Comparison of MODIS cloud microphysical properties with in-situ measurements over the Southeast Pacific

Abstract

Abstract. Utilizing the unique characteristics of the cloud over the Southeast Pacific (SEP) off the coast of Chile during the VOCALS field campaign, we compared satellite remote sensing of cloud microphysical properties against insitu data from multi-aircraft observations, and studied the extent to which these retrieved properties are sufficiently constrained and consistent to reliably quantify the influence of aerosol loading on cloud droplet sizes. After constraining the spatial-temporal coincidence between satellite retrievals and in-situ measurements, we selected 17 non-drizzle comparison pairs. For these cases the mean aircraft profiling times were within one hour of Terra overpasses at both projected and un-projected (actual) aircraft positions for two different averaging domains of 5 km and 25 km. Retrieved quantities that were averaged over a larger domain of 25 km compared better statistically with in-situ observations than averages over a smaller domain of 5 km. Comparison at projected aircraft positions was slightly better than un-projected aircraft positions for some parameters. Overall, both MODISretrieved effective radius and LWP were larger but highly correlated with the in-situ measured effective radius and LWP, e.g., for averaging domains of 5 km, the biases are up to 1.75 µm and 0.02 mm whilst the correlation coefficients are about 0.87 and 0.85, respectively. The observed effective radius difference between the two decreased with increasing cloud drop number concentration (CDNC), and increased with increasing cloud geometrical thickness. Compared to the absolute effective radius difference, the correlations between the relative effective radius difference and CDNC or cloud geometric thickness are weaker. For averaging domains of 5 km and 25 km, the correlation coefficients between MODIS-retrieved and in-situ measured CDNC are 0.91 and 0.93 with fitting slopes of 1.23 and 1.27, respectively. If the cloud adiabaticity is taken into account, better agreements are achieved for both averaging domains (the fitting slopes are 1.04 and 1.07, respectively). Our comparison and sensitivity analysis of simulated retrievals demonstrate that both cloud geometrical thickness and cloud adiabaticity are important factors in satellite retrievals of effective radius and cloud drop number concentration. The large variabilities in cloud geometrical thickness and adiabaticity, the dependencies of cloud microphysical properties on both quantities (as demonstrated in our sensitivity study of simulated retrievals), and the inability to accurately account for either of them in retrievals lead to some uncertainties and biases in satellite retrieved cloud effective radius, cloud liquid water path, and cloud drop number concentration. However, strong correlations between satellite retrievals and in-situ measurements suggest that satellite retrievals of cloud effective radius, cloud liquid water path, and cloud drop number concentration can be used to investigate aerosol indirect effects qualitatively.

Satellite retrieval of cloud condensation nuclei concentrations by using clouds as CCN chambers

“…To avoid significant distortion of r e by coalescence, we avoided heavily precipitating clouds at their tops (r e 0> 18 μm). MODIS r e is larger than aircraft in situ measurements by 10-15% (34)(35)(36). This is probably not a problem for retrieved r e based on the VIIRS Imager (30), because it is lower by a similar amount with respect to MODIS r e .…”

“…The observed 26% underestimate in CCN could have been caused by a 10% systematic overestimate in the retrieved r e . This is quite probable, because MODIS-retrieved r e was found to be larger by 10-15% than aircraft in situ measurements (34)(35)(36). An underestimate of satellite-versus surface-measured CCN can be also caused by a systematic decrease of CCN number concentration (N CCN ) between the surface and cloud base heights.…”

Study of global cloud droplet number concentration with A-Train satellites

“…(1), the true droplet number concentration is a product of N and k when a gamma distribution of the droplets is considered. Results of deviations from this hypothesis have been investigated through comparison with the in situ observations (Painemal and Zuidema, 2011;Min et al, 2012). The MODIS CDNC values (derived with f ad = 0.8) are quite close to in situ observations for stratocumulus over the Chile-Peru coast.…”

Ocean–Cloud–Atmosphere–Land Interactions in the Southeastern Pacific: The VOCALS Program

“…Saide et al (2012b) argued that WRF-Chem simulates marine cloud-aerosol interactions at a level sufficient for applications in forecasting weather and air quality and studying aerosol climate forcing and may do so with the reliability required for policy analysis. Both studies emphasize the importance of reproducing gradients of aerosol and cloud droplet concentrations that typify the SEP. Saide et al (2012b), on the basis of the remarkably robust correspondence of satellite-derived cloud droplet concentrations to in situ VOCALS data (Painemal and Zuidema 2011;King et al 2013;Min et al 2012), are assimilating satellite-observed cloud droplet concentration into WRF-Chem to better constrain aerosol properties below the clouds. WRF-Chem has also been used to provide the first model-based regional quantification over the SEP of the aerosol indirect effects on climate (Q. and to describe a freetropospheric transport pathway for aerosol transport to the remote SEP (George et al 2013).…”