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Zongsheng Zheng, Chenyu Hu, Zhaorong Liu, Jianbo Hao, Qian Hou, and Xiaoyi Jiang

Abstract

A tropical cyclone, also known as a typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damage to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant preprocessing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on a convolutional neural network (CNN). The Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud bands (SCBs) more effectively and reduces the overfitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10 000 multiple sensor satellite cloud images from the National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3, and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas, and SCBs, which facilitate classifying and forecasting typhoon intensity.

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Felix Erdmann, Olivier Caumont, and Eric Defer

Abstract

Coincident Geostationary Lightning Mapper (GLM) and National Lightning Detection Network (NLDN) observations are used to build a generator of realistic lightning optical signal in the perspective to simulate Lightning Imager (LI) signal from European NLDN-like observations. Characteristics of GLM and NLDN flashes are used to train different machine-learning (ML) models, which predict simulated pseudo-GLM flash extent, flash duration, and event number per flash (targets) from several NLDN flash characteristics. Comparing statistics of observed GLM targets and simulated pseudo-GLM targets, the most suitable ML-based target generators are identified. The simulated targets are then further processed to obtain pseudo-GLM events and flash-scale products. In the perspective of lightning data assimilation, flash extent density (FED) is derived from both observed and simulated GLM data. The best generators simulate accumulated hourly FED sums with a bias of 2% to the observation while cumulated absolute differences remain of about 22%. A visual comparison reveals that hourly simulated FED features local maxima at the similar geolocations as the FED derived from GLM observations. However, the simulated FED often exceeds the observed FED in regions of convective cores and high flash rates. The accumulated hourly area with FED > 0 flashes per 5 km × 5 km pixel simulated by some pseudo-GLM generators differs by only 7%–8% from the observed values. The recommended generator uses a linear support vector regressor (linSVR) to create pseudo-GLM FED. It provides the best balance between target simulation, hourly FED sum, and hourly electrified area.

Open access
Mika P. Malila, Patrik Bohlinger, Susanne Støle-Hentschel, Øyvind Breivik, Gaute Hope, and Anne Karin Magnusson

Abstract

We propose a methodology for despiking ocean surface wave time series based on a Bayesian approach to data-driven learning known as Gaussian process (GP) regression. We show that GP regression can be used for both robust detection of erroneous measurements and interpolation over missing values, while also obtaining a measure of the uncertainty associated with these operations. In comparison with a recent dynamical phase space–based despiking method, our data-driven approach is here shown to lead to improved wave signal correlation and spectral tail consistency, although at a significant increase in computational cost. Our results suggest that GP regression is thus especially suited for offline quality control requiring robust noise detection and replacement, where the subsequent analysis of the despiked data is sensitive to the accidental removal of extreme or rare events such as abnormal or rogue waves. We assess our methodology on measurements from an array of four collocated 5-Hz laser altimeters during a much-studied storm event in the North Sea covering a wide range of sea states.

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S. J. Lentz, A. Kirincich, and A. J. Plueddemann

Abstract

Acoustic Doppler current profilers (ADCP) do not provide reliable water velocity measurements near the sea surface or bottom because acoustic sidelobe reflections from the boundary contaminate the Doppler velocity measurements. The apparent depth of the center of the sidelobe reflection is z sl = ha[1 − cos(θ)], where ha is the distance from the ADCP acoustic head to the sea surface and θ is the ADCP beam angle. However, sidelobe contamination extends one and a half ADCP bins below z sl as the range gating of the acoustic return causes overlap between adjacent ADCP bins. Consequently, the contaminated region z < z sl + 3Δz/2 is deeper than traditionally suggested, with a dependence on bin size Δz. Direct observations confirming both the center depth of the sidelobe reflection and the depth of contamination are presented for six bottom-mounted, upward-looking ADCPs. The sidelobe reflection is isolated by considering periods of weak wind stresses when the sea surface is smooth and there is nearly perfect reflection of the main beams away from the ADCP and hence little acoustic return from the main beams to the ADCP.

