MM5伴随模式同化系统中云导风的同化对调整模式地形参数的试验研究
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摘要
四维伴随变分同化方法作为一种提高数值天气预报的有效方法受到了很多国内外专家
的关注。本文详细地介绍了构造伴随模式的基本方法-伴随码技术以及伴随模式中权重、尺
度因子、下降算法的选取,接着介绍了以变分法为基础,以伴随码技术为核心的MM5伴随
模式同化系统。以2002年7月23日的天气过程为例,将常规资料和非常规云导风资料应用于
MM5伴随模式同化系统,进行了修正模式地形参数的试验研究。通过对不同初始地形的修
正试验表明, MM5伴随模式同化系统能够对地形误差进行修正,并得到与资料更为协调一
致的地形。另外,利用非常规云导风资料能够比仅使用常规资料更有效地修正模式地形参数。
风场敏感性试验表明,在进行数值模拟时,针对不同模拟区域的地貌,适当增加某层风场的
权重,可以更有效地调整模式地形,使修正后的地形与资料更协调。最后,将修正后的地形
场作为模式地形进行数值模拟,发现用修正后的地形特别是经云导风资料修正后的地形作为
模式地形所得的预报结果比使用未经修正的地形作为模式地形有所改善。同时,将集合预报
的思想用于地形调整,结果发现,将集合平均的地形作为模式地形,可以在预报中得到一个
比较稳定的预报结果,避免单一个例预报的偶然性。这为伴随模式同化系统的更广泛应用提
供了一种新的思路。
As an efficient approach to improve the accuracy of numerical weather prediction (NWP), Four-dimensional Adjoint Variational Assimilation is focused on by many national meteorologists. In this paper the technique of adjoint codes which is a basic means of constructing adjoint model and the selection of weight, scaling and descend arithmetic are introduced amply. Then the MM5 Adjoint-model Assimilation System that is based on variational method with adjoint codes technique is introduced. Model terrain adjustment is carried out by means of the MM5 adjoint-model assimilation system (AMAS) using the conventional observation data and non-conventional data cloud-derived winds. Two different initially-estimated terrains (from stochastic intensification and weakening) are given and corrected by use of the AMAS, which indicates much the same assimilation for the terrains differing from each other. The modified terrains are more consistent with the observation data. For the terrain parameter of identical errors, the adjoint model with inclusion of non-conventional cloud-derived winds in will improve even more the AMAS modification to the terrain parameter. As shown in the subsequent sensitivity runs, an increase in the proportion of a certain level wind in different regions will adjust the terrains in effect. To verify effects of modified terrain on NWP, experiment is conducted on a large-scale rainstorm over the Changjiang-Huai basin on 23-24 July, 2002, indicating that the terrain modified by conventional data and especially the one adjusted by cloud-derived winds used as the MM5 surface feature will improve prediction than the unmodified terrain. At the same time, Ensemble Prediction technique is used in terrains modification. It was found that a stable prediction can be gotten and the chanciness of a single case can be avoided after the ensemble averaging terrain is used. A new feasible approach about the widely application of AMAS is offered in this paper.
