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  Subjects -> METEOROLOGY (Total: 106 journals)
Showing 1 - 36 of 36 Journals sorted alphabetically
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 4)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 45)
Advances in Climate Change Research     Open Access   (Followers: 49)
Advances in Meteorology     Open Access   (Followers: 27)
Advances in Statistical Climatology, Meteorology and Oceanography     Open Access   (Followers: 11)
Aeolian Research     Hybrid Journal   (Followers: 7)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 20)
American Journal of Climate Change     Open Access   (Followers: 36)
Atmósfera     Open Access   (Followers: 2)
Atmosphere     Open Access   (Followers: 33)
Atmosphere-Ocean     Full-text available via subscription   (Followers: 16)
Atmospheric and Oceanic Science Letters     Open Access   (Followers: 13)
Atmospheric Chemistry and Physics (ACP)     Open Access   (Followers: 43)
Atmospheric Chemistry and Physics Discussions (ACPD)     Open Access   (Followers: 15)
Atmospheric Environment     Hybrid Journal   (Followers: 72)
Atmospheric Environment : X     Open Access   (Followers: 3)
Atmospheric Research     Hybrid Journal   (Followers: 73)
Atmospheric Science Letters     Open Access   (Followers: 40)
Boundary-Layer Meteorology     Hybrid Journal   (Followers: 32)
Bulletin of Atmospheric Science and Technology     Hybrid Journal   (Followers: 5)
Bulletin of the American Meteorological Society     Open Access   (Followers: 63)
Carbon Balance and Management     Open Access   (Followers: 6)
Ciencia, Ambiente y Clima     Open Access   (Followers: 1)
Climate     Open Access   (Followers: 8)
Climate and Energy     Full-text available via subscription   (Followers: 6)
Climate Change Economics     Hybrid Journal   (Followers: 44)
Climate Change Responses     Open Access   (Followers: 23)
Climate Dynamics     Hybrid Journal   (Followers: 45)
Climate Law     Hybrid Journal   (Followers: 6)
Climate of the Past (CP)     Open Access   (Followers: 6)
Climate of the Past Discussions (CPD)     Open Access   (Followers: 1)
Climate Policy     Hybrid Journal   (Followers: 51)
Climate Research     Hybrid Journal   (Followers: 9)
Climate Resilience and Sustainability     Open Access   (Followers: 21)
Climate Risk Management     Open Access   (Followers: 10)
Climate Services     Open Access   (Followers: 4)
Climatic Change     Open Access   (Followers: 69)
Current Climate Change Reports     Hybrid Journal   (Followers: 17)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 6)
Dynamics of Atmospheres and Oceans     Hybrid Journal   (Followers: 19)
Earth Perspectives - Transdisciplinarity Enabled     Open Access   (Followers: 1)
Economics of Disasters and Climate Change     Hybrid Journal   (Followers: 13)
Energy & Environment     Hybrid Journal   (Followers: 24)
Environmental and Climate Technologies     Open Access   (Followers: 3)
Environmental Dynamics and Global Climate Change     Open Access   (Followers: 21)
Frontiers in Climate     Open Access   (Followers: 4)
GeoHazards     Open Access   (Followers: 2)
Global Meteorology     Open Access   (Followers: 20)
International Journal of Atmospheric Sciences     Open Access   (Followers: 25)
International Journal of Biometeorology     Hybrid Journal   (Followers: 3)
International Journal of Climate Change Strategies and Management     Hybrid Journal   (Followers: 29)
International Journal of Climatology     Hybrid Journal   (Followers: 28)
International Journal of Environment and Climate Change     Open Access   (Followers: 20)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 3)
Journal of Agricultural Meteorology     Open Access  
Journal of Applied Meteorology and Climatology     Hybrid Journal   (Followers: 42)
Journal of Atmospheric and Oceanic Technology     Hybrid Journal   (Followers: 33)
Journal of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 133)
Journal of Atmospheric Chemistry     Hybrid Journal   (Followers: 23)
Journal of Climate     Hybrid Journal   (Followers: 56)
Journal of Climate Change and Health     Open Access   (Followers: 4)
Journal of Climatology     Open Access   (Followers: 4)
Journal of Hydrology and Meteorology     Open Access   (Followers: 39)
Journal of Hydrometeorology     Hybrid Journal   (Followers: 10)
Journal of Integrative Environmental Sciences     Hybrid Journal   (Followers: 4)
Journal of Meteorological Research     Full-text available via subscription   (Followers: 2)
Journal of Meteorology and Climate Science     Full-text available via subscription   (Followers: 21)
Journal of Space Weather and Space Climate     Open Access   (Followers: 30)
Journal of the Atmospheric Sciences     Hybrid Journal   (Followers: 83)
Journal of the Meteorological Society of Japan     Partially Free   (Followers: 7)
Journal of Weather Modification     Full-text available via subscription   (Followers: 4)
Mediterranean Marine Science     Open Access   (Followers: 2)
Meteorologica     Open Access   (Followers: 2)
Meteorological Applications     Open Access   (Followers: 4)
Meteorological Monographs     Hybrid Journal   (Followers: 1)
Meteorologische Zeitschrift     Full-text available via subscription   (Followers: 4)
Meteorology and Atmospheric Physics     Hybrid Journal   (Followers: 29)
Mètode Science Studies Journal : Annual Review     Open Access  
Michigan Journal of Sustainability     Open Access   (Followers: 1)
Modeling Earth Systems and Environment     Hybrid Journal   (Followers: 1)
Monthly Notices of the Royal Astronomical Society     Hybrid Journal   (Followers: 13)
Monthly Weather Review     Hybrid Journal   (Followers: 30)
Nature Climate Change     Full-text available via subscription   (Followers: 145)
Nature Reports Climate Change     Full-text available via subscription   (Followers: 40)
Nīvār     Open Access   (Followers: 1)
npj Climate and Atmospheric Science     Open Access   (Followers: 6)
Open Atmospheric Science Journal     Open Access   (Followers: 6)
Open Journal of Modern Hydrology     Open Access   (Followers: 5)
Revista Iberoamericana de Bioeconomía y Cambio Climático     Open Access   (Followers: 1)
Russian Meteorology and Hydrology     Hybrid Journal   (Followers: 4)
Space Weather     Full-text available via subscription   (Followers: 27)
Studia Geophysica et Geodaetica     Hybrid Journal   (Followers: 1)
Tellus A     Open Access   (Followers: 21)
Tellus B     Open Access   (Followers: 20)
The Cryosphere (TC)     Open Access   (Followers: 8)
The Quarterly Journal of the Royal Meteorological Society     Hybrid Journal   (Followers: 32)
Theoretical and Applied Climatology     Hybrid Journal   (Followers: 14)
Tropical Cyclone Research and Review     Open Access   (Followers: 1)
Urban Climate     Hybrid Journal   (Followers: 5)
Weather     Hybrid Journal   (Followers: 20)
Weather and Climate Dynamics     Open Access   (Followers: 1)
Weather and Climate Extremes     Open Access   (Followers: 18)
Weather and Forecasting     Hybrid Journal   (Followers: 43)
Weatherwise     Hybrid Journal   (Followers: 18)
气候与环境研究     Full-text available via subscription   (Followers: 2)

