In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice ...In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0 GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square （rms） difference of MY SIA derived from the algorithm of this study are 0.65×10^6 km^2 and 0.69×10^6 km^2 during January to March, -0.06×10^6 km^2 and 0.14×10^6 km^2during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×10^6 km^2 and 0.84×10^6 km^2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang＇s, Lomax＇ and NASA TEAM algorithms.展开更多
The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） d...The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.展开更多
A retrieval algorithm of arctic sea ice concentration （SIC） based on the brightness temperature data of “HY-2” scanning microwave radiometer has been constructed. The tie points of the brightness temperature were ...A retrieval algorithm of arctic sea ice concentration （SIC） based on the brightness temperature data of “HY-2” scanning microwave radiometer has been constructed. The tie points of the brightness temperature were selected based on the statistical analysis of a polarization gradient ratio and a spectral gradient ratio over open water （OW）, first-year ice （FYI）, and multiyear ice （MYI） in arctic. The thresholds from two weather filters were used to reduce atmospheric effects over the open ocean. SIC retrievals from the “HY-2” radiom-eter data for idealized OW, FYI, and MYI agreed well with theoretical values. The 2012 annual SIC was calcu-lated and compared with two reference operational products from the National Snow and Ice Data Center （NSIDC） and the University of Bremen. The total ice-covered area yielded by the “HY-2” SIC was consistent with the results from the reference products. The assessment of SIC with the aerial photography from the fifth Chinese national arctic research expedition （CHINARE） and six synthetic aperture radar （SAR） images from the National Ice Service was carried out. The “HY-2” SIC product was 16% higher than the values de-rived from the aerial photography in the central arctic. The root-mean-square （RMS） values of SIC between “HY-2” and SAR were comparable with those between the reference products and SAR, varying from 8.57% to 12.34%. The “HY-2” SIC is a promising product that can be used for operational services.展开更多
With the rapid increase in the number of three-dimensional （3D） models each year, to quickly and easily find the part desired has become a big challenge of enterprises. Meanwhile, many methods and algorithms have be...With the rapid increase in the number of three-dimensional （3D） models each year, to quickly and easily find the part desired has become a big challenge of enterprises. Meanwhile, many methods and algorithms have been proposed for part retrieval. However, most of the existing methods are designed lbr mechanical parts, and can not be well worked for sheet metal part re- trieval. An approach to feature-based retrieval of sheet metal parts is presented. Firstly, the features frequently used in sheet metal part design are chosen as the ＂＇key words＂ in retrieval. Based on those features, a relative position model is built to express the different relationships of the features in 3D space. Secondly, a description method of the model is studied. With the descrip- tion method the relative position of features in sheet metal parts can be expressed by four location description matrices. Thirdly, based on the relative position model and location description matrices, the equivalent definition of relationships of two feature groups is given which is the basis to calculate the similarity of two sheet metal parts. Next, the tbrmula of retrieval algorithm for sheet metal parts is given. Finally, a prototype system is developed to test and verify the effectiveness of the retrieval method suggested. Experiments verify that the new method is able to meet the requirements of searching sheet metal parts and possesses potentials in practical application.展开更多
WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this pape...WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.展开更多
Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of cli...Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of ＂The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change： Carbon Budget and Relevant Issues＂. The carbon flux inversion system is composed of two separate parts： the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing（IAPCAS）, and Carbon Tracker-China（CT-China）, developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite（GOSAT） measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 exchange is-0.34 Pg C yr^（-1）（±0.08 Pg C yr^（-1））, with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.展开更多
A convolutional neural network is employed to retrieve the time-domain envelop and phase of few-cycle femtosecond pulses from transient-grating frequency-resolved optical gating(TG-FROG) traces.We use theoretically ge...A convolutional neural network is employed to retrieve the time-domain envelop and phase of few-cycle femtosecond pulses from transient-grating frequency-resolved optical gating(TG-FROG) traces.We use theoretically generated TGFROG traces to complete supervised trainings of the convolutional neural networks,then use similarly generated traces not included in the training dataset to test how well the networks are trained.Accurate retrieval of such traces by the neural network is realized.In our case,we find that networks with exponential linear unit(ELU) activation function perform better than those with leaky rectified linear unit(LRELU) and scaled exponential linear unit(SELU).Finally,the issues that need to be addressed for the retrieval of experimental data by this method are discussed.展开更多
