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  Subjects -> METEOROLOGY (Total: 110 journals)
Showing 1 - 36 of 36 Journals sorted alphabetically
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 43)
Advances in Climate Change Research     Open Access   (Followers: 28)
Advances in Meteorology     Open Access   (Followers: 24)
Advances in Statistical Climatology, Meteorology and Oceanography     Open Access   (Followers: 7)
Aeolian Research     Hybrid Journal   (Followers: 6)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 18)
American Journal of Climate Change     Open Access   (Followers: 27)
Atmósfera     Open Access   (Followers: 3)
Atmosphere     Open Access   (Followers: 25)
Atmosphere-Ocean     Full-text available via subscription   (Followers: 14)
Atmospheric and Oceanic Science Letters     Open Access   (Followers: 10)
Atmospheric Chemistry and Physics (ACP)     Open Access   (Followers: 47)
Atmospheric Chemistry and Physics Discussions (ACPD)     Open Access   (Followers: 14)
Atmospheric Environment     Hybrid Journal   (Followers: 72)
Atmospheric Environment : X     Open Access   (Followers: 3)
Atmospheric Research     Hybrid Journal   (Followers: 69)
Atmospheric Science Letters     Open Access   (Followers: 36)
Boundary-Layer Meteorology     Hybrid Journal   (Followers: 31)
Bulletin of Atmospheric Science and Technology     Hybrid Journal   (Followers: 1)
Bulletin of the American Meteorological Society     Open Access   (Followers: 49)
Carbon Balance and Management     Open Access   (Followers: 4)
Change and Adaptation in Socio-Ecological Systems     Open Access   (Followers: 4)
Ciencia, Ambiente y Clima     Open Access   (Followers: 3)
Climate     Open Access   (Followers: 5)
Climate Change Economics     Hybrid Journal   (Followers: 14)
Climate Change Research Letters     Open Access   (Followers: 7)
Climate Change Responses     Open Access   (Followers: 8)
Climate Dynamics     Hybrid Journal   (Followers: 44)
Climate law     Hybrid Journal   (Followers: 7)
Climate of the Past (CP)     Open Access   (Followers: 5)
Climate of the Past Discussions (CPD)     Open Access  
Climate Policy     Hybrid Journal   (Followers: 36)
Climate Research     Hybrid Journal   (Followers: 6)
Climate Risk Management     Open Access   (Followers: 4)
Climate Services     Open Access   (Followers: 3)
Climate Summary of South Africa     Full-text available via subscription   (Followers: 2)
Climatic Change     Open Access   (Followers: 61)
Current Climate Change Reports     Hybrid Journal   (Followers: 4)
Developments in Atmospheric Science     Full-text available via subscription   (Followers: 27)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 5)
Dynamics of Atmospheres and Oceans     Hybrid Journal   (Followers: 18)
Earth Perspectives - Transdisciplinarity Enabled     Open Access  
Economics of Disasters and Climate Change     Hybrid Journal   (Followers: 2)
Energy & Environment     Hybrid Journal   (Followers: 23)
Environmental and Climate Technologies     Open Access   (Followers: 4)
Environmental Dynamics and Global Climate Change     Open Access   (Followers: 6)
Frontiers in Climate     Open Access   (Followers: 2)
GeoHazards     Open Access   (Followers: 1)
Global Meteorology     Open Access   (Followers: 17)
International Journal of Atmospheric Sciences     Open Access   (Followers: 21)
International Journal of Biometeorology     Hybrid Journal   (Followers: 1)
International Journal of Climatology     Hybrid Journal   (Followers: 31)
International Journal of Environment and Climate Change     Open Access   (Followers: 3)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 2)
Journal of Agricultural Meteorology     Open Access  
