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- Time-Series Embeddings from Language Models: A Tool for Wind Direction
Nowcasting-
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Abstract: Abstract Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo (Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u (east–west) and v (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for 20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology.
PubDate: 2024-06-01
- Character of Convective Systems Producing Short-Term Heavy Precipitation
in Central China Revealed by Kilometer and Minute Interval Observations-
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Abstract: Abstract Accurate forecasting of heavy precipitation in central China is still a challenge, within which a key issue is our still incomplete understanding of the convective systems (CSs) responsible for such events. In this study, through use of an iterative rain-cell tracking algorithm, the macroscale characteristics (scale, intensity, duration, etc.) of the CSs that produced 595 short-term heavy precipitation events in Hunan Province, central China, are quantitatively analyzed, based on radar reflectivity, echo top, and rainfall observations at 1-km and 6-min intervals in April–September of 2016–2018. The results show that CSs present significant seasonal and diurnal features. Spring CSs usually cover a larger echo area with stronger convective cores and thus generate more precipitation than summer CSs, though summer CSs develop more vigorously and frequently. CSs initiated at 1400–1600 local time are characterized by the strongest convection and a smaller spatiotemporal scale, causing violent and transient showers with typical areal precipitation of 0.5–1 mm km−2, but less total precipitation. Further analyses of the relationships among the scale, intensity, duration, and total precipitation of CSs reveal that the convective intensity is linearly correlated to the spatiotemporal scale of CSs, with the duration increasing on average by 0.0372 h dBZ−1; the echo area is significantly correlated to the total precipitation, and the duration and rainfall amount are connected with the area expansion rate (AER) of CSs: when the AER exceeds 50%, CSs expand rapidly with increasing total precipitation, but the duration is shorter. These findings provide a helpful reference for the forecasting of short-term heavy precipitation induced by CSs in central China.
PubDate: 2024-06-01
- A 10-yr Rainfall and Cloud-to-Ground Lightning Climatology over Coastal
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Abstract: Abstract A comparative analysis of the spatiotemporal distribution characteristics of rainfall and lightning in coastal and inland areas of Guangdong Province of China during the pre-summer rainy season (PSRS) from 2008 to 2017 reveals distinct patterns. In the inland target region (ITR), rainfall is concentrated in the central and eastern mountainous areas. It exhibits a bimodal diurnal variation, with peaks in the afternoon and morning. The afternoon peak becomes more pronounced during the post-monsoon-onset period because of the increased rainfall frequency. Similarly, in the coastal target region (CTR), rainfall concentrates around mountainous peripheries. However, CTR’s rainfall is weaker than ITR’s during the pre-monsoon-onset period, primarily associated with the lower-level moisture outflow in CTR, but it strengthens significantly during the post-monsoon-onset period owing to enhanced moisture inflow. CTR’s diurnal rainfall variation transitions from bimodal to a single broad peak during the post-monsoon-onset period, influenced by changes in both rainfall frequency and intensity. In contrast to rainfall, the spatiotemporal distribution of lightning centers remains relatively stable during the PSRS. The strongest center is located over ITR’s plains west of the rainfall center, with a secondary center in the western plains of CTR. Lightning activity significantly increases during the post-monsoon-onset period, particularly in ITR, primarily because of the increased lightning hours. The diurnal lightning flash density and lightning hours show a single afternoon peak in the two target regions, and the timing of the peak in ITR is approximately two hours later than in CTR. Composite circulation analysis indicates that during early morning, the lower atmosphere is nearly neutral in stratification. The advected warm, moist, unstable airflow, combined with topography, favors convection initiation. In the afternoon, solar radiation increases thermal instability, further enhancing the convection frequency and intensity. Improved moisture and thermal conditions contribute to an increase in rainfall and lightning during the post-monsoon-onset period. Moreover, the occurrence of lightning is found to be closely linked to the most unstable convective available potential energy, low-level vertical wind shear, and updraft intensity.
