两类陆面模式的模拟和同化性能比较分析
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摘要
陆面模式作为天气和气候模式的组成部分之一,其模拟好坏对天气预报和气候预测都有很大影响。陆面模式种类繁多,国际上已开展大量陆面模式比较工作,以期对不同陆面模式的模拟性能和参数化方案进行客观评估。尽管如此,由于大型野外陆面过程观测实验不断开展,人们对陆面过程的认识水平正逐渐加深,陆面物理过程和参数化方案逐渐趋于完善,伴随而来的是陆面模式更新速度加快。因此,为了全面考察新模式的性能,有必要不断开展陆面模式的比较研究工作。
本文正是在上述背景下,除采用传统的数值模拟方法外,更重要的是采用目前很少在模式比较中运用到的数据同化技术,计划从数值模拟和数据同化两个方面入手,评估目前流行的陆面模式NOAH LSM,比较其在离线运行时和与大气边界层耦合运行时对陆面和边界层状态的模拟和估算性能(注：以下称为“离线运行的陆面模式”和“与大气边界层耦合运行的陆面模式”,并分别简写为LSM和SCM),以期弥补这方面的研究的不足。
为了全面客观评估两类模式,论文特精心设计3组试验场景：第一组为无植被覆盖下的裸土变干过程；第二组为有植被覆盖和降水发生的陆面过程。前两组试验属于观测系统模拟试验(简称理想试验),试验中所有资料均来源于中尺度WRF模式的输出,即“真实”的陆面和大气状态是准确知道的,排除了模式预报误差和观测误差的不确定对评估的干扰。第三组为实际的陆面过程,驱动数据为实际观测资料,模拟和同化结果也与对应的实际观测资料相比较,目的是比较两类模式在实际运用中的具体表现。
在第一种模拟情况下,SCM模拟的地表热通量和表层土壤温湿与LSM的结果比较一致,主要差别在峰值,相反对较深层土壤温湿模拟差别很小。在第二种模拟情况下,两类模式模拟的地表热通量间差异增大,但表层土壤温湿的差别反而减小,说明在耦合模式中降水和植被对地表热通量与土壤温湿的影响机制不同,地表热通量大小受降水和植被的共同影响。在第三种模拟情况下,对土壤湿度,SCM的模拟较好而LSM的较差；对土壤温度,两类模式模拟的都不好；对2m高度大气温度、湿度和10m高度风,对LSM来说它们为模式的输入量,自然不能再重新模拟,而SCM均对它们有一定模拟能力,其中对温度模拟最好,但与观测值仍存在差别；对地表热通量,SCM模拟相对较好,但在大气稳定度发生转换时结果变差；对边界层内温湿风廓线,LSM不能提供,而SCM的模拟结果在不同时刻表现不一致,相对来说对风场的模拟结果较好。
理想同化试验表明：两类模式同化表层土壤湿度和温度观测都可以减小模式状态估算误差,但同化效果不一样,一系列敏感试验表明：增加同化观测类型还可以提升SCM估算地表热通量的精度,同时改善边界层状态；增加土壤分层数目、减小同化时间间隔均有利于估算精度的提高；对背景场误差协方差阵进行放大和局地化处理能改善同化效果；样本大小和分布形态直接决定同化性能,对具有很好时空代表性的样本,即使样本数目较少也可以达到同化目的；SCM估计的边界层状态对近地层大气温湿风观测的误差增加非常敏感,但对观测误差减小不是特别敏感。
实际观测资料的同化试验表明：6小时一次同化实际观测能提升整层土壤湿度和中间两层土壤温度的估算精度,但表层土壤温度估算值较差,2m高度大气温度和湿度改进有限,10m高度风基本无改进,地表热通量的改进也很小,边界层内温湿风基本无改进。原因是观测资料6小时才有一次,同化时间间隔明显偏大,同时不像理想同化试验那样,此时模式预报误差很大,严重影响整体同化效果。
总之,两类模式模拟的土壤温度和湿度间差别较小,而对受陆-气相互作用影响较大的地表热通量的模拟则差别较大。在同化性能方面,LSM估算的土壤湿度好于SCM的,但土壤温度差于后者；平均而言,LSM估算的地表热通量好于SCM的；在估算大气边界层状态方面,SCM具有绝对优势,但由于受该模式预报误差大和观测资料少的影响,与实际观测相比估算精度不是太高。最后,根据以上两类模式的比较结果,我们得出如下结论：若仅用于模拟和估算陆面状态,最好选用LSM；若用来同化常规气象台站观测资料为边界层模式提供高精度初始场,可以选用SCM。
Land surface model (LSM) is an important part of a weather forecasting model and/or a climate model, which has a large impact on the accuracy in the weather forecasting and climate prediction. Because of large number of land surface models, the international community has carried out lots of projects for intercomparison of land surface parameterization schemes by evaluating the outputs of these models and parameterization schemes. Despite this, due to the rapid progresses in the field land surface observation experiment, the more knowledge are acquired about the land surface processes, the land surface parameterization scheme has become more and more perfect with a new version of LSM frequently releasing out, so more work on LSM intercomparison is very necessary. Under these backgrounds, the dissertation is going to evaluate a widely used LSM by using numerical simulation and data assimilation (DA) technique when the LSM is driven offline by the meteorological observations or coupled with the atmospheric boundary layer (ABL), i.e., an offline NOAH LSM (hereinafter referred to as LSM) and a single column model (SCM). Our comparative study may make a contribution to model development and provide a good suggestion for the model developers.