Open access
David J. Serke, Scott M. Ellis, Sarah A. Tessendorf, David E. Albo, John C. Hubbert, and Julie A. Haggerty

Abstract

Detection of in-flight icing hazard is a priority of the aviation safety community. The “Radar Icing Algorithm” (RadIA) has been developed to indicate the presence, phase, and relative size of supercooled drops. This paper provides an evaluation of RadIA via comparison to in situ microphysical measurements collected with a research aircraft during the 2017 “Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment” (SNOWIE) field campaign. RadIA uses level-2 dual-polarization radar moments from operational National Weather Service WSR-88D and a numerical weather prediction model temperature profile as inputs. Moment membership functions are defined based on the results of previous studies, and fuzzy logic is used to combine the output of these functions to create a 0 to 1 interest for detecting small-drop, large-drop, and mixed-phase icing. Data from the two-dimensional stereo (2D-S) particle probe on board the University of Wyoming King Air aircraft were categorized as either liquid or solid phase water with a shape classification algorithm and binned by size. RadIA interest values from 17 cases were matched to statistical measures of the solid/liquid particle size distributions (such as maximum particle diameter) and values of LWC from research aircraft flights. Receiver operating characteristic area under the curve (AUC) values for RadIA algorithms were 0.75 for large-drop, 0.73 for small-drop, and 0.83 for mixed-phase cases. RadIA is proven to be a valuable new capability for detecting the presence of in-flight icing hazards from ground-based precipitation radar.

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Ali Tokay, Annakaisa von Lerber, Claire Pettersen, Mark S. Kulie, Dmitri N. Moisseev, and David B. Wolff

Abstract

Performance of the Precipitation Imaging Package (PIP) for estimating the snow water equivalent (SWE) is evaluated through a comparative study with the collocated National Oceanic and Atmospheric Administration National Weather Service snow stake field measurements. The PIP together with a vertically pointing radar, a weighing bucket gauge, and a laser-optical disdrometer was deployed at the NWS Marquette, Michigan, office building for a long-term field study supported by the National Aeronautics and Space Administration’s Global Precipitation Measurement mission Ground Validation program. The site was also equipped with a weather station. During the 2017/18 winter, the PIP functioned nearly uninterrupted at frigid temperatures accumulating 2345.8 mm of geometric snow depth over a total of 499 h. This long record consists of 30 events, and the PIP-retrieved and snow stake field measured SWE differed less than 15% in every event. Two of the major events with the longest duration and the highest accumulation are examined in detail. The particle mass with a given diameter was much lower during a shallow, colder, uniform lake-effect event than in the deep, less cold, and variable synoptic event. This study demonstrated that the PIP is a robust instrument for operational use, and is reliable for deriving the bulk properties of falling snow.

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George A. Seaver and Douglas Butler

Abstract

SeaLite Engineering, after a 10-yr research and development program, has produced an operational antifouling system for ocean sensors and instruments designed for low power consumption and long (>1 yr) deployments. The important innovation is the replacement of pumps by gravity and external motion to significantly reduce energy consumption. Also, a prototype system for autonomous underwater vehicle control surfaces is now undergoing laboratory testing. The effectiveness of SeaLite’s technology has been demonstrated year-round in northern estuaries and in the Gulf of Mexico, the latter by an independent agency. The process leading to this result was, first starting in 2010, an extensive laboratory evaluation of electrode alloys, calibration of chlorine production versus electric power input, and the location for attaching electrodes to various objects requiring protection from fouling. After 2015 the experimentation moved to the ocean, first in a Cape Cod estuary and then to the Gulf of Mexico. Comparisons with a mechanical antifouling system were done in situ, and with a UV antifouling system from AML Oceanographic, Ltd., by comparing the data. Starting in 2019, the development of biofilms, from their initial deposition through the extra polysaccharide substance stage, were experimentally investigated by taking samples from an estuary near SeaLite’s laboratory. Biofilms on microscope slides and water column samples were collected. This was done in different seasons, from spring bloom, summer doldrums, autumn temperature decline, and the winter freeze. The objective was to determine the level of biofilm growth that would require antifouling, and its’ seasonal, temperature, and solar radiation dependence, and thus to conserve power.