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Journal of Meteorological Research
Number of Followers: 2  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 2095-6037 - ISSN (Online) 2198-0934
Published by Springer-Verlag Homepage  [2469 journals]
  • CMIP6 Projections of the “Warming-Wetting” Trend in Northwest China
           and Related Extreme Events Based on Observational Constraints

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      Abstract: Abstract This study presents the improved future projections of the climate “warming—wetting” trend and climate extremes with different return periods in Northwest China at different global warming levels. The projections are based on the Coupled Model Intercomparison Project phase 6 (CMIP6) simulations constrained by the high-resolution observation dataset using the equidistant cumulative distribution functions (EDCDF) method. The results indicate that the climate will experience continuous warming and wetting as reflected by average temperature and total precipitation over Northwest China, especially under the scenario of the shared socioeconomic pathway 5—representative concentration pathway 8.5 (SSP5–8.5). Most parts of Northwest China will continue to warm in the future more than global average. Spatially, areas with prominent “warming—wetting” trends will be mainly distributed in western Northwest China. It is worth noting that extreme heat and precipitation events will also increase with the climate warming and wetting over Northwest China. Moreover, frequencies of rarer extreme events will increase more apparently than weaker extreme events and frequency increase of extreme heat events responds to global warming faster than that of extreme precipitation events. Limiting global warming within 2°C relative to 1850–1900 would slowdown the increase in extreme heat events and considerably suppress the increase in frequencies of extreme precipitation events, especially the rare (i.e., 50-yr) extreme events.
      PubDate: 2022-04-01
       
  • Sensitivity of Lake-Effect Convection to the Lake Surface Temperature over
           Poyang Lake in China

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      Abstract: Abstract In this study, high-resolution weather research and forecasting (WRF) simulations are used to explore the sensitivity of lake-effect convection over Poyang Lake (PL) to the change of lake surface temperature (LST). A control experiment (CTR) with climate mean LST (303 K) is compared with six sensitivity experiments (CTR−1/2/3K and CTR+1/2/3K) in which the LSTs are set based on the mean LST difference of 6 K between the maximum and minimum. The results show that the CTR experiment reasonably reproduces the lake-effect convection, and the lake-effect convection in sensitivity experiments is significantly influenced by the LST. With the increase of LST, the initiation time of the lake-effect convection is advanced gradually, while the initiation location moves PL from its shore. The lake-effect convection strengthens (weakens) in the increase-temperature CTR+1/2/3K (decrease-temperature CTR−1/2/3K) experiments, but the lake-effect convection does not monotonically strengthen with the LST, for the strongest one occurring in the CTR+1K experiment. The corresponding diagnostic analysis shows that the upward sensible heat flux and latent heat flux over PL increase with the LST, resulting in the enhancement of the lake-land breeze and the enlargement of the convective available potential energy (CAPE). This is the main reason for the changes in the initiation time and location, as well as the intensity of lake-effect convection in different experiments. In addition, the non-monotonous variation of the level of free convection, which is mainly induced by the non-monotonous variation of the lifting condensation level, is responsible for the non-monotonous variation of the lake-effect convection intensity with the LST.
      PubDate: 2022-04-01
       
