SEASONAL EVOLUTION OF VERTICAL PROFILE OF PRECIPITABLE WATER CONTENT AND CLOUD-BASED ATTENUATION AT EHF BAND OVER A TROPICAL LOCATION USING GBMRR

2020 ◽  
Vol 79 (14) ◽  
pp. 1245-1258
Author(s):  
J. S. Ojo ◽  
S. D. Adeyeye
Keyword(s):  
Water Content ◽  
Vertical Profile ◽  
Precipitable Water ◽  
Seasonal Evolution ◽  
Precipitable Water Content ◽  
Tropical Location

Environments of Formation of Severe Squalls and Tornadoes Causing Large-scale Windthrows in the Forest Zone of European Russia and the Ural

2021 ◽  
pp. 35-49
Author(s):  
N. A. Kalinin ◽  
◽  
A. N. Shikhov ◽  
A. V. Chernokulsky ◽  
S. V. Kostarev ◽  
...  
Keyword(s):  
Water Content ◽  
Convective Instability ◽  
Wind Shear ◽  
Large Scale ◽  
Precipitable Water ◽  
European Russia ◽  
Strong Wind ◽  
Forest Zone ◽  
Principal Role ◽  
Precipitable Water Content

The environments of 53 severe squalls and tornadoes that caused large-scale windthrows in the forest zone of European Russia and the Ural in 1989–2019 are analyzed. The CFSR and ERA-5 reanalyses and sounding data were used to estimate characteristics of the environments including convective instability indices. It was found that the substantial temperature gradient on the atmospheric front (9.6C/500 km on average) and the jet stream presence in the lower or middle troposphere oriented along the frontal zone are important factors to estimate environments of the formation of severe squalls and tornadoes. In most cases, squalls and tornadoes require a combination of high precipitable water content (40 mm on average), moderate or high convective instability (CAPE >1000 J/kg), and moderate or strong wind shear. High precipitable water content and strong convective instability are important for the formation of squalls, while low-level wind shear plays a principal role for the tornado generation.

Four-dimensional variational assimilation of SSM/I precipitable water content data

1998 ◽  
Vol 124 (549) ◽  
pp. 1743-1770 ◽  
Author(s):  
M.-A. Filiberti ◽  
L. Eymard ◽  
F. Rabier ◽  
P. Courtier ◽  
J. N. Thépaut
Keyword(s):  
Water Content ◽  
Precipitable Water ◽  
Variational Assimilation ◽  
Precipitable Water Content

Total Column Ozone, Precipitable Water Content and Aerosol Optical Thickness Over Atigre Village, a Tropical Station: First Observations

MAPAN ◽  
2019 ◽  
Vol 34 (4) ◽  
pp. 451-463 ◽  
Author(s):  
Dada P. Nade ◽  
Swapnil S. Potdar ◽  
Rani P. Pawar ◽  
Santosh T. Mane ◽  
S. Chandra ◽  
...  
Keyword(s):  
Water Content ◽  
Optical Thickness ◽  
Precipitable Water ◽  
Aerosol Optical Thickness ◽  
Total Column Ozone ◽  
Precipitable Water Content ◽  
Column Ozone ◽  
Tropical Station ◽  
Total Column

scholarly journals Characteristics of Surface Temperature and Its Relationship with Meteorological Elements in China in the Last 73 Years

2021 ◽  
Vol 943 (1) ◽  
pp. 012011
Author(s):  
Jinhao Wu
Keyword(s):  
Relative Humidity ◽  
Surface Temperature ◽  
Water Content ◽  
Wind Field ◽  
Precipitable Water ◽  
Negative Potential ◽  
Human Society ◽  
Positive Potential ◽  
Precipitable Water Content ◽  
Meteorological Elements

Abstract Climate, as the natural environment on which human life depends, is intricately linked to human society. This paper focuses on the characteristics of temperature and its relationship with meteorological elements in China in the last 73 years. The data of this research is from NCEP/NCAR Reanalysis Monthly Means. This study adopts the Empirical Orthogonal Function (EOF) and Singular Value Decomposition (SVD) methods to study the surface temperature characteristics within China, and the synergistic variation between surface temperature and precipitable water content, wind field, and relative humidity in China. The results show that 1980s is a turning point for changes in surface temperature, precipitable water content, wind field, and relative humidity in China. Before 1980s, the temperature in China is low, while after this period, the temperature in China is high and China’s exposure to global warming has increased. Temperature is dominated by negative potential-phase oscillations with relative humidity and wind fields. In the north, temperature and precipitable water content have negative potential-phase oscillations, while temperature and precipitable water content have positive potential-phase oscillations in the south. In the central region of Xinjiang, temperature and precipitable water content have weak negative potential-phase oscillations, while temperature and wind field have positive potential-phase oscillations.