的关注。本文详细地介绍了构造伴随模式的基本方法-伴随码技术以及伴随模式中权重、尺
度因子、下降算法的选取,接着介绍了以变分法为基础,以伴随码技术为核心的MM5伴随
模式同化系统。以2002年7月23日的天气过程为例,将常规资料和非常规云导风资料应用于
MM5伴随模式同化系统,进行了修正模式地形参数的试验研究。通过对不同初始地形的修
正试验表明, MM5伴随模式同化系统能够对地形误差进行修正,并得到与资料更为协调一
致的地形。另外,利用非常规云导风资料能够比仅使用常规资料更有效地修正模式地形参数。
风场敏感性试验表明,在进行数值模拟时,针对不同模拟区域的地貌,适当增加某层风场的
权重,可以更有效地调整模式地形,使修正后的地形与资料更协调。最后,将修正后的地形
场作为模式地形进行数值模拟,发现用修正后的地形特别是经云导风资料修正后的地形作为
模式地形所得的预报结果比使用未经修正的地形作为模式地形有所改善。同时,将集合预报
的思想用于地形调整,结果发现,将集合平均的地形作为模式地形,可以在预报中得到一个
比较稳定的预报结果,避免单一个例预报的偶然性。这为伴随模式同化系统的更广泛应用提
供了一种新的思路。
As an efficient approach to improve the accuracy of numerical weather prediction (NWP), Four-dimensional Adjoint Variational Assimilation is focused on by many national meteorologists. In this paper the technique of adjoint codes which is a basic means of constructing adjoint model and the selection of weight, scaling and descend arithmetic are introduced amply. Then the MM5 Adjoint-model Assimilation System that is based on variational method with adjoint codes technique is introduced. Model terrain adjustment is carried out by means of the MM5 adjoint-model assimilation system (AMAS) using the conventional observation data and non-conventional data cloud-derived winds. Two different initially-estimated terrains (from stochastic intensification and weakening) are given and corrected by use of the AMAS, which indicates much the same assimilation for the terrains differing from each other. The modified terrains are more consistent with the observation data. For the terrain parameter of identical errors, the adjoint model with inclusion of non-conventional cloud-derived winds in will improve even more the AMAS modification to the terrain parameter. As shown in the subsequent sensitivity runs, an increase in the proportion of a certain level wind in different regions will adjust the terrains in effect. To verify effects of modified terrain on NWP, experiment is conducted on a large-scale rainstorm over the Changjiang-Huai basin on 23-24 July, 2002, indicating that the terrain modified by conventional data and especially the one adjusted by cloud-derived winds used as the MM5 surface feature will improve prediction than the unmodified terrain. At the same time, Ensemble Prediction technique is used in terrains modification. It was found that a stable prediction can be gotten and the chanciness of a single case can be avoided after the ensemble averaging terrain is used. A new feasible approach about the widely application of AMAS is offered in this paper.
引文
1. 沈桐立,田永祥等.数值天气预报(修订版).北京:气象出版社,2003:237.
2. Sasaki Y. An objective analysis based on variational method. J Meteor Soc Japan, 1958, 36(3) :77-88.
3. Sasaki Y. Some basic formalisms in numerical variational analysis. Mon Wea Rev, 1970, 98(12) :875-883.
4. Gressmen G P. An operational objective analysis system. Mon Wea Rev, 1959, 87(7) :367-374.
5. Lorence A C A. Global three-dimensional multivariate statistical interpolation scheme. Mon Wea Rev, 1981, 109(3) :701-721.
6. LeDimet F X,Talagrand O. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus, 1986, 38A(2) :97-110.
7. Talagrand O,Coutier P. Variational assimilation of meteorological observation with the adjoint vorticity equation. I : Theory. Quart J R Meteor Soc, 1987, 11 3(4) : 1311-1328.
8. Coutier P,Talagrand O. Variational assimilation of meteorological observation with the adjoint vorticity equation.Ⅱ: Numerical results. Quart J R Meteor Soc, 1987, 113(4) :1329-1347.
9. Hoffmann R N. A four-dimensional analysis exactly satisfying equations of motion. Mon Wea Rev, 1986, 114(2) :388-397.
10. Lewis J M,Derber J C. The use of adjoint equations to solve a variational adjustment problem with advective constrains. Tellus, 1985, 37A(4) :309-322.
11. Marchuk G I. Numerical methods in weather prediction. New York: Academic Press, 1974.
12. Marchuk G I,Skribe Y K. Numerical calculation of the oceans and continents. Izvestiya Atm and Ocean Phys, 1975, 12(5) :459-469.
13. Penenko V V, Obratsov N N. A variational initialization method for the field of the meteorological elements (English translation). Soviet Meteorology and Hydrology, 1976, 11(1) :1-11.
