The Evolution of Springtime Water Vapor Over Beijing Observed by a High Dynamic Raman Lidar System: Case Studies

Raman lidar is an effective technique to retrieve the vertical distribution of atmospheric water vapor. For the first time, we present water vapor profiles retrieved by a high dynamic Raman lidar system over the Beijing area for representative cases in spring 2014, within the framework of the Aerosol Multi-wavelength Polarization Lidar Experiment project. In springtime, water vapor content over Beijing is generally low but with a strong daily variability. Its evolution is strongly coupled with winds and aerosols, with clouds also exerting a distinct impact. Northwesterly winds is found to be the most important factor impacting the temporal variability of water vapor mixing ratio (WVMR), and WVMR is found to be negatively correlated with wind speed. Moreover, we find that clouds tend to cause significant increases in the standard deviation of WMVR measurement, and relative humidity sharply increase below the cloud base. During a typical pollution episode, water vapor strongly covaries with aerosols due to hygroscopic growth effect and transport mechanism. Both water vapor and aerosols exhibit the highest variability within the planetary boundary layer (PBL), where the development and dissipation of haze mainly occur. Within the PBL, water vapor and aerosol concentration demonstrate different evolution features at different altitudes during the haze process, with a delayed increase and early decrease for higher altitudes. Back trajectory analysis using the hybrid single-particle Lagrangian trajectory model indicates that this phenomenon is most likely associated with different sources of the air mass at different altitudes.

[1]  Kenneth Sassen,et al.  Cloud Type and Macrophysical Property Retrieval Using Multiple Remote Sensors , 2001 .

[2]  J. Goldsmith,et al.  Turn-key Raman lidar for profiling atmospheric water vapor, clouds, and aerosols. , 1997, Applied optics.

[3]  A. Bucholtz,et al.  Rayleigh-scattering calculations for the terrestrial atmosphere. , 1995, Applied optics.

[4]  A. Tompkins A Prognostic Parameterization for the Subgrid-Scale Variability of Water Vapor and Clouds in Large-Scale Models and Its Use to Diagnose Cloud Cover , 2002 .

[5]  H. Che,et al.  Spatio-temporal variation trends of satellite-based aerosol optical depth in China during 1980-2008 , 2011 .

[6]  Liubin Huang,et al.  Carbonyl compounds over urban Beijing: Concentrations on haze and non-haze days and effects on radical chemistry , 2016 .

[7]  M. Molina,et al.  Elucidating severe urban haze formation in China , 2014, Proceedings of the National Academy of Sciences.

[8]  Glenn Rolph,et al.  Real-time Environmental Applications and Display sYstem: READY , 2017, Environ. Model. Softw..

[9]  Allan I. Carswell,et al.  Automated method for lidar determination of cloud-base height and vertical extent. , 1992, Applied optics.

[10]  David N. Whiteman,et al.  Raman Lidar Measurements During the International H2O Project , 2006 .

[11]  David N. Whiteman,et al.  Atmospheric water vapor measurements: Comparison of microwave radiometry and lidar , 1992 .

[12]  Jie Chen,et al.  Observational and modeling studies of urban atmospheric boundary-layer height and its evolution mechanisms , 2006 .

[13]  Steven C. Reising,et al.  Optimization of Background Information and Layer Thickness for Improved Accuracy of Water-Vapor Profile Retrieval from Ground-Based Microwave Radiometer Measurements at K-Band , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  Nicola Spinelli,et al.  Implementation of High Dynamic Raman Lidar System for 3D Map of Particulate Optical Properties and Their Time Evolution , 2013 .

[15]  J. Klett Lidar inversion with variable backscatter/extinction ratios. , 1985, Applied optics.

[16]  Barbara J. Turpin,et al.  Secondary organic aerosol formation in cloud and fog droplets: a literature evaluation of plausibility , 2000 .

[17]  P. Di Girolamo,et al.  Water vapour profiles from Raman lidar automatically calibrated by microwave radiometer data during HOPE , 2015 .

