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  Subjects -> METEOROLOGY (Total: 112 journals)
Showing 1 - 36 of 36 Journals sorted by number of followers
Journal of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 199)
Nature Climate Change     Full-text available via subscription   (Followers: 134)
Journal of the Atmospheric Sciences     Hybrid Journal   (Followers: 81)
Atmospheric Environment     Hybrid Journal   (Followers: 73)
Atmospheric Research     Hybrid Journal   (Followers: 69)
Climatic Change     Open Access   (Followers: 66)
Journal of Climate     Hybrid Journal   (Followers: 54)
Bulletin of the American Meteorological Society     Open Access   (Followers: 51)
Atmospheric Chemistry and Physics (ACP)     Open Access   (Followers: 48)
Climate Policy     Hybrid Journal   (Followers: 45)
Climate Dynamics     Hybrid Journal   (Followers: 44)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 43)
Nature Reports Climate Change     Full-text available via subscription   (Followers: 37)
Atmospheric Science Letters     Open Access   (Followers: 36)
Journal of Applied Meteorology and Climatology     Hybrid Journal   (Followers: 35)
Journal of Atmospheric and Oceanic Technology     Hybrid Journal   (Followers: 34)
Monthly Weather Review     Hybrid Journal   (Followers: 34)
International Journal of Climatology     Hybrid Journal   (Followers: 31)
American Journal of Climate Change     Open Access   (Followers: 31)
Advances in Climate Change Research     Open Access   (Followers: 31)
Boundary-Layer Meteorology     Hybrid Journal   (Followers: 31)
Journal of Hydrology and Meteorology     Open Access   (Followers: 29)
Developments in Atmospheric Science     Full-text available via subscription   (Followers: 28)
Weather and Forecasting     Hybrid Journal   (Followers: 28)
The Quarterly Journal of the Royal Meteorological Society     Hybrid Journal   (Followers: 27)
Journal of Space Weather and Space Climate     Open Access   (Followers: 27)
Climate Change Economics     Hybrid Journal   (Followers: 26)
Atmosphere     Open Access   (Followers: 26)
Meteorology and Atmospheric Physics     Hybrid Journal   (Followers: 26)
Space Weather     Full-text available via subscription   (Followers: 25)
Advances in Meteorology     Open Access   (Followers: 24)
Energy & Environment     Hybrid Journal   (Followers: 24)
International Journal of Atmospheric Sciences     Open Access   (Followers: 22)
Tellus A     Open Access   (Followers: 22)
International Journal of Climate Change Strategies and Management     Hybrid Journal   (Followers: 22)
Journal of Atmospheric Chemistry     Hybrid Journal   (Followers: 21)
Tellus B     Open Access   (Followers: 21)
Dynamics of Atmospheres and Oceans     Hybrid Journal   (Followers: 19)
Weather     Hybrid Journal   (Followers: 19)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 18)
Global Meteorology     Open Access   (Followers: 17)
Weather and Climate Extremes     Open Access   (Followers: 16)
Climate Resilience and Sustainability     Open Access   (Followers: 15)
Atmosphere-Ocean     Full-text available via subscription   (Followers: 15)
Atmospheric Chemistry and Physics Discussions (ACPD)     Open Access   (Followers: 15)
Journal of Meteorology and Climate Science     Full-text available via subscription   (Followers: 14)
Theoretical and Applied Climatology     Hybrid Journal   (Followers: 13)
Monthly Notices of the Royal Astronomical Society     Hybrid Journal   (Followers: 13)
Climate Change Responses     Open Access   (Followers: 12)
Atmospheric and Oceanic Science Letters     Open Access   (Followers: 11)
Journal of Hydrometeorology     Hybrid Journal   (Followers: 11)
Environmental Dynamics and Global Climate Change     Open Access   (Followers: 8)
Current Climate Change Reports     Hybrid Journal   (Followers: 8)
Climate Change Research Letters     Open Access   (Followers: 7)
Advances in Statistical Climatology, Meteorology and Oceanography     Open Access   (Followers: 7)
Climate     Open Access   (Followers: 6)
Open Journal of Modern Hydrology     Open Access   (Followers: 6)
Climate Risk Management     Open Access   (Followers: 6)
Mathematics of Climate and Weather Forecasting     Open Access   (Followers: 6)
Aeolian Research     Hybrid Journal   (Followers: 6)
Climate Research     Hybrid Journal   (Followers: 6)
Journal of the Meteorological Society of Japan     Partially Free   (Followers: 6)
Change and Adaptation in Socio-Ecological Systems     Open Access   (Followers: 5)
The Cryosphere (TC)     Open Access   (Followers: 5)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 5)
International Journal of Environment and Climate Change     Open Access   (Followers: 5)
Climate of the Past (CP)     Open Access   (Followers: 5)
Urban Climate     Hybrid Journal   (Followers: 4)
Environmental and Climate Technologies     Open Access   (Followers: 4)
Climate and Energy     Full-text available via subscription   (Followers: 4)
The Cryosphere Discussions (TCD)     Open Access   (Followers: 4)
Carbon Balance and Management     Open Access   (Followers: 4)
Weatherwise     Hybrid Journal   (Followers: 4)
Meteorological Applications     Hybrid Journal   (Followers: 4)
Journal of Integrative Environmental Sciences     Hybrid Journal   (Followers: 4)
Russian Meteorology and Hydrology     Hybrid Journal   (Followers: 3)
Meteorologische Zeitschrift     Full-text available via subscription   (Followers: 3)
npj Climate and Atmospheric Science     Open Access   (Followers: 3)
Atmospheric Environment : X     Open Access   (Followers: 3)
Journal of Climate Change     Full-text available via subscription   (Followers: 3)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3)
Economics of Disasters and Climate Change     Hybrid Journal   (Followers: 3)
Frontiers in Climate     Open Access   (Followers: 3)
Ciencia, Ambiente y Clima     Open Access   (Followers: 3)
Journal of Climatology     Open Access   (Followers: 3)
Atmósfera     Open Access   (Followers: 3)
Climate Services     Open Access   (Followers: 3)
Open Atmospheric Science Journal     Open Access   (Followers: 2)
GeoHazards     Open Access   (Followers: 2)
Journal of Weather Modification     Full-text available via subscription   (Followers: 2)
Meteorological Monographs     Hybrid Journal   (Followers: 2)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 2)
Meteorologica     Open Access   (Followers: 2)
Climate Summary of South Africa     Full-text available via subscription   (Followers: 2)
气候与环境研究     Full-text available via subscription   (Followers: 1)
Journal of Meteorological Research     Full-text available via subscription   (Followers: 1)
Bulletin of Atmospheric Science and Technology     Hybrid Journal   (Followers: 1)
Michigan Journal of Sustainability     Open Access   (Followers: 1)
Tropical Cyclone Research and Review     Open Access   (Followers: 1)
International Journal of Biometeorology     Hybrid Journal   (Followers: 1)
Modeling Earth Systems and Environment     Hybrid Journal   (Followers: 1)
Mediterranean Marine Science     Open Access   (Followers: 1)
Large Marine Ecosystems     Full-text available via subscription   (Followers: 1)
Weather and Climate Dynamics     Open Access  
Journal of Agricultural Meteorology     Open Access  
Nīvār     Open Access  
Revista Iberoamericana de Bioeconomía y Cambio Climático     Open Access  
Mètode Science Studies Journal : Annual Review     Open Access  
Earth Perspectives - Transdisciplinarity Enabled     Open Access  
Climate of the Past Discussions (CPD)     Open Access  
Revista Brasileira de Meteorologia     Open Access  
Studia Geophysica et Geodaetica     Hybrid Journal  