14. Hall M C G,Cacuci D G. Physical interpretation of the adjoint function for sensitivity analysis of atmospheric models.J A S, 1983, 40(10) :2537-2546.
15. Courtier P. and Talagrand O. Variational assimilation of meteorological observations with the direct and adjoint shallow-water equations Tellus, 1990, 42A(5) :53 1-549.
16. Thepaut J N, Courtier P. Four dimension variational assimilation using the adjoint of a multilevel equation model.Quart J R Meteo Soc,,1991,117(5) :1225-1254.
17. Navon I M, Zou X, Derber J et ai. Variational data assimilation with an adiabatic version of the NMC spectral model. Mon Wea Rev, 1992, 120(7) :1433-1446.
18. Winston C C,Chang L P. Development of a four-dimension variational analysis system using the adjoint method at GLA. part I: Dynamic. Mon Wea Rev, 1992, 120(8) :1661-1673.
19. Zou X Y,Kuo H, Guo Y R. Assimilation of atmosphere radio refractivity using a nonhydrostatic adjoint model. Mon Wea Rev, 1995, 123(7) : 2229-2247.
20. Zou X,Navon I M.Sela J G Variational data assimilation with moist threshold process using the NMC spectral model. Tellus, 1993, 45A(5) : 370-387.
21. Dusanka Z, Fedor M. Four-dimension variational assimilation of precipitation data. Mon WeaRev, 1995, 123(4) : 1112-1127.
22. Holt T R,Chang S W. Impact of SSM/I precipitable water assimilation on mesoscale simulations of the ERICA IOP4 cyclone. Preprints 10 th conf on numerical weather prediction Amer Meteo Soc, 1994(2) :164-186.
23. Zou X. Rainfall assimilation through an optimal control of initial and boundary conditions in a limited-area mesoscale model.Mon Wea Rev, 1996, 124(12) :2859-2882.
24. Kuo Y H,Zou X,Guo Y R. Variational assimilation of precipitable using a nonhydrostatic mesoscale adjoint model.Part I: moisture retrieval and sensitivity experiments. Mon Weu Rev, 1996, 124(1) : 122-147.
25. Tadaski,Tsuyuki T. Variational data assimilation in tropics using precipitation data .Part Ⅱ:3-D model. Mon. WeaRev, 1996, 124(11) , 2545-2561.
26. Fillion L,Ronald M E. Variational assimilation of precipitation data using moist convective parameterization schemes: AID-Var study. Mon Wea Rev, 1997, 125(11) : 2917-2942.
27. Andrew S, Jones I C C,Thomas H. Data assimilation of satellite-derived heating rates as proxy surface wetness data into a regional atmospheric mesoscale model.Part I: Meteorology. Mon Wea Rev, 1998, 26(3) :634-645.
28. 徐辉、穆穆、罗德海.非线性优化方法在数值模式敏感性分析中的应用.自然科学进展, 2003,13,1213-1216.
29. Hall M C G,Caucci D G,Schlesinger M E. Sensitivity analysis of a radiative convective model by the adjoint method. J Atmos Sci, 1982, 39(9) :2038-2050.
30. Cacucci D G. Sensitivity th(?)y for nonlinear system.I:Nonlincar functional analysis approach. J Matht Phys,1981, 22(12) :2794-2802.
31. Derber J C. A variational continuous assimilation technique. Mon Wea Rev, 1989, 117(11) : 2437-2446.
32. Ronald M E, Tomislava V. Sensitivity analysis using an adjoint of the PSU-NCAR Mesoscale Model.Mon Wea Rev, 1992, 120(8) :1644-1660.
33. Bao J W, Ronald M E. An adjoint examination of a nudging method for data assimilation. Mon Wea Rev, 1997, 125(6) : 1355-1373.
34. Pu Z X. Sensitivity of forecast errors to initial conditions with a quasi-inverse linear method. Mon Wea Rev, 1997, 125(10) : 2479-2503.
