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Abstract Historically, the precipitation trend over the past few decades in the Contiguous United States (CONUS) exhibits a “Dry‐West Wet‐East” pattern; this is manifested by recent droughts/floods in the western/eastern US. However, it remains elusive what atmospheric phenomenon has potentially driven such a remarkable, and impactful precipitation pattern. Here we found that a coupled climate mode—the Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by using Atmospheric Model Intercomparison Project simulations available from the Coupled Model Intercomparison Project Phase 6.

This paper proposes an algorithm based on the long short-term memory (LSTM) network to improve the quality of high-frequency surface wave radar current measurements. In order to address the limitations of traditional high-frequency radar inversion algorithms, which solely rely on electromagnetic inversion and disregard physical oceanography, this study incorporates a bottom-mounted acoustic Doppler current profiler (ADCP) and towed ADCP into LSTM training. Additionally, wind and tidal oceanography data were included as inputs. This study compared high-frequency radar current data before and after calibration. The results indicated that both towed and bottom-mounted ADCP enhanced the quality of HF radar monitoring data. By comparing two methods of calibrating radar, we found that less towed ADCP data input is required for the same high-frequency radar data calibration effect. Furthermore, towed ADCP has a significant advantage in calibrating high-frequency radar data due to its low cost and wide calibration range. However, as the location of the calibrated high-frequency radar data moves further away from the towing position, the calibration error also increases. This article conducted sensitivity studies on the times and different positions of using towed ADCP to calibrate high-frequency radar data, providing reference for the optimal towing path and towing time for future corrections of high-frequency radar data.

The performance of a high-frequency (HF) radar network situated within the Pearl River Estuary from 17 July to 13 August 2022 is described via a comparison with seven acoustic Doppler current profilers (ADCPs). The radar network consists of six OSMAR-S100 compact HF radars, with a transmitting frequency of 13–16 MHz and a direction-finding technique. Both the radial currents and vector velocities showed good agreement with the ADCP results (coefficient of determination r2: 0.42–0.78; RMS difference of radials: 11–21.6 cm s−1; bearing offset Δθ: −4.8°–16.1°; complex correlation coefficient γ: 0.62–0.96; and phase angle α: −24.3°–17.8°). For these radars, the Δθ values are not constant but vary with azimuthal angles. The relative positions between the HF radar and ADCPs, as well as factors such as the presence of island terrain obstructing the signal, significantly influence the errors. The results of spectral analysis also demonstrate a high level of consistency and the capability of HF radar to capture diurnal and semidiurnal tidal frequencies. The tidal characteristics and the Empirical Orthogonal Function (EOF) results measured by the HF radars also resemble the ADCPs and align with the characteristics of the estuarine current field.

An unmanned aerial vehicle (UAV) observation platform obtained the first vertical profiles of particle number size distribution (PNSD) from 7 to 16 July 2022 on the eastern slope of the Tibetan Plateau (ESTP). The results were from two flanks at the Chuni (CN) and Tianquan (TQ) sites, which are alongside a mountain (Mt. Erlang). The observations revealed a significant negative correlation between the planetary boundary layer height (PBLH) and the particle number concentration (PNC), and the correlation coefficient was −0.19. During the morning, the rise in the PBLH at the CN and TQ sites caused decreases of 16.43% and 58.76%, respectively, in the PNC. Three distinct profile characteristics were classified: Type I, the explosive growth of fine particles with a size range of 130–272 nm under conditions of low humidity, strong wind shear, and northerly winds; Type II, the process of particles with a size range of 130–272 nm showing hygroscopic growth into larger particles (e.g., 226–272 nm) under high humidity conditions (RH > 85%), with a maximum vertical change rate of about −1653 # cm−3 km−1 for N130–272 and about 3098 # cm−3 km−1 for N272–570; and Type III, in which during the occurrence of a surface low-pressure center and an 850 hPa low-vortex circulation in the Sichuan Basin, polluting air masses originating from urban agglomeration were transported to the ESTP region, resulting in an observed increase in the PNC below 600 nm. Overall, this study sheds light on the various factors affecting the vertical profiles of PNSD in the ESTP region, including regional transport, meteorological conditions, and particle growth processes, helping us to further understand the various features of the aerosol and atmospheric physical character in this key region.