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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]
  • Long-Term Rainfall Information Forecast by Utilizing Constrained Amount of
           Observation through Artificial Neural Network Approach

    • Abstract: Estimating models are becoming increasingly crucial in highlighting the nonlinear connections of the massive level of rough information and chaotic components. The study demonstrates a modern approach utilizing a created artificial neural network (ANN) method that may be an alternative strategy to conventional factual procedures for advancing rainfall estimate execution. A case study was presented for Turkey’s Düzce and Bolu neighboring territories located on the Black Sea’s southern coast. This study’s primary aim is to create an ANN model unique in the field to generate satisfactory results even with limited data. The proposed technique is being used to estimate rainfall and make predictions regarding future precipitation. Bolu daily average rainfall by month data and a limited number of Düzce rainfall data were used. Missing forecasts and potential rainfall projections will be examined in the fundamental research. This research further focuses on ANN computational concepts and develops a neural network for rainfall time series forecasting. The emphasis of this study was a feed-forward backpropagation network. The Levenberg–Marquardt algorithm (LMA) was implemented for training a two-layer feed-forward ANN for the missing rainfall data prediction part of this research. The inaccessible rainfall parameters for Düzce were determined for the years 1995 to 2009. From 2010 to 2020, a two-layer feed-forward ANN was trained using the gradient descent algorithm to forecast daily average rainfall data by month. The findings reported in this study guide researchers interested in implementing the ANN forecast model for an extended period of missing rainfall data.
      PubDate: Thu, 06 May 2021 07:05:00 +000
       
