Abstract

Observations of monthly and seasonal nightly water vapor variations over Hefei utilizing L625 lidar water vapor data observed from 2000 to 2008 is the focus of this study. The experimental setup and main parameters of the L625 lidar for water vapor measurement are first presented, then the measurement principle of water vapor and data processing methods are introduced. The water vapor measurement precision of the lidar system was analyzed by comparison with radiosonde. Monthly and seasonal water vapor profiles were built by analyzing 2000–2008 lidar data. In the vertical direction, results show that water vapor content decreases gradually with height. The more the water vapor content in the low atmosphere, the faster the decay rate with altitude. As far as monthly variation, the water vapor content first increases and then decreases with month. The maximum content of water vapor appears in July, at mixing ratio of 15.6g/kg at 1km. The seasonal variability of water vapor content is rather obvious. In summer the water vapor mixing ratio reaches up to 15.0g/kg at 1km, and in winter it is only 3.9g/kg at the same altitude. Interannual variation of water vapor content differs between seasons (as revealed in the standard deviation of data) where summer is least stable and autumn is the most stable. Precipitable water vapor is calculated from water vapor mean profiles at 1–4km and the relationship between precipitable water vapor and precipitation is also investigated. A clear positive correlation is found with Pearson correlation coefficients (R) 0.933 between monthly precipitation and mean precipitable water vapor, as well a clear positive correlation between seasonal precipitation and seasonal mean precipitable water vapor (R=0.988). Precipitation conversion efficiency (PCE) is calculated from precipitation and precipitable water vapor. The monthly PCE reaches its maximum in October at 25.8%, and drops to its minimum in January at 11.5%. Seasonal PCE's minimum is 15.2% in autumn and 23.7% in winter, at maximum.

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