Journal of Applied Meteorology and Climatology     Hybrid Journal   (Followers: 35)
Journal of Atmospheric and Oceanic Technology     Hybrid Journal   (Followers: 33)
Journal of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 199)
Journal of Atmospheric Chemistry     Hybrid Journal   (Followers: 21)
Journal of Climate     Hybrid Journal   (Followers: 54)
Journal of Climate Change     Full-text available via subscription   (Followers: 2)
Journal of Climatology     Open Access   (Followers: 3)
Journal of Hydrology and Meteorology     Open Access   (Followers: 29)
Journal of Hydrometeorology     Hybrid Journal   (Followers: 11)
Journal of Integrative Environmental Sciences     Hybrid Journal   (Followers: 4)
Journal of Meteorological Research     Full-text available via subscription   (Followers: 1)
Journal of Meteorology and Climate Science     Full-text available via subscription   (Followers: 14)
Journal of Space Weather and Space Climate     Open Access   (Followers: 27)
Journal of the Atmospheric Sciences     Hybrid Journal   (Followers: 79)
Journal of the Meteorological Society of Japan     Partially Free   (Followers: 6)
Journal of Weather Modification     Full-text available via subscription   (Followers: 2)
Large Marine Ecosystems     Full-text available via subscription   (Followers: 1)
Mathematics of Climate and Weather Forecasting     Open Access   (Followers: 6)
Mediterranean Marine Science     Open Access   (Followers: 1)
Meteorologica     Open Access   (Followers: 2)
Meteorological Applications     Hybrid Journal   (Followers: 4)
Meteorological Monographs     Hybrid Journal  
Meteorologische Zeitschrift     Full-text available via subscription   (Followers: 3)
Meteorology and Atmospheric Physics     Hybrid Journal   (Followers: 26)
Mètode Science Studies Journal : Annual Review     Open Access  
Michigan Journal of Sustainability     Open Access   (Followers: 1)
Modeling Earth Systems and Environment     Hybrid Journal  
Monthly Notices of the Royal Astronomical Society     Hybrid Journal   (Followers: 14)
Monthly Weather Review     Hybrid Journal   (Followers: 34)
Nature Climate Change     Full-text available via subscription   (Followers: 126)
Nature Reports Climate Change     Full-text available via subscription   (Followers: 35)
Nīvār     Open Access  
npj Climate and Atmospheric Science     Open Access   (Followers: 3)
Open Atmospheric Science Journal     Open Access   (Followers: 2)
Open Journal of Modern Hydrology     Open Access   (Followers: 6)
Revista Brasileira de Meteorologia     Open Access  
Revista Iberoamericana de Bioeconomía y Cambio Climático     Open Access  
Russian Meteorology and Hydrology     Hybrid Journal   (Followers: 3)
Space Weather     Full-text available via subscription   (Followers: 24)
Studia Geophysica et Geodaetica     Hybrid Journal  
Tellus A     Open Access   (Followers: 22)
Tellus B     Open Access   (Followers: 21)
The Cryosphere (TC)     Open Access   (Followers: 5)
The Cryosphere Discussions (TCD)     Open Access   (Followers: 4)
The Quarterly Journal of the Royal Meteorological Society     Hybrid Journal   (Followers: 27)
Theoretical and Applied Climatology     Hybrid Journal   (Followers: 12)
Tropical Cyclone Research and Review     Open Access   (Followers: 1)
Urban Climate     Hybrid Journal   (Followers: 4)
Weather     Hybrid Journal   (Followers: 19)
Weather and Climate Dynamics     Open Access  
Weather and Climate Extremes     Open Access   (Followers: 16)
Weather and Forecasting     Hybrid Journal   (Followers: 28)
Weatherwise     Hybrid Journal   (Followers: 4)
气候与环境研究     Full-text available via subscription   (Followers: 1)

