Publisher: Hindawi   (Total: 343 journals)

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Showing 1 - 200 of 343 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.343, CiteScore: 1)
Active and Passive Electronic Components     Open Access   (Followers: 8, SJR: 0.136, CiteScore: 0)
Advances in Acoustics and Vibration     Open Access   (Followers: 51, SJR: 0.147, CiteScore: 0)
Advances in Aerospace Engineering     Open Access   (Followers: 62)
Advances in Agriculture     Open Access   (Followers: 12)
Advances in Artificial Intelligence     Open Access   (Followers: 21)
Advances in Astronomy     Open Access   (Followers: 47, SJR: 0.257, CiteScore: 1)
Advances in Bioinformatics     Open Access   (Followers: 20, SJR: 0.565, CiteScore: 2)
Advances in Biology     Open Access   (Followers: 11)
Advances in Chemistry     Open Access   (Followers: 34)
Advances in Civil Engineering     Open Access   (Followers: 51, SJR: 0.539, CiteScore: 1)
Advances in Computer Engineering     Open Access   (Followers: 8)
Advances in Condensed Matter Physics     Open Access   (Followers: 11, SJR: 0.315, CiteScore: 1)
Advances in Decision Sciences     Open Access   (Followers: 4, SJR: 0.303, CiteScore: 1)
Advances in Electrical Engineering     Open Access   (Followers: 52)
Advances in Electronics     Open Access   (Followers: 101)
Advances in Emergency Medicine     Open Access   (Followers: 15)
Advances in Endocrinology     Open Access   (Followers: 6)
Advances in Environmental Chemistry     Open Access   (Followers: 10)
Advances in Epidemiology     Open Access   (Followers: 9)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.161, CiteScore: 1)
Advances in Geology     Open Access   (Followers: 19)
Advances in Geriatrics     Open Access   (Followers: 6)
Advances in Hematology     Open Access   (Followers: 13, SJR: 0.661, CiteScore: 2)
Advances in Hepatology     Open Access   (Followers: 3)
Advances in High Energy Physics     Open Access   (Followers: 25, SJR: 0.866, CiteScore: 2)
Advances in Human-Computer Interaction     Open Access   (Followers: 21, SJR: 0.186, CiteScore: 1)
Advances in Materials Science and Engineering     Open Access   (Followers: 30, SJR: 0.315, CiteScore: 1)
Advances in Mathematical Physics     Open Access   (Followers: 8, SJR: 0.218, CiteScore: 1)
Advances in Medicine     Open Access   (Followers: 3)
Advances in Meteorology     Open Access   (Followers: 24, SJR: 0.48, CiteScore: 1)
Advances in Multimedia     Open Access   (Followers: 1, SJR: 0.173, CiteScore: 1)
Advances in Nonlinear Optics     Open Access   (Followers: 6)
Advances in Numerical Analysis     Open Access   (Followers: 9)
Advances in Nursing     Open Access   (Followers: 37)
Advances in Operations Research     Open Access   (Followers: 13, SJR: 0.205, CiteScore: 1)
Advances in Optical Technologies     Open Access   (Followers: 4, SJR: 0.214, CiteScore: 1)
Advances in Optics     Open Access   (Followers: 7)
Advances in OptoElectronics     Open Access   (Followers: 6, SJR: 0.141, CiteScore: 0)
Advances in Orthopedics     Open Access   (Followers: 11, SJR: 0.922, CiteScore: 2)
Advances in Pharmacological and Pharmaceutical Sciences     Open Access   (Followers: 8, SJR: 0.591, CiteScore: 2)
Advances in Physical Chemistry     Open Access   (Followers: 13, SJR: 0.179, CiteScore: 1)
Advances in Polymer Technology     Open Access   (Followers: 14, SJR: 0.299, CiteScore: 1)
Advances in Power Electronics     Open Access   (Followers: 42, SJR: 0.184, CiteScore: 0)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 28)
Advances in Regenerative Medicine     Open Access   (Followers: 4)
Advances in Software Engineering     Open Access   (Followers: 11)
Advances in Statistics     Open Access   (Followers: 9)
Advances in Toxicology     Open Access   (Followers: 4)
Advances in Tribology     Open Access   (Followers: 15, SJR: 0.265, CiteScore: 1)
Advances in Urology     Open Access   (Followers: 13, SJR: 0.51, CiteScore: 1)
Advances in Virology     Open Access   (Followers: 8, SJR: 0.838, CiteScore: 2)
AIDS Research and Treatment     Open Access   (Followers: 2, SJR: 0.758, CiteScore: 2)
Analytical Cellular Pathology     Open Access   (Followers: 3, SJR: 0.886, CiteScore: 2)
Anatomy Research Intl.     Open Access   (Followers: 4)
Anemia     Open Access   (Followers: 6, SJR: 0.669, CiteScore: 2)
Anesthesiology Research and Practice     Open Access   (Followers: 15, SJR: 0.501, CiteScore: 1)
Applied and Environmental Soil Science     Open Access   (Followers: 18, SJR: 0.451, CiteScore: 1)
Applied Bionics and Biomechanics     Open Access   (Followers: 7, SJR: 0.288, CiteScore: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 14)
Archaea     Open Access   (Followers: 4, SJR: 0.852, CiteScore: 2)
Autism Research and Treatment     Open Access   (Followers: 34)
Autoimmune Diseases     Open Access   (Followers: 3, SJR: 0.805, CiteScore: 2)
Behavioural Neurology     Open Access   (Followers: 9, SJR: 0.786, CiteScore: 2)
Biochemistry Research Intl.     Open Access   (Followers: 6, SJR: 0.437, CiteScore: 2)
Bioinorganic Chemistry and Applications     Open Access   (Followers: 11, SJR: 0.419, CiteScore: 2)
BioMed Research Intl.     Open Access   (Followers: 5, SJR: 0.935, CiteScore: 3)
Biotechnology Research Intl.     Open Access   (Followers: 1)
Bone Marrow Research     Open Access   (Followers: 2, SJR: 0.531, CiteScore: 1)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 4, SJR: 0.867, CiteScore: 1)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 8, SJR: 0.548, CiteScore: 1)
Canadian Respiratory J.     Open Access   (Followers: 3, SJR: 0.474, CiteScore: 1)
Cardiology Research and Practice     Open Access   (Followers: 11, SJR: 1.237, CiteScore: 4)
Cardiovascular Therapeutics     Open Access   (Followers: 2, SJR: 1.075, CiteScore: 2)
Case Reports in Anesthesiology     Open Access   (Followers: 11)
Case Reports in Cardiology     Open Access   (Followers: 8, SJR: 0.219, CiteScore: 0)
Case Reports in Critical Care     Open Access   (Followers: 12)
Case Reports in Dentistry     Open Access   (Followers: 7, SJR: 0.229, CiteScore: 0)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 17)
Case Reports in Endocrinology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 3)
Case Reports in Genetics     Open Access   (Followers: 2)
Case Reports in Hematology     Open Access   (Followers: 9)
Case Reports in Hepatology     Open Access   (Followers: 2)
Case Reports in Immunology     Open Access   (Followers: 6)
Case Reports in Infectious Diseases     Open Access   (Followers: 6)
Case Reports in Medicine     Open Access   (Followers: 3)
Case Reports in Nephrology     Open Access   (Followers: 5)
Case Reports in Neurological Medicine     Open Access   (Followers: 1)
Case Reports in Obstetrics and Gynecology     Open Access   (Followers: 11)
Case Reports in Oncological Medicine     Open Access   (Followers: 2, SJR: 0.204, CiteScore: 1)
Case Reports in Ophthalmological Medicine     Open Access   (Followers: 3)
Case Reports in Orthopedics     Open Access   (Followers: 6)
Case Reports in Otolaryngology     Open Access   (Followers: 7)
Case Reports in Pathology     Open Access   (Followers: 7)
Case Reports in Pediatrics     Open Access   (Followers: 7)
Case Reports in Psychiatry     Open Access   (Followers: 17)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 12)
Case Reports in Rheumatology     Open Access   (Followers: 10)
