Automated Tracking of Shallow Cloud Lifetimes with AHI during CAMP2EX: Status and Some Case Studies
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Ship tracks appear as long winding linear features in satellite images and are produced by aerosols from ship exhausts changing low cloud properties. They are one of the best examples of aerosol‐cloud interaction experiments. However, manually finding ship tracks from satellite data on a large scale is prohibitively costly while a large number of samples are required to improve our understanding. Here we train a deep neural network to automate finding ship tracks. The neural network model generalizes well as it not only finds ship tracks labeled by human experts but also detects those that are occasionally missed by humans. It finds more ship tracks than all previous studies combined and produces a map of ship track distributions off the California coast that matches well with known shipping traffic. Our technique will enable studying aerosol effects on low clouds using ship tracks on a large scale, which will potentially narrow the uncertainty of the aerosol‐cloud interactions.
Results are presented from multispectral radiation measurements made within a marine stratocumulus cloud layer modified by ship-track effluents. The measurements showed that, compared with nearby noncontaminated clouds not affected by pollution, the upwelling intensity field of the modified stratocumulus clouds increased at a nonabsorbing wavelength in the visible region and decreased in the NIR, where absorption by liquid water is significant. The observations are consistent with an increased optical thickness, a reduced effective radius of the cloud droplets, and a reduced absorption in the contaminated cloud layer compared to a noncontaminated cloud.
Ship tracks represent a natural laboratory to study the effects of aerosols on clouds. A number of observations and simulations have shown that increased droplet concentrations in ship tracks increase their total cross-sectional area, thereby enhancing cloud albedo and providing a negative radiative forcing at the surface and the top of the atmosphere. In some cases, cloud water has been found to be enhanced in ship tracks, which has been attributed to suppression of drizzle and implies an enhanced susceptibility of cloud albedo to droplet concentrations. However, more recently compiled observations indicate that cloud water is instead reduced in daytime ship tracks on average. Such a response is consistent with cloud-burning due to solar absorption by soot (the semi-direct radiative forcing of aerosols), recently suggested to be suppressing trade cumulus cloud coverage over the Indian Ocean. We will summarize observational evidence and present large-eddy simulations that consider these competing mechanisms in the effects of aerosols on cloud albedo.
Nimbus TOMS data have led to advancements among many volcano-related scientific disciplines, from the initial ability to quantify SO2 clouds leading to derivations of eruptive S budgets and fluxes, to tracking of individual clouds, assessing global volcanism and atmospheric impacts. Some of the major aspects of TOMS-related research, listed below, will be reviewed and updated: (1) Measurement of volcanic SO2 clouds: Nimbus TOMS observed over 100 individual SO2 clouds during its mission lifetime; large explosive eruptions are now routinely and reliably measured by satellite. (2) Eruption processes: quantification of SO2 emissions have allowed assessments of eruption sulfur budgets, the evaluation of "excess" sulfur, and inferences of H2S emissions. (3) Detection of ash: TOMS data are now used to detect volcanic particulates in the atmosphere, providing complementary analyses to infrared methods of detection. Paired TOMS and AVHRR studies have provided invaluable information on volcanic cloud compositions and processes. (4) Cloud tracking and hazard mitigation: volcanic clouds can be considered gigantic tracers in the atmosphere, and studies of the fates of these clouds have led to new knowledge of their physical and chemical dispersion in the atmosphere for predictive models. (5) Global trends: the long term data set has provided researchers an unparalleled record of explosive volcanism, and forms a key component in assessing annual to decadal trends in global S emissions. (6) Atmospheric impacts: TOMS data have been linked to independent records of atmospheric change, in order to compare cause and effect processes following a massive injection of SO2 into the atmosphere. (7) Future TOMS instruments and applications: Nimbus TOMS has given way to new satellite platforms, with several wavelength and resolution modifications. New efforts to launch a geostationary TOMS could provide unprecedented observations of volcanic activity.
