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Eva–Maria Walz, Marlon Maranan, Roderick van der Linden, Andreas H. Fink, and Peter Knippertz

Abstract

Current numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best currently high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) (IMERG) “final run” in a ± 15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as Extended Probabilistic Climatology (EPC) and compute it on a 0.1°×0.1° grid for 40°S–40°N and the period 2001–2019. In order to reduce and standardize information, a mixed Bernoulli-Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive webtool to the scientific community.

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Allison C. Michaelis, Andrew C. Martin, Meredith A. Fish, Chad W. Hecht, and F. Martin Ralph

Abstract

A complex and underexplored relationship exists between atmospheric rivers (ARs) and mesoscale frontal waves (MFWs). The present study further explores and quantifies the importance of diabatic processes to MFW development and the AR-MFW interaction by simulating two ARs impacting Northern California’s flood-vulnerable Russian River watershed using the Model for Prediction Across Scales-Atmosphere (MPAS-A) with and without the effects of latent heating. Despite the storms’ contrasting characteristics, diabatic processes within the system were critical to the development of MFWs, the timing and magnitude of integrated vapor transport (IVT), and precipitation impacts over the Russian River watershed in both cases. Low-altitude circulations and lower-tropospheric moisture content in and around the MFWs are considerably reduced without latent heating, contributing to a decrease in moisture transport, moisture convergence, and IVT. Differences in IVT are not consistently dynamic (i.e., wind-driven) or thermodynamic (i.e., moisture-driven), but instead vary by case and by time throughout each event. For one event, AR conditions over the watershed persisted for 6 h less and the peak IVT occurred 6 h earlier and was reduced by ~17%; weaker orographic and dynamic precipitation forcings reduced precipitation totals by ~64%. Similarly, turning off latent heating shortened the second event by 24 h and reduced precipitation totals by ~49%; the maximum IVT over the watershed was weakened by ~42% and delayed by 18 h. Thus, sufficient representation of diabatic processes, and by inference, water vapor initial conditions, is critical for resolving MFWs, their feedbacks on AR evolution, and associated precipitation forecasts on watershed scales.

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Tsung-Yung Lee, Chun-Chieh Wu, and Rosimar Rios-Berrios

Abstract

The impact of low-level flow (LLF) direction on the intensification of intense tropical cyclones under moderate deep-layer shear is investigated based on idealized numerical experiments. The background flow profiles are constructed by varying the LLF direction with the same moderate deep-layer shear. When the maximum surface wind speed of the simulation without background flow reaches 70 knots, the background flow profiles are imposed. After a weakening period in the first 12 h, the members with upshear-left-pointing LLF (fast-intensifying group) intensify faster between 12–24 h than those members (slow-intensifying group) with downshear-right-pointing LLF. The fast-intensifying group experiences earlier development of inner-core structures after 12 h, such as potential vorticity below the mid-troposphere, upper-level warm core, eyewall axisymmmetrization, and moist entropy gradient, while the inner-core features of the slow-intensifying group remain relatively weak and asymmetric. The FI group experiences smaller tilt increase and stronger mid-level PV ring development. The upshear-left convection during 6–12 h is responsible for the earlier development of the inner core by reducing ventilation, providing axisymmetric heating and benefiting the eyewall development. The LLF of the fast-intensifying group enhances surface heat fluxes in the downshear side, resulting in higher energy supply to the upshear-left convection from the boundary layer. In all, this study provides new insights on the impact of LLF direction on intense storms under moderate shear by modulating the surface heat fluxes and eyewall convection.