For this purpose, three experimental settings with different weather and land surface conditions are designed. The first one is that the bare soil becomes drying out with no precipitation, and the second one is that the land is coved by grass and there are rainfall processes. Under these two settings, the two models are all driven by the outputs from the mesoscale Weather Research and Forecasting (WRF) model so the experimentation belongs to one kind of Observing System Simulation Experiments (OSSEs) with no model errors. The third one is that the two models are driven by meteorological observations and moreover the model outputs are compared with observations.
Under the first situation, there is a difference between model outputs from the two models but the differences of the surface soil temperature and soil moisture from the two models are very small; also the two surface heat fluxes are different and their difference changes quickly with time. Under the second situation, there is a large change in the partitioning of available surface energy between the sensible and latent heat fluxes with the sensible heat fluxes decreasing and latent heat fluxes largely increasing, and also the fluxes difference becomes large from the two models with the largest difference appearing when the air stability change.
Under the third situation, the tests show that there is a certain deviation between the simulated and observed due to uncertainties in boundary conditions and model parameters, but the outputs still reflect the atmospheric characteristics and trends in the near-surface layer. The SCM simulated soil moisture is better than that with the LSM; for the simulation of soil temperature, both models do not have a good performance; for2-m air temperature and humidity and10-m wind, LSM cannot simulate, while SCM has a relatively good performance and the simulated temperature is best among three model states, but still has a relatively large error; the simulation of surface heat fluxes with SCM is better, however, it becomes worse when the ABL states change; LSM cannot simulate the ABL state profiles while the performance of SCM is not consistent with a better simulation of the ABL wind.
OSSE shows that the errors of estimation can be greatly reduced by assimilating the near-surface soil moisture and temperature observations, but the assimilation effects are different between the two models. Assimilating more types of observations by SCM will further improve the estimates of surface heat flux, and meanwhile effectively improve the boundary layer. More soil layers or smaller assimilation time interval can effectively increase the assimilation effect. Localization or inflation of background error covariance matrix will improve the performance of data assimilation. The sample size and its forms of background ensemble have a very large impact on the data assimilation; if the sample has a good representative, better estimates can be obtained even if the ensemble number is small. The ABL states estimated with SCM are more sensitive to the increase while are not to the decrease of observational errors in the near-surface atmospheric states.
Assimilating the real observations into SCM once every6hours can effectively improve the estimation of soil moisture profile and two mid-layer soil temperature estimates, but does not significantly improve the estimates of the near-surface soil temperature,2-m air temperature and humidity,10-m wind, surface heat flux estimates, and the ABL states. The reason is that the time interval is6hours between two successive observations so the DA frequency is small. Besides, unlike the OSSE, the prediction error is so large that seriously influences the DA effects.
In summary, the difference of soil moisture and soil temperature simulated with two models is small while that of surface heat fluxes is large which are greatly influenced by the interaction between the land and atmosphere. In the context of comparison between two-model DA performance, the soil moisture estimates by LSM is better than that by SCM but the soil temperature estimates becomes worse; on the average, the estimates of surface heat fluxes by LSM is better than that by SCM; SCM can be combined with DA method to estimate the ABL states while LSM cannot do by assimilating the near-surface atmospheric observations. However, due to large prediction error and long observational time interval, the estimates of ABL states are not good in comparison with the corresponding observations. Therefore, if the model is used to simulate and estimate the land surface states, we suggest adopting LSM; If the model is used to provide initial ABL states, we suggest using SCM.
