A scanning Raman lidar for observing the spatio-temporal distribution of water vapor

2016 ◽  
Vol 150-151 ◽  
pp. 21-30 ◽  
Author(s):  
Masanori Yabuki ◽  
Makoto Matsuda ◽  
Takuji Nakamura ◽  
Taiichi Hayashi ◽  
Toshitaka Tsuda
Keyword(s):  
Water Vapor ◽  
Temporal Distribution ◽  
Raman Lidar ◽  
Spatio Temporal

Quantitative study of atmospheric rivers in the Indian subcontinent

2021 ◽  
Author(s):  
Rosa V. Lyngwa ◽  
Munir Ahmad Nayak
Keyword(s):  
Water Vapor ◽  
Indian Subcontinent ◽  
Temporal Distribution ◽  
Large Degree ◽  
Water Vapor Transport ◽  
Boreal Summer ◽  
Atmospheric Rivers ◽  
Wind Speeds ◽  
Minimum Requirements ◽  
Spatio Temporal

<p>The principal sources of freshwater in India include precipitation, glaciers, and snowmelt. The former dominates the country’s annual river water contribution, which is important for agriculture and livelihood of the residents, and the latter two sources contribute at a much lower fraction in comparison to precipitation to even meet the minimum requirements. However, there is a large degree of variations in their spatio-temporal distribution throughout the country. India receives a major portion of its annual precipitation during the boreal summer (June – September). The well-known but relatively unexplored contributors to precipitation in India are atmospheric rivers (ARs). This study aims to understand the main climatological and dynamical differences between the Indian summer monsoon (ISM) and ARs in boreal summer. Zonal (‘u’) and meridional (‘v’) wind speeds, integrated water vapor transport (IVT), and integrated water vapor (IWV) are used to identify distinct features in ARs in the Indian sub-continent that can be used to distinguish them from ISM. The major differences between the two synoptic features were found in the increased zonal wind speed and moisture inputs during AR events, which often result in extreme precipitation and floods. Besides understanding them, the identification of ARs in this region and accounting for their existential contribution to moisture during peak rainfall seasons is critical for further hydrological impacts studies.</p>

scholarly journals Amazonian terrestrial water balance inferred from satellite-derived water vapor isotopes

2021 ◽  
Author(s):  
Mingjie Shi ◽  
John Worden ◽  
Adriana Bailey ◽  
David Noone ◽  
Camille Risi ◽  
...  
Keyword(s):  
Water Vapor ◽  
Water Balance ◽  
Temporal Distribution ◽  
Satellite Observations ◽  
Amazon Forest ◽  
Tightly Coupled ◽  
Terrestrial Water ◽  
Spatio Temporal ◽  
The Difference ◽  
Variable Bias

Abstract The evolution of the Amazon forest is tightly coupled to its terrestrial water balance (evapotranspiration minus precipitation, or ET-P), as an increase in ET-P reduces soil moisture, increasing water stress. However, large differences of ~ 50% between current monthly estimates of ET-P make it challenging to confidently quantify its spatio-temporal distribution and evolution. Here, we show that new satellite observations of the HDO/H2O ratio of water vapor, spanning 2003 to 2020, constrain estimates of the Amazon water balance with monthly precision of ~ 20%. The HDO/H2O ratio of water vapor is sensitive to the difference between ET and P, rather than to either flux alone, because lighter isotopes preferentially evaporate and heavier isotopes preferentially condense. Consequently, variable bias and sensitivity errors that result from combining different ET and precipitation products are minimized with this proxy. Our analysis demonstrates these data can quantify the spatial patterns of Amazon water balance from monthly to interannual time scales.

Influence of Planting Density on Root Spatio-Temporal Distribution of Different Types of Maize under High-Yielding Cultivation Conditions

2013 ◽  
Vol 38 (7) ◽  
pp. 1286-1294 ◽  
Author(s):  
Zong-Xin LI ◽  
Yuan-Quan CHEN ◽  
Qing-Cheng WANG ◽  
Kai-Chang LIU ◽  
Wang-Sheng GAO ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Planting Density ◽  
Cultivation Conditions ◽  
Different Types ◽  
Spatio Temporal

SPATIO-TEMPORAL DISTRIBUTION OF URANIUM CONCENTRATION IN SHALLOW AQUIFERS OF ARAVALLI TERRANE (NORTH GUJARAT), INDIA

2019 ◽  
Author(s):  
Rudra Mohan Pradhan ◽  
◽  
Karrie A. Weber ◽  
Karrie A. Weber ◽  
Daniel Snow ◽  
...  
Keyword(s):  
Uranium Concentration ◽  
Temporal Distribution ◽  
Shallow Aquifers ◽  
Spatio Temporal ◽  
North Gujarat

scholarly journals Variation Characteristics and Transportation of Aerosol, NO2, SO2, and HCHO in Coastal Cities of Eastern China: Dalian, Qingdao, and Shanghai

2021 ◽  
Vol 13 (5) ◽  
pp. 892
Author(s):  
Xiaomei Li ◽  
Pinhua Xie ◽  
Ang Li ◽  
Jin Xu ◽  
Zhaokun Hu ◽  
...  
Keyword(s):  
Eastern China ◽  
Temporal Distribution ◽  
Correlation Coefficients ◽  
Optical Absorption Spectroscopy ◽  
Near Surface ◽  
Mixing Ratios ◽  
Variation Characteristics ◽  
Spatio Temporal ◽  
Gas Volume ◽  
Transport Flux

This paper studied the method for converting the aerosol extinction to the mass concentration of particulate matter (PM) and obtained the spatio-temporal distribution and transportation of aerosol, nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO) based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations in Dalian (38.85°N, 121.36°E), Qingdao (36.35°N, 120.69°E), and Shanghai (31.60°N, 121.80°E) from 2019 to 2020. The PM2.5 measured by the in situ instrument and the PM2.5 simulated by the conversion formula showed a good correlation. The correlation coefficients R were 0.93 (Dalian), 0.90 (Qingdao), and 0.88 (Shanghai). A regular seasonality of the three trace gases is found, but not for aerosols. Considerable amplitudes in the weekly cycles were determined for NO2 and aerosols, but not for SO2 and HCHO. The aerosol profiles were nearly Gaussian, and the shapes of the trace gas profiles were nearly exponential, except for SO2 in Shanghai and HCHO in Qingdao. PM2.5 presented the largest transport flux, followed by NO2 and SO2. The main transport flux was the output flux from inland to sea in spring and winter. The MAX-DOAS and the Copernicus Atmosphere Monitoring Service (CAMS) models’ results were compared. The overestimation of NO2 and SO2 by CAMS is due to its overestimation of near-surface gas volume mixing ratios.

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