卫星遥感海表温度资料和高度计资料的变分同化
收稿日期: 2009-08-18
修回日期: 2010-04-07
网络出版日期: 2011-07-20
基金资助
财政部行业专项[GYHY(QX) 20072625];中国气象局风云气象卫星遥感开发与应用项目( FiDAF22205)
Variation assimilation using satellite data of sea surface temperature and altimeter
Received date: 2009-08-18
Revised date: 2010-04-07
Online published: 2011-07-20
利用国家气候中心正在发展的第二代全球海洋资料同化系统(BCC_GODAS2.0), 针对多变量同化的协调性问题, 发展了一种基于三维变分框架(3DVAR)下的高度计和海表温度(SST)相互约束的同化方法。该方法使海面高度和 SST 资料在同一个动力约束关系下进行同化。 在一般方法中, 海面高度和 SST 观测项是代价函数中 2 个独立的观测项, 海面高度项引入动力高度计算公式, 海表温度项用统计关系进行垂向投影。在代价函数的实际求解的计算过程中, 虽然其总体积分效应受海面高度观测的约束, 但整个水柱中各层温盐分析变量的调整是无序的。针对这个问题, 文章提出一种新的同化方案。 该方案将SST的观测项并入海面高度观测项中, 海面高度的一部分, 确切说是上层海洋部分, 由 SST 决定, 因此至少在 SST 的统计关系能影响到深度的上层海洋, 在代价函数的求解过程中, 温盐的调整是受较强的统计关系约束的, 而这种统计关系的有效性已经在很多 SST 的同化试验中被其他学者广泛应用并证明。 利用该方法, 对1993—1997年的AVHRR卫星遥感海表温度资料进行变分同化试验, 用TAO、 OISST 和 SODA 数据集进行检验证明, 通过对卫星遥感资料的同化能够有效改进对海洋温度和盐度的估计。海洋表层的月平均温、盐度的总均方根误差相对同化前分别降低了 0.67℃和 0.2‰。在混合层中, 同化效果较好。
肖贤俊,何娜,张祖强,刘怀明,王东晓 . 卫星遥感海表温度资料和高度计资料的变分同化[J]. 热带海洋学报, 2011 , 30(3) : 1 -8 . DOI: 10.11978/j.issn.1009-5470.2011.03.001
A new method for assimilating altimeter and sea surface temperature (SST) data was developed for the second gen- eration Global Ocean Data Assimilation System of the Beijing Climate Center. The method allows the altimeter and SST data to be assimilated under one dynamic constraint based on the three-dimensional variation (3DVAR). In the cost function, the background was temperature and salinity from the model. The analytical variables were temperature and salinity. The sea sur- face high and SST were from the observations. The formula of steric high was introduced in the observation term. Because the steric high formula only constrained the integral of temperature and salinity in a water column from sea level to 1000 m, it will be unprincipled for the adjustment about the temperature and salinity analytics in the calculation of iteration absenting being short of accurate describing of vertical error covariance. Unfortunately we could not obtain the accurate covariance. To resolve this problem, the authors put the SST observation into the sea surface high observation term, making one part of the steric high be determined by the SST. So at least in the upper ocean, the adjustment of temperature and salinity is constrained by the SST according to appropriately chosen vertical relationship for them, which was already shown to be possible in many methods for SST assimilation. This new method was applied in the BCC_GODAS2.0. Five years (1993?2002) of assimilation experiment was performed to test this method, with the assimilation of AVHRR SST and TOPEX/Poseidon altimeter data. The experiment showed this method was successful. The root-mean-square errors for monthly temperature and salinity were reduced 0.67℃and 0.2 psu, respectively compared with the results without the assimilation. All the results were evaluated with independent observations or reanalysis data, such as TAO, OISST and SODA data. The improvement was significant, particularly in the mixed layer.
[1] FISCHER M, LATIF M, FLUGEL M, et al. The impact of data assimilation on ENSO simulation and prediction[J]. Mon Wea Rev, 1997, 127: 819-829.