35. Nils G, Xiang Y. Sensitivity experiments with spectral HIRLAM and its adjoint Tellus, 1996, 48A(4) :501-517.
36. David R S, Bao J W. Optimal determination of nudging coefficients using the adjoint equations. Tellus, 1993, 45A(5) : 358-369.
37. 郜吉东,丑纪范.数值天气预报中的两类反问题及一种数值解法--理想试验.气象学报, 1994,52(2) :129-139.
38. 郜吉东,丑纪范.数值模式初值的敏感性程度对四维同化的影响--基于Lorenz系统的 研究.气象学报,1995,53(4) :471-497.
39. 蒲朝霞,丑纪范.对中尺度遥感资料进行四维同化的共轭方法及数值研究.高原气象, 1994,13(4) :419-429.
40. 邱崇践.变分四维同化方法中的不连续问题.兰州人学学报(自然科学版),1997,33(1) : 115-119.
41. 陈子通,沈桐立,丁一汇,孙麟平.中尺度数值模式的资料同化系:(一)伴随模式的 设计.南京气象学院学报,1998,21(2) :165-172.
42. 沈桐立,陈子通,丁一汇,孙麟平.中尺度数值模式的资料同化系:(二)伴随模式系 统的检验和试验.南京气象学院学报,1998,21(2) :173-180.
43. 龚建东,邱崇践,王强,陈伟民.区域四维变分资料同化的数值试验.气象学报,1999, 57(2) :131-142.
44. 冯伍虎,邱崇践.变分四维同化方法若干问题的数值试验.高原气象,1999,18(2) :138-146.
45. 李晓莉,沈桐立,孙麟平.中尺度模式的四维伴随同化系统及其同化试验.南京气象学院 学报,2002,25(1) :36-44.
46. 王栋梁,沈桐立.中尺度模式MM5的四维变分资料同化系统.南京气象学院学报,2002, 25(5) :603-610.
47. Eyre J.R., GA.Kelly, et al. Assimilation of TOVS radiance information through one-dimensional variational analysis. Q.J.Roy.Met.Soc.1993,119:1427-1463.
48. Eyre J.R., GA.Kelly, et al. Assimilation of TOVS radiance information through one-dimensional variational analysis. Q.J.Roy.Met.Soc.1993, 119:1427-1463.
49. 沈桐立,闵锦忠,吴诚鸥.有限区域卫星云图资料变分分析的试验研究.高原气象, 1996,15(1) :58-457.
50. 闵锦忠,沈桐立,陈海山等.卫星云图资料反演的质量控制及变分同化数值试验.应用 气象学报,2000,11(4) :410-418.
51. 张守峰、王诗文.在台风业务系统中使用卫星云导风资料的试验.气象,1999,25(8) : 22-25.
52. 肖庆农、伍荣生.地形对于气流运动影响的数值研究,气象学报,1995,53:38-49.
53. 李国平.青藏高原动力气象学.北京:气象出版社,2002.
54. 许绍祖.大气物理学基础.北京:气象出版社,1993:55-67.
2. Sasaki Y. An objective analysis based on variational method. J Meteor Soc Japan, 1958, 36(3) :77-88.
3. Sasaki Y. Some basic formalisms in numerical variational analysis. Mon Wea Rev, 1970, 98(12) :875-883.
4. Gressmen G P. An operational objective analysis system. Mon Wea Rev, 1959, 87(7) :367-374.
5. Lorence A C A. Global three-dimensional multivariate statistical interpolation scheme. Mon Wea Rev, 1981, 109(3) :701-721.
6. LeDimet F X,Talagrand O. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus, 1986, 38A(2) :97-110.
7. Talagrand O,Coutier P. Variational assimilation of meteorological observation with the adjoint vorticity equation. I : Theory. Quart J R Meteor Soc, 1987, 11 3(4) : 1311-1328.
