Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

Abstract

This study presents radar-based precipitation estimates collected during the 2-month U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM)-NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR and XSAPR, respectively) for rainfall estimation products to distances within 100 km of the Lamont, Oklahoma, ARM facility. The study utilizes a dense collection of collocated ARM, NASA Global Precipitation Measurement, and nearby surface Oklahoma Mesonet gauge records to evaluate radar-based hourly rainfall products and campaign-optimized methods over individual gauges and for areal rainfall characterizations. Rainfall products are also evaluated against the performance of a regional NWS Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band dual-polarization radar product. Results indicate that the CSAPR system may achieve similar point-and areal-gauge bias and rootmean-square (RMS) error performance to a WSR-88D reference for the variety of MC3E deep convective events sampled. The best campaign rainfall performance was achieved when using radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The XSAPRs demonstrate limited capabilities, having modest success in comparison with the WSR-88D reference for hourly rainfall accumulations that are under 10 mm. All rainfall estimation methods exhibit a reduction by a factor of 1.5-2.5 in RMS errors for areal accumulations over a 15-km 2 NASA dense gauge network, with the smallest errors typically associated with dual-polarization radar methods.

The Green Ocean: Precipitation Insights from the GoAmazon2014/5 Experiment

“…As a function of radar wavelength, the 'a'-coefficient values decrease as wavelength decreases (e.g., quantities more closely related to the rainfall rate). SGP dual-polarization relationships are consistent with previous studies (e.g., Giangrande et al, 2014b), providing confidence in the appropriateness of disdrometer processing. Note, minor discrepancies in SGP R(Z) relations may be related to our filtering of large drops > 5 mm that disproportionately influence Z measurements at sites including SGP wherein melting hail is more regularly observable.…”

On Polarimetric Radar Signatures of Deep Convection for Model Evaluation: Columns of Specific Differential Phase Observed during MC3E*

“…Also shown as white and black lines in panel (a) are subjectively selected sub-samples of the full histogram, to demonstrate non-stationarity of K DP column height statistics. C-SAPR K DP > 3.5 ° km −1 volume above the melting level vs. domain-averaged rain rates for rain above 90 mm h −1 , derived using specific attenuation (Ryzhkov et al 2014;Giangrande et al 2014). Domain is blue circle in Fig.…”

“…Additionally, a polarimetric rainfall estimation based on specific attenuation was used to derive rain rates from C-SAPR polarimetric radar variables (Ryzhkov et al 2014;Giangrande et al 2014). Giangrande et al (2014) analyzed these data and compared them with estimates from X-band polarimetric radars as well as rain gauges; C-SAPR rain rates were shown to be in good agreement with rain gauges.…”

“…Giangrande et al (2014) analyzed these data and compared them with estimates from X-band polarimetric radars as well as rain gauges; C-SAPR rain rates were shown to be in good agreement with rain gauges. Here we analyze rain rates above the 40 and 90 mm hr −1 thresholds to illustrate convective and particularly intense rain rates, respectively.…”

Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E

“…The technical specifications of the ARM scanning radars are listed in Table 2. Radar reflectivity observed by CSAPR-I7 was corrected for attenuation in rain using the CSAPR-I7 differential phase measurements as implemented using available open-source ARM python code utilities (e.g., Bringi and Chandrasekar, 2001;Giangrande et al, 2013bGiangrande et al, , 2014Helmus and Collis, 2016). Because XSAPR reflectivity returns were significantly attenuated in rain, only XSAPR Doppler velocity measurements that are immune to partial attenuation were used in our retrievals.…”