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BINGTIAN LI, ZEXUN WEI, YONGGANG WANG, XINYU GUO, TENGFEI XU, and XIANQING LV

Abstract

An enhanced harmonic analysis (S_TIDE) approach is adopted to examine the seasonal variations of internal tidal amplitudes in the northern South China Sea (SCS). Results of idealized experiments reveal that the seasonality can be captured by S_TIDE. By applying S_TIDE to mooring data, observed seasonality of internal tidal amplitudes in the northern SCS are explored. Not diurnal and semidiurnal internal tides (ITs), but overtides and long-period constituents of ITs exhibit clear seasonal cycles. However, differences between amplitudes of the eastward velocity and the northward counterpart are evident for K1, M2 and MK3, which may be caused by the intensification of background currents. Amplitudes of those ITs are stronger at intersection time between spring and summer in the eastward direction, but weaker in the northward direction. EOF analysis reveals that modes of diurnal ITs are higher than those of seimidiurnal ITs, which induces relatively more complicated seasonal variations. In addition to intensification of background currents, influences of surface tides and stratification will also induce variations of internal tidal amplitudes, introducing tremendous difficulty in predicting variation trends of internal tidal amplitudes, which greatly reduces predictability of ITs.

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Emily M. Riley Dellaripa, Aaron Funk, Courtney Schumacher, Hedanqiu Bai, and Thomas Spangehl

Abstract

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolution. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the sub-grid scale. This work adapts Quickbeam to the dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate-to-heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land/ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP sub-grid algorithm by applying a more realistic, non-deterministic approach to assigning GCM grid box convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated sub-grid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud cover assumption.

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V.V. Sterlyadkin, K.V. Kulikovsky, A.V. Kuzmin, E.A. Sharkov, and M.V. Likhacheva

Abstract

A direct optical method for measuring the “instantaneous” profile of the sea surface with an accuracy of 1 mm and a spatial resolution of 3 mm is described. Surface profile measurements can be carried out on spatial scales from units of millimeters to units of meters with an averaging time of 10−4 s. The method is based on the synchronization of the beginning of scanning a laser beam over the sea surface and the beginning of recording the radiation scattered on the surface onto the video camera matrix. The heights of all points of the profile are brought to a single point in time, which makes it possible to obtain “instantaneous” profiles of the sea surface with the frequency of video recording. The measurement technique and data processing algorithm are described. The errors of the method are substantiated. The results of field measurements of the parameters of sea waves are presented: amplitude spectra, distribution of slopes at various spatial averaging scales. The applied version of the wave recorder did not allow recording capillary oscillations, but with some modernization it will be possible. The method is completely remote, does not distort the properties of the surface, is not affected by wind, waves and sea currents, it allows you to measure the proportion of foam on the surface. The possibility of applying the proposed method at any time of the day and in a wide range of weather conditions has been experimentally proved.

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

Abstract

Tropical cyclone, also known as typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damages to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to the disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant pre-processing 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 convolutional neural network (CNN). Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud band (SCB) more effectively and reduces over-fitting 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 of 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 facilitates classify and forecast typhoon intensity.

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Martin Hurtado

Abstract

In a previous work, a weather radar algorithm with low computational cost has been developed to estimate the background noise power from the data collected at each radial. The algorithm consists of a sequence of steps designed to identify signal-free range volumes which are subsequently used to estimate the noise power. In this paper, we derive compact-closed form expressions to replace the numerical formulations used in the first two steps of the algorithm proposed in the original paper. The goal is to facilitate efficient implementation of the algorithm.

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Lucas M. Merckelbach and Jeffrey R. Carpenter

Abstract

Autonomous, buoyancy-driven ocean gliders are increasingly used as a platform for the measurement of turbulence microstructure. In the processing of such measurements, there is a sensitive (quartic) dependence of the turbulence dissipation rate, ϵ, on the speed of flow past the sensors, or alternatively, the speed of the glider through the ocean water column. The mechanics of glider flight is therefore examined by extending previous flight models to account for the effects of ocean surface waves. It is found that due to the relatively small buoyancy changes used to drive gliders, the surface wave-induced motion, superimposed onto the steady-state motion, follows to a good approximation the motion of the wave orbitals. Errors expected in measuring ϵ at the ocean near-surface due to wave-induced relative velocities are generally less than 10%. However, pressure perturbations associated with the wave motion can be significant when using the glider-measured pressure signal to infer the glider vertical velocity. This effect of surface waves is only present in the shallow water regime, and can also affect glider depth measurements. It arises from an incomplete cancellation of the wave-induced pressure perturbation with the hydrostatic component due to vertical glider displacements, whereas for deep-water waves this cancellation is complete.

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Theodore M. McHardy, James R. Campbell, David A. Peterson, Simone Lolli, Richard L. Bankert, Anne Garnier, Arunas P. Kuciauskas, Melinda L. Surratt, Jared W. Marquis, Steven D. Miller, Erica K. Dolinar, and Xiquan Dong

Abstract

We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite 16 (GOES-16) and developed with collocated Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) channel-4 (1.378 μm) radiance and CALIOP 0.532-μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378-μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine-learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the channel-4 radiance as a function of AMF. The algorithm detects nearly 50% of subvisual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semiquantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378-μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an overland algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

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James S. Bennett, Frederick R. Stahr, Charles C. Eriksen, Martin C. Renken, Wendy E. Snyder, and Lora J. Van Uffelen

Abstract

Seagliders are buoyancy-driven autonomous underwater vehicles whose subsurface position estimates are typically derived from velocities inferred using a flight model. We present a method for computing velocities and positions during the different phases typically encountered during a dive–climb profile based on a buoyancy-driven flight model. We compare these predictions to observations gathered from a Seaglider deployment on the acoustic tracking range in Dabob Bay (200 m depth, mean vehicle speeds ~30 cm s−1), permitting us to bound the position accuracy estimates and understand sources of various errors. We improve position accuracy estimates during long vehicle accelerations by numerically integrating the flight model’s fundamental momentum-balance equations. Overall, based on an automated estimation of flight-model parameters, we confirm previous work that predicted vehicle velocities in the dominant dive and climb phases are accurate to <1 cm s−1, which bounds the accumulated position error in time. However, in this energetic tidal basin, position error also accumulates due to unresolved depth-dependent flow superimposed upon an inferred depth-averaged current.

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Jiali Zhang, Liang Zhang, Anmin Zhang, Lianxin Zhang, Dong Li, and Xuefeng Zhang

Abstract

Sound Speed Profile (SSP) affecting underwater acoustics is closely related to the temperature and the salinity fields. It is of great value to obtain the temperature and the salinity information through the high-precision sound speed profiles. In this paper, a data assimilation scheme by introducing sound speed profiles as a new constraint is proposed within the framework of 3DVAR data assimilation (referenced as SSP-constraint 3DVAR (SSPC-3DVAR) ), which aims at improving the analysis accuracy of initial fields of the temperature and salinity in coastal sea areas. In order to validate the performance of the new assimilation scheme, ideal experiments are firstly carried out to show the advantages of the new proposed SSPC-3DVAR. Then the temperature, the salinity, and the SSP observations from field experiments in a coastal area are assimilated into the Princeton Ocean Model to validate the performance of short-time forecasts, adopting the SSPC-3DVAR scheme. Results show that it is efficient to improve the estimate accuracy by as much as 14.6% (11.1%) for the temperature (salinity), compared with the standard 3DVAR. It demonstrates that the proposed SSPC-3DVAR approach works better in practice than the standard 3DVAR and will primarily benefit from variously and widely distributed observations in the future.

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