. Therefore, this study investigates the statistically optimal (using threat score (TS) as a metric) quantiles of precipitation in an ensemble forecast system constructed using the WRF V4.0 model. The main conclusions are as follows: The threat-score-optimal quantiles for light rain, moderate rain, heavy rain, rainstorm, and heavy rainstorm forecasts are 40%–60%, 60%–70%, 60%–80%, 70%–80%, and 80%, respectively. Overall, the optimal quantile increases with the rise in precipitation magnitude or the extension of forecast lead time. All the optimal quantile forecast products have higher TS than the corresponding control forecast, ensemble mean forecast, and probability-matched ensemble mean forecast products. The merged threat-score-optimal quantile forecast product formed by combining the optimal quantile forecasts of different precipitation magnitudes shows obvious advantages over other products in statistical verification and case studies, and it shows good potential to be operationally implemented in the future.

Meteorological conditions and source emissions in grassland areas are quite different from those in urban areas, which significantly impacts the spatiotemporal characteristics of black carbon (BC). To obtain the characteristics of BC in the typical grassland environment in China, continuous observations of BC were carried out in Etuoke Banner, a typical grassland environment in Ordos, from September 8 to December 1, 2022. BC in Etuoke Banner in autumn is 22.4–4667.5 ng m⁻³, and the average concentration is 456.6 ng m⁻³, accounting for 2.20% of the mass fraction of PM_2.5. BC_liquid (BC generated from the combustion of liquid fuels) is the main component of BC (accounting for 79.2%); the average concentration is 361.7 ng m⁻³. The diurnal variations of BC, BC_liquid, and BC_solid (BC generated from the combustion of solid fuels) are bimodal, with peaks at 08:00 and 18:00. The first peak is mainly related to traffic sources, cooking sources, and incomplete combustion of carbon-containing substances; the second peak may be caused by emissions from residential cooking sources under the influence of meteorological conditions unfavorable to diffusion. The diurnal variation of absorption Ångström exponent (AAE) is unimodal, with the peak at 14:00. With the increase in BC mass concentration, AAE and visibility gradually decreased, wind speed first decreased and then increased, P and RH gradually increased, and the contribution of biomass combustion sources to BC decreased. In contrast, the contribution of traffic sources to BC increased. The evolution characteristics of atmospheric pollutants differed with the increase in BC concentration. The potential sources and affecting areas of BC and PM_2.5 are mainly concentrated around Etuoke Banner and can affect the North China Plain after 48 h of transmission.

The present study aimed to evaluate performance sensitivity to the Cumulus Parameterization Scheme (CPS) used for the Weather Research and Forecasting (WRF) model to predict the atmospheric river-related precipitation (ARP) event with 206 and 57 mm, highest and area-averaged precipitation (AAP) per 24-h, respectively, that occurred over the central mountainous basins of Iran on 31 March 2019. For this purpose, experiments were designed using the 12 (almost all) CPSs available in WRF v4. To verify the predicted precipitation (from the inner 4-km domain), both point-scale and grid-scale comparisons were performed against gauge- and satellite-based observational data at three accumulation time-scales (12-, 18-, and 24-h) and in three distinct sub-regions. All scores obtained from the different statistical metrics used, are in complete agreement with a strongly dependent performance of WRF on the CPS used. In addition, the use of Kain-Fritsch, KF-CuP, and Grell-3 CPSs could provide a realistic picture of impending heavy precipitation for WRF. Contrary, the New SAS, Tiedtke, and Zhang-McFarlane CPSs did not perform satisfactorily in predicting the ARP event. As a result, CPSs with the “momentum transport” option in their modification mechanism are unlikely to adequately simulate the conversion of incoming low-level moisture from atmospheric river to precipitation. However, precipitation predictions are more accurate at the 24-h accumulation time-scale than at the 12- and 18-h. Also, a dry bias in the predictions is expected as the terrain elevation and accumulation time-scale decrease and the distance from the core of the precipitation field increases.

It is widely known that precipitation is a key variable of the hydrological cycle that is strongly affected by recent climate changes. Therefore, there is a growing interest in research activities focused on alteration of rainfall regime, as it conditions the planning of countermeasures against flood and landslide hazards. The available literature about precipitation tendencies over Italian peninsula offers a limited number of studies about recent changes of extreme events and precipitation intensity. This work aims at adding a contribution to fill this research gap, investigating the changes in rainfall regime observed over the 2002–2021 period in the Campania region (southern Italy). To pursue this aim, a dataset including daily precipitation records collected at 107 stations was analysed both through 11 indices developed by the Expert Team on Climate Change Detection and Indices and through the Standardized Precipitation Index in order to detect signals of changes in extreme events and to assess tendencies towards drier or wetter conditions. The Theil-Sen method and the Mann–Kendall non-parametric test were employed to evaluate the trends and their statistical significance. The main results emerging from this work are (i) an increasing tendency in precipitation intensity and in the frequency of occurrence of heavy rainfall events in autumn, mainly in the northern part of the region and in the mountainous areas, (ii) an upward trend of the duration of the longest wet spell in the coastal areas and (iii) an increasing trend of dry spells in spring and in summer in the Gulf of Salerno.

Subseasonal-to-seasonal (S2S) forecasts span the prediction range of weeks to 2–3 months ahead, bridging the gap between medium-range and seasonal weather forecasts. There has been growing interest in S2S forecasts in recent years, largely because of the many potential uses of forecasts spanning these timescales. However, the skill of S2S forecasts beyond the first 2 weeks or so is poor, potentially limiting the usability of these forecasts. We show in this study that when considering accumulated temperatures, there is in fact good forecasting skill over Europe for accumulation periods up to 30 days ahead. Using a set of S2S hindcasts, we show using both a deterministic and a probabilistic measure of skill that the accumulated 2-metre temperature forecasts out to 30 days are skilful over most of Europe. In summer, South West Europe has highest skill, while in winter North East Europe has highest skill. As an example application of such forecasts, we also evaluate the skill for summer cooling degree-days (CDD) and winter heating degree-days (HDD). For 30-day winter HDD, there is good skill in all four European regions; for 30-day summer CDD, the skill is limited in North West Europe, but still good in other regions.