Figure - available from: Frontiers in Earth Science
This content is subject to copyright.
Source publication
This paper develops a new method for the diagnosis and prediction of the evaporation duct heights on the sea, which has certain reference significance for the study of the evaporation ducts. Based on traditional diagnostic and predictive models of evaporation duct heights, a new diagnostic model is proposed. By determining the overall Richardson nu...
Similar publications
PM2.5 (particulate matter with a size/diameter ≤ 2.5 μm) is an important air pollutant that affects human health, especially in urban environments. However, as time-series data of PM2.5 are non-linear and non-stationary, it is difficult to predict future PM2.5 distribution and behavior. Therefore, in this paper, we propose a hybrid short-term urban...
Citations
... Javeed et al. [39] proposed a modified artificial-neural-network (ANN)-based model to predict the refractivity. Mai et al. [40] applied the Darwinian evolutionary algorithm to realize the short-term prediction of the EDH. Zhao et al. [41] proposed a pure data-driven back propagation neural network (BPNN) EDH prediction model. ...
The evaporation duct is a particular type of atmospheric structure that always appears on the open ocean. Predicting the evaporation duct height (EDH) accurately and in a timely manner is of great significance for the practical application of marine wireless communication equipment. Understanding the characteristics of EDH time series is an essential prerequisite for establishing an appropriate prediction model. Moreover, the sampling timescales of EDH data may influence the dynamic characteristics of the EDH time series as well. In this study, EDH time series datasets at three timescales, hourly, daily, and monthly, were constructed as the case study. Statistical methods, namely the augmented Dickey–Fuller test and Ljung–Box test, were adopted to verify the stationary and white noise characteristics of the EDH time series. Then, rescaled range analysis was applied to calculate the Hurst exponent to study the fractal characteristics of the EDH time series. An extensive analysis and discussion of the chaotic dynamics of the EDH time series are provided. From the perspective of nonlinear dynamics, the phase space was constructed from the time delay τ and embedding dimension m, which were calculated from the mutual information method and the Grassberger–Procaccia algorithm, respectively. The maximum Lyapunov exponent was also calculated by the small data volume method to explore the existence of chaos in the EDH time series. According to our analysis, the EDH time series are stationary and have a non-white noise characteristic. The Hurst exponents for all three timescales were greater than 0.5, indicating the predictability of the EDH time series. The phase space diagrams exhibited strange attractors in a well-defined region for all the timescales, suggesting that the evolution of the EDH time series can possibly be explained by deterministic chaos. All of the maximum Lyapunov exponents were positive, confirming the chaos in the EDH time series. Further, stronger chaotic characteristics were found for the finer-resolution time series than the coarser-resolution time series. This study provides a new perspective for scholars to understand the fluctuation principles of the evaporation duct at different timescales. The findings from this study also lay a theoretical and scientific foundation for the future application of chaotic prediction methods in the research on the evaporation duct.
... For example, the most frequently used ED model, the Navy Atmospheric Vertical Surface Layer Model (NAVSLaM), is based on the Monin-Obukhov similarity theory (MOST, see Foken, 2006). The work of Mai et al. (2020) is also based on MOST, except with a slightly different approach from NAVSLaM. The MOST-based approaches have the underlying assumption of stationarity and horizontal homogeneity, which can be a poor assumption in the littoral regions or any regions with large temporal and spatial variations. ...
Evaporative Ducts (ED) are common refractive features that exist due to the persistent/strong vertical moisture gradient inherent to the lowest 10s of meters of the marine atmospheric surface layer (MASL). The ED plays a large role in the propagation of signals from the surface‐ or ship‐based radar/communications systems due to the ducting of electromagnetic (EM) waves. Previous studies have characterized ED structure using mesoscale and surface layer models which are based on the Monin‐Obukhov similarity theory. As a result, only the spatially/temporally averaged mean ED structure has been examined. Conversely, this study focuses on ED variability occurring over turbulent energy‐containing eddy scales by utilizing large‐eddy simulations (LES) of the MASL. This innovative approach reveals that the LES‐resolved refractivity perturbations are directly linked to MASL large eddy dynamic and thermodynamic processes. In the thermally unstable MASL, significant turbulent ED variability is noted, with regions of increased ED heights associated with convective updrafts and positive moisture perturbations. In contrast, the thermally stable MASL is shown to exhibit significantly less ED variability over the LES domain. Since current surface layer models have difficulties in calculating ED properties in the thermally stable MASL, utilization of LES is helpful to gain an understanding of the ED in these conditions. A conceptual model of turbulent ED variation is proposed to describe the relationship between MASL dynamics/thermodynamic processes, state variable perturbations, and refractive variations.
