Evaluation of long‐term surface‐retrieved cloud droplet number concentration with in situ aircraft observations

2016 
A new cloud-droplet number concentration (NDROP) value added product (VAP) has been produced at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for the 13 years from January 1998 to January 2011. The retrieval is based on surface radiometer measurements of cloud optical depth from the multi-filter rotating shadow-band radiometer (MFRSR) and liquid water path from the microwave radiometer (MWR). It is only applicable for single-layered warm clouds. Validation with in situ aircraft measurements during the extended-term aircraft field campaign, Routine ARM Aerial Facility (AAF) CLOWD Optical Radiative Observations (RACORO), shows that the NDROP VAP robustly reproduces the primary mode of the in situ measured probability density function (PDF), but produces a too wide distribution, primarily caused by frequent high cloud-droplet number concentration. Our analysis shows that the error in the MWR retrievals at low liquid water paths is one possible reason for this deficiency. Modification through the diagnosed liquid water path from the coordinate solution improves not only the PDF of the NDROP VAP but also the relationship between the cloud-droplet number concentration and cloud-droplet effective radius. Consideration of entrainment effects rather than assuming an adiabatic cloud improves the values of the NDROP retrieval by reducing themore » magnitude of cloud-droplet number concentration. Aircraft measurements and retrieval comparisons suggest that retrieving the vertical distribution of cloud-droplet number concentration and effective radius is feasible with an improvement of the parameter representing the mixing effects between environment and clouds and with a better understanding of the effect of mixing degree on cloud properties.« less
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    45
    References
    3
    Citations
    NaN
    KQI
    []