Abstract
Idealized simulations of tropical, marine convection depict shallow, nonprecipitating cumuli located beneath the 0°C level transitioning into cumulonimbi that reach up to 12 km and higher. ...The timing of the transition was only weakly related to environmental stability, and 13 of the 15 simulations run with 5 different lapse-rate profiles had rain develop at nearly the same time after model start. The key quantity that apparently controlled deep convective formation was vertical acceleration inside cloudy updrafts between cloud base and the 0°C level. Below a critical value of updraft vertical acceleration, little rainfall occurred. Just as the domain-mean updraft acceleration reached the critical value, the first convection quickly grew to past 12 km altitude. Then, as acceleration increased above the critical value, rain rate averaged in the model domain increased quickly over about a 3-h-long period. The specific value of the critical updraft acceleration depended on how updrafts were defined and in what layer the acceleration was averaged; however, regardless of how criticality was defined, a robust relationship between domain-mean updraft vertical acceleration and rain rate occurred. Positive acceleration of updrafts below the 0°C level was present below 2.75 km and was largest in the 500 m above cloud base. However, the maximum difference between updraft and environmental temperatures occurred between 2 and 3 km. The domain-mean Archimedean buoyancy of updrafts relative to some reference state was a poor predictor for domain-mean rain rate. The exact value of the critical updraft acceleration likely depends on numerous other factors that were not investigated.
Significance Statement
A numerical model is utilized to investigate potential thermodynamic and dynamic quantities related to the growth of cumulus clouds into cumulonimbus clouds over tropical oceans when the atmosphere is sufficiently moist to support rainfall. Archimedean buoyancy alone cannot be used to predict rain rate reliably. Instead the total buoyancy not relative to an arbitrary reference state must be considered. The simulated relationship between total vertical acceleration in updrafts and rain rate was robust. While the processes that control the vertical acceleration remain unclear, our results highlight the importance of observing processes that occur on spatial scales of tens of meters and temporal scales of a few minutes.
Abstract
Radar and rawinsonde data from four ground-based observing stations in the tropical Indo-Pacific warm pool were used to identify possible associations of environmental state variables and ...their vertical profiles with radar-derived rain rate inside a mesoscale radar domain when the column-integrated relative humidity (CRH) exceeds 80%. At CRH exceeding 80%, a wide range—from near 0 to ~50 mm day−1—in rain rate is observed; therefore, tropospheric moisture was a necessary but insufficient condition for deep convection. This study seeks to identify possible factors that inhibit rainfall when the atmosphere is sufficiently moist to support large precipitation rates. The domain-mean rain rate was highly sensitive to the areal coverage of intense, convective rainfall that occurs. There were two fundamentally different instances in which convective area was low. One was when the radar domain is primarily occupied by weakly precipitating, stratiform echoes. The other was when the radar domain contained almost no precipitating echoes of any type. While the former was dependent upon the stage of the convective life cycle seen by radar, the latter was probably dependent upon the convective environment. Areal coverage of convective echoes was largely determined by the number of individual convective echoes rather than their sizes, so changes in the clear-air environment of updrafts might have governed how many updrafts grew into deep cumulonimbi. The most likely environmental influence on convective rainfall identified using rawinsonde data was 900–700-hPa lapse rate; however, processes occurring on spatial scales smaller than a radar domain were probably also important but not investigated.
Composite circumnavigating Madden‐Julian oscillation (MJO) events in Version 2 of the NASA Modern Era Reanalysis for Research and Applications (MERRA‐2) reanalysis propagate as convectively coupled ...Kelvin waves over the Western Hemisphere and moisture waves like that described by Adames and Kim (2016) over the warm pool. Estimated zonally variable phase speeds of coupled Kelvin waves in the tropics are calculated by determining the “effective static stability” experienced by the wave. The wave is structured similarly to a classically derived deep tropospheric Kelvin wave, and its phase speed is up to 33 m s−1 or 40 m s−1 over the central/eastern Pacific or Atlantic/equatorial Africa, respectively, during boreal winter. Theoretically, estimated phase speeds of convectively coupled Kelvin waves over the tropical warm pool are greater than 15 m s−1, much faster than the propagation of the reanalyzed MJO. A complete theory for MJO propagation around the globe must allow both coupled Kelvin waves and moisture waves.
