A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> METEOROLOGY (Total: 106 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Journal Cover
Advances in Atmospheric Sciences
Journal Prestige (SJR): 0.956
Citation Impact (citeScore): 2
Number of Followers: 50  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1861-9533 - ISSN (Online) 0256-1530
Published by Springer-Verlag Homepage  [2468 journals]
  • A Study on the Assessment and Integration of Multi-Source
           Evapotranspiration Products over the Tibetan Plateau

    • Free pre-print version: Loading...

      Abstract: Abstract Evapotranspiration (ET) is a crucial variable in the terrestrial water, carbon, and energy cycles. At present, a large number of multisource ET products exist. Due to sparse observations, however, great challenges exist in the evaluation and integration of ET products in remote and complex areas such as the Tibetan Plateau (TP). In this paper, the applicability of the multiple collocation (MC) method over the TP is evaluated for the first time, and the uncertainty of multisource ET products (based on reanalysis, remote sensing, and land surface models) is further analyzed, which provides a theoretical basis for ET data fusion. The results show that 1) ET uncertainties quantified via the MC method are lower in RS-based ET products (5.95 vs. 7.06 mm month−1) than in LSM ET products (10.22 vs. 17.97 mm month−1) and reanalysis ET estimates (7.27 vs. 12.26 mm month−1). 2) A multisource evapotranspiration (MET) dataset is generated at a monthly temporal scale with a spatial resolution of 0.25° across the TP during 2005–15. MET has better performance than any individual product. 3) Based on the fusion product, the total ET amount over the TP and its patterns of spatiotemporal variability are clearly identified. The annual total ET over the entire TP is approximately 380.60 mm. Additionally, an increasing trend of 1.59±0.85 mm yr−1 over the TP is shown during 2005–15. This study provides a basis for future studies on water and energy cycles and water resource management over the TP and surrounding regions.
      PubDate: 2024-03-01
       
  • Cloud-Type-Dependent 1DVAR Algorithm for Retrieving Hydrometeors and
           Precipitation in Tropical Cyclone Nanmadol from GMI Data

    • Free pre-print version: Loading...

      Abstract: Abstract Understanding the structure of tropical cyclone (TC) hydrometeors is crucial for detecting the changes in the distribution and intensity of precipitation. In this study, the GMI brightness temperature and cloud-dependent 1DVAR algorithm were used to retrieve the hydrometeor profiles and surface rain rate of TC Nanmadol (2022). The Advanced Radiative Transfer Modeling System (ARMS) was used to calculate the Jacobian and degrees of freedom (ΔDOF) of cloud water, rainwater, and graupel for different channels of GMI in convective conditions. The retrieval results were compared with the Dual-frequency Precipitation Radar (DPR), GMI 2A, and IMERG products. It is shown that from all channels of GMI, rain water has the highest ΔDOF, at 1.72. According to the radiance Jacobian to atmospheric state variables, cloud water emission dominates its scattering. For rain water, the emission of channels 1–4 dominates scattering. Compared with the GMI 2A precipitation product, the 1DVAR precipitation rate has a higher correlation coefficient (0.713) with the IMERG product and can better reflect the location of TC precipitation. Near the TC eyewall, the highest radar echo top indicates strong convection. Near the melting layer where Ka-band attenuation is strong, the double frequency difference of DPR data reflects the location of the melting. The DPR drop size distribution (DSD) product shows that there is a significant increase in particle size below the melting layer in the spiral rain band. Thus, the particle size may be one of the main reasons for the smaller rain water below the melting layer retrieved from 1DVAR.
      PubDate: 2024-03-01
       
  • Aircraft Observation and Simulation of the Supercooled Liquid Water Layer
           in a Warm Conveyor Belt over North China

    • Free pre-print version: Loading...

      Abstract: Abstract This paper studied a snow event over North China on 21 February 2017, using aircraft in-situ data, a Lagrangian analysis tool, and WRF simulations with different microphysical schemes to investigate the supercooled layer of warm conveyor belts (WCBs). Based on the aircraft data, we found a fine vertical structure within clouds in the WCB and highlighted a 1–2 km thin supercooled liquid water layer with a maximum Liquid Water Content (LWC) exceeding 0.5 g kg−1 during the vertical aircraft observation. Although the main features of thermodynamic profiles were essentially captured by both modeling schemes, the microphysical quantities exhibited large diversity with different microphysics schemes. The conventional Morrison two-moment scheme showed remarkable agreement with in-situ observations, both in terms of the thermodynamic structure and the supercooled liquid water layer. However, the microphysical structure of the WCB clouds, in terms of LWC and IWC, was not apparent in HUJI fast bin scheme. To reduce such uncertainty, future work may focus on improving the representation of microphysics in bin schemes with in-situ data and using similar assumptions for all schemes to isolate the impact of physics.
      PubDate: 2024-03-01
       
  • The First Global Map of Atmospheric Ammonia (NH3) as Observed by the
           HIRAS/FY-3D Satellite

    • Free pre-print version: Loading...