Case Reports in Surgery     Open Access   (Followers: 12)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 12)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 5)
Child Development Research     Open Access   (Followers: 20, SJR: 0.144, CiteScore: 0)
Chinese J. of Engineering     Open Access   (Followers: 2, SJR: 0.114, CiteScore: 0)
Chinese J. of Mathematics     Open Access  
Chromatography Research Intl.     Open Access   (Followers: 5)
Complexity     Hybrid Journal   (Followers: 7, SJR: 0.531, CiteScore: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.403, CiteScore: 1)
Computational Biology J.     Open Access   (Followers: 7)
Computational Intelligence and Neuroscience     Open Access   (Followers: 13, SJR: 0.326, CiteScore: 1)
Concepts in Magnetic Resonance Part A     Open Access   (Followers: 1, SJR: 0.354, CiteScore: 1)
Concepts in Magnetic Resonance Part B, Magnetic Resonance Engineering     Open Access   (Followers: 1, SJR: 0.26, CiteScore: 1)
Conference Papers in Science     Open Access   (Followers: 2)
Contrast Media & Molecular Imaging     Open Access   (Followers: 2, SJR: 0.842, CiteScore: 3)
Critical Care Research and Practice     Open Access   (Followers: 13, SJR: 0.499, CiteScore: 1)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 9, SJR: 0.512, CiteScore: 2)
Depression Research and Treatment     Open Access   (Followers: 19, SJR: 0.816, CiteScore: 2)
Dermatology Research and Practice     Open Access   (Followers: 4, SJR: 0.806, CiteScore: 2)
Diagnostic and Therapeutic Endoscopy     Open Access   (SJR: 0.201, CiteScore: 1)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 6, SJR: 0.279, CiteScore: 1)
Disease Markers     Open Access   (Followers: 1, SJR: 0.9, CiteScore: 2)
Economics Research Intl.     Open Access   (Followers: 1)
Education Research Intl.     Open Access   (Followers: 19)
Emergency Medicine Intl.     Open Access   (Followers: 10, SJR: 0.298, CiteScore: 1)
Enzyme Research     Open Access   (Followers: 5, SJR: 0.653, CiteScore: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 29, SJR: 0.683, CiteScore: 2)
Game Theory     Open Access   (Followers: 1)
Gastroenterology Research and Practice     Open Access   (Followers: 1, SJR: 0.768, CiteScore: 2)
Genetics Research Intl.     Open Access   (Followers: 1, SJR: 0.61, CiteScore: 2)
Geofluids     Open Access   (Followers: 5, SJR: 0.952, CiteScore: 2)
Hepatitis Research and Treatment     Open Access   (Followers: 6, SJR: 0.389, CiteScore: 2)
Heteroatom Chemistry     Open Access   (Followers: 3, SJR: 0.333, CiteScore: 1)
HPB Surgery     Open Access   (Followers: 7, SJR: 0.824, CiteScore: 2)
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 5, SJR: 1.27, CiteScore: 2)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 1, SJR: 0.627, CiteScore: 2)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 78, SJR: 0.232, CiteScore: 1)
Intl. J. of Agronomy     Open Access   (Followers: 6, SJR: 0.311, CiteScore: 1)
Intl. J. of Alzheimer's Disease     Open Access   (Followers: 12, SJR: 0.787, CiteScore: 3)
Intl. J. of Analytical Chemistry     Open Access   (Followers: 22, SJR: 0.285, CiteScore: 1)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 11, SJR: 0.233, CiteScore: 1)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 21)
Intl. J. of Biodiversity     Open Access   (Followers: 3)
Intl. J. of Biomaterials     Open Access   (Followers: 5, SJR: 0.511, CiteScore: 2)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 3, SJR: 0.501, CiteScore: 2)
Intl. J. of Breast Cancer     Open Access   (Followers: 14, SJR: 1.025, CiteScore: 2)
Intl. J. of Cell Biology     Open Access   (Followers: 4, SJR: 1.887, CiteScore: 4)
Intl. J. of Chemical Engineering     Open Access   (Followers: 8, SJR: 0.327, CiteScore: 1)
Intl. J. of Chronic Diseases     Open Access   (Followers: 1)
Intl. J. of Combinatorics     Open Access   (Followers: 1)
Intl. J. of Computer Games Technology     Open Access   (Followers: 10, SJR: 0.287, CiteScore: 2)
Intl. J. of Corrosion     Open Access   (Followers: 11, SJR: 0.194, CiteScore: 1)
Intl. J. of Dentistry     Open Access   (Followers: 8, SJR: 0.649, CiteScore: 2)
Intl. J. of Differential Equations     Open Access   (Followers: 8, SJR: 0.191, CiteScore: 0)
Intl. J. of Digital Multimedia Broadcasting     Open Access   (Followers: 5, SJR: 0.296, CiteScore: 2)
Intl. J. of Electrochemistry     Open Access   (Followers: 10)
Intl. J. of Endocrinology     Open Access   (Followers: 4, SJR: 1.012, CiteScore: 3)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 7)
Intl. J. of Food Science     Open Access   (Followers: 5, SJR: 0.44, CiteScore: 2)
Intl. J. of Forestry Research     Open Access   (Followers: 3, SJR: 0.373, CiteScore: 1)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.868, CiteScore: 3)
Intl. J. of Geophysics     Open Access   (Followers: 5, SJR: 0.182, CiteScore: 1)
Intl. J. of Hepatology     Open Access   (Followers: 4, SJR: 0.874, CiteScore: 2)
Intl. J. of Hypertension     Open Access   (Followers: 8, SJR: 0.578, CiteScore: 1)
Intl. J. of Inflammation     Open Access   (SJR: 1.264, CiteScore: 3)
Intl. J. of Inorganic Chemistry     Open Access   (Followers: 4)
Intl. J. of Manufacturing Engineering     Open Access   (Followers: 2)
Intl. J. of Mathematics and Mathematical Sciences     Open Access   (Followers: 3, SJR: 0.177, CiteScore: 0)
Intl. J. of Medicinal Chemistry     Open Access   (Followers: 6, SJR: 0.31, CiteScore: 1)
Intl. J. of Metals     Open Access   (Followers: 7)
Intl. J. of Microbiology     Open Access   (Followers: 8, SJR: 0.662, CiteScore: 2)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 3, SJR: 0.136, CiteScore: 1)
Intl. J. of Navigation and Observation     Open Access   (Followers: 20, SJR: 0.267, CiteScore: 2)
Intl. J. of Nephrology     Open Access   (Followers: 2, SJR: 0.697, CiteScore: 1)
Intl. J. of Oceanography     Open Access   (Followers: 8)
Intl. J. of Optics     Open Access   (Followers: 9, SJR: 0.231, CiteScore: 1)
Intl. J. of Otolaryngology     Open Access   (Followers: 3)
Intl. J. of Partial Differential Equations     Open Access   (Followers: 2)
Intl. J. of Pediatrics     Open Access   (Followers: 6)
Intl. J. of Peptides     Open Access   (Followers: 2, SJR: 0.46, CiteScore: 1)
Intl. J. of Photoenergy     Open Access   (Followers: 3, SJR: 0.341, CiteScore: 1)
Intl. J. of Plant Genomics     Open Access   (Followers: 4, SJR: 0.583, CiteScore: 1)
Intl. J. of Polymer Science     Open Access   (Followers: 28, SJR: 0.298, CiteScore: 1)
Intl. J. of Population Research     Open Access   (Followers: 4)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.123, CiteScore: 1)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 6)
Intl. J. of Rheumatology     Open Access   (Followers: 4, SJR: 0.645, CiteScore: 2)
Intl. J. of Rotating Machinery     Open Access   (Followers: 2, SJR: 0.193, CiteScore: 1)
Intl. J. of Spectroscopy     Open Access   (Followers: 8)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 3, SJR: 0.279, CiteScore: 1)
Intl. J. of Surgical Oncology     Open Access   (Followers: 1, SJR: 0.573, CiteScore: 2)
Intl. J. of Telemedicine and Applications     Open Access   (Followers: 7, SJR: 0.403, CiteScore: 2)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.782, CiteScore: 2)
Intl. J. of Zoology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Intl. Scholarly Research Notices     Open Access   (Followers: 233)