  • Impacts of Air-Sea Energy Transfer on Typhoon Modelling

    • Abstract: The Coupled Ocean-Atmosphere-Wave-Sediment Transport model has been used to simulate Super Typhoon Yutu (2018). The impacts of four momentum transfer parameterization schemes (COARE, TY, OT, and DN) and three heat transfer parameterization schemes (COARE, GR, and ZK) on typhoon modelling have been studied by means of the track, intensity, and radial structure of typhoon. The results show that the track of Yutu is not sensitive to the choice of parameterization scheme, while the combinations of different parameterization schemes affect the intensity of Yutu. Among the four momentum flux parameterization schemes, three wave-state-based schemes (TY, OT, and DN) provide better intensity results than the wind-speed-based COARE scheme, but the differences between the three wave-state-based schemes are not obvious. Among the three heat flux parameterization schemes, the results of the GR scheme are slightly better than those of the COARE scheme, and both the GR and COARE schemes are significantly better than the ZK scheme, from which the intensity of Yutu is underpredicted obviously. The influence of the combination of different parameterization schemes on the intensity of the typhoon is also reflected in the radial structure of the typhoon, and the radial structure of the typhoon simulated by experiments with stronger typhoon intensity also develops faster. Differences of intensity between experiments are due mainly to the differences in sea surface heat flux, the enthalpy transferred from sea surface to the atmosphere has a significant impact on the bottom atmosphere wind field, and there is a strong correspondence between the distribution of enthalpy flux and the bottom wind field.
      PubDate: Thu, 15 Apr 2021 09:05:01 +000
       
  • Characteristics and Development Mechanisms of Northeast Cold Vortices

    • Abstract: The northeast cold vortices (NECVs) in May-September during 1989–2018 are classified, based on the 6 h NCEP/NCAR reanalysis data (2.5° × 2.5°) and observational data from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) provided by China Meteorological Administration. Meanwhile, characteristics and development mechanisms for NECVs of different types are also analyzed. In the recent 30 years, the occurrences of NECV processes have been increasing year by year, with an average of 7.4 times per year in Northeast China and a duration of 3–5 days on average for each process. NECVs mostly occur in late spring and early summer, and the longest time influenced by NECVs exceeds 19 days, with annual means of 9.9 days, 8.8 days, and 7.0 days in May, June, and July, respectively. The frequency of weak NECVs is about 1.2 times that of strong NECVs. Strong NCVs in late spring and early autumn as well as weak MCVs in summer are with high-frequency occurrences. It is found that when NCVs occur in late spring and early autumn, the upper-level westerly jets are relatively stronger, thus strengthening the divergence in the upper troposphere and the vortex circulation. The circulation fields in upper and lower levels cooperate with the strong jets, promoting the continuous development and maintenance of the cold vortices. Apart from the jets and circulation, the lower central potential height combined with the obvious cold-core and stronger ascending motions favor the NCV’s development. In addition, the dry intrusion has a strong promotion due to the stronger lower-level cold advection and downward intrusion of high potential vorticity. However, when MCVs occur in summer, things are just the opposite.
      PubDate: Wed, 14 Apr 2021 06:50:01 +000
       
  • The Impact of Tropical Cyclones in Premonsoonal Season on Local Convection
           over the Al-Hajar Mountains in Oman during 2007–2018

    • Abstract: The tropical cyclones (TCs) and convective storms have a significant impact on triggering widespread flooding in vulnerable areas. However, it is not clear whether the TCs stimulate the convective precipitation over the Al-Hajar Mountains in Oman or suppress it although it had been shown in many studies that TCs suppress the local convection development. This study aims to test the applicability of the hypothesis that the TCs suppress the convection over mountainous to the Al-Hajar Mountains. In order to test the hypothesis of tropical cyclones’ impact on local convection development over the Al-Hajar Mountains, this study considers three different cases during the premonsoon season between 2007 and 2018. The results revealed that weak local convection is reported over the Al-Hajar Mountains during the presence of the cyclones in the Arabian Sea and during the period of their direct impacts. The rainfall in these cases was mainly from stratiform clouds. Therefore, the impact of TCs on the convective suppression over the mountains is applicable to the Al-Hajar Mountains. This study will provide decision-makers and policy creators with knowledge as to whether the Al-Hajar Mountains are susceptible and vulnerable to the risk of torrential downpour, flash flooding, and thunderstorms as what is believed to be.
      PubDate: Mon, 12 Apr 2021 10:05:01 +000
       