The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings.

A machine learning neural network-based design for shipborne ADCP navigation is proposed to improve the quality of high-frequency radar measurements. In traditional inversion algorithms for HF radars, sea surface velocity is directly extracted from electromagnetic echoes without constraints from oceanographic processes. Hence, we incorporated oceanographic information from observational data into seabed radar inversion results via an LSTM neural network model to enhance data accuracy. Through a series of numerical simulation experiments, we showed improved data accuracy and feasibility by incorporating both fixed-point and navigation observational data. The results indicate a significant reduction in (related) errors. This study has implications for guiding future navigation observations.

An algorithm based on a long short-term memory (LSTM) network is proposed to reduce errors from high-frequency surface wave radar current measurements. In traditional inversion algorithms, the radar velocities are derived from electromagnetic echo signals, with no constraints imposed by physical oceanographic processes. In this study, sea surface winds and tides are included in the LSTM algorithm to improve radar data. These physical factors provide the LSTM network with more oceanic information by which to constrain and improve its training efficiency. The results show that the domain-averaged root-mean-square errors of the radar-derived velocities are reduced from 0.22 to 0.09 m/s for the whole radar observation area. The overall correlation coefficient increases from 0.37 to 0.88. To provide a practical strategy for future field work, we conduct a set of sensitivity experiments, showing that the LSTM network based on one single point can be applied to other data points within a sub-domain.

High-frequency (HF) radar data, derived from a pair of newly developed radar stations in the Pearl River Estuary (PRE) of China, were validated through comparison with in situ surface buoys, ADCP measurements, and model simulations in this study. Since no in situ observations are available in the radar observing domain, a regional high-resolution ocean model covering the entire PRE and its adjacent seas was first established and validated with in situ measurements, and then the HF radar data quality was examined against the model simulations. The results show that mean flows and tidal ellipses derived from the in situ buoys and ADCP were in very good agreement with the model. The model–radar data comparison indicated that the radar obtained the best data quality within the central overlapping area between the two radar stations, with the errors increasing toward the coast and the open ocean. Near the coast, the radar data quality was affected by coastlines and islands that prevent HF radar from delivering high-quality information for determining surface currents. This is one of the major drawbacks of the HF radar technique. Toward the open ocean, where the wind is the only dominant forcing on the tidal currents, we found that the poor data quality was most likely contaminated by data inversion algorithms from the Shangchuan radar station. A hybrid machine-learning-based inversion algorithm including traditional electromagnetic analysis and physical oceanography factors is needed to develop and improve radar data quality. A new radar observing network with about 10 radar stations is developing in the PRE and its adjacent shelf, this work assesses the data quality of the existing radars and identifies the error sources, serving as the first step toward the full deployment of the entire radar network.

Aneurysmal bone cyst (ABC) is a benign, distending, osteolytic and locally aggressive bone tumor that is mostly associated with trauma. Approximately 1% of bone tumors are ABCs, which are most prevalent in adolescents and are usually detected in the spine and long tubular bones. The diagnosis of ABC mainly relies on histopathology, malignant transformation is rare, and the chance of malignancy increases if there are multiple recurrences. Due to the rarity of reports of malignant transformation of ABCs into osteosarcoma, there is still considerable debate on the appropriate treatment strategy. The current paper presents a case of aneurysmal bone cyst malignant to osteosarcoma and the therapeutic measures to provide expertise for the diagnosis and treatment of ABCs that are malignant to osteosarcoma. [ABSTRACT FROM AUTHOR]