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Similar Journals
Journal Cover
Advances in Meteorology
Journal Prestige (SJR): 0.48
Citation Impact (citeScore): 1
Number of Followers: 24  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1687-9309 - ISSN (Online) 1687-9317
Published by Hindawi Homepage  [343 journals]
  • Evaluation of Future Climate and Potential Impact on Streamflow in the
           Upper Nan River Basin of Northern Thailand

    • Abstract: Water resources in Northern Thailand have been less explored with regard to the impact on hydrology that the future climate would have. For this study, three regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX) of Coupled Model Intercomparison Project 5 (CMIP5) were used to project future climate of the upper Nan River basin. Future climate data of ACCESS_CCAM, MPI_ESM_CCAM, and CNRM_CCAM under Representation Concentration Pathways RCP4.5 and RCP8.5 were bias-corrected by the linear scaling method and subsequently drove the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) to simulate future streamflow. This study compared baseline (1988–2005) climate and streamflow values with future time scales during 2020–2039 (2030s), 2040–2069 (2050s), and 2070–2099 (2080s). The upper Nan River basin will become warmer in future with highest increases in the maximum temperature of 3.8°C/year for MPI_ESM and minimum temperature of 3.6°C/year for ACCESS_CCAM under RCP8.5 during 2080s. The magnitude of changes and directions in mean monthly precipitation varies, with the highest increase of 109 mm for ACESSS_CCAM under RCP 4.5 in September and highest decrease of 77 mm in July for CNRM, during 2080s. Average of RCM combinations shows that decreases will be in ranges of −5.5 to −48.9% for annual flows, −31 to −47% for rainy season flows, and −47 to −67% for winter season flows. Increases in summer seasonal flows will be between 14 and 58%. Projection of future temperature levels indicates that higher increases will be during the latter part of the 20th century, and in general, the increases in the minimum temperature will be higher than those in the maximum temperature. The results of this study will be useful for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset negative impacts of future changes in climate. In addition, the results will also be valuable for agriculturists and hydropower planners.
      PubDate: Sat, 24 Oct 2020 08:05:01 +000
  • Changes in Extreme Precipitation in the Mekong Basin

    • Abstract: Extreme precipitation events can trigger many natural disasters like floods, mudslides, and landslides. Understanding historical changes in extreme precipitation is critical for disaster prevention and risk assessment. The Mekong River Basin (MB) is vulnerable to natural disasters related to extreme precipitation. In the past ten years, the MB has experienced some destructive extreme precipitation events. Our concern is whether the historical extreme precipitation events in the MB have increased in a warming climate. This study investigates the spatiotemporal changes in extreme precipitation in the MB from 1951 to 2015 using a high-quality precipitation product and eight indices of extreme precipitation. These indices consistently indicate that the trend in extreme precipitation in the Upper Mekong Basin (UMB) is opposite to that in the Lower Mekong Basin (LMB). Extreme precipitation has generally decreased in the UMB but increased in the LMB. The areas with significant increasing extreme precipitation are mainly located in Laos, Vietnam, and Cambodia. The areas with a statistically significant decline in extreme precipitation primarily occur in the Lancang (China’s section of the Mekong river) and Thailand. Also, the magnitude of changes in extreme precipitation is significantly larger in the LMB than that in the UMB, which potentially increases flooding risks in the LMB. The findings from this study are useful for guiding disaster-prevention efforts in the MB.
      PubDate: Thu, 22 Oct 2020 16:05:01 +000
  • Main Factors Influencing Winter Visibility at the Xinjin Flight College of
           the Civil Aviation Flight University of China

    • Abstract: Utilizing routine hourly meteorological data of Xinjin Airport and daily average PM2.5 concentration data for Chengdu, winter visibility characteristics at Xinjin Airport between 2013 and 2017 and their relationship with meteorological conditions and particulate matter were analyzed. Between 2013 and 2017, the average winter visibility in Xinjin Airport was lowest in January, followed by that in December. The occurrence frequency of haze days in winter was much higher than that of nonhaze (clean) days, being 90.2% and 9.8%, respectively. These were mainly mild haze days, with an occurrence frequency of 44.4%, while severe haze days occurred the least, with a frequency of 7.7%. The linear and nonlinear relationships between winter visibility, meteorological factors, and PM2.5 were measured using daily data in winter from 2013 to 2016. The linear correlation between PM2.5 concentration and visibility was the most evident, followed by that of relative humidity. Visibility had a higher nonlinear correlation with PM2.5 concentration, relative humidity, and dew point depression. When relative humidity was between 70% and 80%, the negative correlation between visibility and PM2.5 concentration was the most significant and could be described by a power function. The multivariate linear regression equation of PM2.5 concentration and relative humidity could account for 65.9% of the variation in winter visibility, and the multivariate nonlinear regression equation of PM2.5 concentration, relative humidity, and wind speed could account for 68.1% of the variation in winter visibility. These two equations reasonably represented the variation in winter visibility in 2017.
      PubDate: Tue, 20 Oct 2020 05:20:00 +000
  • Changes in Temperature Trends and Extremes over Saudi Arabia for the
           Period 1978–2019

    • Abstract: Climate change is posing severe threats to human health through its impacts on food, water supply, and weather. Saudi Arabia has frequently experienced record-breaking climate extremes over the last decade, which have had adverse socioeconomic effects on many sectors of the country. The present study explores the changes in average temperature and temperature extremes over Saudi Arabia using an updated daily temperature dataset for the period 1978–2019. Also, changes in frequency and percentile trends of extreme events, as well as in absolute threshold-based temperature extremes, are analyzed at seasonal and annual time scales. The results are robust in showing an increase in both temperature trends and temperature extremes averaged over the second period (2000–2019) when compared to the first period (1980–1999). Over the period 1978–2019, the minimum temperature for the country increased (0.64°C per decade) at a higher rate than the maximum temperature (0.60°C per decade). The rate of increase in the minimum and maximum temperatures was reported as 0.48 and 0.71°C per decade, respectively, for the period 1978–2009. The minimum temperature increased by 0.81°C per decade for the second period compared to an increase of 0.47°C per decade for the first period. The significant increase in minimum temperature has resulted in a decreasing linear trend in the diurnal temperature range over recent decades. The maximum (minimum) temperature increased at a higher rate for Jan-Mar (Jun-Nov) with the highest increase of 0.82 (0.89)°C per decade occurring in March (August). The analysis shows a substantial increase (decrease) in the number of warm (cold) days/nights over the second period compared to the first period. The number of warm days (nights) significantly increased by about 13 (21) days per decade, and there is a significant decrease of about 11 (13) days per decade of cold days (nights). The seasonal analysis shows that this increase in warm days/nights is enhanced in boreal summer, with a reduction in the number of cold days/nights in winter. These results indicate that the warming climate of Saudi Arabia is accelerating in recent decades, which may have severe socioeconomic repercussions in many sectors of the country.
      PubDate: Sat, 17 Oct 2020 08:35:01 +000
  • CNOP-P-Based Parameter Sensitivity Analysis for North Atlantic Oscillation
           in Community Earth System Model Using Intelligence Algorithms

