Search

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 $P(\cdot)$ 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.

The variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on Bayesian theory can overcome the limitation of the traditional wind streak algorithm by introducing background wind and considering all sources of error, but its optimal solution is unstable and the time latency is long. In this paper, we propose a new wind retrieval method by applying optimal interpolation (OI) theory to construct a formula that considers the SAR information, background information coming from the numerical prediction model, and their associated wellcharacterized errors. The retrieved wind vector can be acquired by the analytic solution of the OI formula. Experimental results of the simulation data and Sentinel-1 SAR data show that the OI wind retrieval method can effectively reduce the background wind error and is more sensitive to wind speed than wind direction. Compared with other methods, the accuracy of the OI method is similar to that of the VAR method, but significantly higher than that of the direct wind retrieval (DIRECT) method. The time latency of the OI method is the shortest, and the calculation efficiency is much higher than that of the VAR method. The OI method can be effectively applied to SAR wind retrieval and has unique advantages.Sea surface wind is a crucial parameter for studying the physical quantity of the sea surface and plays an important role in many fields such as weather forecasts [1, 2], wind energy resource management [3,4], wave numerical simulation [5,6], and oil spill monitoring [7,8]. Because of the limited spatial and temporal coverage, high-precision sea surface wind acquired from buoys, ships and offshore platforms are not meeting the growing demand [9].In recent decades, with the development of satellite remote sensing, the technology of acquiring sea surface wind using satellite sensor detection data has gradually matured and improved. Among various satellite sensors, microwave radiometers and scatterometers play an important role in providing global sea surface wind. However, microwave radiometers and scatterometers can acquire only low spatial resolution (12.5-50 km) sea surface wind. This relatively low spatial resolution is

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.

InORCID account, please go to the Account Update page (http://mts.hindawi.com/update/) in our Manuscript Tracking System and after you have logged in click on the ORCID link at the top of the page. This link will take you to the ORCID website where you will be able to create an account for yourself. Once you have done so, your new ORCID will be saved in our Manuscript Tracking System automatically."?>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.

An idealized model is used to simulate radio occultation bending angles and residual ionospheric errors. The test results of the proposed simulation method agree with those of previous studies that use end-to-end simulation tools. Also, a new residual ionospheric error model proposed by Healy and Culverwell (2015) is verified in this letter by characterizing the key parameter, κ. A simple model, κ(a) = A − B × (a − 20)/60, is used to estimate the values of κ, where A and B are constants that indicate the magnitude and variation of the values of κ, respectively, and a represents the impact height. When the modelled values of κ are applied in performing ionospheric corrections, the residual ionospheric errors decrease from approximately 5 × 10−8 rad to 1 × 10−9 rad at a latitude of 40°N during the daytime and at a solar activity level of F10.7 = 210. Though the proposed model does not assess other error terms, such as those associated with asymmetry and noise, it will likely prove to be an effective tool for describing idealized residual ionospheric errors in radio occultation, and the features of the κ values identified in this study may be helpful in improving ionospheric correction methods.

New satellite-derived latent and sensible heat fluxes are performed by using WindSat wind speed, WindSat sea surface temperature, the European Centre for Medium-range Weather Forecasting (ECMWF) air humidity, and ECMWF air temperature from 2004 to 2014. The 55 moored buoys are used to validate them by using the 30 min and 25 km collocation window. Furthermore, the objectively analyzed air-sea heat fluxes (OAFlux) products and the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis 2 (NCEP2) products are also used for global comparisons. The mean biases of sensible and latent heat fluxes between WindSat flux results and buoy flux data are–0.39 and–8.09 W/m2, respectively. In addition, the root-mean-square (RMS) errors of the sensible and latent heat fluxes between them are 5.53 and 24.69 W/m2, respectively. The RMS errors of sensible and latent heat fluxes are observed to gradually increase with an increasing buoy wind speed. The difference shows different characteristics with an increasing sea surface temperature, air humidity, and air temperature. The zonal average latent fluxes have some high regions which are mainly located in the trade wind zones where strong winds carry dry air in January, and the maximum value centers are found in the eastern waters of Japan and on the US east coast. Overall, the seasonal variability is pronounced in the Indian Ocean, the Pacific Ocean, and the Atlantic Ocean. The three sensible and latent heat fluxes have similar latitudinal dependencies; however, some differences are found in some local regions.

