Abstract:In view of the limitation that the traditional historical frequency matching method (Hist-FMM) cannot effectively correct the positional deviation of precipitation areas, an improved frequency matching correction scheme based on low-level circulation similarity (Sim-FMM) is proposed, which uses wind direction and speed at 925 hPa as dynamic constraints. By analyzing the coupling relationship between precipitation and the wind field at 925 hPa, together with sensitivity experiments, the optimal parameter scheme for Sim-FMM was established. For general precipitation correction, a wide tolerance window (30° for wind direction and 4 m·s?1 for wind speed) without seasonal constraints is adopted to ensure sample representativeness. For heavy rainfall correction, a narrower tolerance window (10° for wind direction and 1 m·s?1 for wind speed) combined with seasonal constraints is applied to balance circulation similarity and climatological characteristics. Based on surface observations and ECMWF forecast data from December 2023 to May 2026, the correction performance of Sim-FMM was evaluated. The results show that Sim-FMM outperforms Hist-FMM in precipitation correction. For winter rainfall events, Sim-FMM effectively reduces widespread false alarms by matching analogous circulation samples, increasing the rain/no-rain accuracy by 1.9% relative to Hist-FMM. For local heavy rainfall events in spring and summer, Sim-FMM alleviates the systematic underestimation of rainstorm magnitude, improving the rainstorm threat score (TS) by 8.4% relative to Hist-FMM during 2024-2026. Nevertheless, Sim-FMM performs slightly worse for autumn heavy rainfall, which may be attributed to the highly concentrated seasonal features of autumn rainfall over Hainan Island and the inability of the regional mean wind field at a single time to fully depict the heterogeneous structures of complex systems such as typhoons and shear lines.

Abstract:Based on the Local Climate Zone (LCZ) framework, this study systematically analyzes the characteristics of temperature differences across various underlying surface types and their response mechanisms to meteorological conditions. Using hourly temperature, wind speed, and precipitation data (2010–2021) from 37 meteorological stations within the Fifth Ring Road of Beijing, combined with field surveys and satellite imagery, the stations were classified into five LCZ types. The results indicate that the main urban area exhibits a temperature gradient dominated by building density. The compact high-rise zone (LCZ 1) serves as a stable heat center, with average temperatures significantly higher than those of vegetated zones. Thermal differences among LCZ types follow a seasonal pattern of intensifying in autumn and winter while weakening in spring and summer, with the most pronounced disparities occurring during winter nights. Furthermore, precipitation significantly reduces thermal differences between LCZs through evaporative cooling, while simultaneously highlighting the dominant role of surface properties. Finally, weak wind conditions favor the maintenance of thermal gradients, whereas strong wind environments promote the homogenization of the temperature field.

Abstract:Based on observation data from 76 national meteorological stations in Hubei Province and ERA5 reanalysis data during 2000-2024, 108 rain-snow events are classified by means of the T-mode principal component analysis (TPCA). The temporal distributions, circulation configurations, and impact characteristics of the rain-snow events under different circulation types are then analyzed. The results show that over the 25 years (2000—2024), Hubei Province experienced 4.3 rain-snow events on average annually, with a clear inter-annual variability. These events occurred mainly in January and February, accounting for 74% of the total. Based on the circulation patterns at 500 hPa, these rain-snow events are classified into three types: the Lake Baikal High-Ridge Type (48.1%), Lake Baikal Low-Trough Type (30.6%), and Lake Baikal Northwesterly Flow Type (21.3%). In the composite mean fields of the three circulation types, the Lake Baikal High-Ridge Type features the deepest southern-branch trough at 500 hPa and the strongest influence of the surface cold high. The Lake Baikal Low-Trough Type is characterized by the strongest southwesterly jet at 700 hPa, with a maximum wind speed of 20 m·s?1, and the most favorable dynamical conditions. The Lake Baikal Northwesterly Flow Type has the weakest southern-branch trough. The three circulation types of rain-snow events produces distinct impacts in Hubei Province. The Lake Baikal Low-Trough Type has a higher daily average maximum precipitation (15.34 mm) and deeper average maximum snow depth (9.33 cm) than the other two types and is more prone to produce deep snow cover. The Lake Baikal High-Ridge Type has the largest average value of total precipitation (19.63 mm), the highest mean numbers of snow-covered and wire-icing stations, and the greatest wire-icing intensity with a mean maximum wire-icing diameter of 3.83 mm. It also has the longest duration of rain-snow event, lasting for 2.22 d on average. The Lake Baikal Northwesterly Flow Type generally generates smaller precipitation amount, the weakest snow accumulation and wire icing, and the shortest event duration. These findings could provide a reference for circulation-pattern-based forecasting of rain-snow events and disaster risk assessment in Hubei Province.