PubDate: 2024-06-01
- Raindrop Size Distributions in the Zhengzhou Extreme Rainfall Event on 20
July 2021: Temporal–Spatial Variability and Implications for Radar QPE-
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Abstract: Abstract In this study, a regional Parsivel OTT disdrometer network covering urban Zhengzhou and adjacent areas is employed to investigate the temporal–spatial variability of raindrop size distributions (DSDs) in the Zhengzhou extreme rainfall event on 20 July 2021. The rain rates observed by disdrometers and rain gauges from six operational sites are in good agreement, despite significant site-to-site variations of 24-h accumulated rainfall ranging from 198.3 to 624.1 mm. The Parsivel OTT observations show prominent temporal–spatial variations of DSDs, and the most drastic change was registered at Zhengzhou Station where the record-breaking hourly rainfall of 201.9 mm over 1500–1600 LST (local standard time) was reported. This hourly rainfall is characterized by fairly high concentrations of large raindrops, and the mass-weighted raindrop diameter generally increases with the rain rate before reaching the equilibrium state of DSDs with the rain rate of about 50 mm h−1. Besides, polarimetric radar observations show the highest differential phase shift (Kdp) and differential reflectivity (Zdr) near surface over Zhengzhou Station from 1500 to 1600 LST. In light of the remarkable temporal–spatial variability of DSDs, a reflectivity-grouped fitting approach is proposed to optimize the reflectivity–rain rate (Z–R) parameterization for radar quantitative precipitation estimation (QPE), and the rain gauge measurements are used for validation. The results show an increase of mean bias ratio from 0.57 to 0.79 and a decrease of root-mean-square error from 23.69 to 18.36 for the rainfall intensity above 20.0 mm h−1, as compared with the fixed Z–R parameterization. This study reveals the drastic temporal–spatial variations of rain microphysics during the Zhengzhou extreme rainfall event and warrants the promise of using reflectivity-grouped fitting Z–R relationships for radar QPE of such events.
PubDate: 2024-06-01
- Quantitative Applications of Weather Satellite Data for Nowcasting:
Progress and Challenges-
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Abstract: Abstract Monitoring and predicting highly localized weather events over a very short-term period, typically ranging from minutes to a few hours, are very important for decision makers and public action. Nowcasting these events usually relies on radar observations through monitoring and extrapolation. With advanced high-resolution imaging and sounding observations from weather satellites, nowcasting can be enhanced by combining radar, satellite, and other data, while quantitative applications of those data for nowcasting are advanced through using machine learning techniques. Those applications include monitoring the location, impact area, intensity, water vapor, atmospheric instability, precipitation, physical properties, and optical properties of the severe storm at different stages (pre-convection, initiation, development, and decaying), identification of storm types (wind, snow, hail, etc.), and predicting the occurrence and evolution of the storm. Satellite observations can provide information on the environmental characteristics in the preconvection stage and are very useful for situational awareness and storm warning. This paper provides an overview of recent progress on quantitative applications of satellite data in nowcasting and its challenges, and future perspectives are also addressed and discussed.
PubDate: 2024-06-01
- Sub-Seasonal Predictability of the Northeast China Cold Vortex in BCC and
ECMWF S2S Model Forecasts for 2006–2021-
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Abstract: Abstract As an important atmospheric circulation system in the mid–high latitudes of East Asia, the Northeast China cold vortex (NCCV) substantially influences weather and climate in this region. So far, systematic assessment on the performance of numerical prediction of the NCCVs has not been carried out. Based on the Beijing Climate Centre (BCC) and the ECMWF model hindcast and forecast data that participated in the Sub-seasonal to Seasonal (S2S) Prediction Project, this study systematically examines the performance of both models in simulating and forecasting the NCCVs at the sub-seasonal timescale. The results demonstrate that the two models can effectively capture the seasonal variations in the intensity, active days, and spatial distribution of NCCVs; however, the duration of NCCVs is shorter and the intensity is weaker in the models than in the observations. Diagnostic analysis shows that the differences in the intensity and location of the East Asian subtropical westerly jet and the wave train pattern from North Atlantic to East Asia may be responsible for the deficient simulation of NCCV events in the S2S models. Nonetheless, in the deterministic forecasts, BCC and ECMWF provide skillful prediction on the anomalous numbers of NCCV days and intensity at a lead time of 4–5 (5–6) pentads, and the skill limit of the ensemble mean is 1–2 pentads longer than that of individual members. In the probabilistic forecasts of daily NCCV activities, BCC and ECMWF exhibit a forecasting skill of approximately 7 and 11 days, respectively; both models show seasonal dependency in the simulation performance and forecast skills of NCCV events, with better performance in winter than in summer. The results from this study provide helpful references for further improvement of the S2S prediction of NCCVs.