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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]
  • Development and Evaluation of a Hydrometeorological Forecasting System

    • Abstract: In this study, an experimental hydrometeorological forecasting system was developed based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model. The system downloads global real-time ocean, atmosphere, and wave forcing data, producing regional forecasts every day. A coastal area in South China, encompassing Hainan Island, Leizhou Peninsula, and surrounding sea areas, was chosen as the study domain. A series of 72-hour forecasting simulations were conducted in the area, lasting from July 27 to August 31, 2019. The forecasts throughout August were chosen for evaluation with station observations, along with two sets of reanalysis data, ERA5 and CLDAS. The evaluation results revealed that the COAWST model had high potential for routine forecasting operations. The 24 h forecasts, with a lead time of 24 hours, had high accuracy, while the 48 h and 72 h forecasts did not differ greatly in terms of performance. The distributions of bias between forecast and reanalysis data showed obvious differences between land and sea, with more forecasted precipitation and lower temperatures in land grids than in sea grids. In most cases, the forecasts were closer to ERA5 in terms of means and other statistical measures. The forecasts enlarged the land-sea differences of temperature when compared with ERA5 and strengthened summer monsoon with more moisture transported to land areas. Resulting from that, a forecasted bias of lower surface pressure, higher air humidity, stronger south wind, and so forth was also detected over the domain but at low values.
      PubDate: Tue, 19 Jan 2021 07:05:01 +000
  • Numerical Simulation of Near-Surface Wind during a Severe Wind Event in a
           Complex Terrain by Multisource Data Assimilation and Dynamic Downscaling

    • Abstract: Accurate forecast and simulation of near-surface wind is a great challenge for numerical weather prediction models due to the significant transient and intermittent nature of near-surface wind. Based on the analyses of the impact of assimilating in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) satellite radiance data on the simulation of near-surface wind during a severe wind event, using the new generation mesoscale Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system, the dynamic downscaling of near-surface wind is further investigated by coupling the microscale California Meteorological (CALMET) model with the WRF and its 3DVAR system. Results indicate that assimilating in situ and ATOVS radiance observations strengthens the airflow across the Alataw valley and triggers the downward transport of momentum from the upper atmosphere in the downstream area of the valley in the initial conditions, thus improving near-surface wind simulations. Further investigations indicate that the CALMET model provides more refined microtopographic structures than the WRF model in the vicinity of the wind towers. Although using the CALMET model achieves the best simulation of near-surface wind through dynamic downscaling of the output from the WRF and its 3DVAR assimilation, the simulation improvements of near-surface wind speed are mainly within 1 m s−1. Specifically, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. The observed near-surface wind directions in the weak wind conditions are better simulated in the coupled model with CALMET downscaling than in the WRF and its 3DVAR system. It is concluded that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak, and the downscaling effects are mainly manifested in the simulation of near-surface wind directions.
      PubDate: Wed, 30 Dec 2020 12:50:01 +000
  • Diurnal Variation of Seasonal Precipitation over the CONUS: A Comparison
           of Gauge Observations with TRMM Data

    • Abstract: Diurnal variation of precipitation is a fundamental periodic signal of local climate. Comprehensive study of diurnal variation of precipitation is helpful in studying the formation of local climate and validating satellite precipitation products. In this study, a comparison is drawn between precipitation gauge observations and Tropical Rainfall Measuring Mission (TRMM) 3B42 data on diurnal variation of precipitation. First, using the K-means clustering algorithm, stations with gauge observations and pixels with TRMM data are divided into different groups according to the diurnal variation of precipitation, respectively. In each group, the stations have similar diurnal variation of precipitation. Then maps of diurnal variation of precipitation for gauge observations and TRMM data are obtained. According to these maps, the diurnal variation of precipitation over the contiguous United States (CONUS) presents seasonal variability in both gauge observations and TRMM data. In addition, the diurnal variation of precipitation shows clustered features in space. However, the spatial patterns of the obtained maps do not match, and the TRMM satellite data perform poorly in capturing the hourly precipitation event. Finally, the possible mechanism behind the prevailing nocturnal precipitation over the middle of the CONUS is discussed, with the prevailing nocturnal precipitation judged likely to be strongly related to the mountain-plains solenoid (MPS) circulation.
      PubDate: Thu, 24 Dec 2020 16:35:00 +000
  • Corrigendum to “Projected Changes in Precipitation Extremes over Shaanxi
           Province, China, in the 21st Century”

