scholarly journals Characteristics of Heavy Storms and the Scaling Relation with Air Temperature by Event Process-Based Analysis in South China

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 185 ◽  
Author(s):  
Cuilin Pan ◽  
Xianwei Wang ◽  
Lin Liu ◽  
Dashan Wang ◽  
Huabing Huang
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
South China ◽  
Meteorological Factors ◽  
Rainfall Data ◽  
Precipitation Extremes ◽  
Scaling Relation ◽  
Convective Storms ◽  
Warm Front ◽  
Storm Characteristics

The negative scaling rate between precipitation extremes and the air temperature in tropic and subtropic regions is still a puzzling issue. This study investigates the scaling rate from two aspects, storm characteristics (types) and event process-based temperature variations. Heavy storms in South China are developed by different weather systems with unique meteorological characteristics each season, such as the warm-front storms (January), cold-front storms (April to mid-May), monsoon storms (late May to June), convective storms, and typhoon storms (July to September). This study analyzes the storm characteristics using the hourly rainfall data from 1990 to 2017; compares the storm hyetographs derived from the one-minute rainfall data during 2008–2017; and investigates the interactions between heavy storms and meteorological factors including air temperature, relative humidity, surface pressure, and wind speed at 42 weather stations in Guangzhou during 2015–2017. Most storms, except for typhoon and warm-front storms, had a short duration (3 h) and intense rates (~13 mm/h) in Guangzhou, South China. Convective storms were dominant (50%) in occurrence and had the strongest intensity (15.8 mm/h). Storms in urban areas had stronger interactions with meteorological factors and showed different hyetographs from suburban areas. Meteorological factors had larger variations with the storms that occurred in the day time than at night. The air temperature could rise 6 °C and drop 4 °C prior to and post-summer storms against the diurnal mean state. The 24-hour mean air temperature prior to the storms produced more reliable scaling rates than the naturally daily mean air temperature. The precipitation extremes showed a peak-like scaling relation with the 24-hour mean air temperature and had a break temperature of 28 °C. Below 28 °C, the relative humidity was 80%–100%, and it showed a positive scaling rate. Above 28 °C, the negative scaling relation was likely caused by a lack of moisture in the atmosphere, where the relative humidity decreased with the air temperature increase.

scholarly journals Effect of meteorological factors on influenza-like illness from 2012 to 2015 in Huludao, a northeastern city in China

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6919 ◽  
Author(s):  
Ying-Long Bai ◽  
De-Sheng Huang ◽  
Jing Liu ◽  
De-Qiang Li ◽  
Peng Guan
Keyword(s):  
Relative Humidity ◽  
Vapor Pressure ◽  
Air Temperature ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Lag Time ◽  
Temperature Minimum ◽  
The Relationship ◽  
Maximum Air Temperature ◽  
Minimum Air Temperature

Background This study aims to describe the epidemiological patterns of influenza-like illness (ILI) in Huludao, China and seek scientific evidence on the link of ILI activity with weather factors. Methods Surveillance data of ILI cases between January 2012 and December 2015 was collected in Huludao Central Hospital, meteorological data was obtained from the China Meteorological Data Service Center. Generalized additive model (GAM) was used to seek the relationship between the number of ILI cases and the meteorological factors. Multiple Smoothing parameter estimation was made on the basis of Poisson distribution, where the number of weekly ILI cases was treated as response, and the smoothness of weather was treated as covariates. Lag time was determined by the smallest Akaike information criterion (AIC). Smoothing coefficients were estimated for the prediction of the number of ILI cases. Results A total of 29, 622 ILI cases were observed during the study period, with children ILI cases constituted 86.77%. The association between ILI activity and meteorological factors varied across different lag periods. The lag time for average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity were 2, 2, 1, 1 and 0 weeks, respectively. Average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity could explain 16.5%, 9.5%, 18.0%, 15.9% and 7.7% of the deviance, respectively. Among the temperature indexes, the minimum temperature played the most important role. The number of ILI cases peaked when minimum temperature was around −13 °C in winter and 18 °C in summer. The number of cases peaked when the relative humidity was equal to 43% and then began to decrease with the increase of relative humidity. When the humidity exceeded 76%, the number of ILI cases began to rise. Conclusions The present study first analyzed the relationship between meteorological factors and ILI cases with special consideration of the length of lag period in Huludao, China. Low air temperature and low relative humidity (cold and dry weather condition) played a considerable role in the epidemic pattern of ILI cases. The trend of ILI activity could be possibly predicted by the variation of meteorological factors.

