precipmicrophys-kazrlidar > Retrieval of warm precipitation microphysicsData Source Type(s) > PI

Intrieri et al. (1993) and later O’Connor et al. (2005) proposed a technique to constrain water drop size distribution using lidar backscatter (related to water drop cross-section) and Doppler spectral width (related to the width of the water drop size distribution). This radar-lidar technique can be used to estimate precipitation rate and microphysical characteristics at all levels in the subcloud layer when collocated radar and ceilometer observations are available. We apply this technique to the vertically pointing ARM ceilometer lidar and Ka-band Zenith Radar (KAZR2) pair. The O’Connor et al. (2005) technique requires ceilometer backscatter to be calibrated and remapped to the radar spatio-temporal resolution (here 2 s x 30 m). Ceilometer backscatter is calibrated following a variation of the O'Connor et al. (2004) technique by scaling observed path-integrated backscatter in thick stratocumulus to match theoretical cloud lidar ratio values. Satisfactory conditions for ceilometer backscatter calibration are identified as the first (in time) 20-min periods each day with standard deviation of lidar ratio smaller than 1.5. The observed backscatter during the “satisfactory 20-min period” are input to Hogan (2006)’s multi scattered model to determine a daily backscatter calibration factor. For days where satisfactory conditions are not observed, a climatological calibration factor of 1.35 is used to calibrate the observed backscatter. Calibrated ceilometer backscatter is subsequently mapped on the KAZR2 time-height grid using a nearest-neighbor approach. Following Kollias et al. (2019), KAZR2 calibration is performed using collocated surface-based Parsivel laser disdrometer-equivalent radar reflectivity estimates during light precipitation events.

This radar-lidar technique generates time-height maps of precipitation rate from 200 m above ground level to 90 m below cloud base height that are filtered for aerosol contamination. We use the clear-sky -- according to KAZR2 -- -calibrated lidar backscatter signals as a reference for aerosol behavior, lidar-calibrated backscatter values below the mean clear-sky-calibrated backscatter value at each height, are systematically removed from the analysis to leave only drizzle signals. In additional to aerosol contaminated returns, unphysical values with median diameter smaller than 10 μm or equal or large to 1000 μm are also removed from our analysis. Since rain events above this intensity also occur at the ENA, gaps in the rain rate retrieval are filled using relationships between KAZR2-calibrated radar reflectivity (Z; mm6m-3) and rain rate (R; mm hr-1) in the form Z = aRb. We estimated the a and b parameters of this relationships using a collection of all the surface laser distrometer measurements collected at the ENA between 2016 and 2018.  Note that Z-R relationships are only applied to fill gaps in the rain rate retrieval where radar reflectivity is larger than 0 dBZ, thus ensuring that radar reflectivity is dominated by rain with minimum contribution from cloud droplets. The combined retrieval is expected to capture all rain with intensities above 10-3mm hr-1

Given that both these retrievals rely on the assumption of droplet sphericity, they are only applicable in warm rain. Similar to others before, we identify the melting layer height, below which the rain rate retrievals are valid, using KAZR linear depolarization ratio (LDR) measurements (Sandford et al. 2017). First we approximate the height of the zero-degree isotherm using the closest in time sounding; Then a melting layer is identified if the profile of hourly maximum LDR presents a peak associated with a LDR gradient larger than 15 dB km-1 within 1 km from the zero-degree isotherm.


This product relies on a combination of radar reflectivity and lidar backscatter measurements to retrieve the microphysical properties of light precipitation including rain rate without assumptions about the water particle size distribution (Intrieri et al. 1993; O'Connor et al. 2005). 

This technique is an alternative to previous techniques, some of which relied on surface disdrometer measurements to characterize warm precipitation. At ENA, surface disdrometer measurements may be especially unsuitable to characterize light precipitation since there i) a large fraction of the precipitation does not reach the surface (Yang et al. 2018), ii) precipitation reaching the ground typically does so with an intensity below the detection limit of most optical-based disdrometers ( approx. 10^-2 mm hr^-1), and iii) evaporation is an active process such that water drop size distribution information retrieved at one height may not be appropriate to represent the entire atmospheric column. 

This product additionally provides context to the light rain microphysical retrieval including information about cloud location, liquid water path, height of the melting layer, and retrievals of turbulence intensity.


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Data Details

Developed By Katia Lamer | Pavlos Kollias | zeen zhu
Contact Katia Lamer
Resource(s) Data Directory
Data format Netcdf
Site ENA
Content time range 1 October 2015 - 29 September 2018
Attribute accuracy Error variables are provided in the file and estimated using error propagation using look-up table assuming an uncertainty on the drizzle drop size distribution shape parameter (mu) of 1. Reflectivity-weighted droplet terminal fall velocity estimates associated with mu estimates larger than 7 are believed to be less reliable.
Positional accuracy No formal positional accuracy tests were conducted.
Data Consistency and Completeness Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
Access Restriction No access constraints are associated with this data.
Use Restriction No use constraints are associated with this data.
File naming convention productname_yyyymmdd
Citations Lamer, K., B. Puigdomenech Treserras, Z. Zhu, B. Isom, N. Bharadwaj, and P. Kollias (2019 in review), Characterization of Shallow Oceanic Precipitation using Profiling and Scanning Radar Observations at the Eastern North Atlantic ARM Observatory, Atmos. Meas. Tech. Discuss.

Kollias, P., B. Puigdomenech Treserras, and A. Protat (2019 in review), Calibration of the 2007-2017 record of ARM Cloud Radar Observations using CloudSat, Atmos. Meas. Tech. Discuss.

O'Connor, E. J., A. J. Illingworth, and R. J. Hogan (2004), A technique for autocalibration of cloud lidar, Journal of Atmospheric and Oceanic Technology, 21(5), 777-786.

Borque, P., E. Luke, and P. Kollias (2016), On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars, Journal of Geophysical Research: Atmospheres, 121(10), 5972-5989.

O'Connor, E. J., R. J. Hogan, and A. J. Illingworth (2005), Retrieving stratocumulus drizzle parameters using Doppler radar and lidar, Journal of Applied Meteorology, 44(1), 14-27.