      Abstract: Abstract Atmospheric ammonia (NH3) is a chemically active trace gas that plays an important role in the atmospheric environment and climate change. Satellite remote sensing is a powerful technique to monitor NH3 concentration based on the absorption lines of NH3 in the thermal infrared region. In this study, we establish a retrieval algorithm to derive the NH3 column from the Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Chinese FengYun (FY)-3D satellite and present the first atmospheric NH3 column global map observed by the HIRAS instrument. The HIRAS observations can well capture NH3 hotspots around the world, e.g., India, West Africa, and East China, where large NH3 emissions exist. The HIRAS NH3 columns are also compared to the space-based Infrared Atmospheric Sounding Interferometer (IASI) measurements, and we find that the two instruments observe a consistent NH3 global distribution, with correlation coefficient (R) values of 0.28–0.73. Finally, some remaining issues about the HIRAS NH3 retrieval are discussed.
      PubDate: 2024-03-01
       
  • Frontogenesis and Frontolysis of a Cold Filament Driven by the
           Cross-Filament Wind and Wave Fields Simulated by a Large Eddy Simulation

    • Free pre-print version: Loading...

      Abstract: Abstract The variations of the frontogenetic trend of a cold filament induced by the cross-filament wind and wave fields are studied by a non-hydrostatic large eddy simulation. Five cases with different strengths of wind and wave fields are studied. The results show that the intense wind and wave fields further break the symmetries of submesoscale flow fields and suppress the levels of filament frontogenesis. The changes of secondary circulation directions—that is, the conversion between the convergence and divergence of the surface cross-filament currents with the downwelling and upwelling jets in the filament center—are associated with the inertial oscillation. The filament frontogenesis and frontolysis caused by the changes of secondary circulation directions may periodically sharpen and smooth the gradient of submesoscale flow fields. The lifecycle of the cold filament may include multiple stages of filament frontogenesis and frontolysis.
      PubDate: 2024-03-01
       
  • A Long-Time-Step-Permitting Tracer Transport Model on the Regular
           Latitude–Longitude Grid

    • Free pre-print version: Loading...

      Abstract: Abstract If an explicit time scheme is used in a numerical model, the size of the integration time step is typically limited by the spatial resolution. This study develops a regular latitude–longitude grid-based global three-dimensional tracer transport model that is computationally stable at large time-step sizes. The tracer model employs a finite-volume flux-form semi-Lagrangian transport scheme in the horizontal and an adaptively implicit algorithm in the vertical. The horizontal and vertical solvers are coupled via a straightforward operator-splitting technique. Both the finite-volume scheme’s one-dimensional slope-limiter and the adaptively implicit vertical solver’s first-order upwind scheme enforce monotonicity. The tracer model permits a large time-step size and is inherently conservative and monotonic. Idealized advection test cases demonstrate that the three-dimensional transport model performs very well in terms of accuracy, stability, and efficiency. It is possible to use this robust transport model in a global atmospheric dynamical core.
      PubDate: 2024-03-01
       
  • Seasonal Variation of the Sea Surface Temperature Growth Rate of ENSO

    • Free pre-print version: Loading...

      Abstract: Abstract El Niño–Southern Oscillation (ENSO) exhibits a distinctive phase-locking characteristic, first expressed during its onset in boreal spring, developing during summer and autumn, reaching its peak towards winter, and decaying over the next spring. Several studies have demonstrated that this feature arises as a result of seasonal variation in the growth rate of ENSO as expressed by the sea surface temperature (SST). The bias towards simulating the phase locking of ENSO by many state-of-the-art climate models is also attributed to the unrealistic depiction of the growth rate. In this study, the seasonal variation of SST growth rate in the Niño-3.4 region (5°S–5°N, 120°–170°W) is estimated in detail based on the mixed layer heat budget equation and recharge oscillator model during 1981–2020. It is suggested that the consideration of a variable mixed layer depth is essential to its diagnostic process. The estimated growth rate has a remarkable seasonal cycle with minimum rates occurring in spring and maximum rates evident in autumn. More specifically, the growth rate derived from the meridional advection (surface heat flux) is positive (negative) throughout the year. Vertical diffusion generally makes a negative contribution to the evolution of growth rate and the magnitude of vertical entrainment represents the smallest contributor. Analysis indicates that the zonal advective feedback is regulated by the meridional immigration of the intertropical convergence zone, which approaches its southernmost extent in February and progresses to its northernmost location in September, and dominates the seasonal variation of the SST growth rate.
      PubDate: 2024-03-01
       
  • Comparison of the Minimum Bounding Rectangle and Minimum Circumscribed
           Ellipse of Rain Cells from TRMM

    • Free pre-print version: Loading...

