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Significant wave height (SWH) is a key parameter for monitoring the state of waves. Accurate and long-term SWH forecasting is significant to maritime shipping and coastal engineering. This study proposes a transformer model based on an attention mechanism to achieve the forecasting of SWHs. The transformer model can capture the contextual information and dependencies between sequences and achieves continuous time series forecasting. Wave scale classification is carried out according to the forecasting results, and the results are compared with gated recurrent unit (GRU) and long short-term memory (LSTM) machine-learning models and the key laboratory of MArine Science and NUmerical Modeling (MASNUM) numerical wave model. The results show that the machine-learning models outperform the MASNUM within 72 h, with the transformer being the best model. For continuous 12 h, 24 h, 36 h, 48 h, 72 h, and 96 h forecasting, the average mean absolute errors (MAEs) of the test sets were, respectively, 0.139 m, 0.186 m, 0.223 m, 0.254 m, 0.302 m, and 0.329 m, and the wave scale classification accuracies were, respectively, 91.1%, 99.4%, 86%, 83.3%, 78.9%, and 77.5%. The experimental results validate that the transformer model can achieve continuous and accurate SWH forecasting, as well as accurate wave scale classification and early warning of waves, providing technical support for wave monitoring.

The three-dimensional structure, horizontal and vertical propagation characteristics, and convection–circulation coupling of the convectively coupled westward-propagating mixed Rossby–gravity (MRG) waves are examined by classifying the waves based on their amplitude. Convective signals of the MRG waves were identified and isolated using empirical orthogonal function analysis of wavenumber–frequency-filtered outgoing longwave radiation (OLR) data. It was found that about 50% of the MRG waves occur during the August–November months, and this strong seasonality was considered while characterizing the MRG waves. Five strong and five weak MRG wave seasons were identified during 1979–2019, based on seasonal wave amplitude, and through this classification, significant differences in the strength of convection–circulation coupling, zonal scale of circulation, vertical structure, and propagation characteristics of MRG waves were brought out. It was also found that the seasonal mean background state is significantly different during strong and weak MRG wave seasons. While a La Niña–like background state was found to favor enhanced MRG wave activity, the MRG wave activity is mostly suppressed during an El Niño–like background state. The presence of extratropical wave intrusions is another factor that distinguishes the strong MRG wave seasons from the weak ones. Eastward- and northeastward-propagating extratropical wave trains from the South Atlantic to the east Indian Ocean were observed during strong MRG wave seasons.

A deep and cold vortex circulation often occurs over northeast China. Known as the northeast China cold vortex (NCCV), the phenomenon is most active from May to mid-June and can lead to extremely cold local temperatures. This study used rotated principle component analysis to categorize NCCV events into four types, which were characterized by ridges (or blocks) over the following regions: Lake Baikal (BKL), the Yenisei River valley (YNS), the Ural Mountains (UR), and the Yakutsk–Okhotsk region (YO). On the intraseasonal time scale, it was found that BKL- and YNS-type NCCVs formed when the wave train height anomalies originating from the North Atlantic and Europe propagated to East Asia. In contrast, YO- and UR-type NCCVs formed in conjunction with the development of a meridional dipole pattern over northeast Asia. The existence of a blocking-type circulation over the Yakutsk–Okhotsk region favored maintenance of the NCCV circulation for the long-lived (more than 5 days) NCCV events of the four types. The typical circulation over northeast Asia for the long-lived NCCV event was closely associated with wave breaking, whereas the short-lived (3–5 days) event showed only wave propagation. The YNS-type NCCV caused cold surface air temperatures (SAT) not only over northeast China, but also over central and south China, whereas the other three types led only to regional cold SAT anomalies over northeast China. All four types of NCCVs caused a precipitation increase over northeast China, and this effect was broader for the UR- and YO-type NCCVs than that for BKL- and YNS-type NCCVs.

Four state-of-the-science numerical weather prediction (NWP) models were used to perform mountain wave (MW)-resolving hindcasts over the Drake Passage of a 10-day period in 2010 with numerous observed MW cases. The Integrated Forecast System (IFS) and the Icosahedral Nonhydrostatic (ICON) model were run at Δ x ≈ 9 and 13 km globally. The Weather Research and Forecasting (WRF) Model and the Met Office Unified Model (UM) were both configured with a Δ x = 3-km regional domain. All domains had tops near 1 Pa ( z ≈ 80 km). These deep domains allowed quantitative validation against Atmospheric Infrared Sounder (AIRS) observations, accounting for observation time, viewing geometry, and radiative transfer. All models reproduced observed middle-atmosphere MWs with remarkable skill. Increased horizontal resolution improved validations. Still, all models underrepresented observed MW amplitudes, even after accounting for model effective resolution and instrument noise, suggesting even at Δ x ≈ 3-km resolution, small-scale MWs are underresolved and/or overdiffused. MW drag parameterizations are still necessary in NWP models at current operational resolutions of Δ x ≈ 10 km. Upper GW sponge layers in the operationally configured models significantly, artificially reduced MW amplitudes in the upper stratosphere and mesosphere. In the IFS, parameterized GW drags partly compensated this deficiency, but still, total drags were ≈6 times smaller than that resolved at Δ x ≈ 3 km. Meridionally propagating MWs significantly enhance zonal drag over the Drake Passage. Interestingly, drag associated with meridional fluxes of zonal momentum (i.e., ) were important; not accounting for these terms results in a drag in the wrong direction at and below the polar night jet.