[2] HURLBURT H E, FOX D N, METZGER J. Statistical inference of weakly correlated subthermocline fields from satellite altimeter data[J]. Geophys Res, 1990, 95: 11375-11409.
[3] HOLLAND W R. Altimeter data assimilation into ocean circulation models—Some preliminary results[C]//ANDERSON D L T, WILLEBRAND J. Oceanic Circulation Models: Combining Data and Dynamics. Netherlands: Kluwer Academic Publisher, 1989: 203-232.
[4] HAINES K. A direct method for assimilating sea surface height data into ocean models with adjustments to the deep circulation[J]. J Phys Oceanogr, 1991, 21: 843-868.
[5] TANF Y M, KlEEMAN R. A new strategy for assimilating SST data for ENSO predictions[J]. Geophys Res Lett, 2002, 29, 1841, doi: 10.1029/2002GL0148606.
[6] SHU YEQIANG, ZHU JIANG, WANG DONGXIAO, et al. Performance of four sea surface temperature assimilation schemes in the South China Sea[J]. Continental Shelf Research, 2009, 29(11/12): 1489-1501.
[7] GAO SHAN, WANG FAN, LI MINGKUI, et al. Application of altimetry data assimilation on mesoscale eddies simulation[J]. Sci China Ser D-Earth Sci, 2008, 51(1): 142-151.
[8] LERMUSIAUX P F J, ANDERSON D G M, LOZANO C J. On the mapping of multivariate geophysical fields: Error and variability subspace estimates[J]. Q J R Meteorol Soc, 2000, 126: 1387-1430.
[9] KEPPENNE C L, RIENECKER M. Assimilation of temperature into an isopycnal ocean general circulation model using a parallel ensemble Kalman filter[J]. J Mar Sys, 2003, 40–41: 363-380.
[10] BURGERS G, BALMASEDA M A, VOSSEPOEL F C, et al. Balanced ocean-data assimilation near the equator[J]. J Phys Oceanogr, 2002, 32: 2509-2519.
[11] TROCCOLI A, BALMASEDA M A, SEGSCHNEIDER J, et al. Salinity adjustments in the presence of temperature data assimilation[J]. Mon Weather Rev, 2002, 130: 89-102.
[12] BALMASEDA M A. Ocean data assimilation for seasonal forecasts[C]//Proceedings of the ECMWF Seminar on recent developments in data assimilation for atmosphere and ocean. 2003: 301-325.
[13] YAN C, ZHU J, LI R. Roles of vertical correlations of background error and T-S relations in estimation of temperature and salinity profiles from sea surface dynamic height[J]. J Geophys Res, 2004, 109, C08010, 18PP, doi: 10.1029/2003JC002224
[14] ZHU J, YAN C X. Nonlinear balance constraints in 3DVAR data assimilation[J]. Science in China: D Series, 2006, 49: 331-336.
[15] KANAMITSU M, EBISUZAKI W, WOOLLEN J, et al. NCEP-DOE AMIP-Ⅱ Reanalysis (R-2)[J]. Bull Amer Meteor Soc, 2002,83: 1631-1643.
[16] 朱江 周广庆 闫长香. 一个三维变分海洋资料同化系统的设计和初步应用[J]. 中国科学: D辑, 2007, 37 (2): 261-271.
[17] XIAO X J, WANG D X, YAN C X, et al. Evaluation of a 3dVAR system for the South China Sea[J]. Prog Nat Sci, 2008, 18: 547-554.
[18] XIAO X J, WANG D X, XU J J. The assimilation experiment in the southwestern South China Sea in summer 2000 [J]. Chinese Sci Bul, 2006, 51: 31-37.
[19] GILLA E, NIILER P P. The theory of the seasonal variability in the ocean[J]. Deep-Sea Res, 1973, 20: 141-177.
/
| 〈 |
|
〉 |