Abstract:Since 2024, the operational operation of most new-generation weather radars in China has adopted three observation modes: precipitation mode 1 (VCP11D), precipitation mode 2 (VCP21D), and clear-air mode (VCP31D). However, a systematic evaluation of their specific data quality differences and the scientific validity of the automatic switching strategy is still lacking. This study uses 11,965 sets of observation data from 117 dual-polarization operational radars nationwide, collected before and after mode switching from April to July 2024, to compare and analyze the performance of the three observation modes in terms of ground clutter suppression capability, sensitivity, and measurement accuracy. Furthermore, by statistically calculating the proportion of effective precipitation echoes in newly added/missing elevation layers before and after mode switching, the study quantitatively evaluates the rationality of the current automatic switching strategy, which is based on thresholds of composite reflectivity (CR), echo top (ET), and vertically integrated liquid water content (VIL). The results show that: (1) In terms of data quality, the three modes exhibit comparable ground clutter suppression capabilities, but the clear-air mode significantly increases the area of ground clutter identification due to its longer pulse width. The clear-air mode has the highest sensitivity, followed by the precipitation modes; however, differences in radar models and refined modifications can reduce sensitivity. The number of pulse accumulations in different observation modes primarily affects the measurement accuracy of differential reflectivity (ZDR) and correlation coefficient (ρhv); fewer accumulations lead to greater dispersion of these two parameters. (2) Regarding switching strategies, the current fixed-threshold strategy fails to effectively capture the vertical structural changes in precipitation, resulting in low utilization of elevation resources. Taking the CINRAD/SAD radar as an example, the switching threshold between the two precipitation modes is set too low, resulting in a median effective precipitation echo proportion of only 27.13% in the newly added elevation layers of VCP11 (5.2°, 7.5°, 8.7°, 12°, and 16.7°), thus failing to fully leverage the advantages of vertical dense observations. Conversely, the threshold for switching from precipitation to clear-air mode is set too high, resulting in a median of 15.66% of precipitation echoes remaining in the missing elevation layers (6.0°, 9.9°, 14.6°, 19.5°) of VCP31 after switching. (3) To address the above issues, considering the limited computational capacity of the Radar Data Acquisition (RDA) subsystem, this paper proposes a simple switching indicator based on the spatial proportion of echoes in common elevation layers. Comparative verification shows that this indicator significantly improves the accuracy of mode switching and the utilization of elevation resources. This study provides a scientific basis for optimizing the observation strategies of China’s new-generation weather radars and enhancing the capability of severe weather monitoring and early warning.

Abstract:This study investigates the decadal modulation of ENSO’s impact on intraseasonal summer precipitation over eastern China. During 1991–2024, early summer (June) following an El Ni?o event tends to see increased precipitation over the middle and lower reaches of the Yangtze River and regions to the south, whereas late summer (August) experiences a decrease in these same regions. This inverse-phase relationship underwent a significant decadal shift around the early 2000s. A comparison between the periods 1991–2003 and 2004–2024 reveals that in the latter period, the relationship between ENSO and precipitation in August of the following year in the middle and lower reaches of the Yangtze River and regions to the south weakened, making it less likely for widespread negative precipitation anomalies to occur in this region. By examining the concurrent changes in sea surface temperature (SST) and atmospheric circulation associated with ENSO events, this study finds that over the western North Pacific in August 2004–2024, weakened easterly anomalies lead to reduced latent heat flux from evaporation, resulting in a shift from negative to positive SST anomalies. Meanwhile, positive SST anomalies over the northern Indian Ocean are comparatively weaker. Consequently, the anomalous anticyclone over the western North Pacific is unable to persist and transitions into a cyclonic circulation, which is detrimental to precipitation deficits over the middle and lower reaches of the Yangtze River and adjacent southern regions. In terms of circulation configuration, at the 500 hPa level, the East Asia–Pacific/Pacific–Japan teleconnection pattern shifts from a “+-” to a “-+” pattern in August 2004–2024; at the 200 hPa level, the East Asian subtropical westerly jet weakens. Both changes are unfavorable for precipitation deficits over the middle and lower reaches of the Yangtze River and southern regions. This research clarifies the decadal modulation of ENSO’s influence on intraseasonal summer precipitation in eastern China, providing a theoretical basis for improving intraseasonal precipitation prediction.

