Remote sensing of water vapor content using ground-based GPS data

Based on the Haihe river basin National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data from 1948 to 2010 and the precipitation data of 53 hydrological stations during 1957–2010, this study analyzed the variation of water vapor content and precipitation, and investigated the correlation between them using several statistical methods. The results showed that the annual water vapor content decreased drastically from 1948 to 2010. It was comparatively high from the late 1940s to the late 1960s and depreciated from the early 1970s. From the southeast to the northwest of the Haihe river basin, there was a decrease in water vapor content. For vertical distribution, water vapor content from the ground to 700 hPa pressure level accounted for 72.9% of the whole atmospheric layer, which indicated that the water vapor of the Haihe river basin was mainly in the air close to the ground. The precipitation in the Haihe river basin during 1957–2010 decreased very slightly. According to the correlation analysis, the precipitation and water vapor content changes showed statistically positive correlation, in addition, their break points were both in the 1970s. Furthermore, the high consistency between the precipitation efficiency and precipitation demonstrates that water vapor content is one of the important factors in the formation of precipitation.

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Retrieval Accuracy Of Water Vapor Content And Bias-error Causes Of The Msr Aboard Mos-1

10.1109/igarss.1990.688619 ◽

2005 ◽

Cited By ~ 2

Author(s):

T. Ojima ◽

K. Maeda ◽

H. Satoh

Keyword(s):

Water Vapor ◽

Water Vapor Content ◽

Bias Error ◽

Retrieval Accuracy

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Selection Of Near-ir Channels For Remote Sensing Of Water Vapor From EOS/MODW

10.1109/igarss.1990.688623 ◽

2005 ◽

Author(s):

Y.J. Kaufman ◽

Bo-Cal Gao

Keyword(s):

Remote Sensing ◽

Water Vapor ◽

Near Ir ◽

Selection Of

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The Apparent Water Vapor Sinks and Heat Sources Associated with the Intraseasonal Oscillation of the Indian Summer Monsoon

Journal of Climate ◽

10.1175/2011jcli4076.1 ◽

2011 ◽

Vol 24 (16) ◽

pp. 4466-4479 ◽

Cited By ~ 24

Author(s):

Sun Wong ◽

Eric J. Fetzer ◽

Baijun Tian ◽

Bjorn Lambrigtsen ◽

Hengchun Ye

Keyword(s):

Remote Sensing ◽

Water Vapor ◽

Heat Source ◽

Summer Monsoon ◽

Indian Summer Monsoon ◽

Intraseasonal Oscillation ◽

Precipitation Anomaly ◽

Specific Humidity ◽

Heat Sources ◽

Test Bed

Abstract The possibility of using remote sensing retrievals to estimate apparent water vapor sinks and heat sources is explored. The apparent water vapor sinks and heat sources are estimated from a combination of remote sensing, specific humidity, and temperature from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS) and wind fields from the National Aeronautics and Space Administration (NASA)’s Goddard Space Flight Center (GSFC)’s Modern Era Retrospective-Analysis for Research and Applications (MERRA). The intraseasonal oscillation (ISO) of the Indian summer monsoon is used as a test bed to evaluate the apparent water vapor sink and heat source. The ISO-related northward movement of the column-integrated apparent water vapor sink matches that of precipitation observed by the Tropical Rainfall Measuring Mission (TRMM) minus the MERRA surface evaporation, although the amplitude of the variation is underestimated by 50%. The diagnosed water vapor and heat budgets associated with convective events during various phases of the ISO agree with the moisture–convection feedback mechanism. The apparent heat source moves northward coherently with the apparent water vapor sink associated with the deep convective activity, which is consistent with the northward migration of the precipitation anomaly. The horizontal advection of water vapor and dynamical warming are strong north of the convective area, causing the northward movement of the convection by the destabilization of the atmosphere. The spatial distribution of the apparent heat source anomalies associated with different phases of the ISO is consistent with that of the diabatic heating anomalies from the trained heating (TRAIN Q1) dataset. Further diagnostics of the TRAIN Q1 heating anomalies indicate that the ISO in the apparent heat source is dominated by a variation in latent heating associated with the precipitation.

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