    • Abstract: Model error, which results from model parameters, can cause the nonnegligible uncertainty in the North Atlantic Oscillation (NAO) simulation. Conditional nonlinear optimal perturbation related to parameter (CNOP-P) is a powerful approach to investigate the range of uncertainty caused by model parameters under a specific constraint. In this paper, we adopt intelligence algorithms to implement the CNOP-P method and conduct the sensitivity analysis of parameter combinations for NAO events in the Community Earth System Model (CESM). Among 28 model parameters of the atmospheric component, the most sensitive parameter combination for the consists of parameter for deep convection (cldfrc_dp1), minimum relative humidity for low stable clouds (cldfrc_rhminl), and the total solar irradiance (solar_const). As for the , the parameter set that can trigger the largest variation of the NAO index (NAOI) is comprised of the constant for evaporation of precip (cldwat_conke), characteristic adjustment time scale (hkconv_cmftau), and the total solar irradiance (solar_const). The most prominent uncertainties of the NAOI () caused by these two combinations achieve 2.12 for and −2.72 for , respectively. In comparison, the maximum level of the NAOI variation resulting from single parameters reaches 1.45 for and −1.70 for . It is indicated that the nonlinear impact of multiple parameters would be more intense than the single parameter. These results present factors that are closely related to NAO events and also provide the direction of optimizing model parameters. Moreover, the intelligence algorithms adopted in this work are proved to be adequate to explore the nonlinear interaction of parameters on the model simulation.
      PubDate: Thu, 15 Oct 2020 12:35:00 +000
  • Spatiotemporal Variations of Extreme Precipitation Events in the Jinsha
           River Basin, Southwestern China

    • Abstract: Climate extremes have attracted widespread attention for their threats to the natural environment and human society. Based on gauged daily precipitation from 1963 to 2016 in four subregions of the Jinsha River Basin (JRB), four extreme precipitation indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) were employed to assess the spatiotemporal variations of extreme precipitation events. Results show the following: (1) Max one-day precipitation amount (RX1day), max consecutive five-day precipitation amount (RX5day), precipitation on very wet days (R95p), and number of heavy precipitation days (R10mm) showed increasing trends in four subregions except for the decline of R10mm in the southeastern and RX5day in the midsouthern. Extreme precipitation has become more intense and frequent in most parts of the JRB. (2) In space, the four extreme precipitation indices increased from the northwest to the southeast. Temporal trends of extreme precipitation showed great spatial variability. It is notable that extreme precipitation increased apparently in higher elevation areas. (3) The abrupt change of extreme precipitation in the northwestern, midsouthern, and southeastern mainly appeared in the late 1990s and the 2000s. For the midnorthern, abrupt change mainly occurred in the late 1980s. This study is meaningful for regional climate change acquaintance and disaster prevention in the JRB.
      PubDate: Wed, 14 Oct 2020 07:50:01 +000
  • Comparison of Machine-Learning Algorithms for Near-Surface Air-Temperature
           Estimation from FY-4A AGRI Data

    • Abstract: Six machine-learning approaches, including multivariate linear regression (MLR), gradient boosting decision tree, k-nearest neighbors, random forest, extreme gradient boosting (XGB), and deep neural network (DNN), were compared for near-surface air-temperature (Tair) estimation from the new generation of Chinese geostationary meteorological satellite Fengyun-4A (FY-4A) observations. The brightness temperatures in split-window channels from the Advanced Geostationary Radiation Imager (AGRI) of FY-4A and numerical weather prediction data from the global forecast system were used as the predictor variables for Tair estimation. The performance of each model and the temporal and spatial distribution of the estimated Tair errors were analyzed. The results showed that the XGB model had better overall performance, with R2 of 0.902, bias of −0.087°C, and root-mean-square error of 1.946°C. The spatial variation characteristics of the Tair error of the XGB method were less obvious than those of the other methods. The XGB model can provide more stable and high-precision Tair for a large-scale Tair estimation over China and can serve as a reference for Tair estimation based on machine-learning models.
      PubDate: Tue, 06 Oct 2020 12:20:01 +000
  • The Impact of Length-Scale Variation When Diagnosing the Standard
           Deviations of Background Error in a 4D-Var System and Filtering Method

    • Abstract: The four-dimensional variational data assimilation (4D-Var) method has been widely employed as an operational scheme in mainstream numerical weather prediction (NWP) centers. In addition to the ensemble data assimilation method, the randomization technique is still used to diagnose the standard deviations of background error in variational data assimilation (VAR) systems; however, such randomization techniques induce sampling noise, which may contaminate the quality of the standard deviations. First, this paper studies the properties of the sampling noise induced by the randomization technique. The results show that the sampling noise is on a small scale displaying high-frequency oscillations around the estimate compared with the estimate and this difference motivates the use of filtering techniques to eliminate the sampling noise effects. The characteristics of the standard deviation field of the control variables are also investigated, and the standard deviation fields of different model parameters have different scales and vary with the vertical model levels. To eliminate such sampling noise, the spectral filtering method used widely in the operational system and a modified spatial averaging approach are investigated. Although both methods have splendid performance in eliminating sampling noise, the spatial averaging approach is more efficient and easier to implement in operational systems. In addition, the optimal filtered results from the spatial averaging approach are dependent on model parameters and vertical levels, which is consistent with the variation in the standard deviation field. Finally, the spatial averaging approach is tested on the operational system at the global scale based on the YH4DVAR and the global NWP system, and the results indicate that the spatial averaging approach has positive effects on both analysis and forecast quality.
      PubDate: Mon, 05 Oct 2020 13:50:01 +000
  • Climate Comfort Evaluation of National 5A TouristAttractions in the
           Mainland of China Based on Universal Thermal Climate Index

    • Abstract: Based on the daily climate data from 839 meteorological stations covering the 2014–2017 period in the mainland of China, the Universal Thermal Climate Indices (UTCI) were calculated and the UTCI of 247 national 5A tourist attractions in the mainland of China are obtained with ordinary kriging interpolation method. Then, a spatial analysis of all the attractions was carried out based on UTCI. The results showed that the mainland of China’s annual average UTCI is generally distributed as strip-belts along a latitudinal direction and the climate comfort level gradually decreases from south to north. Significant regional differences in climate comfort results are obtained between the southeast coastal areas and the northwest inland. It was found that the number of attractions with the best climate comfort level is relatively high in spring and autumn while it is less in summer and winter. Considering the climate comfort levels, the attractions are grouped into five categories of “comfortable during spring and autumn,” “comfortable during winter,” “comfortable during spring, autumn, and winter,” “comfortable during spring, summer, and autumn,” and “uncomfortable during the four seasons” to carry out the study for determining the most convenient period of the year in terms of climate comfort.
      PubDate: Mon, 28 Sep 2020 11:35:01 +000
  • Field Assessment of Neighboring Building and Tree Shading Effects on the
           3D Radiant Environment and Human Thermal Comfort in Summer within Urban
           Settlements in Northeast China

