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When vortex electromagnetic waves propagate through a turbulent atmosphere, the amplitude and phase of the electromagnetic waves are disturbed, creating the scintillation effect. According to the scintillation index of vortex waves, a new method of retrieving the turbulent refraction structure parameter was proposed using a genetic algorithm, and the feasibility of this method was verified by simulated experiments. The numerical results showed that the inversed value obtained by the genetic algorithm was close to the real parameter when the turbulent inner scale and outer scale were fixed. However, there was a gap between the inversed value and the real parameter when only the turbulent outer scale was fixed. These results suggest that vortex wave data can be used for turbulent refraction structure parameter inversion, and they provide new research directions for atmospheric remote sensing.

“Wave turbopause” is defined as the mesospheric altitude level where the temperature fluctuation field indicates a substantial increase in wave amplitude in the vertical direction. It is similar to turbopause in seasonal and latitudinal variations, providing an almost global analysis of turbopause on the basis of satellite measurements rather than localized detection. Previous studies of “wave turbopause” were based on standard deviation of temperature, which is an integrated measure of wave activity. In this study, we distinguish different atmospheric waves and investigate their influences on “wave turbopause”. By comparing the altitude of “wave turbopause” with peak height of amplitude (PHA) for gravity waves and 6.5 days waves (6.5 DWs), whose period is approximately 6.5 days in the mesosphere and lower thermosphere, we find that the seasonal variation in altitude of “wave turbopause” is higher at the winter pole and lower at the summer pole, correlated with PHA for 6.5 DWs but anti-correlated with PHA for gravity waves. We infer that gravity waves reach saturation and break at lower altitudes in the winter when Brunt–Vaisala frequency is also lower between 80 and 100 km altitudes. Finally, the results may imply that seasonal variations of the “wave turbopause” are driven mainly by 6.5 DWs.

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.

Low-rank matrix recovery is a large-scale data analysis and processing technology; its related theory has been widely used in image restoration, image denoising, video background modeling, signal recovery and other fields. This paper proposes an improved inexact augmented Lagrange multiplier (IALM) method to solve the harmonic recovery (HR) problem. After that, the performance of the original IALM and the improved IALM are compared when the sparse matrix is a diagonal sparse matrix satisfying certain conditions, and the results show that the improved IALM algorithm is more stable than the original algorithm. Then the improved strategy of the algorithm is extended to two kinds of occasions in which the positions of non-zero elements in sparse matrix are fixed or random, which provides a way to improve the algorithm in different application scenarios. Finally, the original IALM algorithm and the improved IALM algorithm are used to solve the HR problem, and the experimental results show that the improved IALM algorithm has better solution performance.

Atmospheric refraction is a special meteorological phenomenon mainly caused by gas molecules and aerosol particles in the atmosphere, which can change the propagation direction of electromagnetic waves in the atmospheric environment. Atmospheric refractive index, an index to measure atmospheric refraction, is an important parameter for electromagnetic wave. Given that it is difficult to obtain the atmospheric refractive index of 100 meters (m)–3000°m over the ocean, this paper proposes an improved extreme gradient boosting (XGBoost) algorithm based on comprehensive learning particle swarm optimization (CLPSO) operator to obtain them. Finally, the mean absolute percentage error (MAPE) and root mean-squared error (RMSE) are used as evaluation criteria to compare the prediction results of improved XGBoost algorithm with backpropagation (BP) neural network and traditional XGBoost algorithm. The results show that the MAPE and RMSE of the improved XGBoost algorithm are 39% less than those of BP neural network and 32% less than those of the traditional XGBoost. Besides, the improved XGBoost algorithm has the strongest learning and generalization capability to calculate missing values of atmospheric refractive index among the three algorithms. The results of this paper provide a new method to obtain atmospheric refractive index, which will be of great reference significance to further study the atmospheric refraction.