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C. Key, A. Hicks, and B. M. Notaroš

Abstract

We present improvements over our previous approach to automatic winter hydrometeor classification by means of convolutional neural networks (CNNs), using more data and improved training techniques to achieve higher accuracy on a more complicated dataset than we had previously demonstrated. As an advancement of our previous proof-of-concept study, this work demonstrates broader usefulness of deep CNNs by using a substantially larger and more diverse dataset, which we make publicly available, from many more snow events. We describe the collection, processing, and sorting of this dataset of over 25,000 high-quality multiple-angle snowflake camera (MASC) image chips split nearly evenly between five geometric classes: aggregate, columnar crystal, planar crystal, graupel, and small particle. Raw images were collected over 32 snowfall events between November 2014 and May 2016 near Greeley, Colorado and were processed with an automated cropping and normalization algorithm to yield 224x224 pixel images containing possible hydrometeors. From the bulk set of over 8,400,000 extracted images, a smaller dataset of 14,793 images was sorted by image quality and recognizability (Q&R) using manual inspection. A presorting network trained on the Q&R dataset was applied to all 8,400,000+ images to automatically collect a subset of 283,351 good snowflake images. Roughly 5,000 representative examples were then collected from this subset manually for each of the five geometric classes. With a higher emphasis on in-class variety than our previous work, the final dataset yields trained networks that better capture the imperfect cases and diverse forms that occur within the broad categories studied to achieve an accuracy of 96.2% on a vastly more challenging dataset.

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David Coe, Mathew Barlow, Laurie Agel, Frank Colby, Christopher Skinner, and Jian-Hua Qian

Abstract

A k-means clustering method is applied to daily ERA5 500-hPa heights, sea level pressure, and 850-hPa winds, 1979-2008, to identify characteristic Weather Types (WTs) for Sep-Nov for the Northeast U.S. The resulting WTs are analyzed in terms of structure, frequency of occurrence, typical progressions, precipitation and temperature characteristics, and relation to teleconnections. The WTs are used to make a daily circulation-based distinction between early and late autumn and consider shifts in seasonality.

Seven WTs are identified for the autumn season, representing a range of trough and ridge patterns. The largest average values of precipitation and greatest likelihood of extremes occurs in the Midwestern Trough and Atlantic Ridge patterns. The greatest likelihood of extreme temperatures occurs in the Northeast Ridge. Some WTs are strongly associated with the phase of the North Atlantic Oscillation and Pacific North America pattern, with frequency of occurrence for several WTs changing by more than a factor of two.

The two most common progressions between the WTs are one most frequent in September, Mid-Atlantic Trough to Northeast Ridge to Mid-Atlantic Trough, and one most frequent in mid-October to November, Midwestern Trough to Northeast Trough to Midwestern Trough. This seasonality allows for a daily WT-based distinction between early and late season. A preliminary trend analysis indicates an increase in early season WTs later in the season and a decrease in late season WTs earlier in the season; that is, a shift toward a longer period of warm season patterns and a shorter, delayed period of cold season patterns.

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Samuel J. Childs, Russ S. Schumacher, and Rebecca D. Adams-Selin

Abstract

Shortly after 0600 UTC (midnight MDT) on 9 June 2020, a rapidly intensifying and elongating convective system produced a macroburst and extensive damage in the town of Akron on Colorado’s eastern Plains. Instantaneous winds were measured as high as 51.12 m s−1 at 2.3 m AGL from an eddy covariance (EC) tower, and a 50.45 m s−1 wind gust from an adjacent 10-m tower became the highest official thunderstorm wind gust ever measured in Colorado. Synoptic-scale storm motion was southerly, but surface winds were northerly in a post-frontal airmass, creating strong vertical wind shear. Extremely high-resolution temporal and spatial observations allow for a unique look at pressure and temperature tendencies accompanying the macroburst and reveal intriguing wave structures in the outflow. At 10-Hz frequency, the EC tower recorded a 5-hPa pressure surge in 19 seconds immediately following the strongest winds, and a 15-hPa pressure drop in the following three minutes. Surface temperature also rose 1.5°C in less than one minute, concurrent with the maximum wind gusts, and then fell sharply by 3.5°C in the following minute. Shifting wind direction observations and an NWS damage survey are suggestive of both radial outflow and a gust front passage, and model proximity soundings reveal a well-mixed surface layer topped by a strong inversion and large low-level vertical wind shear. Despite the greatest risk of severe winds forecast to be northeast of Colorado, convection-allowing model forecasts from 6-18 h in advance did show similar structures to what occurred, warranting further simulations to investigate the unique mesoscale and misoscale features associated with the macroburst.