    • Abstract: Shading is one of the most effective strategies to mitigate urban local-scale heat stress during summer. Therefore, this study investigates the effects of shading caused by buildings and trees via exhaustive field measurement research on urban outdoor 3D radiant environment and human thermal comfort. We analyzed the characteristics of micrometeorology and human thermal comfort at shaded areas, and compared the difference between building and tree shading effects as well as that between shaded and sunlit sites. The results demonstrate that mean radiant temperature Tmrt (mean reduction values of 28.1°C for tree shading and 28.8°C for building shading) decreased considerably more than air temperature Ta (mean reduction values of 1.9°C for tree shading and 1.2°C for building shading) owing to shading; furthermore, the reduction effect of shading on UTCI synthesized the variation in the above two parameters. Within the shaded areas, short-wave radiant components (mean standardized values of 0.104 for tree shading and 0.087 for building shading) decreased considerably more than long-wave radiant components (mean standardized values of 0.848 for tree shading and 0.851 for building shading) owing to shading; the proportion of long-wave radiant flux densities absorbed by the reference standing person was high, leading to a relatively high long-wave mean radiant temperature, and R2 between long-wave mean radiant temperature and air temperature exceeded 0.8. Moreover, the directional sky view factor (SVF) was utilized in this study, and it showed significant positive correlation with short-wave radiant flux densities, but no statistically evident correlation with long-wave radiant flux densities. Meanwhile, Tmrt was most relevant with SVFS⟶ with R2 of 0.9756. Furthermore, UTCI rose two categories at the sunlit areas compared with that at the shaded areas. In contrast, Ta and Tmrt played the first positive role in UTCI at shaded and sunlit areas, respectively.
      PubDate: Wed, 23 Sep 2020 07:50:01 +000
  • CP El Niño and PDO Variability Affect Summer Precipitation over East

    • Abstract: The summer precipitation produced by the East Asian summer monsoon (EASM) is significantly affecting agriculture and socioeconomics. Based on the Precipitation Reconstruction dataset in East China from 1950 to 2017, we investigate the spatiotemporal variations of summer precipitation, influencing environmental factors and their relation with the EASM and the Pacific Decadal Oscillation (PDO) in both central Pacific (CP) El Niño developing and decaying years. Results indicate the following: (1) The evolutions of CP El Niño events modulate the summer precipitation anomalies in East China. In the cool PDO phase, CP El Niño causes enhanced precipitation anomalies in the decaying years but less precipitation anomalies in the developing years, and vice versa for the warm PDO phase. (2) Atmospheric circulation anomalies drive the moisture transportation and combine the motion of western Pacific subtropical high resulting in the variation of precipitation patterns. Anomalous cyclone over the western North Pacific and the sustained Western Pacific Subtropical High (WPSH) are favorable for the increment of summer precipitation. (3) The different CP El Niño-EASM relationship is caused by the influences of PDO on the evolution of CP El Niño. CP El Niño develops slowly (decays rapidly) and is associated with rapidly developing (slowly decaying) anomalous warming in the north Indian Ocean during the developing (decaying) years.
      PubDate: Tue, 22 Sep 2020 11:35:00 +000
  • Prediction of Precipitation in the Western Mountainous Regions of China
           Using a Statistical Model

    • Abstract: During the summer in the western mountainous regions of China (WMR), the disasters such as mountain floods, landslides, and debris flows caused by heavy rain occur frequently, which often result in huge economic losses and many casualties. Therefore, it is of great significance to predict the precipitation accurately in these regions. In this paper, a statistical model is established to predict the precipitation in the WMR using the linear regression statistical method, in which the summer area-averaged precipitation anomaly in WMR is taken as the predictand and the prewinter Niño3 SST is taken as the predictor. The results of the return cross test for the historical years from 1979 to 2008 and independent sample return test from 2009 to 2018 show that this statistical model has a good performance in predicting the summer precipitation in the WMR, especially in the flood years. It has better skill in the prediction of WMR precipitation than the dynamical model SINTEX-F.
      PubDate: Tue, 22 Sep 2020 06:20:00 +000
  • Analysis of the Anomalous Signals near the Tropopause before the
           Overshooting Convective System Onset over the Tibetan Plateau

    • Abstract: This study investigates the anomalous signals near the tropopause before the overshooting convective system (OCS) onset over the Tibetan Plateau (TP). It is found that the tropopause height is stable at the maximum height seven and five days before the OCS onset. It then decreases significantly one day before and on the day of the OCS onset. The upward motion in the troposphere is the strongest five days before the OCS onset. From one day before and after the OCS onset, there are strong ascending motions at 500–300 hPa but weak descending motions near the tropopause. It is proposed that the descending of the tropopause height on the day of the OCS onset is caused by frequent tropopause fold events over the eastern TP associated with frequent cold trough intrusion from the north and the southeastward movement of upper-level westerly jet stream. The decrease of the tropopause height is accompanied by the intrusion of stratospheric air with higher potential vorticity (PV). Positive potential vorticity anomalies on 350 K isentropic surface can be noted in the region where the tropopause height decreases one day before and on the day of the OCS onset. With the deepening of the tropopause fold on the day of the OCS onset, there is not only downward motion near the tropopause in the area behind of the fold but also upward motion in the troposphere beneath the folding region. In addition, the upward displacement of isentropic surfaces leads to an upper-level cold pool, which causes a reduction in static stability beneath the PV anomaly on the day of the OCS onset. The upper-level PV anomalies and their associated strong instability in the middle troposphere can trigger convective activities by the release of potential instability on the day of the OCS onset. The overshooting convection is more likely to occur due to lower tropopause height, although upward motion in the troposphere is not the strongest.
      PubDate: Thu, 17 Sep 2020 14:05:03 +000
  • Improved Rainfall Prediction through Nonlinear Autoregressive Network with
           Exogenous Variables: A Case Study in Andes High Mountain Region

    • Abstract: Precipitation is the most relevant element in the hydrological cycle and vital for the biosphere. However, when extreme precipitation events occur, the consequences could be devastating for humans (droughts or floods). An accurate prediction of precipitation helps decision-makers to develop adequate mitigation plans. In this study, linear and nonlinear models with lagged predictors and the implementation of a nonlinear autoregressive model with exogenous variables (NARX) network were used to predict monthly rainfall in Labrado and Chirimachay meteorological stations. To define a suitable model, ridge regression, lasso, random forest (RF), support vector machine (SVM), and NARX network were used. Although the results were “unsatisfactory” with the linear models, the specific direct influences of variables such as Niño 1 + 2, Sahel rainfall, hurricane activity, North Pacific Oscillation, and the same delayed rainfall signal were identified. RF and SVM also demonstrated poor performance. However, RF had a better fit than linear models, and SVM has a better fit than RF models. Instead, the NARX model was trained using several architectures to identify an optimal one for the best prediction twelve months ahead. As an overall evaluation, the NARX model showed “good” results for Labrado and “satisfactory” results for Chirimachay. The predictions yielded by NARX models, for the first six months ahead, were entirely accurate. This study highlighted the strengths of NARX networks in the prediction of chaotic and nonlinear signals such as rainfall in regions that obey complex processes. The results would serve to make short-term plans and give support to decision-makers in the management of water resources.
      PubDate: Thu, 17 Sep 2020 14:05:03 +000
  • Comparison of the Atmospheric 200 hPa Jet’s Analyses between Proper
           Orthogonal Decomposition and Advanced Dynamic Mode Decomposition Method