Key Points
The MJO propagates like a convectively coupled Kelvin wave over the Western Hemisphere
A holistic MJO theory must accommodate moisture waves and convectively coupled Kelvin waves
Propagation is much slower over Pacific for strong MJO events during boreal summer
Anomalies of eastward propagating large‐scale vertical motion with ~30 day variability at Addu City, Maldives, move into the Indian Ocean from the west and are implicated in Madden‐Julian Oscillation ...(MJO) convective onset. Using ground‐based radar and large‐scale forcing data derived from a sounding array, typical profiles of environmental heating, moisture sink, vertical motion, moisture advection, and Eulerian moisture tendency are computed for periods prior to those during which deep convection is prevalent and those during which moderately deep cumulonimbi do not form into deep clouds. Convection with 3–7 km tops is ubiquitous but present in greater numbers when tropospheric moistening occurs below 600 hPa. Vertical eddy convergence of moisture in shallow to moderately deep clouds is likely responsible for moistening during a 3–7 day long transition period between suppressed and active MJO conditions, although moistening via evaporation of cloud condensate detrained into the environment of such clouds may also be important. Reduction in large‐scale subsidence, associated with a vertical velocity structure that travels with a dry eastward propagating zonal wavenumbers 1–1.5 structure in zonal wind, drives a steepening of the lapse rate below 700 hPa, which supports an increase in moderately deep moist convection. As the moderately deep cumulonimbi moisten the lower troposphere, more deep convection develops, which itself moistens the upper troposphere. Reduction in large‐scale subsidence associated with the eastward propagating feature reinforces the upper tropospheric moistening, helping to then rapidly make the environment conducive to formation of large stratiform precipitation regions, whose heating is critical for MJO maintenance.
Key Points
A dry wavenumber 1 zonal wind and vertical velocity MJO signal is shown
Large‐scale reduction in subsidence instrumental in DYNAMO MJO onset
Collection of increasingly voluminous multispectral data from multiple instruments with high spatial resolution has posed both an opportunity and a challenge for maximizing their utilization, ...analysis, and impact. Obtaining accurate estimates of precipitation globally with high temporal resolution is crucial for assessing multiscale hydrologic impacts and providing a constraint for development of numerical models of the atmosphere that provide weather and climate predictions. Precipitation type classification plays an important role in constraining both the inverse problem in satellite precipitation retrievals and latent heat transfer within weather prediction simulations. Precipitation type, however, is often reported deterministically, without uncertainty attached to an estimate. Machine learning techniques are capable of extracting content of interest from large datasets and accurately retrieving discrete and continuous properties of physical systems, but with limited insights to the retrieval components-such as errors and the physical relationship between the observed and retrieved properties. To address this shortcoming, we perform precipitation type classification to introduce a novel tool for decomposing errors of satellite-retrieved products. We use Bayesian neural networks to map global precipitation measurement mission microwave imager observations to dual-frequency precipitation radar-derived precipitation type, which perform comparably to deterministic models, but with the added benefit of providing well-calibrated uncertainties. Through uncertainty decomposition, we demonstrate well-calibrated uncertainties as useful for making decisions concerning high uncertainty predictions, model selection, targeted data analysis, and data collection and processing. Additionally, our Bayesian models enable mathematical confirmation of a data distribution change as the cause for an unacceptable decline in model accuracy.
Abstract
Realistically representing the multiscale interactions between moisture and tropical convection remains an ongoing challenge for weather prediction and climate models. In this study, we ...revisit the relationship between precipitation and column saturation fraction (CSF) by investigating their tendencies in CSF–precipitation space using satellite and radar observations, as well as reanalysis. A well-known, roughly exponential increase in precipitation occurs as CSF increases above a “critical point,” which acts as an attractor in CSF–precipitation space. Each movement away from and subsequent return toward the attractor results in a small net change of the coupled system, causing it to evolve in a cyclical fashion around the attractor. This cyclical evolution is characterized by shallow and convective precipitation progressively moistening the environment and strengthening convection, stratiform precipitation progressively weakening convection, and drying in the nonprecipitating and lightly precipitation regime. This behavior is evident across a range of spatiotemporal scales, suggesting that shortcomings in model representation of the joint evolution of convection and large-scale moisture will negatively impact a broad range of spatiotemporal scales. Novel process-level diagnostics indicate that several models, all implementing versions of the Zhang–McFarlane deep convective parameterization, exhibit unrealistic coupling between column moisture and convection.