... According to the variation of M with height, atmospheric ducts can be divided into the following four categories [10]: surface ducts, surface-based duct, elevated ducts, and evaporation ducts. Figure 1 shows the respective trends of the ducts as a function of height. ...
... The meteorological data used were high resolution meteorological sounding balloon data [27] and the Defense Meteorological Satellite Program (DMSP) inversion data [10] recorded in 2009. The release site of the sounding balloon was a sea area near the equator with longitude and latitude of 151.8° ...
... First, the temperature, air pressure, relative humidity, wind speed and other meteorological parameters at 3 m height recorded by the sounding balloon were extracted and then substituted into the Liuli-2.0 evaporation duct model [10] to calculate the EDH. Finally, all the diagnostic results were recorded as a time series sample set X. Considering factors such as the rapid change of weather conditions over the sea, measurement errors and turbulence etc., it was necessary to preprocess the data for the EDH before employing the algorithm prediction. ...
Evaporation duct is a kind of chaotic phenomenon over the ocean. In this paper, a new nonlinear prediction algorithm, the Darwinian evolutionary algorithm (DEA), is introduced to obtain the specific nonlinear formula P(·) of the chaotic phenomenon. Based on Darwinian natural selection and survival theory, the method first selects a suitable training set of samples, and then produces an initial population before going through an evolutionary process of selection, reproduction and mutation until the optimal individual is found. Finally, a specific expression for a nonlinear chaotic time series is obtained, which can realize the short-term prediction of evaporation duct height (EDH) quickly and accurately. After that, the DEA, the support vector regression (SVR), and the back propagation (BP) neural network were applied to predict the EDH which were formed over the ocean by using sounding data. After interpolation and smoothing of the original data, we selected the first 250 data as training samples and the last 115 data as test samples to test the effect of the EDA algorithm. The results showed that the root mean squared error (RMSE) for the DEA was about 7% less than that of the SVR and 10% less than that of BP neural network; the mean absolute percent error (MAPE) for the DEA was about 9% less than that of the SVR and 15% less than that of BP neural network. In addition, the DEA obtained, for the first time, a nonlinear expression for EDH, which provides an important reference for future research on the evaporation ducts.
Evaporation ducts are abnormal states of the atmosphere in the air-sea boundary layer that directly affect the propagation trajectory of electromagnetic (EM) waves. Therefore, an accurate diagnosis of the evaporation duct height (EDH) is important for studying the propagation trajectory of EM waves in evaporation ducts. Most evaporation duct models (EDMs) based on the Monin-Obukhov similarity theory are empirical methods. Different EDMs have different levels of environmental adaptability. Evaporation duct diagnosis methods based on machine learning methods only consider the mathematical relationship between data and do not explore the physical mechanism of evaporation ducts. To solve the above problems, this study observed the meteorological and hydrological parameters of the five layers of the low-altitude atmosphere in the East China Sea on board the research vessel Xiangyanghong 18 in April 2021 and obtained the atmospheric refractivity profile. An evaporation duct multimodel fusion diagnosis method (MMF) based on a library for support vector machines (LIBSVM) is proposed. First, based on the observed meteorological and hydrological data, the differences between the EDH diagnosis results of different EDMs and MMF were analyzed. When ASTD ≥ 0, the average errors of the diagnostic results of BYC, NPS, NWA, NRL, LKB, and MMF are 2.57 m, 2.92 m, 2.67 m, 3.27 m, 2.57 m, and 0.24 m, respectively. When ASTD < 0, the average errors are 2.95 m, 2.94 m, 2.98 m, 2.99 m, 2.97 m, and 0.41 m, respectively. Then, the EM wave path loss accuracy analysis was performed on the EDH diagnosis results of the NPS model and the MMF. When ASTD ≥ 0, the average path loss errors of the NPS model and MMF are 5.44 dB and 2.74 dB, respectively. When ASTD < 0, the average errors are 5.21 dB and 3.46 dB, respectively. The results show that the MMF is suitable for EDH diagnosis, and the diagnosis accuracy is higher than other models.
Evaporation duct is a kind of special atmospheric stratification that frequently appears on the sea surface, which has an important influence on the propagation and attenuation of electromagnetic waves, and is an important factor affecting the efficiency of marine radars and communication equipment. After the development in more than half a century, evaporation duct height can be obtained by direct detection, theoretical model, inversion and machine learning. Machine learning can explore the hidden laws of data efficiently and has the potential to surpass the traditional theoretical model. In this paper, the Machine Learning methods in evaporation duct research are shown and prospects of machine learning methods in evaporation duct research are given.