  • Effects of Location-Specific Meteorological Factors on COVID-19 Daily
           Infection in a Tropical Climate: A Case of Kuala Lumpur, Malaysia

    • Abstract: Insufficient information on the novel coronavirus (COVID-19) has made it more difficult for the world to tackle its continuous implosion. Meteorological and environmental factors, in both laboratory and epidemiological studies, have been reported to affect the survival and transmission of the virus. In this study, the possible effects of location-specific meteorological parameters in a tropical climate on new daily COVID-19 infection (NDI) are investigated in Kuala Lumpur from 14 March 2020 to 31 August 2020. A generalized additive model (GAM) was imposed on ambient temperature (T) and absolute humidity (AH) to explore their nonlinear relationship with NDI. Piecewise linear regression was then used to further discern the relationships below and above the threshold values of both T and AH. The relationship between T and NDI, which was linear and statistically significant for T > 29.7°C, showed that each unit rise in temperature increases NDI by about 3.210% (CI: 1.372–7.976). AH had a more pronounced linear association with NDI for AH ≤ 22.6 g/m3 but tended to flatten the exposure-response curve above this value. A 1 g/m3 increase in AH increases NDI by 3.807% (CI: 2.064–5.732). Generally, the results indicated a positive association between T and NDI, particularly above 29.7°C, while the association with AH showed a stronger positive relationship below 22.6 g/m3. The implication of this is that COVID-19 could not be suppressed on account of warmer weather as such public health interventions remain imperative.
      PubDate: Sat, 10 Apr 2021 07:05:01 +000
       
  • Spatiotemporal Variations of Drought in the Arid Region of Northwestern
           China during 1950–2012

    • Abstract: There are water resource shortages and frequent drought disasters in the arid region of northwestern China (ARNC). The purpose of this study is to understand the spatiotemporal variations of the droughts in this region and to further estimate future changes. Multiple drought indexes such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrated Palmer drought severity index (SC-PDSI) are used to investigate the temporal and spatial characteristics of the ARNC drought from 1950 to 2012. Our results indicate the following: (1) The drought indexes exhibit significant increasing trends, and the highest drought frequency occurred in the 1960s, followed by a decreasing trend during the next few decades. All four seasons exhibit a wet trend, with a higher drought frequency in summer than in the other seasons. (2) The changes of the drought indexes in the ARNC also exhibit distinct spatial variations, with a wet trend in the subregions of North Xinjiang (NXJ), the Tianshan Mountains (TS), South Xinjiang (SXJ), and the Qilian Mountains (QL), but with a dry trend in the subregions of the Hexi Corridor (HX) and the western part of Inner Mongolia (WIM). (3) There was a major climate variability in the ARNC that occurred in the 1980s, and there were dry and wet climate oscillation periods of 8a, 17a, and >20a.
      PubDate: Mon, 05 Apr 2021 06:20:02 +000
       
  • Analysis of the Response of Urban Water Consumption to Climatic Variables:
           Case Study of Khorramabad City in Iran

    • Abstract: Iran is located in a dry climate belt. Such conditions have made the supply of urban water resources one of the most fundamental management challenges. The amount of water consumed in a city is affected by the weather conditions greatly such that as the weather changes, the amount of water consumed changes as well. In this study, several models including zero-order Pearson’s correlation coefficient, first-order Pearson’s correlation, generalized additive model (GAM), generalized linear model (GLM), support vector machine (SVM-Nu), and simplex optimization algorithm were used in order to identify linear/nonlinear reactions of monthly water consumption to the individual and combined associations of meteorological variables (temperature, air pressure, and relative humidity) in Khorramabad city. Zero-order and first-order correlations showed that, by controlling the air temperature, the effect of pressure and relative humidity on changes in water consumption increase. On the other hand, both individual and combined GAM models showed the same result in the nonlinear response of water consumption to the changes in relative humidity and air pressure. The spline method also revealed that, by eliminating the effect of air temperature, the nonlinear reaction of water consumption to changes in pressure and relative humidity was increasing, and by eliminating the effects of the relative humidity and air pressure, the nonlinear reaction of water consumption to the air temperature was intensified. In general, by decreasing the air pressure and temperature, the amount of urban household water consumption decreases drastically. These conditions are generally provided by entering low-pressure systems.
      PubDate: Fri, 19 Mar 2021 06:05:02 +000
       