PubDate: 2024-06-01
- Improved Simulation of Summer Heavy Rainfall over Beijing and Henan by the
YHGSM with Updated Subgrid Orographic Parameters-
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Abstract: Abstract In numerical weather prediction (NWP), the parameterization of orographic drag plays an important role in representing subgrid orographic effects. The subgrid orographic parameters are the key input to the parameterization of orographic drag. Currently, the subgrid orographic parameters in most NWP models were produced based on elevation datasets generated many years ago, with a coarse resolution and low quality. In this paper, using the latest high-quality elevation data and considering the applicable scale range of the subgrid orographic parameters, we construct the orographic parameters, including the subgrid orographic standard deviation, anisotropy, orientation, and slope, that are required as input to the orographic gravity wave drag (OGWD) parameterization. Finally, we introduce the newly constructed orographic parameters into the Yin-He Global Spectral Model (YHGSM), optimize the description of the orographic effect in the model, and improve the simulation of two typical heavy rainfall events in Beijing and Henan.
PubDate: 2024-06-01
- Improving the Forecasts of Coastal Wind Speeds in Tianjin, China Based on
the WRF Model with Machine Learning Algorithms-
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Abstract: Abstract Characterized by sudden changes in strength, complex influencing factors, and significant impacts, the wind speed in the circum-Bohai Sea area is relatively challenging to forecast. On the western side of Bohai Bay, as the economic center of the circum-Bohai Sea, Tianjin exhibits a high demand for accurate wind forecasting. In this study, three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast (WRF) model for Tianjin. The results showed that the random forest (RF) achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost, while the support vector machine (SVM) performed slightly worse (especially for stronger winds), but it required an approximately 15 times longer computing time. The back propagation (BP) neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the RMSE. In regard to wind speed frequency forecasting, the RF method commendably corrected the forecasts of the frequency of moderate (force 3) wind speeds, while the BP method showed a desirable capability for correcting the forecasts of stronger (force > 6) winds. In addition, the 10-m u and v components of wind (u10 and v10), 2-m relative humidity (RH2) and temperature (T2), 925-hPa u (u925), sea level pressure (SLP), and 500-hPa temperature (T500) were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin, indicating the importance of local dynamical/thermodynamic processes in regulating the wind speed. This study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.
PubDate: 2024-06-01
- Cloud Microphysical Characteristics of Typhoon Meranti (2016) during Its
Rapid Intensification: Model Validation and SST Sensitivity Experiments-
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Abstract: Abstract Cloud microphysics plays an important role in determining the intensity and precipitation of tropical cyclones (TCs). In this study, a high-resolution numerical simulation by WRF (version 4.2) of Typhoon Meranti (2016) during its rapid intensification (RI) period was conducted and validated by multi-source observations including Cloud-Sat and Global Precipitation Mission satellite data. The snow and ice particles content were found to increase most rapidly compared with other hydrometeors during the RI process. Not all hydrometeors continued to increase. The graupel content only increased in the initial RI stage, and then decreased afterwards due to precipitation during the RI process. In addition, sea surface temperature (SST) sensitivity experiments showed that, although the intensity of the TC increased with a higher SST, not all hydrometeors increased. The graupel content continued to increase with the increase in SST, mainly due to the accumulation of more lower-temperature supercooled water vapor at the corresponding height. The content of snow decreased with the increase in SST because stronger vertical motion at the corresponding height affected the aggregation of ice crystals.