A relation between extreme precipitation and surface air temperature over Russia in the last four decades

2021 ◽  
Author(s):  
Maria Aleshina ◽  
Vladimir Semenov ◽  
Alexander Chernokulsky
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Extreme Precipitation ◽  
Surface Air Temperature ◽  
Precipitation Extremes ◽  
Convective Precipitation ◽  
Temperature Range ◽  
Precipitation Temperature ◽  
Moisture Availability ◽  
Precipitation And Temperature

<p>Precipitation extremes are widely thought to intensify with the global warming due to exponential growth, following the Clausius-Clapeyron (C-C) equation of atmosphere water holding capacity with rising temperatures. However, a number of recent studies based on station and reanalysis data for the contemporary period showed that scaling rates between extreme precipitation and temperature are strongly dependent on temperature range, region and moisture availability. Here, we examine the scaling between daily precipitation extremes and surface air temperature over Russian territory for the last four decades using meteorological stations data and ERA-Interim reanalysis. The precipitation-temperature relation is examined for total precipitation amount and, separately, for convective and large-scale precipitation types. In winter, a general increase of extreme precipitation of all types according to C-C relation is revealed. For the Russian Far East region, the stratiform precipitation extremes scale with surface air temperature following even super C-C rates, about two times as fast as C-C. However, in summer we find a peak-like structure of the precipitation-temperature scaling, especially for the convective precipitation in the southern regions of the country. Being consistent with the C-C relationship, extreme precipitation peaks at the temperature range between 15 °C and 20 °C. For the higher temperatures, the negative scaling prevails. Furthermore, it was shown that relative humidity in general decreases with growing temperature in summer. Notably, there appears to be a temperature threshold in the 15-20 °C range, beyond that relative humidity begins to decline more rapidly. This indicates that moisture availability can be the major factor for the peak-shaped relationship between extreme precipitation and temperature revealed by our analysis.</p>

Ranking Potential Evapotranspiration Models in the Prediction of Maize Water Requirements in the PortHarcourt Area of Nigeria

2003 ◽  
Vol 32 (1) ◽  
pp. 51-56 ◽  
Author(s):  
M.J. Ayotamuno
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Factors ◽  
Potential Evapotranspiration ◽  
Empirical Models ◽  
Geographical Region ◽  
Net Radiation ◽  
Temperature Elevation ◽  
Water Requirements

It has been observed that many of the empirical models used to estimate evapotranspiration, although simple, are not sufficiently sensitive in those areas where the temperature is relatively constant but other meteorological factors that promote evapotranspiration vary (Michael, 1978; Hashemi and Haliban, 1979). It is therefore important to verify, or even recalibrate these models when they are used within a new geographical region or climatic area (eg PortHarcourt, Nigeria) if account is to be taken of relational changes between meteorological factors such as net radiation, air temperature, elevation, advection and relative humidity.

The relation of drainage to rainfall and other meteorological factors

1941 ◽  
Vol 31 (1) ◽  
pp. 110-115 ◽  
Author(s):  
P. N. Sahni
Keyword(s):  
Relative Humidity ◽  
Wind Velocity ◽  
Air Temperature ◽  
Meteorological Factors ◽  
Curvilinear Relationship ◽  
The Mean ◽  
The Difference ◽  
June July

Natural drainage periods, i.e. periods between consecutive cessations of flow of the Rothamsted 20 in. drain gauge, were selected for the months of June, July and August. All periods of from 2 to 13 days were taken.A curvilinear relationship between the difference of rainfall and drainage (deficit) and rainfall was established.Residuals from this curve showed no appreciable correlation with the mean air temperature, but there was some slight evidence that the deficit was increased by a decrease in relative humidity or an increase in wind velocity.The residuals showed little correlation with drainage during the previous 3 weeks.