      Abstract: Abstract Based on the TRMM dataset, this paper compares the applicability of the improved MCE (minimum circumscribed ellipse), MBR (minimum bounding rectangle), and DIA (direct indexing area) methods for rain cell fitting. These three methods can reflect the geometric characteristics of clouds and apply geometric parameters to estimate the real dimensions of rain cells. The MCE method shows a major advantage in identifying the circumference of rain cells. The circumference of rain cells identified by MCE in most samples is smaller than that identified by DIA and MBR, and more similar to the observed rain cells. The area of rain cells identified by MBR is relatively robust. For rain cells composed of many pixels (N > 20), the overall performance is better than that of MCE, but the contribution of MBR to the best identification results, which have the shortest circumference and the smallest area, is less than that of MCE. The DIA method is best suited to small rain cells with a circumference of less than 100 km and an area of less than 120 km2, but the overall performance is mediocre. The MCE method tends to achieve the highest success at any angle, whereas there are fewer “best identification” results from DIA or MBR and more of the worst ones in the along-track direction and cross-track direction. Through this comprehensive comparison, we conclude that MCE can obtain the best fitting results with the shortest circumference and the smallest area on behalf of the high filling effect for all sizes of rain cells.
      PubDate: 2024-03-01
       
  • An Initial Perturbation Method for the Multiscale Singular Vector in
           Global Ensemble Prediction

    • Free pre-print version: Loading...

      Abstract: Abstract Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction (NWP) caused by errors in initial conditions (ICs). The traditional Singular Vector (SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System (CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm, energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to (i) improve the relationship between the ensemble spread and the root-mean-square error and (ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short- to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.
      PubDate: 2024-03-01
       
  • The Persistence and Zonal Scale of Atmospheric Dipolar Modes

    • Free pre-print version: Loading...

      Abstract: Abstract This study investigates the relationship between the persistence and the zonal scale of atmospheric dipolar modes (DMs). Results from the daily data of ERA5 and the long-term output of an idealized atmospheric model show that the atmospheric DMs with a broader (narrower) zonal scale dipolar structure possess a longer (shorter) persistence. A detailed vorticity budget analysis indicates that the persistence of a hemispheric-scale DM (1/1 DM) and a regional or sectoral DM (1/8 DM) in the model both largely rely on the persistence of the nonlinear eddy forcing. Linear terms can indirectly reduce the persistence of the anomalous nonlinear eddy forcing in a 1/8 DM by modifying the baroclinicity via the arousal of anomalous vertical motions. Therefore, the atmospheric DMs with a broader (narrower) zonal scale possess a longer (shorter) persistence because the effects of the linear terms are less (more) pronounced when the atmospheric DMs have better (worse) zonal symmetry. Further analyses show that the positive eddy feedback effect is weak or even absent in a 1/8 DM and the high-frequency eddy forcing acts more like a concomitant phenomenon rather than a leading driving factor for a 1/8 DM. Thus, the hemispheric-scale DM and the regional or sectoral DMs are different, not only in their persistence but also in their dynamics.
      PubDate: 2024-03-01
       
  • Downscaling Seasonal Precipitation Forecasts over East Africa with Deep
           Convolutional Neural Networks

    • Free pre-print version: Loading...

      Abstract: Abstract This study assesses the suitability of convolutional neural networks (CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September (JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa, particularly in providing improved forecast products which are essential for end users.
      PubDate: 2024-03-01
       
  • Assessment of Crop Yield in China Simulated by Thirteen Global Gridded
           Crop Models

    • Free pre-print version: Loading...

      Abstract: Abstract Global gridded crop models (GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national- and provincial-scale evaluation of the simulations by 13 GGCMs [12 from the GGCM Intercomparison (GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops (wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national- and provincial-scale crop yield prediction in China.
      PubDate: 2024-03-01
       
  • Recent Ventures in Interdisciplinary Arctic Research: The ARCPATH Project

    • Free pre-print version: Loading...