    • PubDate: Mon, 21 Dec 2020 13:35:01 +000
  • Application of Multiple Detection Data in the Analysis of Snowstorm
           Processes in Xinjiang during the Central Asia Extreme Precipitation
           Observation Test (CAEPOT)

    • Abstract: At present, there is insufficient research on the refinement of the vertical structure of winter snowstorm systems in arid areas, and, compared with the central and eastern China, the observation sites in arid areas of northwestern China are scarce. To deepen the understanding of dynamics and microphysical processes and improve the level of forecasting and warning of snowstorms in northwestern China, the Institute of Desert Meteorology, China Meteorological Administration, Urumqi, carried out the Central Asia Extreme Precipitation Observation Test (CAEPOT) in Yili, Xinjiang, a typical arid region in China in February 2020. This paper uses multiple fine detection datasets obtained from the CAEPOT, including radar wind profiler, ground-based microwave radiometer, and millimeter-wave cloud radar to analyze macroscopic characteristics and microphysical changes of snowstorm system in Xinjiang. Studies have shown that the low trough with sufficient moisture, heat, power conditions, and weakening banded cloud system, which moved eastward from the Aral Sea to the west of Xinjiang during the snowstorm, were the key influencing system of this snowstorm. Before the snowstorm, the vertical shear of the horizontal wind field was severe, which aggravated the instability of the atmosphere, and there was upward motion in the lower atmosphere. A variety of physical quantities related to moisture showed a tendency to increase at the lower level and could be used as an early warning signal for snowstorm about 8 hours in advance, and the cloud and snow particles observed by millimeter-wave cloud radar were simultaneously developing upward and downward from 4 km, providing snowstorm warning 12 hours in advance. During the snowstorm, the horizontal wind speed and vertical speed were obviously enhanced, and the physical quantities related to moisture further increased, and, with the blocking and uplifting of the Tianshan Mountains, the snowstorm increased. The particles collided and grew while falling, resulting in a decrease in particle concentration and an increase in particle radius from high altitude to the ground, eventually resulting in near-ground reflectivity factor up to 30 dBz. In addition, reflectivity factor, physical quantities related to moisture, wind field, particle concentration, and particle radius all had a good correspondence with the beginning, end, and intensity of snowstorm, so when the physical quantities mentioned above weakened and stopped, snowstorm also weakened and stopped. In a word, this research is an important and meaningful work that provides more backgrounds and references for the forecast and warning of snowstorm in northwestern China.
      PubDate: Mon, 21 Dec 2020 07:35:01 +000
  • Mountain Waves Analysis in the Vicinity of the Madrid-Barajas Airport
           Using the WRF Model

    • Abstract: Turbulence and aircraft icing associated with mountain waves are weather phenomena potentially affecting aviation safety. In this paper, these weather phenomena are analysed in the vicinity of the Adolfo Suárez Madrid-Barajas Airport (Spain). Mountain waves are formed in this area due to the proximity of the Guadarrama mountain range. Twenty different weather research and forecasting (WRF) model configurations are evaluated in an initial analysis. This shows the incompetence of some experiments to capture the phenomenon. The two experiments showing the best results are used to simulate thirteen episodes with observed mountain waves. Simulated pseudosatellite images are validated using satellite observations, and an analysis is performed through several skill scores applied to brightness temperature. Few differences are found among the different skill scores. Nevertheless, the Thompson microphysics scheme combined with the Yonsei university PBL scheme shows the best results. The simulations produced by this scheme are used to evaluate the characteristic variables of the mountain wave episodes at windward and leeward and over the mountain. The results show that north-northwest wind directions, moderate wind velocities, and neutral or slightly stable conditions are the main features for the episodes evaluated. In addition, a case study is analysed to evidence the WRF ability to properly detect turbulence and icing associated with mountain waves, even when there is no visual evidence available.
      PubDate: Fri, 18 Dec 2020 15:50:01 +000
  • Spatial Distribution and Temporal Trends in the Daily Precipitation
           Concentration across the Yarlung Tsangpo River Basin: Eastern Himalaya of

