Overlooked examples of cloud self-organization at the mesoscale

Stratocumulus clouds are common in the tropical and subtropical marine boundary layer, and understanding these clouds is important due to their significant impact on the earth's radiation budget. Observations show that the marine boundary layer contains complex, but poorly understood processes, which, from time to time, result in the observable self-organization of cloud structures at scales ranging from a few to a few thousand kilometers. Such shallow convective cloud features, typically observed as hexagonal cells, are known generally as mesoscale cellular convection (MCC). Actinoform clouds are rarer, but visually more striking forms of MCC, which possess a radial structure. Because mesoscale cloud features are typically too large to be observed from the ground, observations of hexagonal cells historically date only to the beginning of satellite meteorology. Examples of actinoform clouds were shown in the venerable “Picture of the Month” series in Monthly Weather Review in the early 1960s, but these clouds were generally forgotten as research focused on hexagonal cells. Recent high-resolution satellite images have, in a sense, “rediscovered” actinoform clouds, and they appear to be much more prevalent than had been previously suspected. We show a number of examples of actinoform clouds from a variety of locations worldwide. In addition, we have conducted a detailed case study of an actinoform cloud system using data from the Multiangle Imaging SpectroRadiometer (MISR) and the Geostationary Operational Environmental Satellite (GOES), including analysis of cloud heights, radiative properties, and the time-evolution of the cloud system. We also examine earlier theories regarding actinoform clouds in light of the new satellite data

Minimum Length from Quantum Mechanics and Classical General Relativity

We derive fundamental limits on measurements of position, arising from quantum mechanics and classical general relativity. First, we show that any primitive probe or target used in an experiment must be larger than the Planck length, $l_P$. This suggests a Planck-size {\it minimum ball} of uncertainty in any measurement. Next, we study interferometers (such as LIGO) whose precision is much finer than the size of any individual components and hence are not obviously limited by the minimum ball. Nevertheless, we deduce a fundamental limit on their accuracy of order $l_P$. Our results imply a {\it device independent} limit on possible position measurements.Comment: 8 pages, latex, to appear in the Physical Review Letter

Aerosol Optical Depths over Oceans: a View from MISR Retrievals and Collocated MAN and AERONET in Situ Observations

In this study, aerosol optical depths over oceans are analyzed from satellite and surface perspectives. Multiangle Imaging SpectroRadiometer (MISR) aerosol retrievals are investigated and validated primarily against Maritime Aerosol Network (MAN) observations. Furthermore, AErosol RObotic NETwork (AERONET) data from 19 island and coastal sites is incorporated in this study. The 270 MISRMAN comparison points scattered across all oceans were identified. MISR on average overestimates aerosol optical depths (AODs) by 0.04 as compared to MAN; the correlation coefficient and root-mean-square error are 0.95 and 0.06, respectively. A new screening procedure based on retrieval region characterization is proposed, which is capable of substantially reducing MISR retrieval biases. Over 1000 additional MISRAERONET comparison points are added to the analysis to confirm the validity of the method. The bias reduction is effective within all AOD ranges. Setting a clear flag fraction threshold to 0.6 reduces the bias to below 0.02, which is close to a typical ground-based measurement uncertainty. Twelve years of MISR data are analyzed with the new screening procedure. The average over ocean AOD is reduced by 0.03, from 0.15 to 0.12. The largest AOD decrease is observed in high latitudes of both hemispheres, regions with climatologically high cloud cover. It is postulated that the screening procedure eliminates spurious retrieval errors associated with cloud contamination and cloud adjacency effects. The proposed filtering method can be used for validating aerosol and chemical transport models

A Quantitative Validation of Multi-Modal Image Fusion and Segmentation for Object Detection and Tracking

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global coverage and granularity in order to test our models’ capabilities to represent structure at finer and broader scales, using many different kinds of instrumentation, that can be fused when applicable. In all cases tested, our models show a strong ability to segment the objects within input scenes, use multiple datasets fused together where appropriate to improve results, and, at times, outperform the pre-existing datasets. The success here will allow this methodology to be used within use concrete cases and become the basis for future dynamic object tracking across datasets from various remote sensing instruments

MISR Global Aerosol Product Assessment by Comparison with AERONET

A statistical approach is used to assess the quality of the MISR Version 22 (V22) aerosol products. Aerosol Optical Depth (AOD) retrieval results are improved relative to the early post- launch values reported by Kahn et al. [2005a], varying with particle type category. Overall, about 70% to 75% of MISR AOD retrievals fall within 0.05 or 20% AOD of the paired validation data, and about 50% to 55% are within 0.03 or 10% AOD, except at sites where dust, or mixed dust and smoke, are commonly found. Retrieved particle microphysical properties amount to categorical values, such as three groupings in size: "small," "medium," and "large." For particle size, ground-based AERONET sun photometer Angstrom Exponents are used to assess statistically the corresponding MISR values, which are interpreted in terms of retrieved size categories. Coincident Single-Scattering Albedo (SSA) and fraction AOD spherical data are too limited for statistical validation. V22 distinguishes two or three size bins, depending on aerosol type, and about two bins in SSA (absorbing vs. non-absorbing), as well as spherical vs. non-spherical particles, under good retrieval conditions. Particle type sensitivity varies considerably with conditions, and is diminished for mid-visible AOD below about 0.15 or 0.2. Based on these results, specific algorithm upgrades are proposed, and are being investigated by the MISR team for possible implementation in future versions of the product