8. Coutier P,Talagrand O. Variational assimilation of meteorological observation with the adjoint vorticity equation.Ⅱ: Numerical results. Quart J R Meteor Soc, 1987, 113(4) :1329-1347.
9. Hoffmann R N. A four-dimensional analysis exactly satisfying equations of motion. Mon Wea Rev, 1986, 114(2) :388-397.
10. Lewis J M,Derber J C. The use of adjoint equations to solve a variational adjustment problem with advective constrains. Tellus, 1985, 37A(4) :309-322.
11. Marchuk G I. Numerical methods in weather prediction. New York: Academic Press, 1974.
12. Marchuk G I,Skribe Y K. Numerical calculation of the oceans and continents. Izvestiya Atm and Ocean Phys, 1975, 12(5) :459-469.
13. Penenko V V, Obratsov N N. A variational initialization method for the field of the meteorological elements (English translation). Soviet Meteorology and Hydrology, 1976, 11(1) :1-11.
14. Hall M C G,Cacuci D G. Physical interpretation of the adjoint function for sensitivity analysis of atmospheric models.J A S, 1983, 40(10) :2537-2546.
15. Courtier P. and Talagrand O. Variational assimilation of meteorological observations with the direct and adjoint shallow-water equations Tellus, 1990, 42A(5) :53 1-549.
16. Thepaut J N, Courtier P. Four dimension variational assimilation using the adjoint of a multilevel equation model.Quart J R Meteo Soc,,1991,117(5) :1225-1254.
17. Navon I M, Zou X, Derber J et ai. Variational data assimilation with an adiabatic version of the NMC spectral model. Mon Wea Rev, 1992, 120(7) :1433-1446.
18. Winston C C,Chang L P. Development of a four-dimension variational analysis system using the adjoint method at GLA. part I: Dynamic. Mon Wea Rev, 1992, 120(8) :1661-1673.
19. Zou X Y,Kuo H, Guo Y R. Assimilation of atmosphere radio refractivity using a nonhydrostatic adjoint model. Mon Wea Rev, 1995, 123(7) : 2229-2247.
20. Zou X,Navon I M.Sela J G Variational data assimilation with moist threshold process using the NMC spectral model. Tellus, 1993, 45A(5) : 370-387.
21. Dusanka Z, Fedor M. Four-dimension variational assimilation of precipitation data. Mon WeaRev, 1995, 123(4) : 1112-1127.
22. Holt T R,Chang S W. Impact of SSM/I precipitable water assimilation on mesoscale simulations of the ERICA IOP4 cyclone. Preprints 10 th conf on numerical weather prediction Amer Meteo Soc, 1994(2) :164-186.
23. Zou X. Rainfall assimilation through an optimal control of initial and boundary conditions in a limited-area mesoscale model.Mon Wea Rev, 1996, 124(12) :2859-2882.
24. Kuo Y H,Zou X,Guo Y R. Variational assimilation of precipitable using a nonhydrostatic mesoscale adjoint model.Part I: moisture retrieval and sensitivity experiments. Mon Weu Rev, 1996, 124(1) : 122-147.
25. Tadaski,Tsuyuki T. Variational data assimilation in tropics using precipitation data .Part Ⅱ:3-D model. Mon. WeaRev, 1996, 124(11) , 2545-2561.
26. Fillion L,Ronald M E. Variational assimilation of precipitation data using moist convective parameterization schemes: AID-Var study. Mon Wea Rev, 1997, 125(11) : 2917-2942.
27. Andrew S, Jones I C C,Thomas H. Data assimilation of satellite-derived heating rates as proxy surface wetness data into a regional atmospheric mesoscale model.Part I: Meteorology. Mon Wea Rev, 1998, 26(3) :634-645.
28. 徐辉、穆穆、罗德海.非线性优化方法在数值模式敏感性分析中的应用.自然科学进展, 2003,13,1213-1216.
29. Hall M C G,Caucci D G,Schlesinger M E. Sensitivity analysis of a radiative convective model by the adjoint method. J Atmos Sci, 1982, 39(9) :2038-2050.