    • Abstract: Understanding the temporal inequality in precipitation is of great importance for water resource management, environmental risk management, and ecological conservation. This study investigated the spatial patterns and trends of the daily precipitation concentration over the Yarlung Tsangpo River Basin using the concentration index (CI) and the Lorentz asymmetric coefficient (LAC). A Mann–Kendall test and Hurst’s rescaled range analysis were used to detect the change in CI trends. The CI ranged from 0.58 to 0.65, suggesting that a quarter of the rainiest days contributed approximately 69–78% of the total precipitation. The LAC analysis indicated that the nonuniform distribution of precipitation was mainly attributed to a large proportion of days with light rainfall. Compared with that of the central region, the daily precipitation in the western and eastern regions was more irregular. At a seasonal scale, the dry season had a less homogeneous spatial distribution of CI compared to that of the wet season. Most areas exhibited no significant trends in CI from 1970 to 2017. A quarter of the stations presented a significant downward trend in CI, which were primarily found in the central and northern regions. In addition, the future trends of CI in most areas mostly agree with those of the current state; however, the majority of stations exhibited an uneven precipitation distribution in the dry season.
      PubDate: Tue, 15 Dec 2020 07:50:01 +000
  • Evaluation and Analysis of Soil Temperature Data over Poyang Lake Basin,

    • Abstract: Soil temperature reflects the impact of local factors, such as the vegetation, soil, and atmosphere of a region. Therefore, it is important to understand the regional variation of soil temperature. However, given the lack of observations with adequate spatial and/or temporal coverage, it is often difficult to use observational data to study the regional variation. Based on the observational data from Nanchang and Ganzhou stations and ERA-Interim/Land reanalysis data, this study analyzed the spatiotemporal distribution characteristics of soil temperature over Poyang Lake Basin. Four soil depths were examined, 0–7, 7–28, 28–100, and 100–289 cm, recorded as ST1, ST2, ST3, and ST4, respectively. The results showed close correlations between observation data and reanalysis data at different depths. Reanalysis data could reproduce the main spatiotemporal distributions of soil temperature over the Poyang Lake Basin but generally underestimated their magnitudes. Temporally, there was a clear warming trend in the basin. Seasonally, the temperature increase was the most rapid in spring and the slowest in summer, except for ST4, which increased the fastest in spring and the slowest in winter. The temperature increase was faster for ST1 than the other depths. The warming trend was almost the same for ST2, ST3, and ST4. An abrupt change of annual soil temperature at all depths occurred in 1997, and annual soil temperatures at all depths were abnormally low in 1984. Spatially, annual soil temperature decreased with latitude, except for the summer ST1. Because of the high temperature and precipitation in summer, the ST1 values were higher around the lake and the river. The climatic trend of soil temperature generally increased from south to north, which was opposite to the distribution of soil temperature. These findings provide a basis for understanding and assessing the variation of soil temperature in the Poyang Lake Basin.
      PubDate: Tue, 08 Dec 2020 14:35:01 +000
  • Quantifying the Location Error of Precipitation Nowcasts