PubDate: 2024-06-01
- Differences in Variations of Long-Lived and Short-Lived Summer Heat Waves
during 1981–2020 over Eastern China and Their Corresponding Large-Scale
Circulation Anomalies-
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Abstract: Abstract Using daily maximum temperature (Tmax) data from 516 observation stations in eastern China from 1981 to 2020, this study employed a relative threshold method to define short- and long-lived heat waves (HWs) by considering regional climate differences to investigate the spatial characteristics and evolution of large-scale circulation during summer HWs. The results demonstrated spatial disparities in the frequency distribution of HWs of different durations and differences in the magnitude of duration and intensity between short- and long-lived HWs. Empirical orthogonal function analysis revealed three dominant spatial modes for both short- and long-lived HWs. The first mode showed that short-lived HWs occur prominently in both northern and southern regions, whereas long-lived HWs mainly occur in the northern region. The second mode was characterized by a meridional dipole pattern in both cases. The third mode exhibited a quadrupole pattern for short-lived HWs and a tripole pattern for long-lived HWs. Differences in the center locations of anomalies in the 500-hPa geopotential height and 850-hPa wind fields significantly influenced the temperature and precipitation anomaly distribution of typical HWs by affecting the warm column in the lower troposphere, cloud distribution, and moisture transport. Moreover, the atmospheric circulation evolution processes of typical HWs associated with the different modes of long- and short-lived HWs were linked to distinct teleconnection patterns. During the three modes of long-lived (short-lived) HWs, there was stronger (weaker) wave flux activity with multiple (single) propagation paths. Stronger westward Atlantic wave train activity at 300 hPa triggered the synergistic action of meridional and zonal wave fluxes, favoring the strengthening and maintenance of positive anomalies in geopotential height of 500 hPa. This may have contributed to the formation of long-lived HWs. These findings provide valuable insights to enhance our understanding and prediction of summer HWs.
PubDate: 2024-06-01
- The Observed Near-Surface Energy Exchange Processes over Arctic Glacier in
Summer-
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Abstract: Abstract Under Arctic warming, near-surface energy transfers have significantly changed, but few studies have focused on energy exchange over Arctic glacier due to limitations in available observations. In this study, the atmospheric energy exchange processes over the Arctic glacier surface were analyzed by using observational data obtained in summer 2019 in comparison with those over the Arctic tundra surface. The energy budget over the glacier greatly differed from that over the tundra, characterized by less net shortwave radiation and downward sensible heat flux, due to the high albedo and icy surface. Most of the incoming solar radiation was injected into the glacier in summer, leading to snow ice melting. During the observation period, strong daily variations in near-surface heat transfer occurred over the Arctic glacier, with the maximum downward and upward heat fluxes occurring on 2 and 6 July 2019, respectively. Further analyses suggested that the maximum downward heat flux is mainly caused by the strong local thermal contrast above the glacier surface, while the maximum upward heat transfer cannot be explained by the classical turbulent heat transfer theory, possibly caused by countergradient heat transfer. Our results indicated that the near-surface energy exchange processes over Arctic glacier may be strongly related to local forcings, but a more in-depth investigation will be needed in the future when more observational data become available.
PubDate: 2024-06-01
- On the Shallowing of Antarctic Low-Level Temperature Inversions Projected
by CESM-LE under RCP8.5-
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Abstract: Abstract Temperature inversions are frequently observed in the boundary layer and lower troposphere of polar regions. Future variations of the low-level temperature inversions in these regions, especially the Antarctic, are still poorly understood. Due to the scarcity of observations in the Antarctic, reanalysis data and numerical simulations are often used in the study of Antarctic climate change. Based on ERA-Interim, ERA5, JRA-55, and NCEP–NCAR reanalysis products, this study examines temporal and spatial variations of Antarctic inversion depth in austral autumn and winter during 1979–2020. Deeper inversions are found to occur over the high plateau areas of the Antarctic continent. Based on the Mann–Kendall test, ERA-Interim and ERA5 data reveal that the Antarctic inversion depth in austral autumn and winter increased during 1992–2007, roughly maintained afterwards, and then significantly decreased since around 2016. The decrease trend is more obvious in the last two months of winter. Overall, JRA-55 better represents the spatial distribution of inversion depth, and ERA-Interim has better interannual variability. The Community Earth System Model Large Ensemble (CESM-LE) 30-member simulations in 1979–2005 were first verified against JRA-55, showing reasonable consistency, and were then used to project the future changes of Antarctic low-level inversion depth over 2031–2050 under RCP8.5. The CESM-LE projection results reveal that the temperature inversion will shallow in the Antarctic at the end of the 21st century, and the decrease in depth in autumn will be more pronounced than that in winter. In particular, the temperature inversion will weaken over the ice-free ocean, while it will remain stable over the ice sheet, showing certain spatial heterogeneity and seasonal dependence on the underlying cryospheric surface conditions. In addition, the decrease of inversion depth is found closely linked with the reduction in sea ice, suggesting the strong effect of global warming on the thermal structure change of the Antarctic.