The Microwave Temperature Sounder-Ⅱ(MWTS-Ⅱ) and Microwave Humidity and Temperature Sounder(MWHTS) onboard the Fengyun-3 C(FY-3 C) satellite can be used to detect atmospheric temperature profiles. The MWTS-II has 13 ...The Microwave Temperature Sounder-Ⅱ(MWTS-Ⅱ) and Microwave Humidity and Temperature Sounder(MWHTS) onboard the Fengyun-3 C(FY-3 C) satellite can be used to detect atmospheric temperature profiles. The MWTS-II has 13 temperature sounding channels around the 60 GHz oxygen absorption band and the MWHTS has 8 temperature sounding channels around the 118.75 GHz oxygen absorption line. The data quality of the observed brightness temperatures can be evaluated using atmospheric temperature retrievals from the MWTS-Ⅱ and MWHTS observations. Here, the bias characteristics and corrections of the observed brightness temperatures are described. The information contents of observations are calculated, and the retrieved atmospheric temperature profiles are compared using a neural network(NN) retrieval algorithm and a one-dimensional variational inversion(1 D-var) retrieval algorithm. The retrieval results from the NN algorithm show that the accuracy of the MWTS-Ⅱ retrieval is higher than that of the MWHTS retrieval, which is consistent with the results of the radiometric information analysis. The retrieval results from the 1 D-var algorithm show that the accuracy of MWTS-Ⅱ retrieval is similar to that of the MWHTS retrieval at the levels from 850-1,000 h Pa, is lower than that of the MWHTS retrieval at the levels from 650-850 h Pa and 125-300 h Pa, and is higher than that of MWHTS at the other levels. A comparison of the retrieved atmospheric temperature using these satellite observations provides a reference value for assessing the accuracy of atmospheric temperature detection at the 60 GHz oxygen band and 118.75 GHz oxygen line. In addition, based on the comparison of the retrieval results, an optimized combination method is proposed using a branch and bound algorithm for the NN retrieval algorithm, which combines the observations from both the MWTS-Ⅱand MWHTS instruments to retrieve the atmospheric temperature profiles. The results show that the optimal combination can further improve the accuracy of MWTS-Ⅱ retrieval and enhance the detection accuracy of atmospheric temperatures near the surface.展开更多
As rain drops change the radiation and scattering characteristic of the oceans and the atmosphere, the wind speed measuring by spaceborne remote sensors under rainy conditions remains challenging for years. On the bas...As rain drops change the radiation and scattering characteristic of the oceans and the atmosphere, the wind speed measuring by spaceborne remote sensors under rainy conditions remains challenging for years. On the basis of a microwave radiometer（RM） loaded on HY-2 satellite, the sensitivity of some brightness temperature（TB）channels to a rain rate and the wind speed are analyzed. Consequently, two TB combinations which show minor sensitivity to rain are obtained. Meanwhile, the sensitivity of the TB combination to the wind speed is even better to the original TB channel. On the basis of these TB combinations, a wind speed retrieval algorithm is developed and compared with Wind Sat all-weather wind speed product, HY-2 RM original wind speed product and buoy in situ data. The wind speed retrieval accuracy is better than 2 m/s for rainy conditions, which is evidently superior to HY-2 RM original product. The applicability of this new algorithm is testified for the wind speed measuring in rainy weather with HY-2 RM.展开更多
Coherent diffractive imaging （CDI） is a lensless imaging technique and can achieve a resolution beyond the Rayleigh or Abbe limit. The ptychographical iterative engine （PIE） is a CDI phase retrieval algorithm that...Coherent diffractive imaging （CDI） is a lensless imaging technique and can achieve a resolution beyond the Rayleigh or Abbe limit. The ptychographical iterative engine （PIE） is a CDI phase retrieval algorithm that uses multiple diffraction patterns obtained through the scan of a localized illumination on the specimen, which has been demonstrated successfully at optical and X-ray wavelengths. In this paper, a general PIE algorithm （gPIE） is presented and demonstrated with an He-Ne laser light diffraction dataset. This algorithm not only permits the removal of the accurate model of the illumination function in PIE, but also provides improved convergence speed and retrieval quality.展开更多
E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can＇t fulfill the storage and picking d...E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can＇t fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.展开更多
The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral b...The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algorithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machinelearning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:①A baseline deep neural network(DNN)with skip connections is developed using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and②sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7%.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the surface reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.展开更多
Aimed at high turbid coastal waters, an improved algorithm for retrieval ofaerosol optical properties from Geostationary Ocean Color Imager （GOCI） is proposed.The algorithm adopts support vector machine （SVM） to s...Aimed at high turbid coastal waters, an improved algorithm for retrieval ofaerosol optical properties from Geostationary Ocean Color Imager （GOCI） is proposed.The algorithm adopts support vector machine （SVM） to separate the interfering signalof phytoplankton pigments, suspended matter and chromophoric dissolved organicmatter （CDOM）. Radioactive Transfer Model （RTM） Rstar5b is utilized to simulate thetransmitting process. The algorithm can retrieve aerosol optical depth （AOD） andaerosol types simultaneously. In the study, the aerosol optical depth was retrieved overthe turbid waters in the summer of 2014 and 2015. The results of inversion werecompared with the corresponding AERONET data and GOCI service product toestimate the accuracy of the advanced method. The study shows that this algorithmhas better performance compared with GOCI service algorithm for turbid water in theYellow Sea.展开更多