Ship tracks are quasi-linear cloud patterns produced from the interaction of ship emissions with low boundary layer clouds. They are visible throughout the diurnal cycle in satellite images from space-borne assets like the Advanced Baseline Imagers (ABI) aboard the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES-R). However, complex atmospheric dynamics often make it difficult to identify and characterize the formation and evolution of tracks. Ship tracks have the potential to increase a cloud's albedo and reduce the impact of global warming. Thus, it is important to study these patterns to better understand the complex atmospheric interactions between aerosols and clouds to improve our climate models, and examine the efficacy of climate interventions, such as marine cloud brightening. Over the course of this 3-year project, we have developed novel data-driven techniques that advance our ability to assess the effects of ship emissions on marine environments and the risks of future marine cloud brightening efforts. The three main innovative technical contributions we will document here are a method to track aerosol injections using optical flow, a stochastic simulation model for track formations and an automated detection algorithm for efficient identification of ship tracks in large datasets.
Abstract. This paper describes the new open-source framework PyFLEXTRKR (Python FLEXible object TRacKeR), a flexible atmospheric feature tracking software package with specific capabilities to track convective clouds from a variety of observations and model simulations. This software can track any atmospheric 2D objects and handle merging and splitting explicitly. The package has a collection of multi-object identification algorithms, scalable parallelization options, and has been optimized for large datasets including global high-resolution data. We demonstrate applications of PyFLEXTRKR on tracking individual deep convective cells and mesoscale convective systems from observations and model simulations ranging from large-eddy resolving (∼100s m) to mesoscale (∼10s km) resolutions. Visualization, post-processing, and statistical analysis tools are included in the package. New Lagrangian analyses of convective clouds produced by PyFLEXTRKR applicable to a wide range of datasets and scales facilitate advanced model evaluation and development efforts as well as scientific discovery.
The discrete vortex cloud approach models a missile airframe's vortex wake by means that are capable of treating a variety of configurations over a range of flow conditions. Attention is given to the sheets of vorticity formed on the lee side of a missile at moderate angles-of-attack. While three-dimensional attached flow models are used to represent the missile body, two-dimensional, incompressible, separated flow models are used to represent the separated vortex wake. The predicted pressure distribution of the body under the influence of the freestream and the separation vortex wake are used to calculate aerodynamic loads on the body. The separation vortex wake is represented by clouds of discrete vortices in cross flow planes normal to the body axis.
A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The report highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features.
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The explosive June 1991 eruptions of Mount Pinatubo produced the largest sulfur dioxide cloud detected by the Total Ozone Mapping Spectrometer (TOMS) during its 13 years of operation: approximately 20 million tons of SO2, predominantly from the cataclysmic June 15th eruption. The SO2 cloud observed by the TOMS encircled the earth in about 22 days (about 21 m/s); however, during the first three days the leading edge of the SO2 cloud moved with a speed that averaged about 35 m/s. Compared to the 1982 El Chichon eruptions, Pinatubo outgassed nearly three times the amount of SO2 during its explosive phases. The main cloud straddled the equator within the first two weeks of eruption, whereas the El Chichon cloud remained primarily in the Northern Hemisphere. The measurements indicate that Mount Pinatubo has produced a much larger and perhaps longer-lasting SO2 cloud; thus, climatic responses to the Pinatubo eruption can exceed those of El Chichon.
The 21 June 2019 eruption of the Raikoke volcano (Kuril Islands, Russia; 48° N, 153° E) produced significant amounts of volcanic aerosols (sulfate and ash) and sulfur dioxide (SO2) gas that penetrated into the lower stratosphere. The dispersed SO2 and sulfate aerosols in the stratosphere were still detectable by multiple satellite sensors for many months after the eruption. For this study of SO2 and aerosol clouds we use data obtained from two of the Ozone Mapping and Profiler Suite sensors on the Suomi National Polar-orbiting Partnership satellite: total column SO2 from the Nadir Mapper and aerosol extinction profiles from the Limb Profiler as well as other satellite data sets. We evaluated the limb viewing geometry effect (the “arch effect”) in the retrieval of the LP standard aerosol extinction product at 674 nm. It was shown that the amount of SO2 decreases with a characteristic period of 8–18 d and the peak of stratospheric aerosol optical depth recorded at a wavelength of 674 nm lags the initial peak of SO2 mass by 1.5 months. Using satellite observations and a trajectory model, we examined the dynamics of an unusual atmospheric feature that was observed, a stratospheric coherent circular cloud of SO2 and aerosol from 18 July to 22 September 2019.