  • Observed Shear-Relative Rainfall Asymmetries Associated with Landfalling
           Tropical Cyclones

    • Abstract: This study examines the shear-relative rainfall spatial distribution of tropical cyclones (TCs) during landfall based on the 19-year (1998–2016) TRMM satellite 3B42 rainfall estimate dataset and investigates the role of upper-tropospheric troughs on the rainfall intensity and distribution after TCs make a landfall over the six basins of Atlantic (ATL), eastern and central Pacific (EPA), northwestern Pacific (NWP), northern Indian Ocean (NIO), southern Indian Ocean (SIO), and South Pacific (SPA). The results show that the wavenumber 1 perturbation can contribute ∼ 50% of the total perturbation energy of total TC rainfall. Wavenumber 1 rainfall asymmetry presents the downshear-left maxima in the deep-layer vertical wind shear between 200 and 850 hPa for all the six basins prior to making a landfall. In general, wavenumber 1 rainfall tends to decrease less if there is an interaction between TCs and upper-level troughs located at the upstream of TCs over land. The maximum TC rain rate distributions tend to be located at the downshear-left (downshear) quadrant under the high (low)-potential vorticity conditions.
      PubDate: Wed, 10 Mar 2021 12:05:00 +000
       
  • Impacts of Sea Surface Temperature and Atmospheric Teleconnection Patterns
           in the Northern Mid-Latitudes on Winter Extremely Cold Events in North
           China

    • Abstract: The frequency distribution of winter extreme cold events (ECEs) in North China and the influences of mid-latitude sea surface temperature anomalies (SSTAs) in the Northern Hemisphere are studied. The results show that (1) the frequency of single station ECEs (SSECEs) in winter increases from southeast to northwest, with a decrease before 2008 and then a significant increase. This trend abrupt change occurs in late winter. (2) When the SST in the North Pacific shows an “El-Niño-like” anomaly in winter, it triggers the negative Arctic Oscillation (−AO), positive Pacific North America (+PNA), and positive Eurasia Pacific (+EUP) atmospheric teleconnection patterns in the mid-lower troposphere. As a result, the ridge to south of Lake Baikal becomes stronger. Meanwhile, SST in the North Atlantic shows a “reversed C” negative anomaly with North Atlantic Oscillation (+NAO), (+PNA)-like and (+EUP)-like patterns, and the ridge to southwest of Lake Baikal becomes stronger. Furthermore, both cause the Siberian High to become weaker in the north and stronger in the south. With the weaker East Asia subtropical jet and stronger East Asia winter monsoon, these factors lead to a significant increase of SSECE frequency in North China. (3) When the SSTA shows an “El Niño-like” developing pattern from summer to autumn in the North Pacific, the winter SSECE frequency will be higher. (4) The purported mechanism between the mid-latitude SSTA and the winter SSECE frequency in North China is the following: the SSTA in the North Pacific in summer and autumn excites atmospheric teleconnection wave trains, and the Atlantic stores these anomaly signals. In winter, the interaction between the SSTAs in the North Pacific and the North Atlantic enhances the Eurasian teleconnection wave train. With the upstream fluctuation energy dispersing downstream, the wave train centers move eastward with the season, resulting in an increase in the frequency of the SSECEs.
      PubDate: Tue, 09 Mar 2021 09:20:01 +000
       
  • Statistical Characteristics of Raindrop Size Distribution during Rainy
           Seasons in Northwest China

    • Abstract: Raindrop size distribution (DSD) is of great significance for understanding the microphysical process of rainfall and the quantitative precipitation estimation (QPE). However, in the past, there was a lack of relevant research on Xinjiang in the arid region of northwest China. In this study, the rainy season data collected by the disdrometer in the Yining area of Xinjiang were used for more than two years, and the characteristics of DSDs for all samples, for two rain types (convective and stratiform), and for six different rain rates were studied. The results showed that nearly 70% of the total samples had a rainfall rate of less than 1 mm·h−1, the convective rain was neither continental nor maritime, and there was a clear boundary between convective rain and stratiform rain in terms of the scattergram of the standardized intercept parameter () versus the mass-weighted average diameter (). When the raindrop diameter was less than 0.7 mm, DSDs of the two rainfalls basically coincided, while when the raindrop diameter was greater than 0.7 mm, DSDs of convective rainfall were located above the stratiform rain. As the rainfall rate increased, increased, while first increased and then decreased. In addition, we deduced the (radar reflectivity-rain rate) relationship and relationship (shape parameter-slope parameter of the gamma DSDs) suitable for the Yining area. These conclusions are conducive to strengthening the understanding of rainfall microphysical processes in arid regions and improving the ability of QPE in arid regions.
      PubDate: Mon, 08 Mar 2021 08:20:01 +000
       