scholarly journals Results of Sun Photometer–Derived Precipitable Water Content over a Tropical Indian Station

2004 ◽  
Vol 43 (10) ◽  
pp. 1452-1459 ◽  
Author(s):  
P. Ernest Raj ◽  
P. C. S. Devara ◽  
R. S. Maheskumar ◽  
G. Pandithurai ◽  
K. K. Dani ◽  
...  
Keyword(s):  
Water Content ◽  
Precipitable Water ◽  
Southwest Monsoon ◽  
Low Latitude ◽  
Clear Sky ◽  
Precipitable Water Content ◽  
Sun Photometer ◽  
The One ◽  
Indian Station ◽  
Tropical Station

Abstract A compact, hand-held multiband sun photometer (ozone monitor) has been used to measure total precipitable water content (PWC) at the low-latitude tropical station in Pune, India (18°32′N, 73°51′E). Data collected in the daytime (0730–1800 LT) during the period from May 1998 to September 2001 have been used here. The daytime average PWC value at this station is 1.13 cm, and the average for only the clear-sky days is 0.75 cm. PWC values between 0.75 and 1.0 cm have the maximum frequency of occurrence. There is a large day-to-day variability due to varied sky and meteorological conditions. Mainly two types of diurnal variations in PWC are observed. The one occurs in the premonsoon summer months of April and May and shows that forenoon values are smaller than afternoon values. The other type occurs in November and December and shows a minimum around noontime. There is a diurnal asymmetry in PWC in which, on the majority of the days, the mean afternoon value is greater than the forenoon value. This asymmetry is more pronounced in the summer and southwest monsoon months (i.e., March–June). Monthly mean PWC is highest in September and lowest in December. The increase in PWC from the winter (December–February) to summer (March–May) seasons is about 50% and from the summer to southwest monsoon seasons (June–September) is almost 98%. Sun photometer–derived PWC shows a fairly good relationship with surface relative humidity and radiosonde-derived PWC, with a correlation coefficient as high as 0.80.

scholarly journals Estimate of the atmospheric turbidity from three broad-band solar radiation algorithms. A comparative study

2004 ◽  
Vol 22 (8) ◽  
pp. 2657-2668 ◽  
Author(s):  
G. López ◽  
F. J. Batlles
Keyword(s):  
Solar Radiation ◽  
Water Content ◽  
Solar Irradiance ◽  
Broad Band ◽  
Precipitable Water ◽  
Random Errors ◽  
Mean Values ◽  
Precipitable Water Content ◽  
Atmospheric Turbidity ◽  
Mean Square Errors

Abstract. Atmospheric turbidity is an important parameter for assessing the air pollution in local areas, as well as being the main parameter controlling the attenuation of solar radiation reaching the Earth's surface under cloudless sky conditions. Among the different turbidity indices, the Ångström turbidity coefficient β is frequently used. In this work, we analyse the performance of three methods based on broad-band solar irradiance measurements in the estimation of β. The evaluation of the performance of the models was undertaken by graphical and statistical (root mean square errors and mean bias errors) means. The data sets used in this study comprise measurements of broad-band solar irradiance obtained at eight radiometric stations and aerosol optical thickness measurements obtained at one co-located radiometric station. Since all three methods require estimates of precipitable water content, three common methods for calculating atmospheric precipitable water content from surface air temperature and relative humidity are evaluated. Results show that these methods exhibit significant differences for low values of precipitable water. The effect of these differences in precipitable water estimates on turbidity algorithms is discussed. Differences in hourly turbidity estimates are later examined. The effects of random errors in pyranometer measurements and cloud interferences on the performance of the models are also presented. Examination of the annual cycle of monthly mean values of β for each location has shown that all three turbidity algorithms are suitable for analysing long-term trends and seasonal patterns.

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