    • Abstract: In precipitation nowcasting, it is common to track the motion of precipitation in a sequence of weather radar images and to extrapolate this motion into the future. The total error of such a prediction consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow isolating the extent of location errors, making it difficult to specifically improve nowcast models with regard to location prediction. In this paper, we introduce a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time Δt ahead of the forecast time t corresponds to the Euclidean distance between the observed and the predicted feature locations at t + Δt. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the German Weather Service. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion from t − 1 to t (LK-Lin1) and t − 4 to t (LK-Lin4) and the other two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear (DIS-Lin1) and Semi-Lagrangian extrapolation (DIS-Rot1). Of those four models, DIS-Lin1 and LK-Lin4 turned out to be the most skillful with regard to the prediction of feature location, while we also found that the model skill dramatically depends on the sinuosity of the observed tracks. The dataset of 376,125 detected feature tracks in 2016 is openly available to foster the improvement of location prediction in extrapolation-based nowcasting models.
      PubDate: Thu, 03 Dec 2020 08:35:01 +000
  • Simulating Heavy Meiyu Rainfall: A Note on the Choice of the Model
           Microphysics Scheme

    • Abstract: Better simulations and predictions of heavy rainfall associated with Meiyu fronts are critical for flood management in the Yangtze River Valley, China. This work systematically evaluates and compares the performances of three microphysics schemes in Weather Research and Forecasting (WRF) Model with regard to simulating properties of a classic Meiyu rainstorm in central China which occurred during a 30-hour period in July 2016, including spatial distribution, rain rate PDF, and lifecycle behavior of local rainfall. Model simulations are validated using both in situ and remote sensing observations. It is found that all three schemes capture the overall spatial distribution of precipitation and the average rainfall intensity changes more rapidly with time in the simulation than in the observation. Further insights are gained through an examination of the budget terms of raindrop and ice-phase hydrometeors in the model. Accretion of cloud droplets by raindrops and melting of ice-phase hydrometeors are the major source of rainwater. Bergeron and riming processes are found to play a prevailing role in the growth of ice-phase hydrometeors in Meiyu rainfall. Large differences in the parameterization of riming process in different schemes lead to significant differences in the simulated growth of ice-phase hydrometeors.
      PubDate: Sun, 29 Nov 2020 09:50:01 +000
  • Bayesian Spatial and Trend Analysis on Ozone Extreme Data in South Korea:

    • Abstract: Background. Extreme events like flooding, extreme temperature, and ozone depletion are happening in every corner of the world. Thus, the need to model such rare events having enormous damage has been getting priorities in most countries of the world. Methods. The dataset contains the ozone data from 29 representative air monitoring sites in South Korea collected from 1991 to 2015. Spatial generalized extreme value (GEV) using maximum likelihood estimation (MLE) and two max-stable and Bayesian kriging models are the statistical models used for analysis. Moreover, predictive performances of these statistical models are compared using measures like root-mean-squared error (RMSE), mean absolute error (MAE), relative bias (rBIAS), and relative mean separation (rMSEP) have been utilized. Results. From the time plot of ozone data, extreme ozone concentration is increasing linearly within the specified period. The return level of ozone concentration after 10, 25, 50, and 100 years have been forecasted and showed that there was an increasing trend in ozone extremes. High spatial variability of ozone extreme was observed, and those areas around the territories were having extreme ozone concentration than the centers. Moreover, Bayesian Kriging brought about relatively the minimum RMSE compared to the other models. Conclusion. The extreme ozone concentration has clearly showed a positive trend and spatial variation. Moreover, among the models considered in the paper, the Bayesian Kriging has been chosen as the better model.
      PubDate: Fri, 27 Nov 2020 14:20:01 +000
  • Long-Term Homogeneity and Trends of Hydroclimatic Variables in Upper Awash
           River Basin, Ethiopia