    • Abstract: In this paper, a frequently employed technique named the sparsity-promoting dynamic mode decomposition (SPDMD) is proposed to analyze the velocity fields of atmospheric motion. The dynamic mode decomposition method (DMD) is an effective technique to extract dynamic information from flow fields that is generated from direct experiment measurements or numerical simulation and has been broadly employed to study the dynamics of the flow, to achieve a reduced-order model (ROM) of the complex high dimensional flow field, and even to predict the evolution of the flow in a short time in the future. However, for standard DMD, it is hard to determine which modes are the most physically relevant, unlike the proper orthogonal decomposition (POD) method which ranks the decomposed modes according to their energy content. The advanced modal decomposition method SPDMD is a variant of the standard DMD, which is capable of determining the modes that can be used to achieve a high-quality approximation of the given field. It is novel to introduce the SPDMD to analyze the atmospheric flow field. In this study, SPDMD is applied to extract essential dynamic information from the 200 hPa jet flow, and the decomposed results are compared with the POD method. To further demonstrate the extraction effect of POD/SPDMD methods on the 200 hPa jet flow characteristics, the POD/SPDMD reduced-order models are constructed, respectively. The results show that both modal decomposition methods successfully extract the underlying coherent structures from the 200 hPa jet flow. And the DMD method provides additional information on the modal properties, such as temporal frequency and growth rate of each mode which can be used to identify the stability of the modes. It is also found that a fewer order of modes determined by the SPDMD method can capture nearly the same dynamic information of the jet flow as the POD method. Furthermore, from the quantitative comparisons between the POD and SPDMD reduced-order models, the latter provides a higher precision than the former, especially when the number of modes is small.
      PubDate: Thu, 17 Sep 2020 11:35:02 +000
  • Characteristics of the South China Sea Monsoon from the Onset to
           Withdrawal before and after 1993/94

    • Abstract: The characteristics and possible impact factors of the South China Sea summer monsoon (SCSSM) evolution from onset to withdrawal before and after 1993/94 are investigated using ERA-Interim, CPC rainfall, and OLR data. During the late-onset period of 1979–1993, the SCSSM was characterized by stronger onset intensity and a gradual withdrawal, resulting in a continuous, strong preflood season in Southern China and a slower rain-belt retreat from north to south China in September. In addition, the rain-belt in the Yangtze River basin persisted much longer during summer. However, during the early-onset period in 1994–2016, the SCSSM is associated with a weaker onset intensity and comparatively faster retreat. The advanced preflood season lasted intermittently throughout May and the whole eastern China precipitation lasted until October when it retreated rapidly, making the rain-belt in Southern China persist for an extended duration. Further analysis indicates that a strong modulation of SCS intraseasonal oscillation (ISO) on the SCSSM evolution is observed. There are two active low-frequency oscillations over the SCS in summer during the late-onset period but three during the early-onset period. The wet ISO in the Northwest Pacific propagating northwestward into the SCS and enhanced SCSSM ISO activity may contribute to the early onset and faster withdrawal after 1993/94. The effect of warm western Pacific sea surface temperatures (SST) on the SCSSM evolution is also discussed.
      PubDate: Wed, 16 Sep 2020 15:35:02 +000
  • Evaluation of Temperature-Based Empirical Models and Machine Learning
           Techniques to Estimate Daily Global Solar Radiation at Biratnagar Airport,

    • Abstract: Global solar radiation (GSR) is a critical variable for designing photovoltaic cells, solar furnaces, solar collectors, and other passive solar applications. In Nepal, the high initial cost and subsequent maintenance cost required for the instrument to measure GSR have restricted its applicability all over the country. The current study compares six different temperature-based empirical models, artificial neural network (ANN), and other five different machine learning (ML) models for estimating daily GSR utilizing readily available meteorological data at Biratnagar Airport. Amongst the temperature-based models, the model developed by Fan et al. performs better than the rest with an of 0.7498 and RMSE of . Feed-forward multilayer perceptron (MLP) is utilized to model daily GSR utilizing extraterrestrial solar radiation, sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity as inputs. ANN3 performs better than other ANN models with an of 0.8446 and RMSE of . Likewise, stepwise linear regression performs better than other ML models with an of 0.8870 and RMSE of . Thus, the model developed by Fan et al. is recommended to estimate daily GSR in the region where only ambient temperature data are available. Similarly, a more robust ANN3 and stepwise linear regression models are recommended to estimate daily GSR in the region where data about sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity are available.
      PubDate: Wed, 16 Sep 2020 12:35:02 +000
  • Review of Sensor Network-Based Irrigation Systems Using IoT and Remote

    • Abstract: The motivation for this review paper came from the developing countries where the economy is mostly dependent on agriculture and climate conditions. Based on current conditions and historical records, profitability in production farming depends on making a right and timely operational decision. Precision farming is a systematic program designed to maximize the productivity of agriculture by carefully tailoring the soil and crop management to meet the specific requirements in each field while preserving environmental quality. This review paper highlights the development of an automated irrigation system with portable wireless sensor networks and decision support methods to remotely measure the environmental parameters in an agriculture field. Radio satellite, mobile phones, sensors, internet-based communication, and microcontroller capture the ecological parameters such as soil moisture, temperature, humidity, and light intensity. The knowledge gained from the sensors is transferred directly to the cloud server by using IoT technology. Users from anywhere in the world can display them through an internet-enabled device. Development of sensor-based application in modern agriculture makes it cost-effective and potentially productive and increases the efficiency through precision agriculture farming. Different limitations have been reported in the previously reviewed publications like the shortage of power in the field that can be solved by using a solar panel that recharges the battery at the same time using electricity. Bluetooth application in the agriculture sector is mainly improved by design system optimization. Problems related to transmission and radio range frequency can be solved by using a power class upgraded antenna.
      PubDate: Mon, 07 Sep 2020 02:20:01 +000
  • Opposite Effects of ENSO on the Rainfall over the Northern and Equatorial
           Great Horn of Africa and Possible Causes

    • Abstract: In this study, we explore the possible mechanism of opposite ENSO effects on summer rainfall in the JJAS region (northern GHA) and autumn rainfall in the OND region (equatorial GHA). The two regions are identified based on the spatial distribution of high seasonal fractions of annual rainfall for the period 1979–2016. The summer rainfall over the JJAS region is negatively correlated with ENSO. It is because the warm Niño3.4 SST triggers zonal wave one pattern in tropics and forces upper-level westerly anomaly and the low-level easterly anomaly over tropical Africa. Thus, the weakened upper-level Tropical Easterly Jet (TEJ) and the low-level westerly over the JJAS region result in deficient rainfall during JJAS over the northern GHA. For the autumn rainfall variability over the equatorial GHA, IOD is a pivotal factor. But, autumn rainfall anomalies are far greater in ENSO and IOD coexisting years than those in IOD alone years. In other words, ENSO has a significant impact on the autumn rainfall over the equatorial GHA by means of IOD. It is because the warming SST, which is fully developed over western Indian Ocean (IO) in autumn of ENSO developing year, causes low-level convergence over the equatorial GHA and enhances upper-level easterly over tropical Africa. Those conditions are favorable for abundant rainfall over the equatorial GHA in autumn.
      PubDate: Thu, 03 Sep 2020 08:35:01 +000
  • A Spatial Interpolation of Meteorological Parameters considering
           Geographic Semantics

