In this study, the overall editing criteria for altimetric data are considered and the geophysical correction models is improved. The datum for various altimetric data is also unified and the method of a full-combined...In this study, the overall editing criteria for altimetric data are considered and the geophysical correction models is improved. The datum for various altimetric data is also unified and the method of a full-combined crossover adjustment for different altimetric tracks is used to improve the radial orbits of Geosat, ERS-1 and ERS-2 data. In addition, the method for determining mean sea surface (MSS) by using multi-altimetric data is developed. The data used to compute WHU2000 MSS include 7-year Topex/Poseidon data (cycles 11-249), 2-year Geosat ERM data (cycles 1-44), 5-year ERS2 data (cycles 1-52) and all ERS-1 168-day data. The WHU2000 MSS is determined with a grid resolution of 2’×2’ within the ± 82° latitude and its precision is better than 0.05 m. Comparing WHU 2000MSS with 3.75’×3.75’ CLS_SHOM98.2 MSS, 3’×3’ GFZ MSS95A and 3.75’×3.75’ OSU MSS95, as external checks, the corresponding standard deviation (STD) of their differences is 0.090 m, 0.211 m and 0.079 m respectively.展开更多
The mean sea surface （MSS） model is an important reference for the study of charting datum and sea level change. A global MSS model named WHU2013, with 2′ × 2′ spatial resolution between 80° S and 84...The mean sea surface （MSS） model is an important reference for the study of charting datum and sea level change. A global MSS model named WHU2013, with 2′ × 2′ spatial resolution between 80° S and 84°N, is established in this paper by combining nearly 20 years of multi-satellite altimetric data that include Topex/Poseidon （T/P）, Jason-1, Jason-2, ERS-2, ENVISAT and GFO Exact Repeat Mission （ERM） data, ERS-1/168, Jason-1/C geodetic mission data and Cryosat-2 low resolution mode （LRM） data. All the ERM data are adjusted by the collinear method to achieve the mean along-track sea surface height （SSH）, and the combined dataset of T/P, Jason-1 and Jason-2 from 1993 to 2012 after collinear adjustment is used as the reference data. The sea level variations in the non-ERM data （geodetic mission data and LRM data） are mainly investigated, and a combined method is proposed to correct the sea level variations between 66°S and 66°N by along-track sea level variation time series and beyond 66°S or 66°N by seasonal sea level variations. In the crossover adjustment between multi-altimetric data, a stepwise method is used to solve the problem of inconsistency in the reference data between the high and low latitude regions. The proposed model is compared with the CNES-CLS2011 and DTU13 MSS models, and the standard derivation （STD） of the differences between the models is about S cm between 80°S and 84°N, less than 3 cm between 66°S and 66°N, and less than 4 cm in the China Sea and its adjacent sea. Furthermore, the three models exhibit a good agreement in the SSH differences and the along-track gradient of SSH following comparisons with satellite altimetry data.展开更多
There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fi...There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.展开更多
基金This work was supported bythe National Natural Science Foundation for Outstanding Young Scientists (Grant No. 49625408).
文摘In this study, the overall editing criteria for altimetric data are considered and the geophysical correction models is improved. The datum for various altimetric data is also unified and the method of a full-combined crossover adjustment for different altimetric tracks is used to improve the radial orbits of Geosat, ERS-1 and ERS-2 data. In addition, the method for determining mean sea surface (MSS) by using multi-altimetric data is developed. The data used to compute WHU2000 MSS include 7-year Topex/Poseidon data (cycles 11-249), 2-year Geosat ERM data (cycles 1-44), 5-year ERS2 data (cycles 1-52) and all ERS-1 168-day data. The WHU2000 MSS is determined with a grid resolution of 2’×2’ within the ± 82° latitude and its precision is better than 0.05 m. Comparing WHU 2000MSS with 3.75’×3.75’ CLS_SHOM98.2 MSS, 3’×3’ GFZ MSS95A and 3.75’×3.75’ OSU MSS95, as external checks, the corresponding standard deviation (STD) of their differences is 0.090 m, 0.211 m and 0.079 m respectively.
基金supported by National 973 Project China (2012CB957703, 2013CB733302)National 863 Project China (2013AA122502)+1 种基金Public Science and Technology Research Funds projects of Surveying, Mapping and Geo-information (201512001)National Natural Science Foundation of China (41210006, 41304003)
文摘The mean sea surface （MSS） model is an important reference for the study of charting datum and sea level change. A global MSS model named WHU2013, with 2′ × 2′ spatial resolution between 80° S and 84°N, is established in this paper by combining nearly 20 years of multi-satellite altimetric data that include Topex/Poseidon （T/P）, Jason-1, Jason-2, ERS-2, ENVISAT and GFO Exact Repeat Mission （ERM） data, ERS-1/168, Jason-1/C geodetic mission data and Cryosat-2 low resolution mode （LRM） data. All the ERM data are adjusted by the collinear method to achieve the mean along-track sea surface height （SSH）, and the combined dataset of T/P, Jason-1 and Jason-2 from 1993 to 2012 after collinear adjustment is used as the reference data. The sea level variations in the non-ERM data （geodetic mission data and LRM data） are mainly investigated, and a combined method is proposed to correct the sea level variations between 66°S and 66°N by along-track sea level variation time series and beyond 66°S or 66°N by seasonal sea level variations. In the crossover adjustment between multi-altimetric data, a stepwise method is used to solve the problem of inconsistency in the reference data between the high and low latitude regions. The proposed model is compared with the CNES-CLS2011 and DTU13 MSS models, and the standard derivation （STD） of the differences between the models is about S cm between 80°S and 84°N, less than 3 cm between 66°S and 66°N, and less than 4 cm in the China Sea and its adjacent sea. Furthermore, the three models exhibit a good agreement in the SSH differences and the along-track gradient of SSH following comparisons with satellite altimetry data.
基金Project(60574030) supported by the National Natural Science Foundation of ChinaKey Project(60634020) supported by the National Natural Science Foundation of China
文摘There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.