In this paper, we conduct research on the multimedia information retrieval algorithm based on the information restructuring and image reconstruction. With the massive growth of information resources, people through va...In this paper, we conduct research on the multimedia information retrieval algorithm based on the information restructuring and image reconstruction. With the massive growth of information resources, people through various retrieval tools for too much information, led directly to information overload. In vector space model and probability retrieval model based on information retrieval tools rarely consider the user＇ s personalized information needs and features, has resulted in a large amount of information retrieval result and correlation information the user＇ s information demand is not big. In order to improve the existing retrieval system, in recent years, scholars to study looked that context information retrieval context factors need to be considered, such as the retrieval time, place and the interactive history, mission, environment and other factors stated or implied in the retrieval process. At present, the context research has become the information behavior, information search process and the research hotspot in the field of information retrieval interaction.展开更多
Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an...Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an approach for the long-term extreme-response analysis of floating structures. A modified gradient-based retrieval algorithm in conjunction with the inverse first-order reliability method(IFORM) is proposed to enable the use of convolution models in long-term extreme analysis of structures with an analytical formula of response amplitude operator(RAO). The proposed algorithm ensures convergence stability and iteration accuracy and exhibits a higher computational efficiency than the traditional backtracking method. However, when the RAO of general offshore structures cannot be analytically expressed, the convolutional integration method fails to function properly. A numerical discretization approach is further proposed for offshore structures in the case when the analytical expression of the RAO is not feasible. Through iterative discretization of environmental contours(ECs) and RAOs, a detailed procedure is proposed to calculate the long-term response extremes of offshore structures. The validity and accuracy of the proposed approach are tested using a floating offshore wind turbine as a numerical example. The long-term extreme heave responses of various return periods are calculated via the IFORM in conjunction with a numerical discretization approach. The environmental data corresponding to N-year structural responses are located inside the ECs, which indicates that the selection of design points directly along the ECs yields conservative design results.展开更多
Accurate monitoring of atmospheric carbon dioxide(CO_(2))is crucial for understanding the global carbon cycle and informing climate policy.Satellite-based remote sensing provides a promising means to obtain global mea...Accurate monitoring of atmospheric carbon dioxide(CO_(2))is crucial for understanding the global carbon cycle and informing climate policy.Satellite-based remote sensing provides a promising means to obtain global measurements of the column-averaged CO_(2) dry air mole fraction(XCO_(2)).However,traditional retrieval algorithms are computationally intensive due to their reliance on iterative radiative transfer simulations.In this study,we introduce the Spectrum Transformer(SpT),a novel neural network model that employs a Transformer-based architecture to enable fast and accurate XCO_(2) retrievals directly from satellite-measured spectra.Unlike previous machine learning approaches,the SpT model effectively handles data drift caused by increasing atmospheric CO_(2) levels without requiring synthetic future data or additional assumptions.Trained exclusively on historical OCO-2 spectra and retrievals from 2017 to 2019,the SpT model demonstrates unbiased generalization to data from 2020 to 2022,achieving high accuracy(root mean square error[RMSE]∼1.5 parts per million[ppm])in“future”retrievals.Through periodic fine-tuning with minimal new data(<10%of all available data),the model maintains even higher accuracy(RMSE∼1.2 ppm),demonstrating its applicability for ongoing missions up to the most recent measurements(2024 April 1).The SpT model reduces computational time from minutes to milliseconds per retrieval,offering an important advancement over traditional methods.Validation against TCCON ground-based measurements confirms the model’s ability to capture seasonal and regional variations in XCO_(2),highlighting its potential for real-time global CO_(2) monitoring.展开更多
Tower-based solar-induced chlorophyll fluorescence(SIF)measurements have yielded crucial datasets for investigating the diurnal patterns of SIF and its relationship with vegetation photosynthesis.This study assessed t...Tower-based solar-induced chlorophyll fluorescence(SIF)measurements have yielded crucial datasets for investigating the diurnal patterns of SIF and its relationship with vegetation photosynthesis.This study assessed the performance of 3 distinct SIF retrieval algorithms,including band shape fitting(BSF),3-band Fraunhofer line discrimination(3FLD),and a data-driven approach based on singular vector decomposition(SVD),for retrieving far-red SIF diurnal patterns from tower-based observations at the 2 flux sites in China.This study analyzed diurnal patterns of SIF and SIF yield,as well as correlations between SIF,near-infrared radiance reflected by vegetation(NIRvR),and gross primary productivity(GPP)at diurnal and seasonal scales.More pronounced inconsistencies in retrieved SIF by different algorithms at noon compared with the morning and afternoon were observed.Similarly,correlations between the SIF and NIRvR or GPP are weaker during midday.This study underscores the need to consider the reliability of SIF data when investigating diurnal patterns,and the necessity for developments in tower-based SIF retrieval algorithms.展开更多