  • Assimilation of MWHS-2/FY-3C 183 GHz Channels Using a Dynamic Emissivity
           Retrieval and Its Impacts on Precipitation Forecasts: A Southwest Vortex
           Case

    • Abstract: The dynamic emissivity retrieved from window channels of the microwave humidity sounder II (MWHS-2) onboard the China Meteorological Administration’s FengYun (FY)-3C polar orbiting satellite can provide more realistic emissivity over lands and potentially improve the numerical weather prediction (NWP) forecasts. However, whether the assimilation with the dynamic emissivity works for the precipitation forecasts over the complex geography is less investigated. In this paper, a typical precipitating case generated by the Southwest Vortex is selected and the Weather Research and Forecasting data assimilation (WRFDA) system is applied to examine the impacts of assimilating MWHS-2/FY-3C with the uses of the emissivity atlas and the dynamic emissivity on the forecasts. The results indicate that the use of the dynamic emissivity retrieved from the 89 GHz channel of MWHS-2/FY-3C apparently increases the used data number for assimilation and does improve the initial fields and the 24-hour forecasts (from 0000 UTC 24 June 2016 to 0000 UTC 25 June 2016) of precipitation distribution and intensity except for the rainfall over 100 mm. But these positive impacts are not evidently better than those with the emissivity atlas. In general, these results still suggest that the future use of the dynamic emissivity in the assimilation over the complex terrain is promising.
      PubDate: Sat, 27 Feb 2021 13:35:01 +000
       
  • Evaluation of Zenith Tropospheric Delay Derived from Ray-Traced VMF3
           Product over the West African Region Using GNSS Observations

    • Abstract: The growing demand for Global Navigation Satellite System (GNSS) technology has necessitated the establishment of a vast and ever-growing network of International GNSS Service (IGS) tracking stations worldwide. The IGS provides highly accurate and highly reliable daily time-series Zenith Tropospheric Delay (ZTD) products using data from the member sites towards the use of GNSS for precise geodetic, climatological, and meteorological applications. However, if for reasons like poor internet connectivity, equipment failure, and power outages, the IGS station is inaccessible or malfunctioning, and gaps are created in the data archive resulting in degrading the quality of the ZTD and precipitable water vapour (PWV) estimation. To address this challenge as a means of providing an alternative data source to improve the continuous availability of ZTD data and as a backup data in the event that the IGS site data are missing or unavailable in West Africa, this paper compares the sitewise operational Vienna Mapping Functions 3 (VMF3) ZTD product with the IGS final ZTD product over five IGS stations in West Africa. Eight different statistical evaluation metrics, such as the mean bias (MB), mean absolute error (MAE), root mean squared error (RMSE), Pearson correlation coefficient (r), coefficient of determination (r2), refined index of agreement (IAr), Nash–Sutcliffe coefficient of efficiency (NSE), and the fraction of prediction within a factor of two (FAC2), are employed to determine the degree of agreement between the VMF3 and IGS tropospheric products. The results show that the VMF3-ZTD product performed excellently and matches very well with the IGS final ZTD product with an average MB, MAE, RMSE, r, r2, NSE, IAr, and FAC2 of 0.38 cm, 0.87 cm, 1.11 cm, 0.988, 0.976, 0.967, 0.992, and 1.00 (100%), respectively. This result is an indication that the VMF3-ZTD product is accurate enough to be used as an alternative source of ZTD data to augment the IGS final ZTD product for positioning and meteorological applications in West Africa.
      PubDate: Sat, 27 Feb 2021 13:20:01 +000
       
  • Evaluating the Performance of a WRF Physics Ensemble in Simulating
           Rainfall over Lao PDR during Wet and Dry Seasons