The extraction of auroral ovals from images acquired by the Ultraviolet Imager is notoriously difficult due to low contrast, inhomogeneity and the presence of dayglow. In this paper, to address these issues, we propose an improved level set segmentation algorithm by incorporating the shape feature and intensity distribution of the auroral oval into the variational framework. The shape term is proposed to keep the annular ring appearance of the auroral oval and avoid the boundary leak by imposing a distance constraint on the inner and outer boundaries. The local information term tackles the difficulty of intensity inhomogeneity by utilizing the statistical distribution in the local window. Experimental results demonstrate that the proposed method obtains more accurate inner and outer boundaries of the auroral oval, comparing to the existing pixel based and level set based methods.

This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s−1 and 0.88°C, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the QuikSCAT measurements, and the RMS error was 13.3°. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer.

Nearshore wind speeds retrieved by WindSat are validated by a comparison with the moored buoy observations near the U.S. west and east coasts. A 30 min and 25 km collection window is used for the WindSat wind data and buoy measurements from January 2004 to December 2014. Comparisons show that the overall root-mean-square error is better than 1.44 m/s near the U.S. coasts, and the result for the east coast is better than that for the west coast. The retrieval accuracy of the descending portions is slightly better than that of the ascending portions. Most buoy-to-buoy variations are not significantly correlated with the coastal topography, the longitude and the distance from the shore or satellite-buoy separation distance. In addition, comparisons between a polarimetric microwave radiometer and a microwave scatterometer are accomplished with the nearshore buoy observations from 2007 to 2008. The WindSat-derived winds tend to be lower than the buoy observations near the U.S. coasts. In contrast, the QuikSCAT-derived winds tend to be higher than the buoy observations. Overall, the retrieval accuracy of WindSat is slightly better than that of QuikSCAT, and these satellite-derived winds are sufficiently accurate for scientific studies.

To evaluate the ocean surface wind vector and the sea surface temperature obtained from WindSat, we compare these quantities over the time period from January 2004 to December 2013 with moored buoy measurements. The mean bias between the WindSat wind speed and the buoy wind speed is low for the low frequency wind speed product (WSPD_LF), ranging from–0.07 to 0.08 m/s in different selected areas. The overall RMS error is 0.98 m/s for WSPD_LF, ranging from 0.82 to 1.16 m/s in different selected regions. The wind speed retrieval result in the tropical Ocean is better than that of the coastal and offshore waters of the United States. In addition, the wind speed retrieval accuracy of WSPD_LF is better than that of the medium frequency wind speed product. The crosstalk analysis indicates that the WindSat wind speed retrieval contains some cross influences from the other geophysical parameters, such as sea surface temperature, water vapor and cloud liquid water. The mean bias between the WindSat wind direction and the buoy wind direction ranges from–0.46° to 1.19° in different selected regions. The overall RMS error is 19.59° when the wind speed is greater than 6 m/s. Measurements of the tropical ocean region have a better accuracy than those of the US west and east coasts. Very good agreement is obtained between sea surface temperatures of WindSat and buoy measurements in the tropical Pacific Ocean; the overall RMS error is only 0.36°C, and the retrieval accuracy of the low latitudes is better than that of the middle and high latitudes.

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 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

Two new parameters (W6H and W6V) were defined that represent brightness temperature increments for different low-frequency channels due to ocean wind. We developed a new wind speed retrieval model inside hurricanes based on W6H and W6V using brightness temperature data from AMSR-E. The AMSR-E observations of 12 category 3–5 hurricanes from 2003 to 2011 and corresponding data from the H*wind analysis system were used to develop and validate the AMSR-E wind speed retrieval model. The results show that the mean bias and the overall root-mean-square (RMS) difference of the AMSR-E retrieved wind speeds with respect to H*wind (HRD Real-time Hurricane Wind Analysis System) analysis data were −0.01 m/s and 2.66 m/s, respectively. One case study showed that W6H and W6V were less sensitive to rain than the observed AMSR-E C-band and X-band brightness temperature data. The AMSR-E retrieval model was further validated by comparing the retrieved wind speeds against stepped-frequency microwave radiometer (SFMR) measurements. The comparison showed an RMS difference of 3.41 m/s and a mean bias of 0.49 m/s.