  • Diagnostic Quantification of the Cloud Water Resource in China during
           2000–2019

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      Abstract: Abstract By using the diagnostic quantification method for cloud water resource (CWR), the three-dimensional (3D) cloud fields of 1° × 1° resolution during 2000–2019 in China are firstly obtained based on the NCEP reanalysis data and related satellite data. Then, combined with the Global Precipitation Climatology Project (GPCP) products, a 1° × 1° gridded CWR dataset of China in recent 20 years is established. On this basis, the monthly and annual CWR and related variables in China and its six weather modification operation sub-regions are obtained, and the CWR characteristics in different regions are analyzed finally. The results show that in the past 20 years, the annual total amount of atmospheric hydrometeors (GMh) and water vapor (GMv) in the Chinese mainland are about 838.1 and 3835.9 mm, respectively. After deducting the annual mean precipitation of China (Ps, 661.7 mm), the annual CWR is about 176.4 mm. Among the six sub-regions, the southeast region has the largest amount of cloud condensation (Cvh) and precipitation, leading to the largest GMh and CWR there. In contrast, the annual Ps, GMh, and CWR are all the least in the northwest region. Furthermore, the monthly and interannual variation trends of Ps, Cvh, and GMh in different regions are identical, and the evolution characteristics of CWR are also consistent with the hydrometeor inflow (Qhi). For the north, northwest, and northeast regions, in spring and autumn the precipitation efficiency of hydrometeors (PEh) is not high (20%–60%), the renewal time of hydrometeors (RTh) is relatively long (5–25 h), and GMh is relatively high. Therefore, there is great potential for the development of CWR through artificial precipitation enhancement (APE). For the central region, spring, autumn, and winter are suitable seasons for CWR development. For the southeast and southwest regions, Ps and PEh in summer are so high that the development of CWR should be avoided. For different spatial scales, there are significant differences in the characteristics of CWR.
      PubDate: 2022-04-01
       
  • Reprocessing 12-yr Microwave Humidity Sounder Historical Data of Fengyun-3
           Satellites

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      Abstract: Abstract Atmospheric water vapor is an essential climate variable (ECV) with extensive spatial and temporal variations. Microwave humidity observations from meteorological satellites provide important information for climate system variables, including atmospheric water vapor and precipitable water, and assimilation in numerical weather prediction (NWP) and reanalysis. As one of the payloads onboard China’s second-generation polar-orbiting operational meteorological Fengyun-3 (FY-3) satellites, the Microwave Humidity Sounder (MWHS) has been continuously observing the global humidity since 2008. The reprocessing of historical FY-3 MWHS data is documented in detail in this study. After calibrating and correcting the data, the quality of the reprocessed dataset is evaluated and the improvement is shown in this study. The results suggest that MWHS observations bias is reduced to approximately 0.8 K, compared with METOP-A Microwave Humidity Sounder (MHS). The temporal variability of MWHS is highly correlated with the instrument temperature. After reprocessing, the scene temperature dependency is mitigated for all 183 GHz channels, and the consistency and stability between FY-3A/B/C are also improved.
      PubDate: 2022-04-01
       
  • Factors Influencing Diurnal Variations of Cloud and Precipitation in the
           Yushu Area of the Tibetan Plateau

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      Abstract: Abstract Using the cloud radar, ground observations, and ECMWF Reanalysis v5 (ERA5) data, we investigate the factors influencing nighttime precipitation during summer in the Yushu area of the Tibetan Plateau (TP). The cloud top height (CTH), cloud base height (CBH), and liquid water content (LWC) are compared between non-precipitation and precipitation days. The results show that the average CTH on precipitation days in Yushu is below 10 km above ground level (AGL) in the daytime, whereas it exceeds 10 km AGL at night, with the maximum at 2300 BT (Beijing Time). The CBH is in-phase with the dewpoint spread. The precipitation intensity and CTH are in-phase with the LWC. The hourly averaged precipitation intensity and convective available potential energy in ERA5 reach their maximums at 2100 BT, which is 3 h ahead of their observed counterparts. There is descending motion in the mid day on non-precipitation days, whereas there is ascending motion at night on precipitation days. In addition, the horizontal wind direction in the lower level (below 5000 m) shows clockwise rotation from morning to night. Wind shear occurs in the mid level of the atmosphere, accompanied by a subtropical westerly jet in the upper level. The difference in horizontal wind speed between 200 and 500 hPa is positively related to the LWC, thereby contributing to the formation of upper-level cloud.
      PubDate: 2022-04-01
       
