Retrieving Routine Rain Rates from a Ka-Band Profiling Radar Platform

 

Submitter:

Chandra, Arunchandra S. — University of Miami

Area of research:

Cloud Processes

Journal Reference:

Chandra A, C Zhang, P Kollias, S Matrosov, and W Szyrmer. 2015. "Automated rain rate estimates using the Ka-band ARM zenith radar (KAZR)." Atmospheric Measurement Techniques, 8(9), 10.5194/amt-8-3685-2015.

Science

An automated algorithm is developed to retrieve routine rain rates from a profiling Ka-Band (35-GHz) ARM zenith radar, known as the KAZR. A one-dimensional, simple, steady state microphysical model is used to estimate impacts of microphysical properties and attenuation on the profiles of radar observations at 35-GHz, thus provide a criteria for identifying situations when attenuation or microphysical processes dominate KAZR observations. High rain rates are retrieved by using the amount of attenuation in rain layers, while low rain rates are retrieved from the reflectivity-rain rate (Ze-R) relation. Observations collected by the KAZR, rain gauge, disdrometer, and precipitating radars are used to validate the proposed approach during the ARM Madden-Julian Oscillation [MJO] Investigation Experiment (AMIE)/Dynamics of the Madden-Julian Oscillation (DYNAMO) field campaigns at Gan Island, Maldives, in the Indian Ocean.

Impact

The routine retrieval of rain rates from the KAZR are implemented in two stages: The lighter rain rates retrieved based on the KAZR reflectivity measurements by applying the Ze-R relation, and the higher rain rates are based on the attenuation technique (A-R). A Doppler velocity threshold of 5 m/s is used for separating the two regimes with dominant microphysical effects and attenuation effects for applying Ze-R and A-R techniques respectively.

The KAZR observations are screened for signal saturation. The portion of the Ze profile that saturates the KAZR receiver are identified based on the first maxima in the reflectivity profile. It is found that less than 2 percent of the total cases (51 of 3180 1-min samples) have saturated layer tops greater than 300 m and were excluded from the rain retrieval. The rain layer of depth 500 m is chosen starting from the lowest location of the identified unsaturated radar resolution gate. The reflectivity information in the rain layer is summarized by calculating the reflectivity averaged over rain layer, reflectivity difference between top and bottom of the rain layer, and the bulk reflectivity gradients between each gate in the rain layer. The rain layer suitable for the attenuation technique was identified when both the reflectivity difference between top and bottom of the rain layer and reflectivity gradients in each gate are negative in order to minimize the effect of clouds.

The relative dominance of attenuation and microphysical effects in the chosen rain layer are assessed by comparing the reflectivity profiles of KAZR and S-Polka (S-Band frequency). Since the attenuation effects on S-Polka reflectivities are negligible, therefore the reflectivity changes in the rain layer observed by the S-Polka above the KAZR are mainly caused by changes in rain microphysics. The reflectivity changes due to microphysics are quantified by subtracting the S-Polka reflectivity from the KAZR reflectivity change. The sensitivity of overall rain rate retrievals from the KAZR for the chosen Doppler velocity threshold and the Ze-R relations (developed based on the disdrometer and KAZR) is quantified.

Summary

First, the number of profiles which have dominant microphysical effects over rain layer attenuation effects are found be around 4 percent. Failing to separate them would result in the bias of 8 percent in the total rain amount.

Secondly, bias in the total rain accumulation would be slightly larger (7.8 percent) if we use KAZR-based Ze-R relation compared to Ze-R relation based on the disdrometer (4.5 percent). Compared to rain gauge, the rain amount from the KAZR slightly overestimates with a difference of 6.88 mm and a bias of 4.42 percent.

Thirdly, by separating the contributions of total rain accumulation into stratiform and convective rain accumulations, it was found that the stratiform rain accumulation from the KAZR is slightly larger compared to gauge accumulation, where as the convective rain accumulation from the KAZR is slightly smaller.

Lastly, the rain rate statistics are decomposed for stratiform, convective, and total rain events from the KAZR, rain gauge, and disdrometer, and from the S-Polka and SMART-R. The peak of the stratiform rain rate probability distribution function from the KAZR coincides with the other measurements, where as the peak of the convective rain probability distribution function is slightly shifted towards lower rain rates. Also, KAZR rain retrievals have slightly more occurrences of higher rain rates for stratiform events and slightly less occurrence of higher rain rates for convective events.