The high costs and low deployment rates of floating offshore wind turbines (FOWTs) remain the main barriers to meeting the UK’s 2030 offshore wind targets. Although mass and serial production have the potential to alleviate these challenges, current industry practice still largely relies on bespoke designs tailored to individual projects or offshore sites. This research develops a practical metocean classification methodology to support the design of FOWTs for generalised, globally applicable classes of environmental conditions, thereby enabling standardisation and economies of scale. The proposed approach uses metocean data from reanalysis products and employs robust analysis to extract design-driving metrics relevant to the design of floating support structures. To identify unbiased boundaries between regions with similar metocean characteristics, data-driven clustering techniques are employed. A key element of the methodology is extreme value analysis. Two classical approaches–the Generalised Extreme Value (GEV) and the Generalised Pareto Distribution (GPD) methods–are compared and validated. A novel automated threshold-selection procedure is developed to ensure consistent peaks-over-threshold (POT) extraction across a global grid of offshore locations. Based on the derived extreme wind speed and significant wave height values, additional features are extracted, including the associated wave peak period and wind-wave misalignment. Finally, a fuzzy C-means clustering approach is applied to define metocean zones. The methodology has been developed with continuous feedback from industrial partners to ensure practical applicability. The results for UK waters are presented to demonstrate its potential use in supporting the standardised FOWT design.

Machine learning methods are well established in the classification of quasars (QSOs). However, the advent of light-curve observations adds a great amount of complexity to the problem. Our goal is to use the Zwicky Transient Facility (ZTF) to create a catalog of QSOs. We process the ZTF DR20 light curves with a transformer artificial neural network and combine different surveys with extreme gradient boosting. Based on ZTF g-band and Wide-field Infrared Survey Explorer (WISE) observations, we find 4,849,574 objects classified as QSOs with confidence higher than 90% (QZO). We robustly classify objects fainter than the 5σ signal-to-noise ratio (SNR) limit at g = 20.8 by requiring g < nobs/80 + 20.375. For 33% of QZO objects, with available WISE data, we publish redshifts with estimated error Δz/(1 + z) = 0.14. We find that ZTF classification is superior to the Pan-STARRS static bands, and on par with WISE and Gaia measurements, but the light curves provide the most important features for QSO classification in the ZTF data set. Using ZTF g-band data with at least 100 observational epochs per light curve, we obtain a 97% F1 score for QSOs. We find that with 3 day median cadence, a survey time span of at least 900 days is required to achieve a 90% QSO F1 score. However, one can obtain the same score with a survey time span of 1800 days and the median cadence prolonged to 12 days.

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

An internal wave is a propagating disturbance within a stable density-stratified fluid. The internal seiche amplitude is often estimated through theories that describe the amplitude growth based on the Bulk Richardson number (Ri). However, most theoretical formulations neglect secondary effects that may influence the evolution of internal seiches. Since these waves have been pointed out as the most important process of vertical mixing, influencing the biogeochemical fluxes in stratified basins, the wrong estimation may have several impacts on the prediction of the system dynamics. This research paid particular attention to the importance of secondary effects that may play a major role on the basin-scale internal wave amplitude, especially related to the interaction between internal waves and lake boundaries, internal wave depth, and mixing processes due to turbulence. Based on a set of methods, which include auto- and cross-correlations, spectral analysis, and mathematical models, we analyzed the effect of total water depth, wind-resonance, and higher vertical modes on the amplitude growth. We based our analysis on underwater temperature measurements and meteorological data obtained from two small thermally-stratified basins, complemented with numerical simulations. We introduce here a new parametrization which takes into account the total water depth (H), lake length (L), epilimnion thickness (he), as well as the resonance effect. We observed that the rate of amplitude growth decreases compared to linear theory when Rihe/L≤1. In these cases, we suggests that previous theories overestimate the internal seiche amplitude, neglecting the instabilities generated near the wave crest due to weak stability and wave interactions. However, under shallow thermocline conditions, due to extra pressure in the upper layer, the vertical displacement may be higher than that predicted by the linear theory.

Climate change increases the frequency and intensity of extreme precipitation, which in combination with rising population enhances exposure to major floods. An improved understanding of the atmospheric processes that cause extreme precipitation events would help to advance predictions and projections of such events. To date, such analyses have typically been performed rather unsystematically and over limited areas (e.g., the U.S.) which has resulted in contradictory findings. Here we present the Multi-Object Analysis of Atmospheric Phenomenon algorithm that uses a set of 12 common atmospheric variables to identify and track tropical and extra-tropical cyclones, cut-off lows, frontal zones, anticyclones, atmospheric rivers (ARs), jets, mesoscale convective systems (MCSs), and equatorial waves. We apply the algorithm to global historical data between 2001–2020 and associate phenomena with hourly and daily satellite-derived extreme precipitation estimates in major climate regions. We find that MCSs produce the vast majority of extreme precipitation in the tropics and some mid-latitude land regions, while extreme precipitation in mid and high-latitude ocean and coastal regions are dominated by cyclones and ARs. Importantly, most extreme precipitation events are associated with phenomena interacting across scales that intensify precipitation. These interactions are a function of the intensity (i.e., rarity) of extreme events. The presented methodology and results could have wide-ranging applications including training of machine learning methods, Lagrangian-based evaluation of climate models, and process-based understanding of extreme precipitation in a changing climate.

Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.