  • Deep Learning for Seasonal Precipitation Prediction over China

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      Abstract: Abstract Despite significant progress having been made in recent years, the forecast skill for seasonal precipitation over China remains limited. In this study, a deep-learning-based statistical prediction model for seasonal precipitation over China was developed. The model was trained to learn the distribution of the seasonal precipitation using simultaneous general circulation data. First, it was pre-trained with the hindcasts of several general circulation models (GCMs), and evaluation of the test set suggested that the pre-trained model could basically reproduce the GCM-predicted precipitation, with the anomaly pattern correlation coefficients (PCCs) greater than 0.80. Then, transfer learning was applied by using ECMWF Reanalysis v5 (ERA5) data and gridded precipitation observational data over China, to further correct the systemic errors in the model. As a result, using general circulation fields from reanalysis as the input, this hybrid model performed reasonably well in simulating the seasonal precipitation over China, with the PCC reaching 0.71. In addition, the results using the circulation fields predicted by GCMs as the input were also assessed. In general, the proposed model improves the PCC over China by 0.10–0.13, as compared to the raw GCM outputs, for lead times of 1–4 months. This deep learning model has been used at the National Climate Center of China Meteorological Administration for the past two years to provide guidance for summer precipitation prediction over China and has performed extremely well.
      PubDate: 2022-04-01
       
  • Fast CO2 Retrieval Using a Semi-Physical Statistical Model for the
           High-Resolution Spectrometer on the Fengyun-3D Satellite

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      Abstract: Abstract China’s Fengyun-3D meteorological satellite launched in December 2016 carries the high-resolution greenhouse-gases absorption spectrometer (GAS) aimed at providing global observations of carbon dioxide (CO2). To date, GAS is one of the few instruments measuring CO2 from the near-infrared spectrum. On orbit, the oxygen (O2) A band suffers a disturbance, and the signal-to-noise ratio (SNR) is significantly lower than the nominal specification. This leads to difficulties in the retrieval of surface pressure and hence a degradation of the retrieval of the column-averaged CO2 dry air mole fraction (XCO2) if a full physics retrieval algorithm is used. Thus, a fast CO2 inverse method, named semi-physical statistical algorithm, was developed to overcome this deficiency. The instrument characteristics, the semi-physical statistical algorithm, and the results of comparison with ground-based measurements over land were introduced in this paper. XCO2 can be obtained from three bands, namely, the O2 A, weak CO2, and strong CO2 bands, with compensation from the Medium Resolution Spectral Imager-2 (MERSI-2) products, ECMWF Reanalysis v5 (ERA-5) data, and Total Carbon Column Observing Network (TCCON) data. The eigenvectors of covariance matrices and the least square fits were used to derive retrieval coefficients and yield cloud-free solutions. In addition to the GAS radiance, some key factors necessary for the accurate estimations of XCO2 were also taken as input information (e.g., air mass, surface pressure, and a priori XCO2). The global GAS XCO2 restricted over land was compared against the simultaneously collocated observations from TCCON. The retrieval algorithm can mitigate the issue caused by the low SNR of the O2 A band to a certain extent. Overall, through site-by-site comparisons, GAS XCO2 agreed well with the average precision (1σ) of 1.52 ppm and bias of −0.007 ppm. The seasonal variation trends of GAS XCO2 can be clearly seen at TCCON sites on the 1-yr timescale.
      PubDate: 2022-04-01
       
  • Upper-Ocean Lateral Heat Transports in the Niño3.4 Region and Their
           Connection with ENSO

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      Abstract: Abstract In the Niño3.4 region (tropical Pacific, 5°S–5°N, 170°–120°W), sea surface temperature (SST) changes are highly correlated with temperature variations in the upper 40-m layer. This study explores the upper-ocean heat budget in the Niño3.4 region using Ocean Reanalysis System 5 (ORAS5) monthly data from 1979 to 2018, with a focus on ocean heat transports at lateral boundaries in the top 40-m layer and their correlation with temperature variations. In the region, there is a well-defined structure of opposite meridional circulation in the upper and lower parts of the thermocline, characterized by divergence in the upper layer above 40 m and convergence in the lower layer. The change of mean temperature in the upper layer is determined by the sum of zonal, meridional, and vertical heat transports, which, however, tend to largely compensate for each other. In general, part of the surface heat flux from the atmosphere to the ocean and the heat transport from the subsurface ocean are transported out of the domain by meridional and zonal currents, leaving only a tiny part to warm or cool the upper ocean. The amplitude of the net surface heat flux effective for the entire 40-m layer of the ocean is weaker than the lateral heat transport. On an interannual timescale, variations of heat transports in both zonal and meridional are positively correlated with temperature anomalies, while the vertical heat transport from the subsurface ocean is negatively correlated. Composite analyses for five El Niño events and five La Niña events also revealed that there is a positive contribution of horizontal transport convergence to temperature anomalies during the evolution of El Niño (warming) and La Niña (cooling), while vertical transport acts against temperature variations.
      PubDate: 2022-04-01
       