PubDate: 2024-06-01
- Enhancement and Removal of PM2.5 by Cold and Warm Air Masses Accompanying
Certain Synoptic Weather Systems across Hangzhou, China during 2014–2020
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Abstract: Abstract Local air pollution is strongly affected by synoptic weather systems, such as fronts, troughs, low-altitude vortices, or high-altitude ridges. Nevertheless, few studies have analyzed the meteorological properties of cold or warm air masses associated to these systems and their impact on local air quality. In this study, hourly observations of fine particulate matter (diameter of up to 2.5 µm, i.e., PM2.5), wind (V), temperature (T), pressure (P), and precipitation (R), acquired in Hangzhou in 2014–2020, were analyzed. From this analysis, weather patterns were categorized into 27 types; 89 and 94 cases illustrating the passage of warm and cold air masses over Hangzhou were identified, respectively; the influence of air mass temperature, wind speed, and wind direction on PM2.5 concentrations and local accumulation or removal was quantified. The main results are as follows. (1) Pollution events occurred more frequently for cold than for warm air masses, but average pollutant concentration was lower for cold air masses; (2) 48% of the cold air mass cases corresponded to PM2.5 decreases and 52% to PM2.5 increases, with strong cold air masses (ΔT24h > 4°C; ∣V∣average > 4 m s−1) markedly reducing local pollution, but weak cold air masses (ΔT24h < 2°C; ∣V∣average < 2 m s−1) primarily inducing pollutant transport and accumulation; (3) for warm air masses, PM2.5 accumulation or removal occurred in 60% and 40% of the cases, respectively: warm air masses (ΔT24h > 4°C) reduced the PM2.5 concentration whereas weaker winds (∣V∣average < 2 m s−1) increased it; and (4) PM2.5 concentration decreased sharply within 4 h after the passage of strong cold air masses, but more gradually within 14 h after the passage of strong warm air masses. These results considerably improve the current understanding of the influence of cold and warm air masses on local pollution patterns.
PubDate: 2024-06-01
- Variations in Column Concentration of Greenhouse Gases in China and Their
Response to the 2015–2016 El Niño Event-
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Abstract: Abstract Since the industrial revolution, enhancement of atmospheric greenhouse gas concentrations as a result of human activities has been the primary cause of global warming. The monitoring and evaluation of greenhouse gases are significant prerequisites for carbon emission control. Using monthly data of global atmospheric carbon dioxide (CO2) and methane (CH4) column concentrations (hereinafter XCO2 and XCH4, respectively) retrieved by the Greenhouse Gas Observation Satellite (GOSAT), we analyzed the variations in XCO2 and XCH4 in China during 2010–2022 after confirming the reliability of the data. Then, the influence of a strong El Niño event in 2015–2016 on XCO2 and XCH4 variations in China was further studied. The results show that the retrieved XCO2 and XCH4 from GOSAT have similar temporal variation trends and significant correlations with the ground observation and emission inventory data of an atmospheric background station, which could be used to assess the variations in XCO2 and XCH4 in China. XCO2 is high in spring and winter while XCH4 is high in autumn. Both XCO2 and XCH4 gradually declined from Southeast China to Northwest and Northeast China, with variation ranges of 401–406 and 1.81–1.88 ppmv, respectively; and the high value areas are located in the middle–lower Yangtze River basin. XCO2 and XCH4 in China increased as a whole during 2010–2022, with rapid enhancement and high levels of XCO2 and XCH4 in several areas. The significant increases in XCO2 and XCH4 over China in 2016 might be closely related to the strong El Niño–Southern Oscillation (ENSO) event during 2015–2016. Under a global warming background in 2015, XCO2 and XCH4 increased by 0.768% and 0.657% in 2016 in China. Data analysis reveals that both the XCO2 and XCH4 variations might reflect the significant impact of the ENSO event on glacier melting in the Tibetan Plateau.
