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What are clouds?
Explores clouds, discusses what they are made of, and describes the different types.
Book
Cloud and Precipitation Microphysics
This book focuses specifically on bin and bulk parameterizations for the prediction of cloud and precipitation at various scales - the cloud scale, mesoscale, synoptic scale, and the global climate scale. It provides a background to the fundamental principles of parameterization physics, including processes involved in the production of clouds, ice particles, liquid water, snow aggregate, graupel and hail. It presents full derivations of the parameterizations, allowing readers to build parameterization packages, with varying levels of complexity based on information in the book. Architectures for a range of dynamical models are given, in which parameterizations form a significant tool for investigating large non-linear numerical systems. Model codes are available online at www.cambridge.org/9780521883382. Written for researchers and advanced students of cloud and precipitation microphysics, this book is also a valuable reference for all atmospheric scientists involved in models of numerical weather prediction.
eBook
ML-CIRRUS
The Midlatitude Cirrus experiment (ML-CIRRUS) deployed the High Altitude and Long Range Research Aircraft (HALO) to obtain new insights into nucleation, life cycle, and climate impact of natural cirrus and aircraft-induced contrail cirrus. Direct observations of cirrus properties and their variability are still incomplete, currently limiting our understanding of the clouds’ impact on climate. Also, dynamical effects on clouds and feedbacks are not adequately represented in today’s weather prediction models.
Here, we present the rationale, objectives, and selected scientific highlights of ML-CIRRUS using the G-550 aircraft of the German atmospheric science community. The first combined in situ–remote sensing cloud mission with HALO united state-of-the-art cloud probes, a lidar and novel ice residual, aerosol, trace gas, and radiation instrumentation. The aircraft observations were accompanied by remote sensing from satellite and ground and by numerical simulations.
In spring 2014, HALO performed 16 flights above Europe with a focus on anthropogenic contrail cirrus and midlatitude cirrus induced by frontal systems including warm conveyor belts and other dynamical regimes (jet streams, mountain waves, and convection). Highlights from ML-CIRRUS include 1) new observations of microphysical and radiative cirrus properties and their variability in meteorological regimes typical for midlatitudes, 2) insights into occurrence of in situ–formed and lifted liquid-origin cirrus, 3) validation of cloud forecasts and satellite products, 4) assessment of contrail predictability, and 5) direct observations of contrail cirrus and their distinction from natural cirrus. Hence, ML-CIRRUS provides a comprehensive dataset on cirrus in the densely populated European midlatitudes with the scope to enhance our understanding of cirrus clouds and their role for climate and weather.
Journal Article
Demonstrating the Potential Impacts of Assimilating FY-4A Visible Radiances on Forecasts of Cloud and Precipitation with a Localized Particle Filter
The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun-4A ( FY-4A ) satellite provides visible radiances that contain critical information on clouds and precipitation. In this study, the impact of assimilating FY-4A /AGRI all-sky visible radiances on the simulation of a convective system was evaluated with an observing system simulation experiment (OSSE) using a localized particle filter (PF). The localized PF was implemented into the Data Assimilation Research Testbed (DART) coupled with the Weather Research and Forecasting (WRF) Model. The results of a 2-day data assimilation (DA) experiment generated encouraging outcome at a synoptic scale. Assimilating FY-4A /AGRI visible radiances with the localized PF significantly improved the analysis and forecast of cloud water path (CWP), cloud coverage, rain rate, and rainfall areas. In addition, some positive impacts were produced on the temperature and water vapor mixing ratio in the vicinity of cloudy regions. Sensitivity studies indicated that the best results were achieved by the localized PF configured with a localization distance that is equivalent to the model grid spacing (20 km) and with an adequately short cycling interval (30 min). However, the localized PF could not improve cloud vertical structures and cloud phases due to a lack of related information in the visible radiances. Moreover, the localized PF was compared with the ensemble adjustment Kalman filter (EAKF) and it was indicated that the localized PF outperformed EAKF even when the number of ensemble members was doubled for the latter, indicating a great potential of the localized PF in assimilating visible radiances.
Journal Article
Learning Scene Dynamics from Point Cloud Sequences
Understanding 3D scenes is a critical prerequisite for autonomous agents. Recently, LiDAR and other sensors have made large amounts of data available in the form of temporal sequences of point cloud frames. In this work, we propose a novel problem—sequential scene flow estimation (SSFE)—that aims to predict 3D scene flow for all pairs of point clouds in a given sequence. This is unlike the previously studied problem of scene flow estimation which focuses on two frames. We introduce the SPCM-Net architecture, which solves this problem by computing multi-scale spatiotemporal correlations between neighboring point clouds and then aggregating the correlation across time with an order-invariant recurrent unit. Our experimental evaluation confirms that recurrent processing of point cloud sequences results in significantly better SSFE compared to using only two frames. Additionally, we demonstrate that this approach can be effectively modified for sequential point cloud forecasting (SPF), a related problem that demands forecasting future point cloud frames. Our experimental results are evaluated using a new benchmark for both SSFE and SPF consisting of synthetic and real datasets. Previously, datasets for scene flow estimation have been limited to two frames. We provide non-trivial extensions to these datasets for multi-frame estimation and prediction. Due to the difficulty of obtaining ground truth motion for real-world datasets, we use self-supervised training and evaluation metrics. We believe that this benchmark will be pivotal to future research in this area. All code for benchmark and models will be made accessible at (https://github.com/BestSonny/SPCM).