    • Abstract: Spatial interpolation of meteorological parameters, closely related to the earth surface, plays important roles in climatological study. However, most of traditional spatial interpolation methods ignore the geographic semantics of interpolation sample points in practical application. This paper attempts to propose an improved inverse-distance weighting interpolation algorithm considering geographic semantics (S-IDW), which adds geographic semantic similarity to the traditional IDW formula and adjusts weight coefficient. In the interpolation process, the geographic semantic differences between sample points and estimation points are considered comprehensively. In this study, 3 groups of land surface temperature data from 2 different areas were selected for experiments, and several commonly used spatial interpolation methods were compared. Experimental results indicated that S-IDW outperformed IDW and several existing spatial interpolation methods, but there were also some abnormal value and interpolation outliers. This method provides a new insight toward the estimation accuracy, data missing, and error correction of spatial attributes related to meteorological parameters.
      PubDate: Wed, 02 Sep 2020 13:20:01 +000
  • Spatiotemporal Assessment of Temperature Data Products for the Detection
           of Warming Trends and Abrupt Transitions over the Largest Irrigated Area
           of Pakistan

    • Abstract: Reliable and accurate temperature data acquisition is not only important for hydroclimate research but also crucial for the management of water resources and agriculture. Gridded data products (GDPs) offer an opportunity to estimate and monitor temperature indices at a range of spatiotemporal resolutions; however, their reliability must be quantified by spatiotemporal comparison against in situ records. Here, we present spatial and temporal assessments of temperature indices (Tmax, Tmin, Tmean, and DTR) products against the reference data during the period of 1979–2015 over Punjab Province, Pakistan. This region is considered as a center for agriculture and irrigated farming. Our study is the first spatiotemporal statistical evaluation of the performance and selection of potential GDPs over the study region and is based on statistical indicators, trend detection, and abrupt change analysis. Results revealed that the CRU temperature indices (Tmax, Tmin, Tmean, and DTR) outperformed the other GDPs as indicated by their higher CC and R2 but lower bias and RMSE. Furthermore, trend and abrupt change analysis indicated the superior performances of the CRU Tmin and Tmean products. However, the Tmax and DTR products were less accurate for detecting trends and abrupt transitions in temperature. The tested GDPs as well as the reference data series indicate significant warming during the period of 1997–2001 over the study region. Differences between GDPs revealed discrepancies of 1-2°C when compared with different products within the same category and with reference data. The accuracy of all GDPs was particularly poor in the northern Punjab, where underestimates were greatest. This preliminary evaluation of the different GDPs will be useful for assessing inconsistencies and the capabilities of the products prior to their reliable utilization in hydrological and meteorological applications particularly over arid and semiarid regions.
      PubDate: Wed, 02 Sep 2020 06:50:01 +000
  • Comparison of Different Analyzing Techniques in Identifying Rainfall
           Trends for Colombo, Sri Lanka

    • Abstract: Identifying rainfall trends in highly urbanized area is extremely important for various planning and implementation activities, including designing, maintaining and controlling of water distribution networks and sewer networks and mitigating flood damages. However, different available methods in trend analysis may produce comparable and contrasting results. Therefore, this paper presents an attempt in comparing some of the trend analysis methods using one of the highly urbanized areas in Sri Lanka, Colombo. Recorded rainfall data for 10 gauging stations for 30 years were tested using the MannKendall test, Sen’s slope estimator, Spearman’s rho test, and innovative graphical method. Results showcased comparable findings among three trend identification methods. Even though the graphical method is easier, it is advised to use it with a proper statistical method due to its identification difficulties when the data scatter has some outliers. Nevertheless, it was found herein that Colombo is under a downward rainfall trend in the month of July where the area receives its major rainfall events. In addition, the area has several upward rainfall trends over the minor seasons and in the annual scale. Therefore, the water management activities in the area have to be revisited for a sustainable use of water resources.
      PubDate: Tue, 01 Sep 2020 01:35:06 +000
  • Spatial and Temporal Variations of Terrestrial Evapotranspiration in the
           Upper Taohe River Basin from 2001 to 2018 Based on MOD16 ET Data

    • Abstract: Evapotranspiration (ET) is an essential component of watershed hydrological cycle. Spatial-temporal variations analyses of evapotranspiration and potential evapotranspiration (PET) have remarkable theoretical and practical significances for understanding the interaction between climate changes and hydrological cycle and optimal allocation of water resources under global warming background. The MODIS-estimated ET agreed well with basin evapotranspiration from water balance principle methods in the study. The spatiotemporal variations results based on MOD16 ET data showed the following: (1) multiyear mean ET and PET were 464.2 mm and 1192.2 mm, and annual ET showed an upward trend at a rate of 3.48 mm/a, while PET decreased significantly at a rate of −8.18 mm/a. The annual ET trend showed a complemental relationship with PET; (2) at the seasonal scale, ET was highest in summer and least in winter, while PET was higher in spring and summer. The change of ET and PET in spring and summer had a great contribution to the annual variations; (3) ET and PET in the northern part were significantly stronger than those in the western and southern parts; (4) ET in cropland increased significantly, while PET decreased obviously in grass and forest; (5) changes of ET and PET were closely related to climatic factors. The rise in temperature caused the increase in ET and the decrease of wind speed contributed more to the decrease in PET. The results can provide a scientific basis for water resources planning and management.
      PubDate: Fri, 28 Aug 2020 12:50:07 +000
  • Trends of Hydroclimate Variables in the Upper Huai River Basin:
           Implications of Managing Water Resource for Climate Change Mitigation

    • Abstract: The present study attempted to investigate the trends of mean annual temperature, precipitation, and streamflow changes to determine their relationships in the upper Huai river basin. The Mann–Kendall (MK), Sen’s slope test estimator, and innovative trend detection (ф) (ITA) methods were used to detect the trends. According to the findings, average annual precipitation shows a descending trend (ф = −0.17) in most stations. An increasing trend was found only in Fuyang station (ф = 1.02). In all stations, the trends of mean annual temperature (ф = 0.36) were abruptly increased. During the past 57 years, the mean air temperature has considerably increased by 12°C/10a. The river streamflow showed a dramatic declining trend in all stations for the duration of the study period (1960–2016) (ф = −4.29). The climate variability in the study region affects the quantity of the streamflow. The river streamflow exhibits decreasing trends from 1965 onwards. The main possible reason for the declining stream flow in the study area is the declining amount of precipitation on some specific months due to the occurrence of climate change. The outcomes of this study could create awareness for the policymakers and members of the scientific community, informing them about the hydroclimatic evolutions across the study basin, and become an inordinate resource for advanced scientific research.
      PubDate: Wed, 26 Aug 2020 14:35:10 +000
  • Triggering Mechanism of an Extreme Rainstorm Process near the Tianshan
           Mountains in Xinjiang, an Arid Region in China, Based on a Numerical