PubDate: 2024-06-01
- The Effect of Moisture in Different Altitude Layers on the Eastward
Propagation of MJO-
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Abstract: Abstract In this study, driven by ERA5 reanalysis data, the Weather Research and Forecasting (WRF) version 4.0 was used to investigate the eastward propagation of the Madden–Julian oscillation (MJO) in the tropical atmosphere during December–February (DJF) of 2007/2008. The experiment with 11 cumulus parameterization schemes respectively shows that the Grell 3D scheme is one of several worse ones in describing MJO activities. In addition, still by use of the Grell 3D scheme, four nudging assimilation experiments for water vapor in all model vertical layers (Ndg_all), lower layers (Ndg_low), middle layers (Ndg_mid), and upper layers (Ndg_upp) were conducted. It is found that when the water vapor in the model approaches to the observed value, the model performance for MJO activities is improved greatly. Among the four nudging simulations, Ndg_all certainly performs best. Although Ndg_mid is important for the MJO-filtered profiles related to moisture, Ndg_low and Ndg_upp exhibit superiority to Ndg_mid in simulating MJO eastward propagation. Ndg_low has advantages when MJO features are represented by zonal wind at 850 hPa and precipitation because the lower-level MJO-filtered moisture is conducive to the existence of lower-level condensational heating to the east of the MJO convective center. Ndg_upp performs better when describing the MJO eastward propagation features by outgoing longwave radiation (OLR) since it can capture the moisture and cloud top temperature of deep convection associated with MJO, as well as front Walker cell. These results suggest that the lower-level moisture is more important in regulating the MJO eastward propagation, and the observed maximum MJO-filtered moisture in the middle troposphere might be a phenomenon accompanying the MJO deep convection, but not a factor controlling its eastward propagation.
PubDate: 2024-06-01
- Tropical Cyclone Monitoring and Analysis Techniques: A Review
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Abstract: Abstract Tropical cyclones (TCs), including tropical depressions and different categories of typhoons, hurricanes, and cyclonic storms, mostly originate over the oceans in the absence of direct observations. Thus, detailed monitoring and analysis of TCs has always been an unsolved problem. In the recent 20 years, great changes have taken place in domestic and foreign TC monitoring techniques, imposing a significant impact on TC operations and research. Some new technologies and products gradually emerge to support operations, with improved monitoring accuracy. In this paper, the progress on TC monitoring and analysis via meteorological satellites, radars, and airplanes in China and the world is reviewed, compared, and summarized, with special focuses on multisatellite fusion observations, in situ aircraft measurements, and some unconventional observation equipment such as rockets, saildrones, and underwater gliders. On this basis, the paper points out future directions for improving TC monitoring and analysis in aid of better TC forecast and early warning.
PubDate: 2024-04-01
DOI: 10.1007/s13351-024-3135-9
- The Short-Duration Heavy Rainfall in Different Quadrants of Northeast
China Cold Vortices-
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Abstract: Abstract The Northeast China cold vortex (NCCV) is one of the main synoptic-scale systems causing short-duration heavy rainfall (SDHR) in Northeast China. Environmental conditions (e.g., water vapor, instability, and vertical wind shear) are known to be distinctly different over the four quadrants of NCCVs, rendering prediction of the SDHR related to NCCVs (NCCV_SDHR) more challenging. Based on 5-yr hourly rainfall observations from 3196 automatic weather stations and ERA5 reanalysis data, 10,232 NCCV_SDHR events were identified and divided into four quadrant groups according to their relative position to the center of the NCCV (CVC). The results show that the southeast quadrant features the highest frequency of SDHR, with stronger intensity, longer duration, and wider coverage; and the SDHR in different quadrants presents different formation mechanisms and varied temporal evolution. A new coordinate system is established relative to the CVC that uses the CVC as the origin and the radius of the NCCV (rCV) as the unit distance. In this new coordinate system, all of the NCCV_SDHR events in the 5-yr study period are synthesized. It is found that the occurrence frequency of NCCV_SDHR initially increases and then decreases with increasing distance from the CVC. The highest frequency occurs mainly between 0.8 and 2.5 times rCV from the CVC in the southeast quadrant. This can be attributed to the favorable conditions, such as convergence of the low-level shear line and abundant water vapor, which are concentrated in this region. Furthermore, high-frequency NCCV_SDHR larger than 50 mm (NCCV_SDHR50) is observed to be closer to the CVC. When NCCV_SDHR50 occurs, the NCCV is in closer proximity to the subtropical high, resulting in stronger low-level convergence and more abundant water vapor. Additionally, there are lower lifting condensation levels and stronger 0–6- and 0–1-km vertical wind shears in these environments. These findings provide a valuable reference for more accurate prediction of NCCV_SDHR.