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 number Rib, the Monin-Obukhov (M-O) length L and the wind speed characteristic parameter u∗, temperature characteristic parameter θ∗ and humidity characteristic parameters q∗ are calculated, and then the evaporation duct height is diagnosed. Taking the diagnosed heights as a time series, and using the support vector regression (SVR) algorithm improved by a simulated annealing operator, then the time series is analyzed by taking three consecutive sample steps as input and the next sample step as output in order to develop an algorithm for predicting future heights. Finally, the prediction results are compared with those from the traditional auto-regressive (AR) algorithm and classical SVR algorithm to identify the advantages and disadvantages of the improved SVR algorithm. The results show that the root-mean-square error (RMSE) of the traditional AR, the classical SVR and the improved SVR algorithms is 0.60, 0.45, and 0.38, and the mean absolute percentage error (MAPE) of the three algorithms is 7.79%, 6.10% and 4.78%, respectively. The prediction error of the improved SVR algorithm is 37% less than that of the traditional AR algorithm and 15% less than that of the classical SVR algorithm, signifying an improvement in its prediction capability.

In this paper, global position system high-resolution sounding data from 1998 to 2008 were used to statistically analyze the spatiotemporal distribution and determine the probability, thickness, and intensity of atmospheric ducts at 12 stations in Alaska. In addition, the singular value decomposition (SVD) was used to examine the relationship between the Arctic vortex and atmospheric ducts. The annual average probability of atmospheric ducts, primarily surface and elevation ducts, was approximately 30% in Alaska. The probability of elevation ducts was greater than that of surface ducts. The Arctic vortex area and intensity index of each subarea were significantly negatively correlated with the occurrence of atmospheric ducts. Thus, when the area of the Arctic vortex increased and the intensity index of each subarea strengthened, the probability of atmospheric ducts decreased and their characteristics weakened.

The traditional method of retrieving atmospheric ducts is to use the special sensor of weather balloons or rocket soundings to obtain information intelligently, and it is very expensive. Today, with the development of technology, it is very convenient to retrieve the atmospheric ducts from Global Navigation Satellite System (GNSS) phase delay and propagation loss observation data, and then the GNSS receiver on the ground forms an automatic receiving sensor. This paper proposes a hybrid decomposition-based multi-objective evolutionary algorithm with adaptive neighborhood sizes (EN-MOEA/ACD-NS), which dynamically imposes some constraints on the objectives. The decomposition-based multi-objective evolutionary algorithm (MOEA/D) updates the solutions through neighboring objectives, the number of which affects the quality of the optimal solution. Properly constraining the optimization objectives can effectively balance the diversity and convergence of the population. The experimental results from the Congress on Evolutionary Computation (CEC) 2009 on test instances with hypervolume (HV), inverted generational distance (IGD), and average Hausdorff distance ∆2 metrics show that the new method performs similarly to the evolutionary algorithm MOEA/ACD-NS, which considers only the dynamic change of the neighborhood sizes. The improved algorithm is applied to the practical problem of jointly retrieving atmospheric ducts with GNSS signals, and its performance further demonstrates its feasibility and practicability.

The problem of atmospheric duct inversion is usually solved as a single objective optimization problem. Based on ground-based Global Positioning System (GPS) phase delay and propagation loss, this paper develops a multi-objective method including the effect of source frequency and receiving antenna height. The diversity and convergence of solution sets are evaluated for seven multi-objective evolutionary algorithms with three performance metrics: Hypervolume (HV), Inverted Generational Distance (IGD), and the averaged Hausdorff distance ( Δ 2 ). The inversion results are compared with the simulation results, and the experimental comparison is conducted on three groups of test situations. The results demonstrate that the ranking of algorithm performance varies because of the different methods used to calculate performance metrics. Moreover, when the algorithms show overwhelming performance using performance metrics, the inversion result is not more close to the real value. In the comparison of computational experiments, it was found that, as the retrieved parameter dimension increases, the inversion result becomes more unstable. When the observed data are sufficient, the inversion result seems to be improved.

