Atmospheric Science Letters最新文献

This study classifies the spatial distribution of heavy precipitation in summer (June–August) from 1979 to 2021 in the three provinces of Northeast China (TPNC) into two patterns by using the self-organizing maps (SOM) neural network, and then quantitatively analyzes their moisture transport channels and sources using the Lagrangian model. The results show that the summer heavy precipitation in TPNC can be divided into the northern and southern patterns according to the distribution of the heavy precipitation. Both patterns of heavy precipitation are affected by the low-level vortex west of TPNC, but the strength and shape of the low vortex are different. The northern pattern is mainly influenced by the westerly flow in the vortex in the mid-high latitudes, which transports moisture from the upstream westerly region into TPNC. The southern pattern is mainly affected by the southerly jet stream southeast of TPNC, which conveys a large amount of moisture from the East Asian summer monsoon region into TPNC. In terms of the summer climatological mean, the northern pattern has a higher precipitation recycling rate, while the southern pattern has a lower recycling rate.

Based on the daily temperature observation data at 1992 stations in China, this study investigates the intra-seasonal variations and trends of extreme warm spells during 1981–2022. The results indicate that the nationwide extreme cold and warm spells have both increased rapidly since the 21st century. However, different from cold extremes which occur evenly in winter months, the nationwide super warm spells have distinct intra-seasonal differences, with more occurrences in February. The number of nationwide extreme warm spells has increased from 8 during 1981–2001 to 14 during 2002–2022, and the intensity has also increased obviously. Due to the spatial differences, the study area is divided into three regions to reveal the detailed features. It is found that the warm spell frequencies in most of China increase from December to February while decrease in northeastern China. Results demonstrate that extreme warm spells are concentrated in late winter in most regions (stations). The significant increasing trends are widespread from northern China to most parts of southern China, and the areas with increasing trend exceeding +0.6 day/decade are concentrated in North China and the middle reaches of the Yangtze River. Linear trends indicate that the significantly increasing trends in late winter contribute a major part to the variation in the whole winter in China.

This study investigates interagency discrepancies among best-track estimates of tropical cyclone (TC) intensity in the western North Pacific, provided by the Joint Typhoon Warning Center (JTWC), the China Meteorological Administration (CMA), and the Regional Specialized Meteorological Center (RSMC) Tokyo during 2013 to 2019. The results reveal evident differences in maximum wind speed (MSW) estimates, where linear systematic differences are significant. However, the Dvorak parameter (CI) numbers derived from the MSWs reported by the three agencies are internally consistent. Further analysis suggests that the remained CI discrepancies are related to differences in the estimation of intensity trends, initial intensities, and TC positions among these datasets. In addition, the CI estimates provided by the JTWC for TCs over the open ocean are generally higher than those reported by the CMA and RSMC. However, the estimates from CMA and RSMC tend to give higher TC intensities for the TCs in the mainland and coastal areas of China and Japan, respectively, than those over the open ocean with the same intensity in JTWC dataset. This pattern potentially reflects the extensive use of surface observations by these two agencies for landfalling and offshore TCs. These results may help the research community to get more accurate details about the TCs in WNP from the best track datasets of different agencies.

During mid-July 2021, an extreme heavy rainfall event (HRE) occurred in Henan Province (hereafter “21.7” HRE), with extreme hourly precipitation of 201.9 mm appearing at Zhengzhou station. Our preliminary analyses of the “21.7” HRE using the observations and ECMWF (European Centre for Medium-Range Weather Forecasts) ERA5 reanalysis data, reached the following conclusions. Favorable configurations of various synoptic weather systems (e.g., strong upper-level high-pressure ridge, intense middle-level low-pressure trough) acted as crucial background conditions for the occurrence of the “21.7” HRE. A 21-h long-lived mesoscale convective vortex (MCV), mainly located in the middle and lower troposphere west of Zhengzhou city, was a key system that produced the extreme hourly rainfall of 201.9 mm·h⁻¹. The MCV's development/sustainment was dominated by the vertical transport of cyclonic vorticity and tilting, as well as the horizontal import of cyclonic vorticity to the vortex's key region. In contrast, the divergence-related vertical shrinking was the most detrimental factor. Lagrangian moisture transport analysis showed that moisture for the extreme heavy rainfall in Zhengzhou on July 20 mainly came from levels below 2200 m, driven by airflows on the peripheries of tropical cyclones IN-FA and CEMPAKA. To enhance the understanding of “21.7” HRE, we suggest more in-depth investigations in the future.