Response to "Toward Unified Satellite Climatology of Aerosol Properties. 3. MODIS Versus MISR Versus AERONET"

A recent paper by Mishchenko et al. compares near-coincident MISR, MODIS, and AERONET aerosol optical depth (AOD) products, and reports much poorer agreement than that obtained by the instrument teams and others. We trace the reasons for the discrepancies primarily to differences in (1) the treatment of outliers, (2) the application of absolute vs. relative criteria for testing agreement, and (3) the ways in which seasonally varying spatial distributions of coincident retrievals are taken into account

JMISR INteractive eXplorer

MISR (Multi-angle Imaging SpectroRadiometer) INteractive eXplorer (MINX) is an interactive visualization program that allows a user to digitize smoke, dust, or volcanic plumes in MISR multiangle images, and automatically retrieve height and wind profiles associated with those plumes. This innovation can perform 9-camera animations of MISR level-1 radiance images to study the 3D relationships of clouds and plumes. MINX also enables archiving MISR aerosol properties and Moderate Resolution Imaging Spectroradiometer (MODIS) fire radiative power along with the heights and winds. It can correct geometric misregistration between cameras by correlating off-nadir camera scenes with corresponding nadir scenes and then warping the images to minimize the misregistration offsets. Plots of BRF (bidirectional reflectance factor) vs. camera angle for points clicked in an image can be displayed. Users get rapid access to map views of MISR path and orbit locations and overflight dates, and past or future orbits can be identified that pass over a specified location at a specified time. Single-camera, level-1 radiance data at 1,100- or 275- meter resolution can be quickly displayed in color using a browse option. This software determines the heights and motion vectors of features above the terrain with greater precision and coverage than previous methods, based on an algorithm that takes wind direction into consideration. Human interpreters can precisely identify plumes and their extent, and wind direction. Overposting of MODIS thermal anomaly data aids in the identification of smoke plumes. The software has been used to preserve graphical and textural versions of the digitized data in a Web-based database

Two massive star-forming regions at early evolutionary stages

We report sensitive ATCA radio-continuum observations toward IRAS 15596-5301 and 16272-4837, two luminous objects (> 2x10^4 Lsun) thought to represent massive star-forming regions in early stages of evolution (due to previously undetected radio emission at the 1-sigma level of 2 mJy per beam). Also reported are 1.2-millimeter continuum and a series of molecular-line observations made with the SEST telescope. For IRAS 15596-5301, the observations reveal the presence of three distinct compact radio-continuum sources associated with a dense molecular core. We suggest that this core contains a cluster of B stars which are exciting compact HII regions that are in pressure equilibrium with the dense molecular surroundings. No radio continuum emission was detected from IRAS 16272-4837 (3-sigma limit of 0.2 mJy). However, a dense molecular core has been detected. The high luminosity and lack of radio emission from this massive core suggests that it hosts an embedded young massive protostar that is still undergoing an intense accretion phase. This scenario is supported by the observed characteristics of the line profiles and the presence of a bipolar outflow detected from observations of the SiO emission. We suggest that IRAS 16272-4837 is a bona fide massive star- forming region in a very early evolutionary stage, being the precursor of an ultra compact HII region.Comment: 25 pages, 9 figures, accepted for publication in The Astrophysical Journa

Application of Topological Data Analysis to Multi-Resolution Matching of Aerosol Optical Depth Maps

Topological data analysis (TDA) combines concepts from algebraic topology, machine learning, statistics, and data science which allow us to study data in terms of their latent shape properties. Despite the use of TDA in a broad range of applications, from neuroscience to power systems to finance, the utility of TDA in Earth science applications is yet untapped. The current study aims to offer a new approach for analyzing multi-resolution Earth science datasets using the concept of data shape and associated intrinsic topological data characteristics. In particular, we develop a new topological approach to quantitatively compare two maps of geophysical variables at different spatial resolutions. We illustrate the proposed methodology by applying TDA to aerosol optical depth (AOD) datasets from the Goddard Earth Observing System, Version 5 (GEOS-5) model over the Middle East. Our results show that, contrary to the existing approaches, TDA allows for systematic and reliable comparison of spatial patterns from different observational and model datasets without regridding the datasets into common grids