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Xiaodong Wu, Falk Feddersen, and Sarah N. Giddings

Abstract

Oceanic density fronts can evolve, be advected, or propagate as gravity currents. Frontal evolution studies require methods to temporally track evolving density fronts. We present an automated method to temporally track these fronts from numerical model solutions. First, at all time steps contiguous density fronts are detected using an edge detection algorithm. A front event, defined as a set of sequential-in-time fronts representing a single time-evolving front, is then identified. At time step i, a front is compared to each front at time step i + 1 to determine if the two fronts are matched. An i front grid point is trackable if the minimum distance to the i + 1 front falls within a range. The i front is forward matched to the i + 1 front when a sufficient number of grid points are trackable and the front moves onshore. A front event is obtained by forward tracking a front for multiple time steps. Within an event, the times that a grid point can be tracked is its connectivity and a pruning algorithm using a connectivity cutoff is applied to extract only the coherently evolving components. This tracking method is applied to a realistic 3-month San Diego Bight model solution yielding 81 front events with duration ≥ 7 h, allowing analyses of front event properties including occurrence frequency and propagation velocity. Sensitivity tests for the method’s parameters support that this method can be straightforwardly adapted to track evolving fronts of many types in other regions from both models and observations.

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Shohei Morino, Naoyuki Kurita, Naohiko Hirasawa, Hideaki Motoyama, Konosuke Sugiura, Matthew Lazzara, David Mikolajczyk, Lee Welhouse, Linda Keller, and George Weidner

Abstract

Surface temperature measurements with naturally ventilated (NV) sensors over the Antarctic Plateau are largely subject to systematic errors caused by solar radiative heating. Here we examined the radiative heating error in Dronning Maud Land on the East Antarctic Plateau using both the newly installed automatic weather stations (AWSs) at NDF and Relay Station and the existing AWSs at Relay Station and Dome Fuji. Two types of NV shields were used in these AWSs: a multiplate radiation shield and a simple cylinder-shaped shield. In austral summer, the temperature bias between the force-ventilated (FV) sensor and the NV sensor never reached zero because of continuous sunlight. The hourly mean temperature errors reached up to 8°C at noon on a sunny day with weak wind conditions. The errors increased linearly with increasing reflected shortwave radiation and decreased nonlinearly with increasing wind speed. These features were observed in both the multiplate and the cylinder-shaped shields. The magnitude of the errors of the multiplate shield was much larger than that of the cylinder-shaped shield. To quantify the radiative errors, we applied an existing correction model based on the regression approach and successfully reduced the errors by more than 70% after the correction. This indicates that we can use the corrected temperature data instead of quality controlled data, which removed warm bias during weak winds in inland Dronning Maud Land.

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Aodhan J. Sweeney and Qiang Fu

Abstract

An observationally based global climatology of the temperature diurnal cycle in the lower stratosphere is derived from 11 different satellites with global positioning system–radio occultation (GPS-RO) measurements from 2006 to 2020. Methods used in our analysis allow for accurate characterization of global stratospheric temperature diurnal cycles, even in the high latitudes where the diurnal signal is small but longer time-scale variability is large. A climatology of the synthetic Microwave Sounding Unit (MSU) and Advanced MSU (AMSU) Temperature in the Lower Stratosphere (TLS) is presented to assess the accuracy of diurnal cycle climatologies for the MSU and AMSU TLS observations, which have traditionally been generated by model data. The TLS diurnal ranges are typically less than 0.4 K in all latitude bands and seasons investigated. It is shown that the diurnal range (maximum minus minimum temperature) of TLS is largest over Southern Hemisphere tropical land in the boreal winter season, indicating the important role of deep convection. The range, phase, and seasonality of the TLS diurnal cycle are generally well captured by the WACCM6 simulation and ERA5 dataset. We also present an observationally based diurnal cycle climatology of temperature profiles from 300 to 10 hPa for various latitude bands and seasons and compare the ERA5 data with the observations.

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