  • Comparison between the Roles of Low-Level Jets in Two Heavy Rainfall
           Events over South China

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      Abstract: Abstract Two heavy rainfall events occurred over the Pearl River Delta during 20–22 May 2020: the first was a warm-sector event and the second a frontal event. Based on ERA5 reanalysis data and observations from wind profilers and Doppler weather radars, the structures and roles of low-level jets (LLJs) during these two heavy rainfall events were analyzed. The results show that: (1) South China was affected by a low-level vortex and a low-level shear line during the two processes. The two heavy rainfall events were both associated with a synoptic-system-related low-level jet (SLLJ) and a boundary layer jet (BLJ). The coupling of the convergence at the exit of the BLJ and the divergence at the entrance of the SLLJ produced strong lifting for the warm-sector heavy rainfall, and the strong convergence between the LLJs and northerly winds as the cold front moved southwards was the main lifting reason for the frontal heavy rainfall. (2) The BLJ was the main transport of water vapor during the two processes. The coupling of the BLJ and SLLJ caused the water vapor convergence to be concentrated in the boundary layer during the first process, whereas the strong convergence between the LLJs and northerly winds led to the lower and middle troposphere having strong water vapor convergence during the second process. (3) During the period of these two heavy rainfall events, the lower and middle troposphere remained unstable. Further analysis show that the differences in the intensity, location, and direction between the BLJ and SLLJ resulted in the pseudo-equivalent potential temperature advection in the boundary layer being significantly larger than in the lower and middle troposphere, which compensated for the energy loss caused by heavy rainfall and maintained the convective instability. These findings add to our knowledge on the roles of LLJs in the pre-summer rainfall over South China.
      PubDate: 2022-04-01
       
  • Role of Anthropogenic Climate Change in Autumn Drought Trend over China
           from 1961 to 2014

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      Abstract: Abstract Understanding the impact of anthropogenic climate change on drought is of great significance to the prevention of its adverse effects. Two drought indices, standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), are used here for the detection and attribution of autumn droughts in China, and for the exploration of the role played by the anthropogenic climate change. SPI is only related to precipitation, but SPEI involves both precipitation and potential evapotranspiration. For their trend’s spatial patterns, the historical simulations (including all forcings, noted as ALL) from 11 models of the Coupled Model Intercomparison Project phase 6, as an ensemble, are able to reproduce their observational counterpart. SPI shows wetting trend in the north of 35°N and drying trend in the south. SPEI shows drying trend in almost whole China. The drying trend in historical simulations ALL is significantly stronger, compared with the counterpart from the accompanying simulations (called NAT) with only natural forcings implemented. This result clearly indicates that anthropogenic climate change plays a dominant role in the enhancement of autumn drought in China. A more rigorous detection work is also performed with the signal’s fingerprint of ALL (and NAT) projected onto the observation and assessed with the background noise from no external-forcing control simulations. The trend pattern signal in ALL is significantly detected in observation for both SPI and SPEI, with a more pronounced signal in SPEI than in SPI, while the signal of NAT is not detected for neither SPI nor SPEI. Finally, extreme droughts (with indices beyond −2) are assessed in terms of probability ratio between ALL and NAT. It is shown that the anthropogenic precipitation change plays a leading role in the south of 35°N, while the anthropogenic temperature change leads in the north.
      PubDate: 2022-04-01
       
  • An Updated Review of Event Attribution Approaches

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      Abstract: Abstract There have been considerable high-impact extreme events occurring around the world in the context of climate change. Event attribution studies, which seek to quantitatively answer whether and to what extent anthropogenic climate change has altered the characteristics—predominantly the probability and magnitude—of particular events, have been gaining increasing interest within the research community. This paper reviews the latest approaches used in event attribution studies through a new classification into three major categories according to how the event attribution question is framed—namely, the risk-based approach, the storyline approach, and the combined approach. Four approaches in the risk-based framing category and three in the storyline framing category are also reviewed in detail. The advantages and disadvantages of each approach are discussed. Particular attention is paid to the ability, suitability, and applicability of these approaches in attributing extreme events in China, a typical monsoonal region where climate models may not perform well. Most of these approaches are applicable in China, and some are more suitable for analyzing temperature events. There is no right or wrong among these approaches, but different approaches have different framings. The uncertainties in attribution results come from several aspects, including different categories of framing, different conditions in climate model approaches, different models, different definitions of the event, and different observational data used. Clarification of these aspects can help to understand the differences in attribution results from different studies.
      PubDate: 2022-04-01
       