30. Cacucci D G. Sensitivity th(?)y for nonlinear system.I:Nonlincar functional analysis approach. J Matht Phys,1981, 22(12) :2794-2802.
31. Derber J C. A variational continuous assimilation technique. Mon Wea Rev, 1989, 117(11) : 2437-2446.
32. Ronald M E, Tomislava V. Sensitivity analysis using an adjoint of the PSU-NCAR Mesoscale Model.Mon Wea Rev, 1992, 120(8) :1644-1660.
33. Bao J W, Ronald M E. An adjoint examination of a nudging method for data assimilation. Mon Wea Rev, 1997, 125(6) : 1355-1373.
34. Pu Z X. Sensitivity of forecast errors to initial conditions with a quasi-inverse linear method. Mon Wea Rev, 1997, 125(10) : 2479-2503.
35. Nils G, Xiang Y. Sensitivity experiments with spectral HIRLAM and its adjoint Tellus, 1996, 48A(4) :501-517.
36. David R S, Bao J W. Optimal determination of nudging coefficients using the adjoint equations. Tellus, 1993, 45A(5) : 358-369.
37. 郜吉东,丑纪范.数值天气预报中的两类反问题及一种数值解法--理想试验.气象学报, 1994,52(2) :129-139.
38. 郜吉东,丑纪范.数值模式初值的敏感性程度对四维同化的影响--基于Lorenz系统的 研究.气象学报,1995,53(4) :471-497.
39. 蒲朝霞,丑纪范.对中尺度遥感资料进行四维同化的共轭方法及数值研究.高原气象, 1994,13(4) :419-429.
40. 邱崇践.变分四维同化方法中的不连续问题.兰州人学学报(自然科学版),1997,33(1) : 115-119.
41. 陈子通,沈桐立,丁一汇,孙麟平.中尺度数值模式的资料同化系:(一)伴随模式的 设计.南京气象学院学报,1998,21(2) :165-172.
42. 沈桐立,陈子通,丁一汇,孙麟平.中尺度数值模式的资料同化系:(二)伴随模式系 统的检验和试验.南京气象学院学报,1998,21(2) :173-180.
43. 龚建东,邱崇践,王强,陈伟民.区域四维变分资料同化的数值试验.气象学报,1999, 57(2) :131-142.
44. 冯伍虎,邱崇践.变分四维同化方法若干问题的数值试验.高原气象,1999,18(2) :138-146.
45. 李晓莉,沈桐立,孙麟平.中尺度模式的四维伴随同化系统及其同化试验.南京气象学院 学报,2002,25(1) :36-44.
46. 王栋梁,沈桐立.中尺度模式MM5的四维变分资料同化系统.南京气象学院学报,2002, 25(5) :603-610.
47. Eyre J.R., GA.Kelly, et al. Assimilation of TOVS radiance information through one-dimensional variational analysis. Q.J.Roy.Met.Soc.1993,119:1427-1463.
48. Eyre J.R., GA.Kelly, et al. Assimilation of TOVS radiance information through one-dimensional variational analysis. Q.J.Roy.Met.Soc.1993, 119:1427-1463.
49. 沈桐立,闵锦忠,吴诚鸥.有限区域卫星云图资料变分分析的试验研究.高原气象, 1996,15(1) :58-457.
50. 闵锦忠,沈桐立,陈海山等.卫星云图资料反演的质量控制及变分同化数值试验.应用 气象学报,2000,11(4) :410-418.
51. 张守峰、王诗文.在台风业务系统中使用卫星云导风资料的试验.气象,1999,25(8) : 22-25.
52. 肖庆农、伍荣生.地形对于气流运动影响的数值研究,气象学报,1995,53:38-49.
53. 李国平.青藏高原动力气象学.北京:气象出版社,2002.
54. 许绍祖.大气物理学基础.北京:气象出版社,1993:55-67.