Journal Article
Regional Cloud Forecast Verification Using Standard, Spatial, and Object-Oriented Methods
This study assesses the accuracy of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) forecasts for clouds within stable and unstable environments (thereafter refers as “stable” and “unstable” clouds). This evaluation is conducted by comparing these forecasts against satellite retrievals through a combination of traditional, spatial, and object-based methods. To facilitate this assessment, the Model Evaluation Tools (MET) community tool is employed. The findings underscore the significance of fine-tuning the MET parameters to achieve a more accurate representation of the features under scrutiny. The study’s results reveal that when employing traditional pointwise statistics (e.g., frequency bias and equitable threat score), there is consistency in the results whether calculated from Method for Object-Based Diagnostic Evaluation (MODE)-based objects or derived from the complete fields. Furthermore, the object-based statistics offer valuable insights, indicating that COAMPS generally predicts cloud object locations accurately, though the spread of these predicted locations tends to increase with time. It tends to overpredict the object area for unstable clouds while underpredicting it for stable clouds over time. These results are in alignment with the traditional pointwise bias scores for the entire grid. Overall, the spatial metrics provided by the object-based verification methods emerge as crucial and practical tools for the validation of cloud forecasts.
Journal Article
Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling
Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are subject to a number of uncertainties relating to the eruption itself (mass of ash emitted and when), parameterisations of physical processes, and the meteorological conditions. To fully explore these uncertainties through atmospheric dispersion model simulations alone may be expensive, and instead, an emulator can be used to increase understanding of uncertainties in the model inputs and outputs, going beyond combinations of source, physical, and meteorological inputs that were simulated by the dispersion model. We emulate the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model for simulations of the Raikoke 2019 eruption and use these emulators to compare simulated ash clouds to observations derived from satellites, constraining NAME source and internal parameters via history matching. We demonstrate that the effect of varying both meteorological scenarios and model parameters can be captured in this way with accurate emulation and using only a small number of runs per meteorological scenario. We show that accounting for meteorological uncertainty simultaneously with other uncertainties may lead to the identification of different sensitive model parameters and may lead to less constrained source and internal NAME parameters; however, through idealised experiments, we argue that this is a reasonable result and is properly accounting for all sources of uncertainty in the model inputs.
Journal Article
The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station
We document, for the first time, how detailed vertical profiles of cloud fraction (CF) change diurnally between 51∘ S and 51∘ N, by taking advantage of 15 months of measurements from the Cloud-Aerosol Transport System (CATS) lidar on the non-sun-synchronous International Space Station (ISS). Over the tropical ocean in summer, we find few high clouds during daytime. At night they become frequent over a large altitude range (11–16 km between 22:00 and 04:00 LT). Over the summer tropical continents, but not over ocean, CATS observations reveal mid-level clouds (4–8 km above sea level or a.s.l.) persisting all day long, with a weak diurnal cycle (minimum at noon). Over the Southern Ocean, diurnal cycles appear for the omnipresent low-level clouds (minimum between noon and 15:00) and high-altitude clouds (minimum between 08:00 and 14:00). Both cycles are time shifted, with high-altitude clouds following the changes in low-altitude clouds by several hours. Over all continents at all latitudes during summer, the low-level clouds develop upwards and reach a maximum occurrence at about 2.5 km a.s.l. in the early afternoon (around 14:00). Our work also shows that (1) the diurnal cycles of vertical profiles derived from CATS are consistent with those from ground-based active sensors on a local scale, (2) the cloud profiles derived from CATS measurements at local times of 01:30 and 13:30 are consistent with those observed from CALIPSO at similar times, and (3) the diurnal cycles of low and high cloud amounts (CAs) derived from CATS are in general in phase with those derived from geostationary imagery but less pronounced. Finally, the diurnal variability of cloud profiles revealed by CATS strongly suggests that CALIPSO measurements at 01:30 and 13:30 document the daily extremes of the cloud fraction profiles over ocean and are more representative of daily averages over land, except at altitudes above 10 km where they capture part of the diurnal variability. These findings are applicable to other instruments with local overpass times similar to CALIPSO's, such as all the other A-Train instruments and the future EarthCARE mission.
Journal Article