  • Contribution of Winter SSTA in the Tropical Eastern Pacific to Changes of
           Tropical Cyclone Precipitation over Southeast China

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      Abstract: Abstract Tropical cyclone precipitation (TCP) accounts for 10%–40% of the boreal summer precipitation that occurs over Southeast China (SEC), causing flood disasters and serious damage. On the decadal scale, TCP increases significantly in SEC while TC frequency decreases in the western North Pacific (WNP) during 1980–2019. Therefore, variations in TCP and the corresponding physical mechanism are investigated in this study. First, an empirical statistical method is introduced to quantify the TCP amount based on accumulated cyclone energy (ACE) and TC frequency with the TCP anomaly decomposed into three items (rainfall frequency, rainfall intensity, and nonlinear item). ACE, as the integration of TC intensity and frequency, is a more effective index than TC frequency for depicting the characteristics of TCP because the contribution of rainfall frequency represented by ACE is higher than that of TC frequency. Then, the physical mechanism affecting the WNP TC activities and TCP in SEC are inspected. Positive sea surface temperature anomaly (SSTA) over the tropical eastern Pacific (TEP) in winter can trigger variations of air—sea interaction over the tropical Pacific, including low-level divergent winds, mid-tropospheric descent flows, high-level convergent winds coupled with negative anomalies of vorticity and humidity over the tropical western Pacific (TWP) in the next summer. These dynamic conditions provide unfavorable environments for TC activities in the WNP and constrain TCP in SEC. Furthermore, more significantly negative SSTA events in the TEP facilitate enhanced ACE along with positive relative vorticity, relative humidity, and upwelling vertical winds anomalies over the coast of SEC after 1998, which is a reasonable explanation for the increasing TCP in SEC.
      PubDate: 2022-04-01
       
  • Uncertainty in TC Maximum Intensity with Fixed Ratio of Surface Exchange
           Coefficients for Enthalpy and Momentum

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      Abstract: Abstract The classical tropical cyclone (TC) maximum intensity theory of Emanuel suggests that the maximum azimuthal wind of TC depends linearly on the ratio of surface exchange coefficients for enthalpy and momentum (Ck and Cd). In this study, a series of sensitivity experiments are conducted with the three-dimensional Cloud Model 1 (CM1), by fixing the ratio of Ck/Cd but varying the specific values of Ck and Cd simultaneously. The results show significant variations in the simulated TC maximum intensity by varying Ck and Cd, even if their ratio is fixed. Overall, the maximum intensity increases steadily with increasing Ck and Cd when their value is smaller than 1.00 × 10−3, and then this increasing trend slows down with further increases in the coefficients. Two previous theoretical frameworks—one based on gradient wind balance and the other incorporating the unbalanced terms—are applied to calculate the maximum potential intensity (PI). The calculated value of the former shows little variation when varying the specific values of Ck and Cd, while the latter shows larger values with increases in both Ck and Cd. Further examination suggests that the unbalanced effect plays a key role in contributing to the increasing intensity with increasing Ck and Cd.
      PubDate: 2022-02-01
       
  • A Case Study on the Rapid Rain-to-Snow Transition in Late Spring 2018 over
           Northern China: Effects of Return Flows and Topography

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      Abstract: Abstract Phase changes in the precipitation processes of early winter and late spring in midlatitude regions represent challenges when forecasting the timing and magnitude of snowfall. On 4 April 2018, a heavy snow process occurred in Beijing and northwestern Hebei Province, becoming the most delayed occurrence of heavy spring snow ever recorded over Beijing in the last 30 years. This paper uses observational and numerical simulation data to investigate the causes for the rapid rain-to-snow (RRTS) phase transition during this process. The following results are obtained. (1) Return flows (RFs), an interesting type of easterly wind, including those at 1000, 925, and 800 hPa, played an important role in this heavy snow process and presented a characteristic “sandwich” structure. The RFs, complex topography, and snow particles that dominated the clouds, were the three key factors for the RRTS transition. (2) The RRTS transition in the plains was directly related to the RF at 925 hPa, which brought about advective cooling initiated approximately 4–6 h before the onset of precipitation. Then, the RF played a role of diabatic cooling when snow particles began to fall at the onset of precipitation. (3) The RRTS transition in the northern part of the Taihang Mountains was closely related to the relatively high altitude that led to a lower surface temperature owing to the vertical temperature lapse rate. Both immediately before and after the onset of precipitation, the snow particles in clouds entrained the middle-level cold air downward, causing the melting layer (from surface to the 0°C-isotherm level) to become very thin; and thus the snow particles did not have adequate time to melt before falling to the ground. (4) The rapid RRTS over the Yanqing mountainous area in the northwest of Beijing could have involved all the three concurrent mechanisms: the advective cooling of RF, the melting cooling of cloud snow particles, and the high-altitude effect. Compared with that in the plain area with less urbanization, the duration of the RRTS in the plain area with significant urbanization was extended by approximately 2 h.
      PubDate: 2022-02-01
       
