Scaling properties of observed and simulated satellite visible radiances

Abstract

Structure functions Sq, which are related to power spectra and used to study turbulence, were computed for GOES‐13 visible radiances measured on 16 May 2015 over French Guiana and adjacent Atlantic Ocean. The nested Global Environmental Multiscale (GEM) numerical weather prediction (NWP) model was run for the same time and area. Cloud data generated by GEM over (300 km)2 domains, with one‐way nesting ending at horizontal grid‐spacing of 0.25 km, were operated on by a 3‐D solar radiative transfer model with resulting radiances degraded to GOES‐13 resolution (~1 km) and Sq computed for them, too. For GOES‐13 radiances, scaling exponents ζ(2) associated with S2, for separation distances between 5 km and 25 km, were typically >0.6 for deep convective and marine boundary layer clouds and <0.4 for shallow cumuli over land. ζ(2) for GEM agreed well with GOES‐13 for deep convective clouds. This suggests that the self‐organizing properties of deep convection in GEM exhibit realistic geometric features, a potentially important point given the link between cloud structure and precipitation, with the latter being much more difficult to measure and assess than visible radiances. Regarding radiances for GEM's marine boundary layer clouds, their Sq differed markedly from GOES‐13's; better resembling fair‐weather cumulus. Likewise, GEM's shallow cumuli over land appear to have bypassed the “scattered” fair‐weather stage and went straight into more organized convection. Thus, it appears that comparing time series of Sq for geostationary satellite data and corresponding modeled radiances has the potential to benefit assessment of cloud system‐resolving models.

Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005–2015

“…The extratropical scaling exponents of 0.42 and 0.44 are similar to, but slightly in excess of the classic Kolmogorov scaling of 1/3 (−5/3 in the power spectral domain). The tropical scaling exponent of 0.62 is in excess of the classic Kolmogorov scaling of 1/3 but is consistent with tropical cloud reflectance variability reported in Barker et al (2017) and mid-tropospheric water vapor mixing ratio in the tropics from the AIRS instrument, e.g., Kahn et al (2011). At finer spatial resolutions there is also evidence of scale breaks dependent on altitude.…”

Accelerating radiative transfer calculations for high‐resolution atmospheric models

“…Also shown are cloud‐top altitudes which, as expected for marine stratus, vary only by ∼0.5 km (Loeb et al, ). Visible radiances were computed by a 3D RT model and provide an impression of the field's structure (Barker et al, ). Various cloud fractions for this domain are shown in Figure .…”

Evaluation of a high‐resolution numerical weather prediction model's simulated clouds using observations from CloudSat, GOES‐13 and in situ aircraft

“…GEM's clouds were assessed using forward models that simulate satellite observations of Geostationary Operational Environmental Satellite (GOES‐13) geostationary visible reflectances and CloudSat cloud‐profiling radar (CPR) reflectivities (Stephens et al , 2002) by operating directly on model‐generated clouds (Bodas‐Salcedo et al , ; Di Michele et al , ; Barker et al , ). This approach contrasts with methods that compare values of model‐generated cloud variables to those inferred from satellite observations.…”