An extensive study collected in situ data along the Yellow Sea(YS) and East China Sea(ECS) to assess the radiometric properties and the concentration of the water constituents derived from Moderate Resolution Imaging ...An extensive study collected in situ data along the Yellow Sea(YS) and East China Sea(ECS) to assess the radiometric properties and the concentration of the water constituents derived from Moderate Resolution Imaging Spectroradiometer(MODIS). Thirteen high quality match-ups were obtained for evaluating the MODIS estimates of Rrs(λ), chlorophyll a(Chl a) and concentrations of suspended particulate sediment matter(SPM). For MODIS Rrs(λ), the mean absolute percentage difference(APD) was in the range of 20%–36%, and the highest uncertainty appeared at 412 nm, whereas the band ratio of Rrs(λ) at 488 nm compared with that at 547 nm was highly consistent, with an APD of 7%. A combination of near-infrared bands and shortwave infrared wavelengths atmosphere correction algorithm(NIR-SWIR algorithm) was applied to the MODIS data, and the estimation accuracy of Rrs were improved at most of the visible spectral bands except 645 nm, 667 nm and 678 nm. Two ocean-colour empirical algorithms for Chl a estimation were applied to the processed data, the results indicated that the accuracy of the derived Chl a values was obviously improved, the four-band algorithms outperformed the other algorithm for measured and simulated datasets, and the minimum APD was 35%. The SPM was also quantified. Two regional and two coastal SPM algorithms were modified according to the in situ data. By comparison, the modified Tassan model had a higher accuracy for the application along the YS and ECS with an APD of 21%. However, given the limited match-up dataset and the potential influence of the aerosol properties on atmosphere correction, further research is required to develop additional algorithms especially for the low Chl a coastal water.展开更多
Soil freeze-thaw(FT)cycles impact soil functions and atmosphere-land interaction,but accurate measurements are very limited.Since surface dielectric properties and microwave emissions are sensitive to the FT state,bri...Soil freeze-thaw(FT)cycles impact soil functions and atmosphere-land interaction,but accurate measurements are very limited.Since surface dielectric properties and microwave emissions are sensitive to the FT state,brightness temperature(TB)measurements at L-band allow retrieval of the FT state.We have demonstrated the potential of a soil FT retrieval algorithm from Soil Moisture Active Passive(SMAP)TB measurements.This retrieval algorithm is formulated regarding Diurnal Amplitude Variation(DAV),which is defined as the difference in TB observations of ascending and descending orbits.The DAV-FT algorithm uses globally fixed parameters.However,parameters should vary regionally considering factors like land cover type,terrain,and climate regions.We introduce Overall Classification Accuracy(OA)to characterize the extraction of DAV annual variation under different parameters.Then,the parameter optimization process,akin to maximum likelihood estimation,selects a combination of parameters to extract the annual variation of the DAV optimally.The DAV-FT algorithm uses optimized parameters,and the results show that compared to using fixed parameters,(a)the area with OA>0.7 increases from 54.43%to 89.36%;(b)consistency with ERA5-Land and SMAP data has improved in southwestern North America,the Qinghai-Tibet Plateau,and southwestern Eurasia,with regions showing over 0.7 consistency reaching 81.28%for ERA5-Land and 79.54% for SMAP-FT;and(c)in situ stations with higher accuracy outnumber those with lower accuracy(48.11%versus 22.97% for fixed parameters,35.14%versus 33.51%for SMAP FT).Furthermore,the algorithm achieves the highest median(0.92)and median accuracy(0.88),compared to fixed parameters and SMAP.展开更多
Accurate retrieval of atmospheric relative humidity(RH)profiles is essential for improving our understanding of atmospheric thermodynamics and climate change.Nevertheless,it remains challenging,as traditional models r...Accurate retrieval of atmospheric relative humidity(RH)profiles is essential for improving our understanding of atmospheric thermodynamics and climate change.Nevertheless,it remains challenging,as traditional models rely exclusively on vertical brightness temperature(BT)observations.Here,we present a novel retrieval algorithm called AngleNet,a groundbreaking deep-learning model that leverages multi-angle BT observation from ground-based microwave radiometers(MWRs).The innovative“multi-angle-aware”module in AngleNet effectively exploits previously underutilized oblique scanning angle data by accurately capturing these nonlinear relationships between BT and RH profiles,and precisely characterizes its vertical fine structure.Based on the 7-year(2018-2024)in situ measurements from Beijing,Nanjing,and Shanghai,validation results reveal that AngleNet achieves substantial improvements,with an average R^(2) of 0.71 and a root mean square error(RMSE)of 10.39%,surpassing conventional models such as LGBM(light gradient boosting machine)and RF(random forest)by over 10% in both metrics,and demonstrating a remarkable 41% increase in R^(2) and a 10% reduction in RMSE compared to the previous BRNN method(batch normalization and robust neural network).Moreover,additional independent validation results demonstrate that AngleNet exhibits excellent stability and retrieval accuracy during periods without radiosonde measurements.Feature analysis and evaluations of the“multi-angle-aware”module indicate that optimal RH retrieval performance is achieved by combining zenith-angle BTs with oblique angles at 30°and 19.2°.AngleNet breakthrough performance is especially notable in consistently capturing complex RH profile features,which are critical for accurate numerical weather forecasting and climate monitoring.展开更多
基金The National Natural Science Foundation of China under contract Nos 41330960 and 41276193 and 41206184
文摘In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0 GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square （rms） difference of MY SIA derived from the algorithm of this study are 0.65×10^6 km^2 and 0.69×10^6 km^2 during January to March, -0.06×10^6 km^2 and 0.14×10^6 km^2during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×10^6 km^2 and 0.84×10^6 km^2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang＇s, Lomax＇ and NASA TEAM algorithms.
基金Research Grant Council under contract No.461907Innovation and Technology Commission under contract No.GHP/026/06+1 种基金partly by China Postdoctoral Science Foundation under contract No.2008041345 for ChengONR under contract NosN00014-05-1-0328 and N00014-05-1-0606 for Zheng
文摘The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.