[18]  James D. Spinhirne,et al.  An Automated Algorithm for Detection of Hydrometeor Returns in Micropulse Lidar Data , 1998 .

[19]  Martin Wirth,et al.  Detection and Analysis of Water Vapor Transport by Airborne Lidars , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  C. Flamant,et al.  LIDAR DETERMINATION OF THE ENTRAINMENT ZONE THICKNESS AT THE TOP OF THE UNSTABLE MARINE ATMOSPHERIC BOUNDARY LAYER , 1997 .

[21]  Keith P. Shine,et al.  Sensitivity of the Earth's climate to height-dependent changes in the water vapour mixing ratio , 1991, Nature.

[22]  S. H. Melfi,et al.  OBSERVATION OF RAMAN SCATTERING BY WATER VAPOR IN THE ATMOSPHERE , 1969 .

[23]  S H Melfi,et al.  Remote measurements of the atmosphere using Raman scattering. , 1972, Applied optics.

[24]  George P. Petropoulos,et al.  Variational Bayes and the Principal Component Analysis Coupled With Bayesian Regulation Backpropagation Network to Retrieve Total Precipitable Water (TPW) From GCOM-W1/AMSR2 , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  David M. Winker,et al.  The Experimental Cloud Lidar Pilot Study (ECLIPS) for cloud-radiation research , 1994 .

[26]  Y Sasano,et al.  Tropospheric aerosol optical properties derived from lidar, sun photometer, and optical particle counter measurements. , 1994, Applied optics.

[27]  Dong Liu,et al.  A new cloud and aerosol layer detection method based on micropulse lidar measurements , 2014 .

[28]  David N. Whiteman,et al.  A Comparison of Water Vapor Measurements Made by Raman Lidar and Radiosondes , 1995 .

[29]  William C. Skamarock,et al.  A time-split nonhydrostatic atmospheric model for weather research and forecasting applications , 2008, J. Comput. Phys..

[30]  Tao Li,et al.  Variation characteristics of water vapor distribution during 2000-2008 over Hefei (31.9°N, 117.2°E) observed by L625 lidar , 2015 .

[31]  A. Ansmann,et al.  Experimental determination of the lidar overlap profile with Raman lidar. , 2002, Applied optics.

[32]  Xiaoquan Song,et al.  Observations of water vapor mixing ratio profile and flux in the Tibetan Plateau based on the lidar technique , 2016 .

[33]  Thierry Leblanc,et al.  Ground-based water vapor raman lidar measurements up to the upper troposphere and lower stratosphere for long-term monitoring , 2012 .

[34]  Fang Zhang,et al.  Persistent sulfate formation from London Fog to Chinese haze , 2016, Proceedings of the National Academy of Sciences.

[35]  H. Burtscher,et al.  Hygroscopic properties of carbon and diesel soot particles , 1997 .

[36]  Alain Hauchecorne,et al.  Water vapor observations up to the lower stratosphere through the Raman lidar during the Maïdo LIdar Calibration Campaign , 2014 .

[37]  S. Bony,et al.  Observational Evidence for Relationships between the Degree of Aggregation of Deep Convection, Water Vapor, Surface Fluxes, and Radiation , 2012 .

[38]  J. Lenoble,et al.  Methodology for the independent calibration of Raman backscatter water-vapor lidar systems. , 1999, Applied optics.

[39]  Chad W. Higgins,et al.  A Raman lidar to measure water vapor in the atmospheric boundary layer , 2013 .

[40]  S. Twomey,et al.  Aerosols, clouds and radiation , 1991 .

[41]  P. Sreenivas,et al.  Estimation of Improvement in Indian Summer Monsoon Circulation by Assimilation of Satellite Retrieved Temperature Profiles in WRF Model , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  S. H. Melfi,et al.  Raman lidar system for the measurement of water vapor and aerosols in the Earth's atmosphere. , 1992, Applied optics.

[43]  Zhanqing Li,et al.  The climatology of planetary boundary layer height in China derived fromradiosonde and reanalysis data , 2016 .