A new precipitation value-added product (VAP) for the Atmospheric Radiation Measurement (ARM) user facility provides quality-controlled disdrometer measurements of drop size distributions (DSDs), rain rates, and polarimetric radar-equivalent quantities. The Laser Disdrometer VAP for Quality-Controlled Measurements, using PyDSD (LDQUANTS) capitalizes on laser disdrometer measurements from ARM fixed observatories and mobile facility deployments.
DSD measurements from laser disdrometers can be used to estimate key precipitation properties, including the rainfall rate, number concentration of drops, and mean raindrop size information. For these quantities to be useful in model evaluation, radar monitoring, or other activities, disdrometer data sets require careful quality control and processing. LDQUANTS uses standard methods adapted from the literature to filter spurious drops and extract important microphysical quantities of parameterized DSDs (e.g., gamma/exponential DSD assumptions and fitting).
In support of radar-based research interests and monitoring, this VAP also estimates radar-equivalent dual-polarization quantities (e.g., Reflectivity Factor Z, Differential Reflectivity ZDR). LDQUANTS computes these quantities by using the T-matrix scattering technique and several wavelength, temperature, and drop shape assumptions.
Now in production, LDQUANTS data sets are available for:
- the Green Ocean Amazon (GoAmazon2014/15) field campaign
- the 2018‒2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina
- routine laser disdrometer data collection at ARM’s Southern Great Plains and Eastern North Atlantic atmospheric observatories.
To provide feedback or to ask a question, please contact the associated translator, Scott Giangrande, or the instrument mentor, Mary Jane Bartholomew.
More information on LDQUANTS can be found on the VAP web page.
To access these data, go to the ARM Data Center. (Go here to request an account to download the data.)
Implementations of the algorithms are from PyDisdrometer (PyDSD), an open-source Python library for working with disdrometer data (Hardin and Guy 2017). Additional testing and ARM data set examples are from Wang et al. (2018).
To cite the LDQUANTS data, please use doi:10.5439/1432694.
References: Hardin J and N Guy. 2017. PyDSD, http://doi.org/10.5281/zenodo.9991.
Wang D, S Giangrande, M Bartholomew, J Hardin, Z Feng, R Thalman, and L Machado. 2018. “The Green Ocean: precipitation insights from the GoAmazon2014/5 experiment.” Atmospheric Chemistry and Physics, 18(12), 10.5194/acp-18-9121-2018.
# # #ARM is a DOE Office of Science user facility operated by nine DOE national laboratories.