基金The International Science and Technology Cooperation Project of China under contract No.2011DFA22260the National Natural Science Foundation of China under contract No.41276191+1 种基金the Public Science and Technology Research Funds Projects of Ocean by the State Oceanic Administration under contract No.201205007-05the Chinese Polar Environment Comprehensive Investigation & Assessment Program by the State Oceanic Administration under contract Nos 2013-02-04 and 2012-04-03-02
文摘A retrieval algorithm of arctic sea ice concentration （SIC） based on the brightness temperature data of “HY-2” scanning microwave radiometer has been constructed. The tie points of the brightness temperature were selected based on the statistical analysis of a polarization gradient ratio and a spectral gradient ratio over open water （OW）, first-year ice （FYI）, and multiyear ice （MYI） in arctic. The thresholds from two weather filters were used to reduce atmospheric effects over the open ocean. SIC retrievals from the “HY-2” radiom-eter data for idealized OW, FYI, and MYI agreed well with theoretical values. The 2012 annual SIC was calcu-lated and compared with two reference operational products from the National Snow and Ice Data Center （NSIDC） and the University of Bremen. The total ice-covered area yielded by the “HY-2” SIC was consistent with the results from the reference products. The assessment of SIC with the aerial photography from the fifth Chinese national arctic research expedition （CHINARE） and six synthetic aperture radar （SAR） images from the National Ice Service was carried out. The “HY-2” SIC product was 16% higher than the values de-rived from the aerial photography in the central arctic. The root-mean-square （RMS） values of SIC between “HY-2” and SAR were comparable with those between the reference products and SAR, varying from 8.57% to 12.34%. The “HY-2” SIC is a promising product that can be used for operational services.
基金National High-tech Research and Development Program of China (2009AA043302)
文摘With the rapid increase in the number of three-dimensional （3D） models each year, to quickly and easily find the part desired has become a big challenge of enterprises. Meanwhile, many methods and algorithms have been proposed for part retrieval. However, most of the existing methods are designed lbr mechanical parts, and can not be well worked for sheet metal part re- trieval. An approach to feature-based retrieval of sheet metal parts is presented. Firstly, the features frequently used in sheet metal part design are chosen as the ＂＇key words＂ in retrieval. Based on those features, a relative position model is built to express the different relationships of the features in 3D space. Secondly, a description method of the model is studied. With the descrip- tion method the relative position of features in sheet metal parts can be expressed by four location description matrices. Thirdly, based on the relative position model and location description matrices, the equivalent definition of relationships of two feature groups is given which is the basis to calculate the similarity of two sheet metal parts. Next, the tbrmula of retrieval algorithm for sheet metal parts is given. Finally, a prototype system is developed to test and verify the effectiveness of the retrieval method suggested. Experiments verify that the new method is able to meet the requirements of searching sheet metal parts and possesses potentials in practical application.
文摘WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.
基金funded by the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues(Grant No.XDA05040200)the National Key Research and Development Program of China(Grant No.2016YFA0600203)+1 种基金the National Natural Science Foundation of China(Grant Nos.41375035 and 31500402)the Chinese Academy of Sciences Strategic Priority Program on Space Science(Grant No.XDA04077300)
文摘Monitoring atmospheric carbon dioxide（CO_2） from space-borne state-of-the-art hyperspectral instruments can provide a high precision global dataset to improve carbon flux estimation and reduce the uncertainty of climate projection. Here, we introduce a carbon flux inversion system for estimating carbon flux with satellite measurements under the support of ＂The Strategic Priority Research Program of the Chinese Academy of Sciences—Climate Change： Carbon Budget and Relevant Issues＂. The carbon flux inversion system is composed of two separate parts： the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing（IAPCAS）, and Carbon Tracker-China（CT-China）, developed at the Chinese Academy of Sciences. The Greenhouse gases Observing SATellite（GOSAT） measurements are used in the carbon flux inversion experiment. To improve the quality of the IAPCAS-GOSAT retrieval, we have developed a post-screening and bias correction method, resulting in 25%–30% of the data remaining after quality control. Based on these data, the seasonal variation of XCO_2（column-averaged CO_2dry-air mole fraction） is studied, and a strong relation with vegetation cover and population is identified. Then, the IAPCAS-GOSAT XCO_2 product is used in carbon flux estimation by CT-China. The net ecosystem CO_2 exchange is-0.34 Pg C yr^（-1）（±0.08 Pg C yr^（-1））, with a large error reduction of 84%, which is a significant improvement on the error reduction when compared with in situ-only inversion.
基金Project supported by the National Key R&D Program of China(Grant No.2017YFB0405202)the National Natural Science Foundation of China(Grant Nos.61690221,91850209,and 11774277)。
文摘A convolutional neural network is employed to retrieve the time-domain envelop and phase of few-cycle femtosecond pulses from transient-grating frequency-resolved optical gating(TG-FROG) traces.We use theoretically generated TGFROG traces to complete supervised trainings of the convolutional neural networks,then use similarly generated traces not included in the training dataset to test how well the networks are trained.Accurate retrieval of such traces by the neural network is realized.In our case,we find that networks with exponential linear unit(ELU) activation function perform better than those with leaky rectified linear unit(LRELU) and scaled exponential linear unit(SELU).Finally,the issues that need to be addressed for the retrieval of experimental data by this method are discussed.