    • Abstract: Understanding long-term trends in hydroclimatic variables is important for future sustainable water resource management as it could show the possible regime shifts in hydrology. The main objective of this study was to analyze the homogeneity and trends of hydroclimatic data of Upper Awash Sab-Basin (UASB) in Oromia, Ethiopia, by employing homogeneity tests and Mann-Kendall and Sen’s slope tests. The data consist of 18 rainfall stations, 8 temperature stations, and 8 flow gauging stations across the UASB. Homogeneity and trends in streamflow, rainfall, and temperature variables were analyzed for the time period 1980 to 2017. In order to analyze homogeneity of hydroclimatic variables, we used four homogeneity tests (Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test) at 5% significance level. Based on the outputs of four homogeneity tests, the results were classified into three categories, namely, “useful,” “doubtful,” and “suspect” to select the homogeneity stations. Mann-Kendall (Z) and Sen’s slope tests (Q) were applied for the selected homogeneous time series to detect the trend and magnitude of changes in hydroclimatic variables. The result showed that most of the stations in annual rainfall and streamflow data series were classified as useful. It is found that 58% of the rainfall stations were homogeneous. It is highlighted that 3 out of 8 discharge gauging stations have an inhomogeneity as they failed from one or a combination of the four tests. The MK revealed significant decreasing trends of annual rainfall in Addis Alem (Q = −19.81), Akaki (Q = −5.60), Hombole (Q = −9.49), and Ghinch (Q = −12.38) stations. The trend of annual temperature was a significant increasing trend in Addis Ababa Bole (Q = 0.05), Addis Ababa Tikur Ambessa (Q = 0.03), Tulu Bolo (Q = 0.07), and Addis Alem (Q = 0.06) stations. The results of discharge showed a significant increasing trend in Bega at Mojo (Q = 0.17) and Hombole (Q = 0.03) gauging stations. In general, the results obtained from discharge, rainfall, and temperature series indicated that most of the stations exhibited no trends in both annual and seasonal time series. It can be concluded that decreases in average annual rainfall totals and increases in mean annual temperature will probably drive sub-basin scale changes in discharge. We believe that the results obtained can fill information gaps on homogeneity and trends of hydroclimatic variables, which is very crucial for future water resource planning and management in the face of climate change.
      PubDate: Tue, 24 Nov 2020 08:05:01 +000
  • Analysis of Seasonal Daytime Urban Thermal Environment Dynamics in a
           Tropical Coastal City Based on the Spatiotemporal Fusion Model

    • Abstract: This study investigated the seasonal variations of daytime urban thermal environment (UTE) based on land surface temperature (LST) in Shenzhen of 2015. The spatial and temporal adaptive reflectance fusion model (STARFM) was used for retrieving seasonal daytime LST at high spatiotemporal resolution by combining MODIS and HJ-1B LST data. Next, the relationship between the land cover and daytime in each season was examined. Finally, daytime LST patterns were classified, and the effects of seasonal variations of high-grade daytime LSTs were analyzed with landscape metrics. The results showed that (1) the STARFM is capable of generating seasonal daytime LST data at high spatiotemporal resolution. (2) Daytime LSTs were generally higher in the western parts of Shenzhen in spring and summer. (3) Daytime LST in each land cover type showed an increasing trend form winter to summer and decreased from summer to autumn. The highest and lowest daytime LSTs in each season were observed in ISAs and water bodies. (4) Landscape metrics provided a quantitative method for describing seasonal variations in daytime LSTs, and it was found that seasons influenced the intensity and extent of daytime LSTs in Shenzhen. These findings may be helpful for urban planners developing regional urban strategies to improve daytime urban thermal comfort conditions.
      PubDate: Mon, 23 Nov 2020 07:50:01 +000
  • Evaluating the Dependence between Temperature and Precipitation to Better
           Estimate the Risks of Concurrent Extreme Weather Events

