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The cloud drop effective radius, Re, of the drop size distribution derived from passive satellite sensors is a key variable used in climate research. Validation of these satellite products often took place in stratiform cloud conditions that favored the assumption of cloud horizontal homogeneity used by the retrieval techniques. However, many studies point to concerns of significant biases in retrieved Re arising from cloud heterogeneity, for example, in cumulus cloud fields. Here, we examine data collected during the 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP²Ex), which, in part, targeted the objective of providing the first detailed evaluation of Re retrieved across multiple platforms and techniques in a cumulus and congestus cloud region. Our evaluation consists of cross comparisons of Re between the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, the Research Scanning Polarimeter (RSP) onboard the NASA P-3 aircraft, and in situ measurements from both the P-3 and Learjet aircrafts that are all taken in close space-time proximity of the same cloud fields. A particular advantage of our approach lies in RSP’s capability to retrieve Re using a bi-spectral MODIS approach and a polarimetric approach, which allows for evaluating bi-spectral and polarimetric Re retrievals from an airborne perspective using the same samples. Averaged over all P-3 flight segments examined here for warm clouds, the RSP-polarimetric, in situ, and the bias adjusted MODIS method of Fu et al. (2019) show comparable median (mean and standard deviations) of Re samples of 9.6 (10.2 ± 4.0) µm, 11.0 (13.6 ± 11.3) µm, and 10.4 (10.8 ± 3.8) µm, respectively. These values are far lower than 15.1 (16.2 ± 5.5) µm and 17.2 (17.7 ± 5.7) µm from the bi-spectral retrievals of RSP and MODIS, respectively. Similar results are observed when Re is segregated by cloud top height and in detailed case studies. The clouds sampled during CAMP²Ex consist of mostly small (mean transect length ~1.4 km) and low clouds (mean cloud top height ~ 1 km), which are much smaller than the trade wind cumuli sampled in past field campaigns such as Rain in Shallow Cumulus over the Ocean (RICO) and the Indian Ocean Experiment (INDOEX). RSP bi-spectral Re shows larger relative values compared to RSP polarimetric Re for smaller and optically thinner clouds. Drizzle, cloud top bumpiness and solarzenith angle, however, are not closely correlated with the overestimate of bi-spectral Re. We show that for shallow, non-drizzling clouds that dominate the liquid cloud cover for the CAMP2Ex region and period, 3D radiative pathways appear to be the leading cause for the large positive biases in bi-spectral retrievals. Because this bias varies with the underlying structure of the cloud field, caution continues to be warranted in studies that use bi-spectral Re retrievals in cumulus cloud fields. [ABSTRACT FROM AUTHOR]

We have developed an algorithm that retrieves the microphysical properties of falling snow from multi-frequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multi-frequency airborne radar observations from the OLYMPEX/RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar, and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and test the sensitivity of the algorithm to the prior assumptions. The results suggest that multi-frequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars, and better locate regions of high-density snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. [ABSTRACT FROM AUTHOR]

This article presents a method to characterize stochastic observables defined by induced surface currents and fields in electromagnetic interactions with uncertain configurations. As the covariance operators of the stochastic distributions and fields are not compact, a strict Karhunen-Loève (KL) approach is not possible. Instead, we apply a point-spectrum regularization by expanding the stochastic quantities on a finite-element-like basis. The coefficients of the KL expansion are approximated analytically in a polynomial-chaos (PC) expansion. The novelty of our approach resides in its ability to handle multiple PC expansions simultaneously and determine the orders of the KL and PC expansions adaptively. This method is illustrated through the example of the voltage induced at the port of a random thin-wire frame illuminated by random plane waves. The results show the accuracy and computational efficiency of the proposed method, which provides a complete characterization of the randomness of the observable. [ABSTRACT FROM AUTHOR]