    • Abstract: Dynamical downscaling of General Circulation Model (GCM) data for any region has been made possible due to a set of physics options and model dynamics within the Weather Research and Forecasting (WRF) model. This study evaluated the performance of an ensemble of physics options in simulating rainfall during wet and dry seasons of Lao PDR. The model evaluation criteria focused on identifying the optimum physics options for a range of scenarios. No single combination of physics options performed well in all scenarios reflecting the importance of using different parameterizations according to the geographic location and the intended application of the results. For the dry season, none of the ensemble members performed satisfactorily for the southern region of Lao PDR, while all the ensemble members performed well for the northern and central regions. While almost all the WRF simulations overestimated the rainfall during the wet season, BMJ for cumulus physics performed better in the northern and central regions, and KF performed better in the south region. The YSU scheme performed best as the planetary boundary layer for both wet and dry seasons, while WSM5 for the wet season and Lin for the dry season gave the best model performance as the microphysics option.
      PubDate: Thu, 11 Feb 2021 13:05:01 +000
       
  • Determination of the Influence of Fuel Switching Regulation on the Sulfur
           Dioxide Content of Air in a Port Area Using DID Model

    • Abstract: Since January 1, 2018, ships berthed at all ports of the three designated emission control areas (ECAs) in China are required to use fuel with sulfur content not exceeding 0.5% (m/m), excluding one hour postarrival and one hour predeparture. To understand changes in SO2 due to this policy, two observation stations were established on Waigaoqiao Dock in the Yangtze River estuary. Three data types were collected from March 2018 to May 2018, namely, wind speed and direction, SO2 concentration, and ships’ arrival and departure times. The statistics indicate that the wind direction changed little during the observation period and SO2 concentration was below 5 µg/m3 77.47% of the time. Meanwhile, ships’ arrival and departure at the dock had a distinct influence on overall SO2 distribution, including occurrence of concentrations ≥5 µg/m3. The three types of data were divided into six groups and a difference-in-difference model was used for analysis. The result shows that SO2 concentration increases due to the use of high-sulfur fuel and is especially significant when the wind is southwesterly. Furthermore, there was a positive correlation between increases in SO2 concentrations over 5 µg/m3 and the number of ships arriving or departing from the port. This study reports the positive impact of fuel switching on air quality and can be used to evaluate adherence to the ECA policy.
      PubDate: Tue, 09 Feb 2021 14:20:02 +000
       
  • Spatiotemporal Rainfall Distribution of Soan River Basin, Pothwar Region,
           Pakistan

    • Abstract: This study evaluates the spatiotemporal rainfall variability over the semimountainous Soan River Basin (SRB) of sub-Himalayan Pothwar region, Pakistan. The temporal rainfall trend analysis of sixteen rain gauges was performed on annual basis with long-term (1981–2016) data. The results depicted that there is substantial year-to-year and season-to-season variability in rainfall patterns, and rainfall patterns are generally erratic in nature. The results highlight that most of the highland rainfall stations showed decreasing trends on annual basis. The central and lowland stations of the study area recorded an increasing trend of rainfall except for Talagang station. The average annual rainfall of the study area ranges between 492 mm and 1710 mm in lowland and high-altitude areas, respectively. Of the whole year’s rainfall, about 70 to 75% fall during the monsoon season. The rainfall spatial distribution maps obtained using the inverse distance weighting (IDW) method, through the GIS software, revealed the major rainfall range within the study area. There is a lack of water during postmonsoon months (November–February) and great differences in rainfall amounts between the mountainous areas and the lowlands. There is a need for the rational management of mountainous areas using mini and check dams to increase water production and stream regulation for lowland areas water availability. The spatiotemporal rainfall variability is crucial for better water resource management schemes in the study area of Pothwar region, Pakistan.
      PubDate: Mon, 08 Feb 2021 08:35:01 +000
       
  • Investigation on the Impacts of COVID-19 Lockdown and Influencing Factors
           on Air Quality in Greater Bangkok, Thailand

    • Abstract: With the outbreak of the COVID-19 pandemic around the world, many countries announced lockdown measures, including Thailand. Several scientific studies have reported on improvements in air quality due to the impact of these COVID-19 lockdowns. This study aims to investigate the effects of the COVID-19 lockdown and its driving influencing factors on air pollution in Greater Bangkok, Thailand, using in situ measurements. Overall, PM2.5, PM10, O3, and CO concentrations presented a significant decreasing trend during the COVID-19 outbreak year based on three periods: the Before Lockdown, Lockdown, and After Lockdown periods, for PM2.5: −0.7%, −15.8%, and −20.7%; PM10: −4.1%, −31.7%, and −6.1%; and O3: −0.3%, −7.1%, and −4.7%, respectively, compared to the same periods in 2019. CO concentrations, especially which had increased by 14.7% Before Lockdown, decreased by −8.0% and −23.6% during the Lockdown and After Lockdown periods, respectively. Meanwhile, SO2 increased by 54.0%, 41.5%, and 84.6%, and NO2 increased by 20.1%, 3.2%, and 26.6%, respectively, for the Before Lockdown, Lockdown, and After Lockdown periods. PCA indicated a significant combination effect of atmospheric mechanisms that were strongly linked to emission sources such as traffic and biomass burning. It has been demonstrated that the COVID-19 lockdown did pause some of these anthropogenic emissions, i.e., traffic and commercial and industrial activities, but not all of them. Even low traffic emissions, on their own, did not cause an absolute reduction in air pollution since there are several primary emission sources that dominate the air quality over Greater Bangkok. Finally, these findings highlight the impact of COVID-19 lockdown measures not only on air pollution levels but on their effects on air pollution characteristics, as well.
      PubDate: Thu, 04 Feb 2021 15:05:01 +000
       