基金Key Fostering Project of the National Space Science Center,Chinese Academy of Sciences(Y62112f37s)National 863 Project of China(2015AA8126027)
文摘The Microwave Temperature Sounder-Ⅱ(MWTS-Ⅱ) and Microwave Humidity and Temperature Sounder(MWHTS) onboard the Fengyun-3 C(FY-3 C) satellite can be used to detect atmospheric temperature profiles. The MWTS-II has 13 temperature sounding channels around the 60 GHz oxygen absorption band and the MWHTS has 8 temperature sounding channels around the 118.75 GHz oxygen absorption line. The data quality of the observed brightness temperatures can be evaluated using atmospheric temperature retrievals from the MWTS-Ⅱ and MWHTS observations. Here, the bias characteristics and corrections of the observed brightness temperatures are described. The information contents of observations are calculated, and the retrieved atmospheric temperature profiles are compared using a neural network(NN) retrieval algorithm and a one-dimensional variational inversion(1 D-var) retrieval algorithm. The retrieval results from the NN algorithm show that the accuracy of the MWTS-Ⅱ retrieval is higher than that of the MWHTS retrieval, which is consistent with the results of the radiometric information analysis. The retrieval results from the 1 D-var algorithm show that the accuracy of MWTS-Ⅱ retrieval is similar to that of the MWHTS retrieval at the levels from 850-1,000 h Pa, is lower than that of the MWHTS retrieval at the levels from 650-850 h Pa and 125-300 h Pa, and is higher than that of MWHTS at the other levels. A comparison of the retrieved atmospheric temperature using these satellite observations provides a reference value for assessing the accuracy of atmospheric temperature detection at the 60 GHz oxygen band and 118.75 GHz oxygen line. In addition, based on the comparison of the retrieval results, an optimized combination method is proposed using a branch and bound algorithm for the NN retrieval algorithm, which combines the observations from both the MWTS-Ⅱand MWHTS instruments to retrieve the atmospheric temperature profiles. The results show that the optimal combination can further improve the accuracy of MWTS-Ⅱ retrieval and enhance the detection accuracy of atmospheric temperatures near the surface.
基金The National Science Foundation for Young Scientists of China under contract 41306183the National High Technology Research and Development Program(863 Program)of China under contract Nos 2013AA09A505 and 2013AA122803
文摘As rain drops change the radiation and scattering characteristic of the oceans and the atmosphere, the wind speed measuring by spaceborne remote sensors under rainy conditions remains challenging for years. On the basis of a microwave radiometer（RM） loaded on HY-2 satellite, the sensitivity of some brightness temperature（TB）channels to a rain rate and the wind speed are analyzed. Consequently, two TB combinations which show minor sensitivity to rain are obtained. Meanwhile, the sensitivity of the TB combination to the wind speed is even better to the original TB channel. On the basis of these TB combinations, a wind speed retrieval algorithm is developed and compared with Wind Sat all-weather wind speed product, HY-2 RM original wind speed product and buoy in situ data. The wind speed retrieval accuracy is better than 2 m/s for rainy conditions, which is evidently superior to HY-2 RM original product. The applicability of this new algorithm is testified for the wind speed measuring in rainy weather with HY-2 RM.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11179009 and 50875013)the Beijing Municipal Natural Science Foundation, China (Grant No. 4102036)the Beijing NOVA Program, China (Grant No. 2009A09)
文摘Coherent diffractive imaging （CDI） is a lensless imaging technique and can achieve a resolution beyond the Rayleigh or Abbe limit. The ptychographical iterative engine （PIE） is a CDI phase retrieval algorithm that uses multiple diffraction patterns obtained through the scan of a localized illumination on the specimen, which has been demonstrated successfully at optical and X-ray wavelengths. In this paper, a general PIE algorithm （gPIE） is presented and demonstrated with an He-Ne laser light diffraction dataset. This algorithm not only permits the removal of the accurate model of the illumination function in PIE, but also provides improved convergence speed and retrieval quality.
文摘E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can＇t fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.
基金supported by the National Natural Science of Foundation of China(41825011,42030608,42105128,and 42075079)the Opening Foundation of Key Laboratory of Atmospheric Sounding,the CMA and the CMA Research Center on Meteorological Observation Engineering Technology(U2021Z03).
文摘The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algorithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machinelearning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:①A baseline deep neural network(DNN)with skip connections is developed using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and②sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7%.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the surface reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.
基金supported by Tianjin Natural Science Foundation Project(14JCYBJC22500)
文摘Aimed at high turbid coastal waters, an improved algorithm for retrieval ofaerosol optical properties from Geostationary Ocean Color Imager （GOCI） is proposed.The algorithm adopts support vector machine （SVM） to separate the interfering signalof phytoplankton pigments, suspended matter and chromophoric dissolved organicmatter （CDOM）. Radioactive Transfer Model （RTM） Rstar5b is utilized to simulate thetransmitting process. The algorithm can retrieve aerosol optical depth （AOD） andaerosol types simultaneously. In the study, the aerosol optical depth was retrieved overthe turbid waters in the summer of 2014 and 2015. The results of inversion werecompared with the corresponding AERONET data and GOCI service product toestimate the accuracy of the advanced method. The study shows that this algorithmhas better performance compared with GOCI service algorithm for turbid water in theYellow Sea.