In recent years, Thorpe analysis has been used to retrieve the characteristics of turbulence in free atmosphere from balloon-borne sensor data. However, previous studies have mainly focused on the mid-high latitude region, and this method is still rarely applied at heights above 30 km, especially above 35 km. Therefore, seven sets of upper air (>35 km) sounding data from the Changsha Sounding Station (28°12′ N, 113°05′ E), China are analyzed with Thorpe analysis in this article. It is noted that, in the troposphere, Thorpe analysis can better retrieve the turbulence distribution and the corresponding turbulence parameters. Also, because of the thicker troposphere at low latitudes, the values of the Thorpe scale L T and turbulent energy dissipation rate ε remain greater in a larger height range. In the stratosphere below the height of 35 km, the obtained ε is higher, and Thorpe analysis can only be used to analyze the characteristics of large-scale turbulence. In the stratosphere at a height of 35–40 km, because of the interference of sensor noise, Thorpe analysis can only help to retrieve the rough distribution position of large-scale turbulence, while it can hardly help with the calculation of the turbulence parameters.

The propagation of Global Positioning System (GPS) signals at low-elevation angles is significantly affected by a surface duct. In this paper, we present an improved algorithm known as NSSAGA, in which simulated annealing (SA) is combined with the non-dominated sorting genetic algorithm II (NSGA-II). Matched-field processing was used to remotely sense the refractivity structure by using the data of ground-based GPS phase delay and propagation loss from multiple antenna heights. The performance was checked by simulation data with and without noise. In comparison with NSGA-II, the new hybrid algorithm retrieved the refractivity structure more efficiently under various noise conditions. We then modified the objective function and found that matched-field processing is more effective than the conventional least-squares method for inferring the refractivity parameters. Comparing the inversion results and in situ sounding data suggests that the improved method presented herein can capture refractivity characteristics in realistic environments.

This study compares wind speeds derived from five satellite microwave radiometers with those directly observed by buoy-mounted anemometers and the global analyses produced by the European Center for Medium-Range Weather Forecasts (ECMWF) model. Buoy comparisons yield wind speed root mean square errors of 0.82 m/s for WindSat, 1.45 m/s for SSMIS F16, 1.39 m/s for SSMIS F17, 1.43 m/s for AMSR-E, and 1.45 m/s for AMSR2. The overall mean bias for each satellite is typically

This article gives an in-depth analysis of the experimental data of the TK-1G sounding rocket installed with the satellite navigation system. It turns out that the data acquisition rate of the rocket sonde is high, making the collection of complete trajectory and meteorological data possible. By comparing the rocket sonde measurements with those obtained by virtue of other methods, we find that the rocket sonde can be relatively precise in measuring atmospheric parameters within the scope of 20–60 km above the ground. This establishes the fact that the TK-1G sounding rocket system is effective in detecting near-space atmospheric environment.

In this paper, we present a new estimation of the atmospheric refractivity profile combining the scattering signal (electromagnetic wave propagation loss) and the direct signal (phase delay). The refractivity profile is modeled using four parameters, i.e., the gradient of the refractivity profile (c1, c2) and the vertical altitude (h1, h2). We apply the NSGA-II (Non-dominated Sorting Genetic Algorithm II), a multiobjective optimization algorithm, to achieve the goals of joint optimization inversion in the inverting process, and compare this method with traditional individual inversion methods. The anti-noise ability of joint inversion is investigated under the noiseless condition and adding noise condition, respectively. The numerical experiments demonstrate that joint inversion is superior to individual inversion. The adding noise test further suggests that this method can estimate synthesized parameters more efficiently and accurately in different conditions. Finally, a set of measured data is tested in the new way and the consequence of inversion shows the joint optimization inversion algorithm has feasibility, effectiveness and superiority in the retrieval of the refractivity profile.