    • Abstract: The current study investigated the triggering mechanism of a record-breaking heavy rain process in the area near the Tianshan Mountains in Xinjiang, an arid region in China, from July 31 to August 1, 2016, based on the simulation using the Weather Research and Forecasting (WRF) model. The results illustrated that the rainstorm system was generated in the middle atmosphere of the western Aksu region near the Tianshan Mountains and gradually evolved into a multicell linear echo during system evolution. The cold air transported from the Tianshan Mountains partly reached the low altitudes during the downhill process, and the warm southwest air from Aksu was lifted, forming oblique updraft airflow. The other part of the cold air converged with the southeastern warm air in the middle atmosphere, and the transportation and convergence of the water vapor related to the southwestern, southeastern, and oblique updraft airflows provided good water vapor conditions for the storm system. Meanwhile, the inclined upward air transported cloud water and ice-phase particles to high altitudes, mixing the two and generating a large amount of supercooled cloud water, which was very beneficial for the development and maintenance of the storm system. These conditions were favorable for power, heat, water vapor, and water condensate particles, which enabled the development and maintenance of the rainstorm system on the convergence line, thus triggering this rare rainstorm process during the movement to the northeast.
      PubDate: Thu, 20 Aug 2020 14:35:06 +000
  • A Survey on the Relationship between Ocean Subsurface Temperature and
           Tropical Cyclone over the Western North Pacific

    • Abstract: The relationship between ocean subsurface temperature and tropical cyclone (TC) over the western North Pacific (WNP) is studied based on the TC best-track data and global reanalysis data during the period of 1948–2012. Here the TC frequency (TCF), lifespan, and genesis position of TCs are analysed. A distinctive negative correlation between subsurface water temperature and TCF is observed, especially the TCF in the southeastern quadrant of the WNP (0–15°N, 150–180°E). According to the detrended subsurface temperature anomalies of the 125 m depth layer in the main TC genesis area (0–30°N, 100–180°E), we selected the subsurface cold and warm years. During the subsurface cold years, TCs tend to have a longer mean lifespan and a more southeastern genesis position than the subsurface warm years in general. To further investigate the causes of this characteristic, the TC genesis potential indexes (GPI) are used to analyse the contributions of environmental factors to TC activities. The results indicate that the negative correlation between subsurface water temperature and TCF is mainly caused by the variation of TCF in the southeastern quadrant of the WNP, where the oceanic and atmospheric environments are related to ocean subsurface conditions. Specifically, compared with the subsurface warm years, there are larger relative vorticity, higher relative humidity, smaller vertical wind shear, weaker net longwave radiation, and higher ocean mixed layer temperature in the southeastern quadrant during cold years, which are all favorable for genesis and development of TC.
      PubDate: Thu, 20 Aug 2020 14:35:06 +000
  • A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction
           Based on an Ensemble of Signal Decomposition Techniques with a Deep
           Learning Network

    • Abstract: The accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development of efficient prediction models. Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. Firstly, the correlation analysis and the two-stage decomposition approach were employed to select suitable inputs for the proposed model. ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. Afterward, all the components obtained through the correction analysis and the two-stage decomposition approach were predicted by using the LSTM network. Finally, the predicted results of all components were aggregated, to formulate an ensemble prediction for the original monthly streamflow series. The predicted results showed that the performance of the proposed model was superior to the other developed models, in respect of several evaluation benchmarks, demonstrating the applicability of the proposed IVL model for monthly streamflow prediction.
      PubDate: Tue, 18 Aug 2020 14:35:06 +000
  • Assessment of WRF Land Surface Model Performance over West Africa

    • Abstract: Simulations with four land surface models (LSMs) (i.e., Noah, Noah-MP, Noah-MP with ground water GW option, and CLM4) using the Weather Research and Forecasting (WRF) model at 12 km horizontal grid resolution were carried out as two sets for 3 months (December–February 2011/2012 and July–September 2012) over West Africa. The objective is to assess the performance of WRF LSMs in simulating meteorological parameters over West Africa. The model precipitation was assessed against TRMM while surface temperature was compared with the ERA-Interim reanalysis dataset. Results show that the LSMs performed differently for different variables in different land-surface conditions. Based on precipitation and temperature, Noah-MP GW is overall the best for all the variables and seasons in combination, while Noah came last. Specifically, Noah-MP GW performed best for JAS temperature and precipitation; CLM4 was the best in simulating DJF precipitation, while Noah was the best in simulating DJF temperature. Noah-MP GW has the wettest Sahel while Noah has the driest one. The strength of the Tropical Easterly Jet (TEJ) is strongest in Noah-MP GW and Noah-MP compared with that in CLM4 and Noah. The core of the African Easterly Jet (AEJ) lies around 12°N in Noah and 15°N for Noah-MP GW. Noah-MP GW and Noah-MP simulations have stronger influx of moisture advection from the southwesterly monsoonal wind than the CLM4 and Noah with Noah showing the least influx. Also, analysis of the evaporative fraction shows sharp gradient for Noah-MP GW and Noah-MP with wetter Sahel further to the north and further to the south for Noah. Noah-MP-GW has the highest amount of soil moisture, while the CLM4 has the least for both the JAS and DJF seasons. The CLM4 has the highest LH for both DJF and JAS seasons but however has the least SH for both DJF and JAS seasons. The principal difference between the LSMs is in the vegetation representation, description, and parameterization of the soil water column; hence, improvement is recommended in this regard.
      PubDate: Fri, 07 Aug 2020 15:50:02 +000
  • Evaluation of Precipitation Forecast of System: Numerical Tools for
           Hurricane Forecast

    • Abstract: Heavy rainfall events, typically associated with tropical cyclones (TCs), provoke intense flooding, consequently causing severe losses to life and property. Therefore, the amount and distribution of rain associated with TCs must be forecasted precisely within a reasonable time to guarantee the protection of lives and goods. In this study, the skill of the Numerical Tool for Hurricane Forecast (NTHF) for determining rainfall pattern, average rainfall, rainfall volume, and extreme amounts of rain observed during TCs is evaluated against Tropical Rainfall Measuring Mission (TRMM) data. A sample comprising nine systems formed in the North Atlantic basin from 2016 to 2018 is used, where the analysis begins 24 h before landfall. Several statistical indices characterising the abilities of the NTHF and climatology and persistence model for rainfalls (R-CLIPER) for forecasting rain as measured by the TRMM are calculated at 24, 48, and 72 h forecasts for each TC and averaged. The model under consideration presents better forecasting skills than the R-CLIPER for all the attributes evaluated and demonstrates similar performances compared with models reported in the literature. The proposed model predicts the average rainfall well and presents a good description of the rain pattern. However, its forecast of extreme rain is only applicable for 24 h.
      PubDate: Wed, 05 Aug 2020 14:20:01 +000
  • Estimation of High-Resolution Global Monthly Ocean Latent Heat Flux from
           MODIS SST Product and AMSR-E Data

    • Abstract: Accurate estimation of satellite-derived ocean latent heat flux (LHF) at high spatial resolution remains a major challenge. Here, we estimate monthly ocean LHF at 4 km spatial resolution over 5 years using bulk algorithm COARE 3.0, driven by satellite data and meteorological variables from reanalysis. We validated the estimated ocean LHF by multiyear observations and by comparison with seven ocean LHF products. Validation results from monthly observations at 96 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA, and RAMA) indicated a bias of less than 7 W/m2 with R2 of more than 0.80 () and with a King–Gupta efficiency (KGE) of over 0.84. Our estimated ocean LHF also performs well in simulating annual variability and predicting between-site variability, as indicated by a bias of lower than 6 W/m2 and an R2 of more than 0.84 (). Overall, the average KGE for estimated ocean LHF increased by 18%–23% compared to other LHF products, indicating robust LHF estimation performance. Importantly, our estimated annual ocean LHF has similar global spatial distribution compared to other LHF products, although there are general differences in LHF values due to the difference in the models and the spatial resolution.
      PubDate: Tue, 04 Aug 2020 07:05:03 +000
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762

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