文摘In this paper, we conduct research on the multimedia information retrieval algorithm based on the information restructuring and image reconstruction. With the massive growth of information resources, people through various retrieval tools for too much information, led directly to information overload. In vector space model and probability retrieval model based on information retrieval tools rarely consider the user＇ s personalized information needs and features, has resulted in a large amount of information retrieval result and correlation information the user＇ s information demand is not big. In order to improve the existing retrieval system, in recent years, scholars to study looked that context information retrieval context factors need to be considered, such as the retrieval time, place and the interactive history, mission, environment and other factors stated or implied in the retrieval process. At present, the context research has become the information behavior, information search process and the research hotspot in the field of information retrieval interaction.
基金Supported by the National Natural Science Foundation of China (Grant Nos.52088102 and 51879287)National Key Research and Development Program of China (Grant No.2022YFB2602301)。
文摘Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an approach for the long-term extreme-response analysis of floating structures. A modified gradient-based retrieval algorithm in conjunction with the inverse first-order reliability method(IFORM) is proposed to enable the use of convolution models in long-term extreme analysis of structures with an analytical formula of response amplitude operator(RAO). The proposed algorithm ensures convergence stability and iteration accuracy and exhibits a higher computational efficiency than the traditional backtracking method. However, when the RAO of general offshore structures cannot be analytically expressed, the convolutional integration method fails to function properly. A numerical discretization approach is further proposed for offshore structures in the case when the analytical expression of the RAO is not feasible. Through iterative discretization of environmental contours(ECs) and RAOs, a detailed procedure is proposed to calculate the long-term response extremes of offshore structures. The validity and accuracy of the proposed approach are tested using a floating offshore wind turbine as a numerical example. The long-term extreme heave responses of various return periods are calculated via the IFORM in conjunction with a numerical discretization approach. The environmental data corresponding to N-year structural responses are located inside the ECs, which indicates that the selection of design points directly along the ECs yields conservative design results.
基金supported by the National Natural Science Foundation of China(grants nos.52276077 and 52120105009).
文摘Accurate monitoring of atmospheric carbon dioxide(CO_(2))is crucial for understanding the global carbon cycle and informing climate policy.Satellite-based remote sensing provides a promising means to obtain global measurements of the column-averaged CO_(2) dry air mole fraction(XCO_(2)).However,traditional retrieval algorithms are computationally intensive due to their reliance on iterative radiative transfer simulations.In this study,we introduce the Spectrum Transformer(SpT),a novel neural network model that employs a Transformer-based architecture to enable fast and accurate XCO_(2) retrievals directly from satellite-measured spectra.Unlike previous machine learning approaches,the SpT model effectively handles data drift caused by increasing atmospheric CO_(2) levels without requiring synthetic future data or additional assumptions.Trained exclusively on historical OCO-2 spectra and retrievals from 2017 to 2019,the SpT model demonstrates unbiased generalization to data from 2020 to 2022,achieving high accuracy(root mean square error[RMSE]∼1.5 parts per million[ppm])in“future”retrievals.Through periodic fine-tuning with minimal new data(<10%of all available data),the model maintains even higher accuracy(RMSE∼1.2 ppm),demonstrating its applicability for ongoing missions up to the most recent measurements(2024 April 1).The SpT model reduces computational time from minutes to milliseconds per retrieval,offering an important advancement over traditional methods.Validation against TCCON ground-based measurements confirms the model’s ability to capture seasonal and regional variations in XCO_(2),highlighting its potential for real-time global CO_(2) monitoring.
基金funded by the National Key Research and Development Program of China(2022YFF1301900)the National Natural Science Foundation of China(42071310 and 42425001).
文摘Tower-based solar-induced chlorophyll fluorescence(SIF)measurements have yielded crucial datasets for investigating the diurnal patterns of SIF and its relationship with vegetation photosynthesis.This study assessed the performance of 3 distinct SIF retrieval algorithms,including band shape fitting(BSF),3-band Fraunhofer line discrimination(3FLD),and a data-driven approach based on singular vector decomposition(SVD),for retrieving far-red SIF diurnal patterns from tower-based observations at the 2 flux sites in China.This study analyzed diurnal patterns of SIF and SIF yield,as well as correlations between SIF,near-infrared radiance reflected by vegetation(NIRvR),and gross primary productivity(GPP)at diurnal and seasonal scales.More pronounced inconsistencies in retrieved SIF by different algorithms at noon compared with the morning and afternoon were observed.Similarly,correlations between the SIF and NIRvR or GPP are weaker during midday.This study underscores the need to consider the reliability of SIF data when investigating diurnal patterns,and the necessity for developments in tower-based SIF retrieval algorithms.
