On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars

Abstract

Coincident profiling observations from Doppler lidars and radars are used to estimate the turbulence energy dissipation rate (ε) using three different data sources: (i) Doppler radar velocity (DRV), (ii) Doppler lidar velocity (DLV), and (iii) Doppler radar spectrum width (DRW) measurements. The agreement between the derived ε estimates is examined at the cloud base height of stratiform warm clouds. Collocated ε estimates based on power spectra analysis of DRV and DLV measurements show good agreement (correlation coefficient of 0.86 and 0.78 for both cases analyzed here) during both drizzling and nondrizzling conditions. This suggests that unified (below and above cloud base) time-height estimates of ε in cloud-topped boundary layer conditions can be produced. This also suggests that eddy dissipation rate can be estimated throughout the cloud layer without the constraint that clouds need to be nonprecipitating. Eddy dissipation rate estimates based on DRW measurements compare well with the estimates based on Doppler velocity but their performance deteriorates as precipitation size particles are introduced in the radar volume and broaden the DRW values. Based on this finding, a methodology to estimate the Doppler spectra broadening due to the spread of the drop size distribution is presented. The uncertainties in ε introduced by signal-to-noise conditions, the estimation of the horizontal wind, the selection of the averaging time window, and the presence of precipitation are discussed in detail.

Rain retrieval from dual‐frequency radar Doppler spectra: validation and potential for a midlatitude precipitating case‐study

“…For such periods, other retrieved parameters, such as D m , must be interpreted cautiously, but are probably still sensible. Indeed, σ air is generally very small compared to the spectrum width of the DSD, σ DSD (Borque et al , ). In Figure (c), D m reaches fairly large values of up to 2.5 mm in both the stratiform and convective parts. In the convective part these large D m are likely the consequence of some drop sorting due to the successive updraughts and downdraughts.…”

Atmospheric Boundary Layer Classification With Doppler Lidar

“…The presence of turbulence is obtained from ε using a threshold‐based approach. Previous studies suggest ε > 10 −4 m 2 /s 3 as a suitable threshold value (e.g., Borque et al, ;O'Connor et al, ;Vakkari et al, ). We selected ε > 10 −5 m 2 /s 3 as our threshold for detecting turbulent mixing and a threshold of ε > 10 −4 m 2 /s 3 for detecting surface‐connected mixing, as discussed below.…”

Revisiting Liquid Water Content Retrievals in Warm Stratified Clouds: The Modified Frisch

“…In PAMTRA, spectral width broadening (Doviak et al, 1979) was accounted for by assuming a half power beam width of 0.6 ∘ , a radar frequency of 35 GHz, a horizontal wind of 10 m/s, and three different eddy dissipation rates, that is, 10 −6 (low), 10 −4 (medium), 10 −2 m 2 /s 3 (high; Borque et al, 2016). The radar noise floor was set to −38 dBZ at 1 km distance and cloud base was assumed to be located at 1,400 m radial distance to a zenith-pointing radar.…”