    • Abstract: Precipitation and temperature are among major climatic variables that are used to characterize extreme weather events, which can have profound impacts on ecosystems and society. Accurate simulation of these variables at the local scale is essential to adapt urban systems and policies to future climatic changes. However, accurate simulation of these climatic variables is difficult due to possible interdependence and feedbacks among them. In this paper, the concept of copulas was used to model seasonal interdependence between precipitation and temperature. Five copula functions were fitted to grid (approximately 10 km × 10 km) climate data from 1960 to 2013 in southern Ontario, Canada. Theoretical and empirical copulas were then compared with each other to select the most appropriate copula family for this region. Results showed that, of the tested copulas, none of them consistently performed the best over the entire region during all seasons. However, Gumbel copula was the best performer during the winter season, and Clayton performed best in the summer. More variability in terms of best copula was found in spring and fall seasons. By examining the likelihoods of concurrent extreme temperature and precipitation periods including wet/cool in the winter and dry/hot in the summer, we found that ignoring the joint distribution and confounding impacts of precipitation and temperature lead to the underestimation of occurrence of probabilities for these two concurrent extreme modes. This underestimation can also lead to incorrect conclusions and flawed decisions in terms of the severity of these extreme events.
      PubDate: Tue, 17 Nov 2020 14:35:02 +000
  • Evaluating the Performance of Secondary Precipitation Products through
           Statistical and Hydrological Modeling in a Mountainous Tropical Basin of

    • Abstract: This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets’ performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India.
      PubDate: Tue, 17 Nov 2020 13:50:01 +000
  • Impact of Multivariate Background Error Covariance on the WRF-3DVAR
           Assimilation for the Yellow Sea Fog Modeling

    • Abstract: Numerical modeling of sea fog is highly sensitive to initial conditions, especially to moisture in the marine atmospheric boundary layer (MABL). Data assimilation plays a vital role in the improvement of initial MABL moisture for sea fog modeling over the Yellow Sea. In this study, the weather research and forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation module are employed for sea fog simulations. Two kinds of background error (BE) covariances with different control variables (CV) used in WRF-3DVAR, that is, CV5 and multivariate BE (CV6), are compared in detail by explorative case studies and a series of application experiments. Statistical verification metrics including probability of detection (POD) and equitable threat scores (ETS) of forecasted sea fog area are computed and compared for simulations with the implementations of CV5 and CV6 in the WRF-3DVAR system. The following is found: (1) there exists a dominant negative correlation between temperature and moisture in CV6 near the sea surface, which makes it possible to improve the initial moisture condition in the MABL by assimilation of observed temperature; (2) in general, the performance of the WRF-3DVAR assimilation with CV6 is distinctly better, and the results of 10 additional sea fog cases clearly suggest that CV6 is more suitable than CV5 for sea fog modeling. Compared to those with CV5, the average POD and ETS of forecasted sea fog area using 3DVAR with CV6 can be improved by 27.6% and 21.0%, respectively.
      PubDate: Tue, 10 Nov 2020 14:35:01 +000
  • Assessing the Response of Satellite Solar-Induced Chlorophyll Fluorescence
           and NDVI to Impacts of Heat Waves on Winter Wheat in the North China Plain

    • Abstract: Global warming has increased the chance of concurrent extreme climate events (weather or climate events that are rare within their statistical reference distributions in a particular place, such as heat waves, floods, and droughts). Crops grow best within specific temperature intervals, and excessive heat is detrimental to the physiological processes of crops and eventually affects yield levels. Analysing historical changes in concurrent extreme high temperatures is critical to preparing for and mitigating the negative effects of climatic change. The North China Plain (NCP) is the most important wheat production area in China. In this study, the spatiotemporal variations in temperature and heat wave trends in the NCP were analysed. Furthermore, we examined the potential of solar-induced chlorophyll fluorescence (SIF) to capture the influence of heat wave impacts on wheat crops in the NCP by comparing satellite remote sensing data of SIF and normalized difference vegetation index (NDVI) and validated ground-based yield data. The results indicate that temperatures and the number of heat wave days in the study region all show increasing trends, especially daily minimum temperature, which has increased by 0.38°C per decade for the past 30 years. Spatially, the southern NCP has suffered greater increasing-temperature trends and more heat wave days than the northern region. Regarding the response of SIF and NDVI to heat waves, SIF can better capture wheat yield decline due to heat waves compared to NDVI; thus, the SIF result indicated more sensitivity to heat waves compared to NDVI.
      PubDate: Thu, 29 Oct 2020 13:50:00 +000
  • 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
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