基金The National Natural Science Foundation of China under contract Nos 41506197 and 41406199the Doctoral Scientific Research Foundation of Liaoning Province under contract No.201501190the Fundamental Research Funds for the Central Universities under contract No.3132017110
文摘An extensive study collected in situ data along the Yellow Sea(YS) and East China Sea(ECS) to assess the radiometric properties and the concentration of the water constituents derived from Moderate Resolution Imaging Spectroradiometer(MODIS). Thirteen high quality match-ups were obtained for evaluating the MODIS estimates of Rrs(λ), chlorophyll a(Chl a) and concentrations of suspended particulate sediment matter(SPM). For MODIS Rrs(λ), the mean absolute percentage difference(APD) was in the range of 20%–36%, and the highest uncertainty appeared at 412 nm, whereas the band ratio of Rrs(λ) at 488 nm compared with that at 547 nm was highly consistent, with an APD of 7%. A combination of near-infrared bands and shortwave infrared wavelengths atmosphere correction algorithm(NIR-SWIR algorithm) was applied to the MODIS data, and the estimation accuracy of Rrs were improved at most of the visible spectral bands except 645 nm, 667 nm and 678 nm. Two ocean-colour empirical algorithms for Chl a estimation were applied to the processed data, the results indicated that the accuracy of the derived Chl a values was obviously improved, the four-band algorithms outperformed the other algorithm for measured and simulated datasets, and the minimum APD was 35%. The SPM was also quantified. Two regional and two coastal SPM algorithms were modified according to the in situ data. By comparison, the modified Tassan model had a higher accuracy for the application along the YS and ECS with an APD of 21%. However, given the limited match-up dataset and the potential influence of the aerosol properties on atmosphere correction, further research is required to develop additional algorithms especially for the low Chl a coastal water.
基金funded by the National Key R&D Program of China(grant no.2022YFF0801404)the Key Research and Development and Achievement Transformation Program of Inner Mongolia Autonomous Region,China(grant no.2025YFDZ0007)+1 种基金the Yan Liyuan-ENSKY Foundation Project of Zhuhai Fudan Innovation Research Institute(grant no.JX240002)the National Natural Science Foundation of China(grant no.42075150).
文摘Soil freeze-thaw(FT)cycles impact soil functions and atmosphere-land interaction,but accurate measurements are very limited.Since surface dielectric properties and microwave emissions are sensitive to the FT state,brightness temperature(TB)measurements at L-band allow retrieval of the FT state.We have demonstrated the potential of a soil FT retrieval algorithm from Soil Moisture Active Passive(SMAP)TB measurements.This retrieval algorithm is formulated regarding Diurnal Amplitude Variation(DAV),which is defined as the difference in TB observations of ascending and descending orbits.The DAV-FT algorithm uses globally fixed parameters.However,parameters should vary regionally considering factors like land cover type,terrain,and climate regions.We introduce Overall Classification Accuracy(OA)to characterize the extraction of DAV annual variation under different parameters.Then,the parameter optimization process,akin to maximum likelihood estimation,selects a combination of parameters to extract the annual variation of the DAV optimally.The DAV-FT algorithm uses optimized parameters,and the results show that compared to using fixed parameters,(a)the area with OA>0.7 increases from 54.43%to 89.36%;(b)consistency with ERA5-Land and SMAP data has improved in southwestern North America,the Qinghai-Tibet Plateau,and southwestern Eurasia,with regions showing over 0.7 consistency reaching 81.28%for ERA5-Land and 79.54% for SMAP-FT;and(c)in situ stations with higher accuracy outnumber those with lower accuracy(48.11%versus 22.97% for fixed parameters,35.14%versus 33.51%for SMAP FT).Furthermore,the algorithm achieves the highest median(0.92)and median accuracy(0.88),compared to fixed parameters and SMAP.
基金supported by the National Nature Science Foundation of China(42030606).
文摘Accurate retrieval of atmospheric relative humidity(RH)profiles is essential for improving our understanding of atmospheric thermodynamics and climate change.Nevertheless,it remains challenging,as traditional models rely exclusively on vertical brightness temperature(BT)observations.Here,we present a novel retrieval algorithm called AngleNet,a groundbreaking deep-learning model that leverages multi-angle BT observation from ground-based microwave radiometers(MWRs).The innovative“multi-angle-aware”module in AngleNet effectively exploits previously underutilized oblique scanning angle data by accurately capturing these nonlinear relationships between BT and RH profiles,and precisely characterizes its vertical fine structure.Based on the 7-year(2018-2024)in situ measurements from Beijing,Nanjing,and Shanghai,validation results reveal that AngleNet achieves substantial improvements,with an average R^(2) of 0.71 and a root mean square error(RMSE)of 10.39%,surpassing conventional models such as LGBM(light gradient boosting machine)and RF(random forest)by over 10% in both metrics,and demonstrating a remarkable 41% increase in R^(2) and a 10% reduction in RMSE compared to the previous BRNN method(batch normalization and robust neural network).Moreover,additional independent validation results demonstrate that AngleNet exhibits excellent stability and retrieval accuracy during periods without radiosonde measurements.Feature analysis and evaluations of the“multi-angle-aware”module indicate that optimal RH retrieval performance is achieved by combining zenith-angle BTs with oblique angles at 30°and 19.2°.AngleNet breakthrough performance is especially notable in consistently capturing complex RH profile features,which are critical for accurate numerical weather forecasting and climate monitoring.