  • Global Rainstorm Disaster Risk Monitoring Based on Satellite Remote
           Sensing

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      Abstract: Abstract Methods of rainstorm disaster risk monitoring (RDRM) based on retrieved satellite rainfall data are studied. Due to significant regional differences, the global rainstorm disasters are not only affected by geography (such as topography and surface properties), but also by climate events. It is necessary to study rainstorm disaster-causing factors, hazard-formative environments, and hazard-affected incidents based on the climate distribution of precipitation and rainstorms worldwide. According to a global flood disaster dataset for the last 20 years, the top four flood disaster causes (accounting for 96.8% in total) related to rainstorms, from most to least influential, are heavy rain (accounting for 61.6%), brief torrential rain (16.7%), monsoonal rain (9.4%), and tropical cyclone/storm rain (9.1%). A dynamic global rainstorm disaster threshold is identified by using global climate data based on 3319 rainstorm-induced floods and rainfall data retrieved by satellites in the last 20 years. Taking the 7-day accumulated rainfall, 3- and 12-h maximum rainfall, 24-h rainfall, rainstorm threshold, and others as the main parameters, a rainstorm intensity index is constructed. Calculation and global mapping of hazard-formative environmental factor and hazard-affected body factor of rainstorm disasters are performed based on terrain and river data, population data, and economic data. Finally, a satellite remote sensing RDRM model is developed, incorporating the above three factors (rainstorm intensity index, hazard-formative environment factor, and hazard-affected body factor). The results show that the model can well capture the rainstorm disasters that happened in the middle and lower reaches of the Yangtze River in China and in South Asia in 2020.
      PubDate: 2022-02-01
       
  • Diagnosing the Dynamic and Thermodynamic Effects for the Exceptional 2020
           Summer Rainy Season in the Yangtze River Valley

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      Abstract: Abstract An exceptional rainy season occurred in the Yangtze River valley of eastern China in June–July 2020. The relative importance of the dynamic and thermodynamic effects on this unusual event is evaluated through the budget equations of moisture and moist static energy (MSE). The moisture budget analysis suggests that the thermodynamic effect contributes to the precipitation anomaly by 8.5% through the advection of abnormal water vapor by mean vertical motion, while the dynamic effect, related to water vapor advection by anomalous vertical motion, has the dominant contribution. The MSE budget analysis further reveals that the anomalous vertical motion is both constrained by the dynamic effect related to changes in atmospheric circulation and the thermodynamic effect related to changes of the atmospheric thermal state, with a ratio of thermodynamic versus total effects estimated at 45.3%. The dynamic effect is linked to the advection of warm and humid air by the abnormal southwesterly wind, which is related with an anomalous anticyclone over the Philippine Sea. The thermodynamic effect is partly induced by the positive advection of anomalous MSE (mainly latent energy) by the mean vertical motion. This analysis of the dynamic and thermodynamic effects is useful to understand the underlying physical mechanisms leading to the unusual rainy season in the Yangtze River valley in summer 2020. It is also helpful to put forward a few speculations on the potential role of global warming whose primary effect is, after all, to change the thermal state of the atmosphere.
      PubDate: 2022-02-01
       
  • Improving the Nowcasting of Strong Convection by Assimilating Both Wind
           and Reflectivity Observations of Phased Array Radar: A Case Study

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      Abstract: Abstract With the advent of the phased array radar (PAR) technology, it is possible to capture the development and evolution of convective systems in a much shorter time interval and with higher spatial resolution than via traditional Doppler radar. Research on the assimilation of PAR observations in numerical weather prediction models is still in its infancy in China. In this paper, the impact of assimilating PAR data on model forecasts was investigated by a case study of a local heavy rainfall event that occurred over Foshan city of Guangdong Province on 26 August 2020, via a series of sensitivity experiments. Both the retrieved three-dimensional wind and hydrometeor fields were assimilated through the nudging method with the Tropical Regional Assimilation Model for South China Sea_Rapid Update Cycle_1km (TRAMS_RUC_1km). The temperature and moisture fields were also adjusted accordingly. The results show that significant improvements are made in the experiments with latent heat nudging and adjustment of the water vapor field, which implies the importance of thermodynamic balance in the initialization of the convective system and highlights the need to assimilate PAR radar observations in a continuous manner to maximize the impact of the data. Sensitivity tests also indicate that the relaxation time should be less than 5 min. In general, for this case, the assimilation of PAR data can significantly improve the nowcasting skill of the regional heavy precipitation. This study is the first step towards operational PAR data assimilation in numerical weather prediction in southern China.
      PubDate: 2022-02-01
       