  • Vegetation Change and Its Response to Climate Change in Yunnan Province,
           China

    • Abstract: The impact of global climate change on vegetation has become increasingly prominent over the past several decades. Understanding vegetation change and its response to climate can provide fundamental information for environmental resource management. In recent years, the arid climate and fragile ecosystem have led to great changes in vegetation in Yunnan Province, so it is very important to further study the relationship between vegetation and climate. In this study, we explored the temporal changes of normalized difference vegetation index (NDVI) in different seasons based on MOD13Q1 NDVI by the maximum value composite and then analyzed spatial distribution characteristics of vegetation using Sen’s tendency estimation, Mann–Kendall significance test, and coefficient of variation model (CV) combined with terrain factors. Finally, the concurrent and lagged effects of NDVI on climate factors in different seasons and months were discussed using the Pearson correlation coefficient. The results indicate that (1) the temporal variation of the NDVI showed that the NDVI values of different vegetation types increased at different rates, especially in growing season, spring, and autumn; (2) for spatial patterns, the NDVI, CV, and NDVI trends had strong spatial heterogeneity owning to the influence of altitudes, slopes, and aspects; and (3) the concurrent effect of vegetation on climate change indicates that the positive effect of temperature on NDVI was mainly in growing season and autumn, whereas spring NDVI was mainly influenced by precipitation. In addition, the lag effect analysis results revealed that spring precipitation has a definite inhibition effect on summer and autumn vegetation, but spring and summer temperature can promote the growth of vegetation. Meanwhile, the precipitation in the late growing season has a lag effect of 1-2 months on vegetation growth, and air temperature has a lag effect of 1 month in the middle of the growing season. Based on the above results, this study provided valuable information for ecosystem degradation and ecological environment protection in the Yunnan Province.
      PubDate: Thu, 04 Feb 2021 08:20:00 +000
       
  • Impact of Large-Scale Climate Indices on Meteorological Drought of Coastal
           Ghana

    • Abstract: The devastating effects of drought on agriculture, water resources, and other socioeconomic activities have severe consequences on food security and water resource management. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. In this study, meteorological droughts over six coastal synoptic stations were investigated using three-month Standardized Precipitation Index (SPI). The dry seasons of November-December-January (NDJ), December-January-February (DJF), and January-February-March (JFM) were the focal seasons for the study. Trends of dry seasons SPIs were evaluated using seasonal Mann–Kendall test. The relationship between drought SPI and ocean-atmosphere climate indices and their predictive ability were assessed using Pearson correlation and Akaike Information Criterion (AIC) stepwise regression method to select best climate indices at lagged timestep that fit the SPI. The SPI exhibited moderate to severe drought during the dry seasons. Accra exhibited a significant increasing SPI trend in JFM, NDJ, and DJF seasons. Besides, Saltpond during DJF, Tema, and Axim in NDJ season showed significant increasing trend of SPI. In recent years, SPIs in dry seasons are increasing, an indication of weak drought intensity, and the catchment areas are becoming wetter in the traditional dry seasons. Direct (inverse) relationship was established between dry seasons SPIs and Atlantic (equatorial Pacific) ocean's climate indices. The significant climate indices modulating drought SPIs at different time lags are a combination of either Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, or AMO for a given station. The AIC stepwise regression model explained up to 48% of the variance in the drought SPI and indicates Nino 3.4, Nino 4, Nino 3, Nino 1 + 2, TNA, TSA, AMM, and AMO have great potential for seasonal drought prediction over Coastal Ghana.
      PubDate: Wed, 03 Feb 2021 14:05:00 +000
       
  • 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
           China

    • 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,
           China

    • 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:
           1991–2015

    • 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
       
 
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