Abstract:Aiming at the problems that the existing evaluation methods of the Shenyang vertical observation system are imperfect and mostly rely on single-equipment data, and the insufficient exploration of the synergistic application value of multi-source vertical observation data in the monitoring and early warning of severe convective weather under cold vortex conditions. This study intends to establish a multi-element data quality evaluation system suitable for the cold vortex circulation background. Meanwhile, it explores the monitoring performance of multi-device collaborative observations during cold-vortex severe convective rainstorms, so as to further enhance the regional three-dimensional vertical monitoring and nowcasting capabilities for cold vortex weather. Based on the observation data from the Shenyang Ground-based Remote Sensing Vertical Observation Comprehensive Station, this paper takes the extremely heavy regional rainstorm process in Liaoning from July 23 to 28, 2024 as an example, combines radiosonde observation data, establishes a multi-element data quality evaluation system for vertical observation suitable for the cold vortex background, and carries out systematic evaluation and application analysis. The results show that: (1) The operational availability of all five types of equipment in the vertical observation system reaches 100%, and the data quality meets the requirements of operational application and analysis; (2) Multi-element fusion analysis reveals that affected by the Northeast cold vortex, water vapor transport from the periphery of Typhoon Gaemi, and the subtropical high, the rapid accumulation of water vapor and the vigorous vertical development of cloud systems, combined with vertical temperature difference, low-level convergence and upward movement, as well as mid-level jet momentum transport, jointly trigger the severe convection process; (3) In terms of forecast and early warning, the wind profile radar can indicate changes in dynamic conditions 6-12 hours in advance; the microwave radiometer combined with GNSS/MET observation can capture water vapor and precursor signals 1.5 hours in advance; the millimeter-wave cloud radar can monitor the imminent development characteristics of convection 30-60 minutes in advance. The study shows that the established multi-element quality evaluation system can effectively support the data quality evaluation and application analysis of the vertical observation system under the cold vortex background, and the multi-equipment collaborative observation shows significant advantages in the identification of dynamic triggers, tracking of water vapor evolution, and imminent early warning of severe convective weather, providing important technical support for the refined monitoring and short-term forecasting of severe convective weather in Liaoning.

Abstract:Reanalysis products hold significant application value in extreme weather risk assessment, renewable energy planning, climate change research, and artificial intelligence. Systematic evaluation of the accuracy and applicability of 2m temperature reanalysis products is a prerequisite for their reliable utilization in scientific investigations. This study employs hourly 2m temperature observations from Chinese automatic weather stations (2021–2022) to evaluate three reanalysis products—CMA Regional Re-Analysis (CMA-RRA), ECMWF Reanalysis v5 (ERA5), and Modern-Era Retrospective Analysis for Research and Applications, v2 (MERRA2). Results indicate that: Summer high-temperatures are concentrated in the Yangtze-Huai River Basin (June) and South China and Sichuan Basin regions (July–August),with 2022 high-temperature intensity surpassing 2021 levels. CMA-RRA exhibiting the smallest RMSE (<2°C) and highest correlation coefficient (R≥0.9). For Sichuan Basin extreme high-temperatures, CMA-RRA effectively captures diurnal variations and spatial patterns, achieving the highest Critical Success Index. While ERA5 shows premature heat onset timing and spatial overestimation, and MERRA2 systematically underestimates both intensity and duration of high-temperature events.