In boreal winter, strong upper-level midlatitude troughs across the Atlantic–Africa–southwestern Asia sector generate substantial tropical–extratropical interaction and have become recognized as important factors in some extreme weather events. As such, they represent important dynamic features to understand and capture in weather forecasts, as well as in climate models for projections on longer timescales. Here, we empirically study the 20% of winter days with strongest trough signatures during 1982–2020 at each longitude across the sector, and show that the trough impact over northern Africa, most notably in central parts, is particularly strong in magnitude, low-latitude extent and persistence, leading to the characterization of a northern Africa mode of several-days weather fluctuation. Weather conditions that follow strong troughs from the eastern Atlantic to the Central Mediterranean include: (i) a warming tendency across much of northern Africa, generally of several Celsius magnitude ahead of the trough, and >1°C even extending to the south of 10° N in central parts and continuing eastward until the Ethiopian Highlands; (ii) precipitation development further north than normal across northern tropical Africa, especially strong over longitudes corresponding to a northward extension of the main Congo rain belt. The intertropical discontinuity and low-level heat low are also shifted significantly north, with the complex of anomalies persisting for several days, beyond the timescale of the trough. For context, at all other trough longitudes across the sector, a warming signal does emerge (statistically significant), but with much shorter persistence (2–3 days), smaller magnitude and extending southward clearly only to 15–20° N. Mid-level tropical plumes of moisture are also typically present for strong troughs from the eastern Atlantic to southwestern Asia, and these alone can lead to weather extremes. However, low-level warming and mid-level moistening are uniquely juxtaposed at low latitudes over central Africa, where a near-equatorial signature develops.

In November 2021, the Royal Meteorological Society Data Assimilation (DA) Special Interest Group and the University of Reading hosted a virtual meeting on the topic of DA for convection-permitting numerical weather prediction. The goal of the meeting was to discuss recent developments and review the challenges including methodological developments and progress in making the best use of observations. The meeting took place over two half days on the 10 and 12 November, and consisted of six talks and a panel discussion. The scientific presentations highlighted some recent work from Europe and the USA on convection-permitting DA including novel developments in the assimilation of observations such as cloud-affected satellite radiances in visible channels, ground-based profiling networks, aircraft data, and radar reflectivity data, as well as methodological advancements in background and observation error covariance modelling and progress in operational systems. The panel discussion focused on key future challenges including the handling of multiscales (synoptic-, meso-, and convective-scales), ensemble design, the specification of background and observation error covariances, and better use of observations. These will be critical issues to address in order to improve short-range forecasts and nowcasts of hazardous weather.

Using the daily 2 m maximum temperature (Tmax), 2 m minimum temperature (Tmin) and cloud cover data measured at ground sites of the China Meteorological Administration in North China from 2000 to 2017, this study investigates the influence of clouds on the daily temperature range (DTR) defined as the difference between Tmax and Tmin. As expected, the cloud cover shows the similar averaged spatial distribution and monthly variation with Tmin. Surprisingly, it also shows the similar average spatial distribution and monthly variation with Tmax, suggesting the more important roles of regions (latitude) and seasons associated with the variations of land surface temperature, which is further related to solar radiation absorbed and surface heat capacity. By comparing monthly variations of temperature between cloudy and clear skies, we find that clouds can weaken Tmax and increase Tmin, and thus decrease DTR. As a result, the spatial distribution of DTR is opposite to the cloud cover. The clouds have relatively stronger impact on Tmin and DTR over mountain region, which is most likely caused by the stronger longwave cloud radiative forcing associated with higher cloud tops over the mountain region.

Boreal winter (December–February) 2020/2021 in the North Atlantic/European region was characterised by a negative North Atlantic Oscillation (NAO) index. Although this was captured within the ensemble spread of predictions from the Met Office Global Seasonal forecast system (GloSea5), with 17% of ensemble members predicting an NAO less than zero, the forecast ensemble mean was shifted towards a positive NAO phase. The observed monthly NAO anomalies were particularly negative in January and February, following an early January sudden stratospheric warming (SSW), and a prolonged period of Phase 6 or 7 of the Madden Julian Oscillation (MJO) in late January/early February. In contrast, predictions showed the expected teleconnection from the observed La Niña, with a positive NAO signal resulting from a weakening of the Aleutian Low leading to a reduction in tropospheric wave activity, an increase in polar vortex strength and a reduced chance of an SSW. Forecasts initialised later in the winter season successfully predicted the negative NAO in January and February once the SSW and MJO were within the medium range timescale. GloSea5 likely over-predicted the strength of the La Niña which we estimate caused a small negative bias in the SSW probability. However, this error is smaller than the uncertainty in SSW probability from the finite forecast ensemble size, emphasising the need for large forecast ensembles. This case study also demonstrates the advantage of continuously updated lagged ensemble forecasts over a ‘burst’ ensemble started on a fixed date, since a change in forecast signal due to events within the season can be detected early and promptly communicated to users.