The characteristics of convective clouds on infrared brightness temperature (BTIR) and brightness temperature difference (BTD) image were analyzed using successive Infrared and Visible Spin-Scan Radiometer (VISSR) data of FY-2, and an integrated detection method of convective clouds using infrared multi-thresholds in combination with tracking techniques was implemented. In this method, BT and BTD thresholds are used to detect severe convection and uncertain clouds, then the tracking technique including overlap ratio, minimum BT change and cross-correlation coefficient is used to detect convection activities in uncertain clouds. The Application test results show that our integrated detection method can effectively detect convective clouds in different life periods, which show a better performance than any single step in it. The statistical results show that the α-type clouds are mostly large-scale systems, and the β- and γ-type clouds have the highest proportion of general type. However, the proportion of weak convective cloud is higher than that of severe ones in γ-type cloud, and an opposite result is found in the β-type.

The properties of the long-term oscillations in the middle atmosphere have been investigated using the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperature data and Fabry–Perot interferometer (FPI) data. Results for SABER temperature show that the semiannual oscillation (SAO) has three amplitude maxima at altitudes of 45, 75, and 85 km, respectively, and shows prominent seasonal asymmetries there. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K on average at each equinox, contributing to the lower mesospheric inversion layer (MIL). It is shown that stronger enhancement can be found at the spring equinox than at the autumn equinox. The triennial oscillation (TO) is significant in the tropical region. The spectral peak of the TO is probably a sub-peak of the quasi-biennial oscillation (QBO) and is due to modulation of QBO. In addition, there may be potential interaction of the TO with SAO at 85 km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The annual oscillation (AO) and SAO are analyzed over Kelan by FPI data. Generally, the amplitudes of FPI wind are smaller than those of the Horizontal Wind Model (HWM07). The comparison between FPI and TIMED Doppler Interferometer (TIDI) winds shows relatively large discrepancy. This may be due to the tidal aliasing in the nighttime results derived from the FPI data. Results also show that the algorithm to derive FPI temperature needs improvements.

In order to monitor nighttime particular matter (PM) air quality in urban area, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The study focuses on the moonless and cloudless nights in Beijing during March–May 2015. A test is carried out by selecting surface PM2.5 data from 12 PM2.5 automatic monitoring stations and the corresponding night city light intensity from DNB. As indicated by the results, the linear correlation coefficient (R) between the results and the corresponding measured surface PM2.5 concentration is 0.91, and the root-mean-square error (RMSE) is 14.02 μg/m3 with the average of 59.39 μg/m3. Furthermore, the BP neural network model shows better accuracy when air relative humility ranges from 40% to 80% and surface PM2.5 concentration exceeds 40 μg/m3. The study provides a superiority approach for monitoring PM2.5 air quality from space with visible light remote sensing data at night.

The realistic and accurate estimation of hurricane intensity is highly desired in many scientific and operational applications. With the advance of passive microwave polarimetry, an alternative opportunity for retrieving wind speed in hurricanes has become available. A wind speed retrieval algorithm for wind speeds above 20 m/s in hurricanes has been developed by using the 6.8 and 10.7 GHz vertically and horizontally polarized brightness temperatures of WindSat. The WindSat measurements for 15 category 4 and category 5 hurricanes from 2003 to 2010 and the corresponding H*wind analysis data are used to develop and validate the retrieval model. In addition, the retrieved wind speeds are also compared to the Remote Sensing Systems (RSS) global all-weather product and stepped-frequency microwave radiometer (SFMR) measurements. The statistical results show that the mean bias and the overall root-mean-square (RMS) difference of the retrieved wind speeds with respect to the H*wind analysis data are 0.04 and 2.75 m/s, respectively, which provides an encouraging result for retrieving hurricane wind speeds over the ocean surface. The retrieved wind speeds show good agreement with the SFMR measurements. Two case studies demonstrate that the mean bias and RMS difference are 0.79 m/s and 1.79 m/s for hurricane Rita-1 and 0.63 m/s and 2.38 m/s for hurricane Rita-2, respectively. In general, the wind speed retrieval accuracy of the new model in hurricanes ranges from 2.0 m/s in light rain to 3.9 m/s in heavy rain.