Abstract:Based on daily precipitation data from 126 national meteorological stations in Yunnan Province from June 2023 to June 2025, this study evaluated the applicability of Fengyun-4B (FY-4B) quantitative precipitation estimation (QPE) daily precipitation products over Yunnan Province using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI), and compared FY-4B QPE with Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) and climate hazards group infrared precipitation with station data (CHIRPS) products. The results show that the precipitation detection capability of FY-4B QPE is superior to that of GPM IMERG and CHIRPS, with the highest CSI (0.44) and the lowest FAR (0.44). However, it exhibits a certain degree of precipitation underestimation (RB=?0.15). In comparison, GPM IMERG has the highest FAR (0.53), and CHIRPS shows a relatively low POD (0.41). FY-4B QPE exhibits a pattern of higher precipitation over the southwest and lower precipitation over the east of Yunnan Province. The precipitation detection capability in southwestern and southern Yunnan is superior to that in the central and eastern regions. The southwestern and southern areas, covering approximately 31% of the province, have an average CSI that is 0.14 higher than that of the central and eastern regions. The 500 to 1000 m elevation range is the optimal application area for FY-4B QPE. FY-4B QPE performs best under light rain, with pronounced underestimation under heavy precipitation. FY-4B QPE is sensitive to seasonal variations, with strong detection capability but a tendency toward underestimation in summer, while overall accuracy decreases somewhat in winter. The evaluation results can provide a scientific reference for the research and operational applications of FY-4B QPE for precipitation monitoring in complex terrain areas of Yunnan Province.

Abstract:Based on the C-band dual-polarization radar data from the Wutaishan Cloud Physics Experimental Base in Shanxi Province and the operational data of artificial rain enhancement, a fuzzy-logic hydrometeor classification algorithm was used to analyze the full-stage microphysical characteristics of a ground-based rocket rain enhancement operation during a mixed rain event on 22 May 2025. The results show that after the seeding of silver iodide (AgI) ice nuclei, ice crystal nuclei formed through heterogeneous nucleation, and the growth of ice-phase particles was promoted through the Bergeron process and collision-coalescence process. During the main impact stage of rain enhancement, ice crystals and wet snow particles showed an inverse relationship in number variation. Snow aggregate particles were mainly produced by the collisional growth of ice crystals, accounting for 38.9% of the total number and becoming the dominant ice-phase hydrometeors, while high-density graupel accounted for only 3.19%. Wet snow particles were mainly derived from aggregate particles above the 0degrees Celsius level, and their generation region was located at 3.6-4.1 km altitude. They were also an important source of the bright band near the 0degrees Celsius level. Based on the differences before and after AgI entered the cloud, a three-stage conceptual model of cloud microphysical evolution was established, including AgI ice nuclei seeded at the supercooled water accumulation zone, ice formation and aggregation growth, and mixed-phase transition near the 0degrees Celsius level with the formation of raindrop.

Abstract:Based on the observation data from 126 national meteorological stations in Yunnan Province from 2016 to 2025 and and ERA5 reanalysis data for the same period, the spatial distribution characteristics of regional heavy fog in Yunnan were diagnosed and analyzed using Rotated Empirical Orthogonal Function (REOF), and fog zoning was carried out combined with topographic factors. Furthermore, machine learning methods such as random forest were employed to establish zonal heavy fog forecasting models, and the results were compared and verified with SCMOC and ECMWF numerical forecast products. The results show that: 1)Heavy fog in Yunnan Province exhibits four dominant spatial modes: an out-of-phase pattern between the southwest and the rest of the region, southeastern Yunnan, northeastern Yunnan, and northwestern Yunnan. Combined with topography, the province is finally divided into four fog zones: Zone 1 (western and northwestern Yunnan), Zone 2 (northeastern and central Yunnan), Zone 3 (southwestern and southern Yunnan), and Zone 4 (southeastern Yunnan). 2)The random forest model achieves an accuracy of 0.82~0.84 for modeling samples and 0.80~0.83 for test samples in each zone, which is significantly superior to decision tree, K-nearest neighbor, logistic regression, and Bayesian methods. 3)There are regional differences in the importance of forecast factors in each fog zone. High-altitude areas (Zone 1 and Zone 2) are strongly dependent on water vapor conditions at 600—700 hPa. Regions significantly affected by eastern industrial activities (Zone 2 and Zone 4) are largely contributed by initial visibility. In relatively low-altitude areas (Zone 3 and Zone 4), the importance of sea level pressure and temperature factors within the boundary layer is more prominent. 4)The comprehensive performance of the short-range heavy fog forecast model established based on random forest is superior to those of two mainstream forecast products, SCMOC and ECMWF. The improvement in forecast effect is more significant in autumn and winter, and Regions 3 and 4 exhibit higher forecast accuracy.. For a regional heavy fog event that lasted two consecutive days, the TS scores of the random forest model are 0.38 and 0.41, which are significantly higher than those of SCMOC (both < 0.1) and ECMWF (0.26 and 0.11, respectively).