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Explore the source record for details and available documents.
A new unsteady, cavitation model is presented wherein the phase change process (bubble growth/collapse) is coupled to the acoustic field in a cryogenic fluid. It predicts the number density and radius of bubbles in vapor clouds by tracking both the aggregate surface area and volume fraction of the cloud. Hence, formulations for the dynamics of individual bubbles (e.g. Rayleigh-Plesset equation) may be integrated within the macroscopic context of a dense vapor cloud i.e. a cloud that occupies a significant fraction of available volume and contains numerous bubbles. This formulation has been implemented within the CRUNCH CFD, which has a compressible real fluid formulation, a multi-element, unstructured grid framework, and has been validated extensively for liquid rocket turbopump inducers. Detailed unsteady simulations of a cavitating ogive in liquid nitrogen are presented where time-averaged mean cavity pressure and temperature depressions due to cavitation are compared with experimental data. The model also provides the spatial and temporal history of the bubble size distribution in the vapor clouds that are shed, an important physical parameter that is difficult to measure experimentally and is a significant advancement in the modeling of dense cloud cavitation.
Numerical Weather Prediction (NWP) centers assimilate cloud-free infrared (IR) radiances because the assimilation of all-sky IR radiances is not yet operationally achievable. The cloud-clearing procedure offers a simpler, but effective strategy that produces cloud-affected radiances suitable for assimilation in partially cloudy regions. Several studies conducted by this team have demonstrated that IR Cloud-Cleared Radiances (CCRs), if thinned more aggressively than clear-sky radiances, can improve analysis and forecasts, particularly in meteorologically active areas. However, CCRs are not used by operational centers due partly to the thought that the process of cloud-clearing may affect latency and introduce difficult-to-control external dependencies. This study presents the results of implementing an Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) cloud-clearing procedure into the NASA Goddard Earth Observing System (GEOS) to demonstrate the portability of the procedure. The AIRS and CrIS cloud-clearing algorithms have been deprived of external dependencies, made customizable to any specific model, and the computational efficiency has been improved via parallelization. The revised AIRS and CrIS cloud-clearing algorithms allow a customized choice of channel selection, the use of a user-specified model's fields as first guess, and can perform in real time. Data assimilation experiments with the hybrid 4DEnVar GEOS system were successfully performed for the 2017 tropical cyclones (TC) season with a focus on three major hurricanes (Harvey, Irma, and Maria). This study shows that assimilation of locally-generated CCRs have a positive impact on both global skill and TC representation, compared to the assimilation of AIRS and CrIS clear-sky radiances, and a comparable or slightly improved impact compared to assimilation of CCRs produced by external sources, such as NASA's Distributed Active Archive Centers and NOAA’s Comprehensive Large Array-data Stewardship System. The customization and computational efficiency of the revised procedure would enable its usability in a real-time forecast context.
On 6 August, 2012, Russia launched two commercial satellites aboard a Proton rocket, and attempted to place them in geosynchronous orbit using a Briz-M upper stage (2012-044C, SSN 38746). Unfortunately, the upper stage failed early in its burn and was left stranded in an elliptical orbit with a perigee in low Earth orbit (LEO). Because the stage failed with much of its fuel on board, it was deemed a significant breakup risk. These fears were confirmed when it broke up 16 October, creating a large cloud of debris with perigees below that of the International Space Station. The debris cloud was tracked by the US Space Surveillance Network (SSN), which can reliably detect and track objects down to about 10 cm in size. Because of the unusual geometry of the breakup, there was an opportunity for NASA Orbital Debris Program Office to request radar assets to characterize the extent of the debris cloud in sizes smaller than the standard debris tracked by the SSN. This paper will describe the observation campaign to measure the small particle distributions of this cloud, and presents the results of the analysis of the data. We shall compare the data to the modelled size distribution, number, and shape of the cloud, and what implications this may have for future breakup debris models. We shall conclude the paper with a discussion how this measurement process can be improved for future breakups.