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Emily V. Fischer, Brittany Bloodhart, Kristen Rasmussen, Ilana B. Pollack, Meredith G. Hastings, Erika Marin-Spiotta, Ankur R. Desai, Joshua P. Schwarz, Stephen Nesbitt, and Deanna Hence

Capsule

This article raises awareness of sexual harassment within the AMS community, and it provides critical research findings previously absent on this important topic in our community.

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Luc Lenain and Nick Pizzo

Abstract

Internal waves are a regular feature of the open ocean and coastal waters. As a train of internal waves propagate, their surface induced currents modulate the surface waves, generating a characteristic rough and smooth banded structure. While the surface expression of these internal waves is well known and has been observed from a variety of remote sensing instruments, direct quantitative observations of the directional properties of the surface gravity wave field modulated by an internal wave remain sparse. In this work, we report on a comprehensive field campaign conducted off the coast of Point Sal, CA in September 2017. Using a unique combination of airborne remote sensing observations, along with in-situ surface and subsurface measurements, we investigate and quantify the interaction between surface gravity and internal wave processes. We find that surface waves are significantly modulated by the currents induced by the internal waves. Through novel observations of ocean topography, we characterize the rapid modification of the directional and spectral properties of surface waves over very short spatial scales (O(100)m or less). Over a range of wavelengths (3-9m waves), geometrical optics and wave action conservation predictions show good agreement with the observed wavenumber spectra in smooth and rough regions of the modulated surface waves. If a parameterization of wave action source terms is used, good agreement is found over a larger range of wavenumbers, down to 4rad/m. These results elucidate properties of surface waves interacting with a submesoscale ocean current, and should provide insight into more general interactions between surface waves and the fine scale structure of the upper ocean.

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Xiaoduo Pan, Xuejun Guo, Xin Li, Xiaolei Niu, Xiaojuan Yang, Min Feng, Tao Che, Rui Jin, Youhua Ran, Jianwen Guo, Xiaoli Hu, and Adan Wu

Capsule

The National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn) integrates and shares scientific datasets for the Tibetan Plateau and its surrounding regions, hosting more than 3500 datasets from a wide range of disciplines. Fifty datasets were highlighted in the article, including an integrated observational dataset collected by the 17 stations of the High-cold Region Observation and Research Network, datasets of the distributions and attributes of permafrost, glacier, snow, and other cryospheric states, a high resolution and long term dataset of the near-surface atmosphere forcing, and datasets collected by scientific expeditions, e.g., the ongoing second Tibetan Plateau Scientific Expedition and Research Program.

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Hua Zheng, Xiao-Hua Zhu, Chuanzheng Zhang, Ruixiang Zhao, Ze-Nan Zhu, and Zhao-Jun Liu

Abstract

Topographic Rossby waves (TRWs) are oscillations generated on sloping topography when water columns travel across isobaths under potential vorticity conservation. Based on our large-scale observations from 2016 to 2019, near 65-day TRWs were first observed in the deep basin of the South China Sea (SCS). The TRWs propagated westward with a larger wavelength (235 km) and phase speed (3.6 km/day) in the north of the array and a smaller wavelength (80 km) and phase speed (1.2 km/day) toward the southwest of the array. The ray-tracing model was used to identify the energy source and propagation features of the TRWs. The paths of the near 65-day TRWs mainly followed the isobaths with a slightly downslope propagation. The possible energy source of the TRWs was the variance of surface eddies southwest of Taiwan. The near 65-day energy propagated from the southwest of Taiwan to the northeast and southwest of the array over ~100–120 and ~105 days, respectively, corresponding to a group velocity of 4.2–5.0 and 10.5 km/day, respectively. This suggests that TRWs play an important role in deep-ocean dynamics and deep current variation, and upper ocean variance may adjust the intraseasonal variability in the deep SCS.

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