scholarly journals Estimation of the Influence of Meteorological Factors on the Potential Evapotranspiration of Yanhe River Basin

2021 ◽  
Author(s):  
yu luo ◽  
Peng Gao ◽  
Xingmin Mu ◽  
dexun Qiu
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
River Basin ◽  
Air Temperature ◽  
Meteorological Factors ◽  
Sensitivity Coefficient ◽  
Downward Trend ◽  
Upward Trend ◽  
Temporal And Spatial

Potential evapotranspiration (ET) is an important expenditure item in the hydrological cycle. Quantitative estimation of the influence of meteorological factors on ET can provide a scientific basis for the study of the impact mechanism of climate change on the hydrological cycle. In this paper, the Penman-Monteith method was used to calculate ET. The Mann-Kendall statistical test and the Inverse Distance Weighting method were used to analyze the temporal and spatial characteristics of the sensitivity coefficient of ET to meteorological factors and contribution rate of meteorological factors to ET. And the reasons for the change of ET were quantitatively explored in combination with the change trend of meteorological factors. The results showed that the average ET in the Yanhe River Basin from 1978 to 2017 was 935.92mm. Except for Ganquan Station, ET showed an upward trend. Generally, the sensitivity coefficient of air temperature (0.08), wind speed (0.19) and solar radiation (0.42) was positive and the sensitivity coefficient of relative humidity (-0.41) was negative. But there were significant temporal and spatial differences. The upward trend of air temperature and solar radiation contributed 1.09% and 0.55% to ET. Respectively, the downward trend of wind speed contributed -0.63% And the downward trend of relative humidity contributed to -0.85% of ET. Therefore, the decrease of relative humidity did not cause the increase of ET in Yanhe River basin. The dominant factor of the upward trend of ET was air temperature. But the dominant factors of ET had significant temporal and spatial differences. The downward trend of wind speed at Ganquan Station contributed -9.16% to ET, which indicated the dominant factor of “evaporation paradox” in Ganquan area was wind speed. Generally, the increase of ET was related to air temperature, wind speed and solar radiation. And the decrease of ET was related to relative humidity.

scholarly journals Mugwort (Artemisia L.) pollen in aeroplankton of Lublin, 2001-2005

2012 ◽  
Vol 59 (2) ◽  
pp. 121-130
Author(s):  
Elżbieta Weryszko-Chmielewska ◽  
Bogusław M. Kaszewski ◽  
Krystyna Piotrowska
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Negative Correlation ◽  
Pollen Season ◽  
Significant Positive Correlation ◽  
Meteorological Factors ◽  
Pollen Grains ◽  
Volumetric Method ◽  
Pollen Concentration ◽  
The Impact

The course of the <i>Artemisia</i> pollen season was recorded in Lublin over a period of five years: 2001-2005. The volumetric method was applied in the studies, using a VPPS 2000 Lanzoni trap. The length of the season was determined by the 98% method. The impact of several meteorological factors on the start and course of the pollen season was analysed. It was found that in the five-year period studied the mugwort pollen season started in the second or third decade of July and lasted 59-90 days. Maximum concentrations in the range of 103-221 pollen grains in 1 m<sup>3</sup> of air were noted between 2 and 9 August. Annual totals of mugwort pollen grains ranged from 1496 to 2532. A significant positive correlation was demonstrated between the <i>Artemisia</i> pollen concentration and air temperature, and a negative correlation between the pollen concentration and air relative humidity and cloudiness. A significant impact of temperature on the start of the <i>Artemisia</i> pollen season was also found.

scholarly journals Estimate of The Dynamical Change of Air TemperAture, Relative Humidity and Dew Point TemperaTure for Some Selected Station in Iraq