Understanding Atrocities is a wide-ranging collection of essays bridging scholarly and community-based efforts to understand and respond to the global, transhistorical problem of genocide. The essays ...in this volume investigate how evolving, contemporary views on mass atrocity frame and complicate the possibilities for the understanding and prevention of genocide. The contributors ask, among other things, what are the limits of the law, of history, of literature, and of education in understanding and representing genocidal violence? What are the challenges we face in teaching and learning about extreme events such as these, and how does the language we use contribute to or impair what can be taught and learned about genocide? Who gets to decide if it's genocide and who its victims are? And how does the demonization of perpetrators of atrocity prevent us from confronting the complicity of others, or of ourselves? Through a multi-focused and multidisciplinary investigation of these questions, Understanding Atrocities demonstrates the vibrancy and breadth of the contemporary state of genocide studies. With contributions by: Amarnath Amarasingam, Andrew R. Basso, Kristin Burnett, Lori Chambers, Laura Beth Cohen, Travis Hay, Steven Leonard Jacobs, Lorraine Markotic, Sarah Minslow, Donia Mounsef, Adam Muller, Scott W. Murray, Christopher Powell, and Raffi Sarkissian
Abstract
Hurricane Matthew locally generated more than 400 mm of rainfall on 8–9 October 2016 over the eastern Carolinas and Virginia as it transitioned into an extratropical cyclone. The heaviest ...precipitation occurred along a swath situated up to 100–200 km inland from the coast and collocated with enhanced low-tropospheric frontogenesis. Analyses from version 3 of the Rapid Refresh (RAPv3) model indicate that rapid frontogenesis occurred over eastern North and South Carolina and Virginia on 8 October, largely over a 12-h time period between 1200 UTC 8 October and 0000 UTC 9 October. The heaviest rainfall in Matthew occurred when and where spiral rainbands intersected the near-surface front, which promoted the lift of conditionally unstable, moist air. Parallel to the spiral rainbands, conditionally unstable low-tropospheric warm, moist oceanic air was advected inland, and the instability was apparently released as the warm air mass rose over the front. Precipitation in the spiral rainbands intensified on 9 October as the temperature gradient along the near-surface front rapidly increased. Unlike in Hurricane Floyd over the mid-Atlantic states, rainfall totals within the spiral rainbands of Matthew as they approached the near-surface front evidently were not enhanced by release of conditional symmetric instability. However, conditional symmetric instability release in the midtroposphere may have enhanced rainfall 200 km northwest of the near-surface front. Finally, although weak cold-air damming occurred prior to heavy rainfall, damming dissipated prior to frontogenesis and did not impact rainfall totals.
In this paper we present a catalog of 4584 eclipsing binaries observed during the first two years (26 sectors) of the TESS survey. We discuss selection criteria for eclipsing binary candidates, ...detection of hitherto unknown eclipsing systems, determination of the ephemerides, the validation and triage process, and the derivation of heuristic estimates for the ephemerides. Instead of keeping to the widely used discrete classes, we propose a binary star morphology classification based on a dimensionality reduction algorithm. Finally, we present statistical properties of the sample, we qualitatively estimate completeness, and we discuss the results. The work presented here is organized and performed within the TESS Eclipsing Binary Working Group, an open group of professional and citizen scientists; we conclude by describing ongoing work and future goals for the group. The catalog is available from http://tessEBs.villanova.edu and from MAST.
Reconstructing spatially continuous two-dimensional fields out of their individually derived building blocks typically introduces artifacts that decrease the overall perceptual quality of the field. ...Machine learning applications encounter such a challenge when patching a U-net-like architecture output. Numerous techniques have been developed to mitigate this problem. Yet, few are informed scalable solutions. The present work manages the stitching of Unet-inferred images using Bayesian deep learning probabilistic output. The ability to preserve a Bayesian prediction's variance while effectively reducing the artifacts within a patched scene is presented through an example of predicting a field related to atmospheric radiance. In areas of high variance, adjacent patches of inferred atmospheric radiances may significantly vary in magnitude, leading to large undesirable spatial gradients in the combined (patched) product. Multiple weighted aggregation strategies and weighting schema are surveyed to investigate how to efficiently decrease artificial gradients in large images constructed by stitching several small predictions while maintaining naturally occurring gradients expected to appear in the mosaiced image. Structural Similarity Index (SSIM) and Visual Information Fidelity (VIF) are used to evaluate the perceptual quality of the resultant images and confirm the successful employment of Bayesian U-nets with well-calibrated uncertainty, yielding geospatial images with fewer artifacts than naive methods. Log-Linear Pooling (LLP) proved to be the optimal aggregation strategy tested for fusing patch uncertainties by retaining per-pixel Gaussian distributions and scaling uncertainties in a principled manner to maintain calibration across the spatial map.
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