Search for: All records

Abstract The glory, a striking optical phenomenon seen from space in unpolarized satellite images can be mapped onto the cloud's droplet sizes with a characteristic scale of 10. Such a mapping allows us to infer the mean and variance of the cloud droplets' radius, an important property that has remained elusive and inaccessible to passive unpolarized satellite sensing. Here, we propose a simple and robust polarization‐like differential approach to map the glory's spectral properties to the desired moments of the droplet size distribution. By taking the differences between two spectrally close channels, we reduce multiple scattering contributions and amplify the single‐scattering signal, thus allowing for a simple and rapidly converging map from glory to droplet size distribution. Moreover, the droplet information reflects the upper part of the cloud, adding another sample to the traditional multiple scattering‐based retrievals that reflect droplet properties deeper in the cloud. 

Abstract Clouds, crucial for understanding climate, begin with droplet formation from aerosols, but observations of this fleeting activation step are lacking in the atmosphere. Here we use a time-gated time-correlated single-photon counting lidar to observe cloud base structures at decimeter scales. Results show that the air–cloud interface is not a perfect boundary but rather a transition zone where the transformation of aerosol particles into cloud droplets occurs. The observed distributions of first-arriving photons within the transition zone reflect vertical development of a cloud, including droplet activation and condensational growth. Further, the highly resolved vertical profile of backscattered photons above the cloud base enables remote estimation of droplet concentration, an elusive but critical property to understanding aerosol–cloud interactions. Our results show the feasibility of remotely monitoring cloud properties at submeter scales, thus providing much-needed insights into the impacts of atmospheric pollution on clouds and aerosol-cloud interactions that influence climate. 

We consider the question of monitoring polarization purity, that is, measuring deviations from orthogonalityδ_τandδ_ϵof an ostensibly orthogonal polarization basis with a reference channel of ellipticityϵand tiltτ. A simple result was recently derived for a phase-sensitive receiver observing unpolarized radiation [IEEE Trans. Geosci. Remote Sens.62,2003610(2024)10.1109/TGRS.2024.3380531]: withρ⁽¹⁾denoting the Pearson complex correlation coefficient between channelcomplex fields, it states that ∓cos⁡(2ϵ)δ_τ±iδ_ϵ≈ρ⁽¹⁾whenδ_τ,ϵ≪1. However, phase-sensitive (in-phase and quadrature) data are seldom available at optical frequencies. To that end, here we generalize the result by deriving a new equation for the polarization “alignment” error:cos²(2ϵ)δ_τ²+δ_ϵ²≈ρ⁽²⁾, whereρ⁽²⁾is the intensity cross-correlation coefficient. Only the measurement of the(real) intensitycross-correlation coefficient is needed when observing unpolarized light. For the special case of a linearly polarized basis, the tilt error is simplyδ_τ≈ρ⁽²⁾, and for the circular basis case, with ellipticity deviationδ_ϵfrom circular helicityπ/4 (the reference channel of opposite helicity),δ_ϵ≈ρ⁽²⁾. These results provide simple means to gauge the quality of polarimeters and depolarizers. 

Linear least squares (LLS) is perhaps the most common method of data analysis, dating back to Legendre, Gauss and Laplace. Framed as linear regression, LLS is also a backbone of mathematical statistics. Here we report on an unexpected new connection between LLS and random walks. To that end, we introduce the notion of a random walk based on a discrete sequence of data samples (data walk). We show that the slope of a straight line which annuls the net area under a residual data walk equals the one found by LLS. For equidistant data samples this result is exact and holds for an arbitrary distribution of steps. 

The Deep Space Climate Observatory (DSCOVR) spacecraft drifts about the Lagrangian point ≈ 1.4 − 1.6 × 10⁶ km from Earth, where its Earth Polychromatic Imaging Camera (EPIC) observes the entire sunlit face of Earth every 1–2 h. In an attempt to detect “signals,” i.e., longer-term changes and semi-permanent features such as the ever-present ocean glitter, while suppressing geographic “noise,” in this study, we introduce temporally and conditionally averaged reflectance images, performed on a fixed grid of pixels and uniquely suited to the DSCOVR/EPIC observational circumstances. The resulting images (maps), averaged in time over months and conditioned on surface/cover type such as land, ocean, or clouds, show seasonal dependence literally at a glance, e.g., by an apparent extent of polar caps. Clear ocean-only aggregate maps feature central patches of ocean glitter, linking directly to surface roughness and, thereby, global winds. When combined with clouds, these blue planet “moving average” maps also serve as diagnostic tools for cloud retrieval algorithms. Land-only images convey the prominence of Earth’s deserts and the variable opacity of the atmosphere at different wavelengths. Insights into climate science and diagnostic and educational tools are likely to emerge from such average reflectance maps. 

Abstract. A large convection–cloud chamber has the potential to produce drizzle-sized droplets, thus offering a new opportunity to investigate aerosol–cloud–drizzle interactions at a fundamental level under controlled environmental conditions. One key measurement requirement is the development of methods to detect the low-concentration drizzle drops in such a large cloud chamber. In particular, remote sensing methods may overcome some limitations of in situ methods. Here, the potential of an ultrahigh-resolution radar to detect the radar return signal of a small drizzle droplet against the cloud droplet background signal is investigated. It is found that using a small sampling volume is critical to drizzle detection in a cloud chamber to allow a drizzle drop in the radar sampling volume to dominate over the background cloud droplet signal. For instance, a radar volume of 1 cubic centimeter (cm3) would enable the detection of drizzle embryos with diameter larger than 40 µm. However, the probability of drizzle sampling also decreases as the sample volume reduces, leading to a longer observation time. Thus, the selection of radar volume should consider both the signal power and the drizzle occurrence probability. Finally, observations from the Pi Convection–Cloud Chamber are used to demonstrate the single-drizzle-particle detection concept using small radar volume. The results presented in this study also suggest new applications of ultrahigh-resolution cloud radar for atmospheric sensing. 

see abstract