2021 ◽  
Vol 910 (1) ◽  
pp. 012010
Author(s):  
Wedyan G. Nassif ◽  
Sundus H. Jaber ◽  
Salwa S. Naif ◽  
Osama T. Al-Taai
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Factors ◽  
Geographical Area ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Relative Value ◽  
Low Humidity ◽  
Dynamical Change

Abstract Relative humidity can be inferred from the dew point values. When the air temperature and dew point temperatures are very close, the air has high relative humidity. The converse is true when there is a large difference between the air temperature and the dew point temperature, indicating the presence of low humidity air. To understand the expected changes in the climatic elements in the atmosphere, changes in temperature behavior, dew point, and relative humidity have been studied This study used data obtained from the European Center (ECMWF), which includes monthly and annual mean temperatures, dew, and relative humidity during the period (1988-2018) for selected stations in Iraq. The highest values of temperature and dew were recorded in July and August, and they were accompanied by a decrease in relative humidity. The highest value of relative humidity was recorded in December and January, accompanied by a decrease in temperature and dew, as we note through the results that there is an inverse relationship between relative humidity, temperature, and dew point Relative humidity changes when the temperature rises or falls, and the relative humidity may be higher in the morning when the temperature drops. The lowest amount of relative humidity during the day is when the temperature rises, the highest temperature value was recorded on 21July 2017 (12:00 PM) for Basra Station, while the highest relative value is humidity in Basra Governorate. Mosul station on January 21, 2014 (12:00 AM), and the reason is due to meteorological factors and the nature of the geographical area.

scholarly journals Influence of Meteorological Factors on the Potential Evapotranspiration in Yanhe River Basin, China

2021 ◽  
Author(s):  
yu luo ◽  
Peng Gao ◽  
Xingmin Mu
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
River Basin ◽  
Air Temperature ◽  
Hydrological Cycle ◽  
Meteorological Factors ◽  
Potential Evapotranspiration ◽  
Sensitivity Coefficients ◽  
The Impact

Potential evapotranspiration (ET) is an essential component of the hydrological cycle, and quantitative estimation of the influence of meteorological factors on ET can provide a scientific basis for studying the impact mechanisms of climate change. In the present research, the Penman-Monteith method was used to calculate ET. The Mann-Kendall statistical test with the inverse distance weighting were used to analyze the spatiotemporal characteristics of the sensitivity coefficients and contribution rates of meteorological factors to ET to identify the mechanisms underlying changing ET rates. The results showed that the average ET for the Yanhe River Basin, China from 1978–2017 was 935.92 mm. Save for a single location (Ganquan), ET increased over the study period. Generally, the sensitivity coefficients of air temperature (0.08), wind speed at 2 m (0.19), and solar radiation (0.42) were positive, while that of relative humidity was negative (-0.41), although significant spatiotemporal differences were observed. Increasing air temperature and solar radiation contributed 1.09% and 0.55% of the observed rising ET rates, respectively; whereas decreasing wind speed contributed -0.63%, and relative humidity accounted for -0.85%. Therefore, it was concluded that the decrease of relative humidity did not cause the observed ET increase in the basin. The predominant factor driving increasing ET was rising air temperatures, but this too varied significantly by location and time (intra- and interannually). Decreasing wind speed at Ganquan Station decreased ET by -9.16%, and was the primary factor underlying the observed, local “evaporation paradox.” Generally, increases in ET were driven by air temperature, wind speed and solar radiation, whereas decreases were derived from relative humidity.

scholarly journals Effect of meteorological factors on photovoltaic power forecast based on the neural network

RSC Advances ◽  
2017 ◽  
Vol 7 (88) ◽  
pp. 55846-55850 ◽  
Author(s):  
Wenbo Xiao ◽  
Jin Dai ◽  
Huaming Wu ◽  
Gina Nazario ◽  
Feng Cheng
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Factors ◽  
Network Models ◽  
Neural Network Models ◽  
Photovoltaic Power ◽  
The Neural Network

In this paper, the effects of meteorological factors (including air temperature, wind speed, and relative humidity) on photovoltaic (PV) power forecast using neural network models have been studied.

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