      Abstract: Abstract This paper celebrates Professor Yongqi GAO’s significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies - ARCPATH (https://www.svs.is/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.
      PubDate: 2024-02-27
       
  • Scientific Advances and Weather Services of the China Meteorological
           Administration’s National Forecasting Systems during the Beijing 2022
           Winter Olympics

    • Free pre-print version: Loading...

      Abstract: Abstract Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas, there is a deficiency of relevant research, operational techniques, and experience. This made providing meteorological services for this event particularly challenging. The China Meteorological Administration (CMA) Earth System Modeling and Prediction Centre, achieved breakthroughs in research on short- and medium-term deterministic and ensemble numerical predictions. Several key technologies crucial for precise winter weather services during the Winter Olympics were developed. A comprehensive framework, known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics, was established. Some of these advancements represent the highest level of capabilities currently available in China. The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality. This included achievements such as the “100-meter level, minute level” downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days. Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed, and many of these techniques have since been integrated into the CMA’s operational national forecasting systems. These accomplishments were facilitated by a dedicated weather forecasting and research initiative, in conjunction with the preexisting real-time operational forecasting systems of the CMA. This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project (SMART2022), and is also a part of their Regional Association (RA) II Research Development Project (Hangzhou RDP). Therefore, the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming high-impact weather forecasting activities. This article provides an overview and assessment of this program and the operational national forecasting systems.
      PubDate: 2024-02-22
       
  • Persistent Variations in the East Asian Trough from March to April and the
           Possible Mechanism

    • Free pre-print version: Loading...

      Abstract: Abstract The East Asian trough (EAT) profoundly influences the East Asian spring climate. In this study, the relationship of the EATs among the three spring months is investigated. Correlation analysis shows that the variation in March EAT is closely related to that of April EAT. Extended empirical orthogonal function (EEOF) analysis also confirms the co-variation of the March and April EATs. The positive/negative EEOF1 features the persistent strengthened/weakened EAT from March to April. Further investigation indicates that the variations in EEOF1 are related to a dipole sea surface temperature (SST) pattern over the North Atlantic and the SST anomaly over the tropical Indian Ocean. The dipole SST pattern over the North Atlantic, with one center east of Newfoundland Island and another east of Bermuda, could trigger a Rossby wave train to influence the EAT in March–April. The SST anomaly over the tropical Indian Ocean can change the Walker circulation and influence the atmospheric circulation over the tropical western Pacific, subsequently impacting the southern part of the EAT in March–April. Besides the SST factors, the Northeast Asian snow cover could change the regional thermal conditions and lead to persistent EAT anomalies from March to April. These three impact factors are generally independent of each other, jointly explaining large variations in the EAT EEOF1. Moreover, the signals of the three factors could be traced back to February, consequently providing a potential prediction source for the EAT variation in March and April.
      PubDate: 2024-02-09
       
  • Shallow Convection Dataset Simulated by Three Different Large Eddy Models

    • Free pre-print version: Loading...

      Abstract: Abstract Shallow convection plays an important role in transporting heat and moisture from the near-surface to higher altitudes, yet its parameterization in numerical models remains a great challenge, partly due to the lack of high-resolution observations. This study describes a large eddy simulation (LES) dataset for four shallow convection cases that differ primarily in inversion strength, which can be used as a surrogate for real data. To reduce the uncertainty in LES modeling, three different large eddy models were used, including SAM (System for Atmospheric Modeling), WRF (Weather Research and Forecasting model), and UCLA-LES. Results show that the different models generally exhibit similar behavior for each shallow convection case, despite some differences in the details of the convective structure. In addition to grid-averaged fields, conditionally sampled variables, such as in-cloud moisture and vertical velocity, are also provided, which are indispensable for calculation of the entrainment/detrainment rate. Considering the essentiality of the entraining/detraining process in the parameterization of cumulus convection, the dataset presented in this study is potentially useful for validation and improvement of the parameterization of shallow convection.
      PubDate: 2024-02-09
       
  • The Unprecedented Extreme Anticyclonic Anomaly over Northeast Asia in July
           2021 and Its Climatic Impacts

    • Free pre-print version: Loading...