PubDate: 2024-04-01
DOI: 10.1007/s13351-024-3055-8
- Predicting PM2.5 Concentration in the Yangtze River Delta Region Using
Climate System Monitoring Indices and Machine Learning-
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Abstract: Abstract Changing meteorological conditions during autumn and winter have considerable impact on air quality in the Yangtze River Delta (YRD) region. External climatic factors, such as sea surface temperature and sea ice, together with the atmospheric circulation, directly affect meteorological conditions in the YRD region, thereby modulating the variation in atmospheric PM2.5 concentration. This study used the evolutionary modeling machine learning technique to investigate the lag relationship between 144 climate system monitoring indices and autumn/winter PM2.5 concentration over 0–12 months in the YRD region. After calculating the contribution ratios and lagged correlation coefficients of all indices over the previous 12 months, the top 36 indices were selected for model training. Then, the nine indices that contributed most to the PM2.5 concentration in the YRD region, including the decadal oscillation index of the Atlantic Ocean and the consistent warm ocean temperature index of the entire tropical Indian Ocean, were selected for physical mechanism analysis. An evolutionary model was developed to forecast the average PM2.5 concentration in major cities of the YRD in autumn and winter, with a correlation coefficient of 0.91. In model testing, the correlation coefficient between the predicted and observed PM2.5 concentrations was in the range of 0.73–0.83 and the root-mean-square error was in the range of 9.5–11.6 µg m−3, indicating high predictive accuracy. The model performed exceptionally well in capturing abnormal changes in PM2.5 concentration in the YRD region up to 50 days in advance.
PubDate: 2024-04-01
DOI: 10.1007/s13351-024-3099-9
- Effect of Meteorological Data Assimilation on Regional Air Quality
Forecasts over the Korean Peninsula-
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Abstract: Abstract The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a type of online coupled chemistry-meteorology model (CCMM), considers the interaction between air quality and meteorology to improve air quality forecasting. Meteorological data assimilation (DA) can be used to reduce uncertainty in meteorological field, which is one factor causing prediction uncertainty in the CCMM. In this study, WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula. The nesting model domains were configured over East Asia (outer domain) and the Korean Peninsula (inner domain). Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA. When the meteorological DA was performed in the outer domain or both the outer and inner domains, the root-mean-square error (RMSE), bias of the predicted particulate matter (PM) concentrations, and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain. This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain, subsequently improving air quality and meteorological predictions. Compared to the experiment without meteorological DA, the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA. The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58–66 h, depending on the case. Therefore, the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.
PubDate: 2024-04-01
DOI: 10.1007/s13351-024-3152-8
- Advances in Atmospheric Radiation: Theories, Models, and Their
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Abstract: Abstract The subject of “atmospheric radiation” includes not only fundamental theories on atmospheric gaseous absorption and the scattering and radiative transfer of particles (molecules, cloud, and aerosols), but also their applications in weather, climate, and atmospheric remote sensing, and is an essential part of the atmospheric sciences. This review includes two parts (Part I and Part II); following the first part on gaseous absorption and particle scattering, this part (Part II) reports the progress that has been made in radiative transfer theories, models, and their common applications, focusing particularly on the contributions from Chinese researchers. The recent achievements on radiative transfer models and methods developed for weather and climate studies and for atmospheric remote sensing are firstly reviewed. Then, the associated applications, such as surface radiation estimation, satellite remote sensing algorithms, radiative parameterization for climate models, and radiative-forcing related climate change studies are summarized, which further reveals the importance of radiative transfer theories and models.
PubDate: 2024-04-01
DOI: 10.1007/s13351-024-3089-y
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