  • QpefBD: A Benchmark Dataset Applied to Machine Learning for Minute-Scale
           Quantitative Precipitation Estimation and Forecasting

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      Abstract: Abstract Nowcasts of strong convective precipitation and radar-based quantitative precipitation estimations have always been hot yet challenging issues in meteorological sciences. Data-driven machine learning, especially deep learning, provides a new technical approach for the quantitative estimation and forecasting of precipitation. A high-quality, large-sample, and labeled training dataset is critical for the successful application of machine-learning technology to a specific field. The present study develops a benchmark dataset that can be applied to machine learning for minute-scale quantitative precipitation estimation and forecasting (QpefBD), containing 231,978 samples of 3185 heavy precipitation events that occurred in 6 provinces of central and eastern China from April to October 2016–2018. Each individual sample consists of 8 products of weather radars at 6-min intervals within the time window of the corresponding event and products of 27 physical quantities at hourly intervals that describe the atmospheric dynamic and thermodynamic conditions. Two data labels, i.e., ground precipitation intensity and areal coverage of heavy precipitation at 6-min intervals, are also included. The present study describes the basic components of the dataset and data processing and provides metrics for the evaluation of model performance on precipitation estimation and forecasting. Based on these evaluation metrics, some simple and commonly used methods are applied to evaluate precipitation estimates and forecasts. The results can serve as the benchmark reference for the performance evaluation of machine learning models using this dataset. This paper also gives some suggestions and scenarios of the QpefBD application. We believe that the application of this benchmark dataset will promote interdisciplinary collaboration between meteorological sciences and artificial intelligence sciences, providing a new way for the identification and forecast of heavy precipitation.
      PubDate: 2022-02-01
       
  • Spatial and Temporal Validation of In-Situ and Satellite Weather Data for
           the South West Agricultural Region of Australia

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      Abstract: Abstract Seasonal variations in weather have significant impacts on crop yields. The accuracy of weather data is an important consideration for crop yield models. This study uses an independent in-situ weather station network to validate the accuracy of monthly temperature and precipitation data from the in-situ weather station network operated by the Bureau of Meteorology (BOM), interpolated gridded data from this network, and satellite weather data for the South West Agricultural Region of Australia. This region covers five classes of the Köppen—Geiger climate classification system and is responsible for 10 billion AUD of agricultural produce annually. A strong bias was found for the maximum temperatures in the Copernicus LST (land surface temperature) satellite product. This bias was linearly correlated with the in-situ temperature and exceeded 20°C in warmer months. Due to the bias’s linear nature, a linear correction was able to reduce the root-mean-square error (RMSE) of the Copernicus LST product by 82%. This process was tested for other regions of Australia and, despite some regional differences, a linear correction consistently reduced RMSE by 80%. The validation process demonstrated that the dataset with reliably the lowest RMSE is the gridded weather data calculated from BOM’s in-situ weather stations. Nearest neighbor in-situ weather stations generally had the next lowest RMSE, followed by weather-station corrected satellite products and lastly the non-weather station corrected satellite products. While the in-situ gridded product generally had the lowest RMSE, there were spatial and seasonal variations. Monthly maximum temperatures were more accurately measured by the bias-corrected Copernicus LST product in the northern and eastern extents (where there is a lower density of BOM in-situ stations). Monthly minimum temperatures from the Copernicus LST product had similar to slightly better RMSE than the Australian Water Availability Project (AWAP) product for the southern half of the study area and the rain-gauge corrected GSMaP (Global Satellite Mapping of Precipitation) product performed similarly to AWAP in the drier months (November–April).
      PubDate: 2022-02-01
       
  • Feature Construction and Identification of Convective Wind from Doppler
           Radar Data

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      Abstract: Abstract Convective wind is one of the common types of severe convective weather. Identification and Forecasting of convective wind are essential. In this paper, five kinds of features are firstly constructed from characteristics of typical convective wind-related echo phenomena based on Doppler radar data. The features include storm motion, high-value reflectivity, high-value velocity, velocity shear, and velocity texture. A severe convective wind (SCW) identification model is then built by applying the above features to the random forest model. With convective wind samples collected over 13 cities of China in June–August 2016, it is found that the probability of detection (POD) of SCW is 78.9%, the false alarm ratio (FAR) is 26.4%, and the critical success index (CSI) is 61.5%. For the convective wind samples that carry typical echo features, the POD, FAR, and CSI range from 89.4% to 99.3%, 4.2% to 16.0%, and 76.4% to 95.1%, respectively. Meanwhile, the POD and negative-case POD of samples without typical echo features are 66.8% and 85.4%, respectively. The experimental results demonstrate that the SCW identification model can classify non-SCW effectively, and performs better with SCW samples carrying typical echo features than without.
      PubDate: 2022-02-01
       
 
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