      Abstract: Abstract This study investigates the evolution of an extreme anomalous anticyclone (AA) event over Northeast Asia, which was one of the dominant circulation systems responsible for the catastrophic extreme precipitation event in July 2021 in Henan, and further explores the significant impact of this AA on surface temperatures beneath it. The results indicate that this AA event over Northeast Asia was unprecedented in terms of intensity and duration. The AA was very persistent and extremely strong for 10 consecutive days from 13 to 22 July 2021. This long-lived and unprecedented AA led to the persistence of warmer surface temperatures beyond the temporal span of the pronounced 500-hPa anticyclonic signature as the surface air temperatures over land in Northeast Asia remained extremely warm through 29 July 2021. Moreover, the sea surface temperatures in the Sea of Japan/East Sea were extremely high for 30 consecutive days from 13 July to 11 August 2021, persisting well after the weakening or departure of this AA. These results emphasize the extreme nature of this AA over Northeast Asia in July 2021 and its role in multiple extreme climate events, even over remote regions. Furthermore, possible reasons for this long-lasting AA are explored, and it is suggested to be a byproduct of a teleconnection pattern over extratropical Eurasia during the first half of its life cycle, and of the Pacific–Japan teleconnection pattern during the latter half.
      PubDate: 2024-02-09
       
  • Alignment of Track Oscillations during Tropical Cyclone Rapid
           Intensification

    • Free pre-print version: Loading...

      Abstract: Abstract Recent studies on tropical cyclone (TC) intensity change indicate that the development of a vertically aligned TC circulation is a key feature of its rapid intensification (RI), however, understanding how vortex alignment occurs remains a challenging topic in TC intensity change research. Based on the simulation outputs of North Atlantic Hurricane Wilma (2005) and western North Pacific Typhoon Rammasun (2014), vortex track oscillations at different vertical levels and their associated role in vortex alignment are examined to improve our understanding of the vortex alignment during RI of TCs with initial hurricane intensity. It is found that vortex tracks at different vertical levels oscillate consistently in speed and direction during the RI of the two simulated TCs. While the consistent track oscillation reduces the oscillation tilt during RI, the reduction of vortex tilt results mainly from the mean track before RI. It is also found that the vortex tilt is primarily due to the mean vortex track before and after RI. The track oscillations are closely associated with wavenumber-1 vortex Rossby waves that are dominant wavenumber-1 circulations in the TC inner-core region. This study suggests that the dynamics of the wavenumber-1 vortex Rossby waves play an important role in the regulation of the physical processes associated with the track oscillation and vertical alignment of TCs.
      PubDate: 2024-02-09
       
  • The Contribution of United States Aircraft Reconnaissance Data to the
           China Meteorological Administration Tropical Cyclone Intensity Data: An
           Evaluation of Homogeneity

    • Free pre-print version: Loading...

      Abstract: Abstract This paper investigates the homogeneity of United States aircraft reconnaissance data and the impact of these data on the homogeneity of the tropical cyclone (TC) best track data for the seasons 1949–1987 generated by the China Meteorological Administration (CMA). The evaluation of the reconnaissance data shows that the minimum central sea level pressure (MCP) data are relatively homogeneous, whereas the maximum sustained wind (MSW) data show both overestimations and spurious abrupt changes. Statistical comparisons suggest that both the reconnaissance MCP and MSW were well incorporated into the CMA TC best track dataset. Although no spurious abrupt changes were evident in the reconnaissance-related best track MCP data, two spurious changepoints were identified in the remainder of the best-track MCP data. Furthermore, the influence of the reconnaissance MSWs seems to extend to the best track MSWs unrelated to reconnaissance, which might reflect the optimistic confidence in making higher estimates due to the overestimated extreme wind “observations”. In addition, the overestimation of either the reconnaissance MSWs or the best track MSWs was greater during the early decades compared to later decades, which reflects the important influence of reconnaissance data on the CMA TC best track dataset. The wind–pressure relationship (WPR) used in the CMA TC best track dataset is also evaluated and is found to overestimate the MSW, which may lead to inhomogeneity within the dataset between the aircraft reconnaissance era and the satellite era.
      PubDate: 2024-02-09
       
  • Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating
           Physics-based Models

    • Free pre-print version: Loading...

      Abstract: Abstract Accurate soil moisture (SM) prediction is critical for understanding hydrological processes. Physics-based (PB) models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes. In addition to PB models, deep learning (DL) models have been widely used in SM predictions recently. However, few pure DL models have notably high success rates due to lacking physical information. Thus, we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions. To this end, we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale (attention model). We further built an ensemble model that combined the advantages of different hybrid schemes (ensemble model). We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory (ConvLSTM) model for 1–16 days of SM predictions. The performances of the proposed hybrid models were investigated and compared with two existing hybrid models. The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models. Moreover, the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions. It is highlighted that the ensemble model outperformed the pure DL model over 79.5% of in situ stations for 16-day predictions. These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.
      PubDate: 2024-02-07
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 100.26.196.222
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-
JournalTOCs
 
 

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> METEOROLOGY (Total: 106 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Similar Journals
HOME > Browse the 73 Subjects covered by JournalTOCs  
SubjectTotal Journals
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 100.26.196.222
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-