本文正是在上述背景下,除采用传统的数值模拟方法外,更重要的是采用目前很少在模式比较中运用到的数据同化技术,计划从数值模拟和数据同化两个方面入手,评估目前流行的陆面模式NOAH LSM,比较其在离线运行时和与大气边界层耦合运行时对陆面和边界层状态的模拟和估算性能(注：以下称为“离线运行的陆面模式”和“与大气边界层耦合运行的陆面模式”,并分别简写为LSM和SCM),以期弥补这方面的研究的不足。
为了全面客观评估两类模式,论文特精心设计3组试验场景：第一组为无植被覆盖下的裸土变干过程；第二组为有植被覆盖和降水发生的陆面过程。前两组试验属于观测系统模拟试验(简称理想试验),试验中所有资料均来源于中尺度WRF模式的输出,即“真实”的陆面和大气状态是准确知道的,排除了模式预报误差和观测误差的不确定对评估的干扰。第三组为实际的陆面过程,驱动数据为实际观测资料,模拟和同化结果也与对应的实际观测资料相比较,目的是比较两类模式在实际运用中的具体表现。
在第一种模拟情况下,SCM模拟的地表热通量和表层土壤温湿与LSM的结果比较一致,主要差别在峰值,相反对较深层土壤温湿模拟差别很小。在第二种模拟情况下,两类模式模拟的地表热通量间差异增大,但表层土壤温湿的差别反而减小,说明在耦合模式中降水和植被对地表热通量与土壤温湿的影响机制不同,地表热通量大小受降水和植被的共同影响。在第三种模拟情况下,对土壤湿度,SCM的模拟较好而LSM的较差；对土壤温度,两类模式模拟的都不好；对2m高度大气温度、湿度和10m高度风,对LSM来说它们为模式的输入量,自然不能再重新模拟,而SCM均对它们有一定模拟能力,其中对温度模拟最好,但与观测值仍存在差别；对地表热通量,SCM模拟相对较好,但在大气稳定度发生转换时结果变差；对边界层内温湿风廓线,LSM不能提供,而SCM的模拟结果在不同时刻表现不一致,相对来说对风场的模拟结果较好。
理想同化试验表明：两类模式同化表层土壤湿度和温度观测都可以减小模式状态估算误差,但同化效果不一样,一系列敏感试验表明：增加同化观测类型还可以提升SCM估算地表热通量的精度,同时改善边界层状态；增加土壤分层数目、减小同化时间间隔均有利于估算精度的提高；对背景场误差协方差阵进行放大和局地化处理能改善同化效果；样本大小和分布形态直接决定同化性能,对具有很好时空代表性的样本,即使样本数目较少也可以达到同化目的；SCM估计的边界层状态对近地层大气温湿风观测的误差增加非常敏感,但对观测误差减小不是特别敏感。
实际观测资料的同化试验表明：6小时一次同化实际观测能提升整层土壤湿度和中间两层土壤温度的估算精度,但表层土壤温度估算值较差,2m高度大气温度和湿度改进有限,10m高度风基本无改进,地表热通量的改进也很小,边界层内温湿风基本无改进。原因是观测资料6小时才有一次,同化时间间隔明显偏大,同时不像理想同化试验那样,此时模式预报误差很大,严重影响整体同化效果。
总之,两类模式模拟的土壤温度和湿度间差别较小,而对受陆-气相互作用影响较大的地表热通量的模拟则差别较大。在同化性能方面,LSM估算的土壤湿度好于SCM的,但土壤温度差于后者；平均而言,LSM估算的地表热通量好于SCM的；在估算大气边界层状态方面,SCM具有绝对优势,但由于受该模式预报误差大和观测资料少的影响,与实际观测相比估算精度不是太高。最后,根据以上两类模式的比较结果,我们得出如下结论：若仅用于模拟和估算陆面状态,最好选用LSM；若用来同化常规气象台站观测资料为边界层模式提供高精度初始场,可以选用SCM。
Land surface model (LSM) is an important part of a weather forecasting model and/or a climate model, which has a large impact on the accuracy in the weather forecasting and climate prediction. Because of large number of land surface models, the international community has carried out lots of projects for intercomparison of land surface parameterization schemes by evaluating the outputs of these models and parameterization schemes. Despite this, due to the rapid progresses in the field land surface observation experiment, the more knowledge are acquired about the land surface processes, the land surface parameterization scheme has become more and more perfect with a new version of LSM frequently releasing out, so more work on LSM intercomparison is very necessary. Under these backgrounds, the dissertation is going to evaluate a widely used LSM by using numerical simulation and data assimilation (DA) technique when the LSM is driven offline by the meteorological observations or coupled with the atmospheric boundary layer (ABL), i.e., an offline NOAH LSM (hereinafter referred to as LSM) and a single column model (SCM). Our comparative study may make a contribution to model development and provide a good suggestion for the model developers.
For this purpose, three experimental settings with different weather and land surface conditions are designed. The first one is that the bare soil becomes drying out with no precipitation, and the second one is that the land is coved by grass and there are rainfall processes. Under these two settings, the two models are all driven by the outputs from the mesoscale Weather Research and Forecasting (WRF) model so the experimentation belongs to one kind of Observing System Simulation Experiments (OSSEs) with no model errors. The third one is that the two models are driven by meteorological observations and moreover the model outputs are compared with observations.
Under the first situation, there is a difference between model outputs from the two models but the differences of the surface soil temperature and soil moisture from the two models are very small; also the two surface heat fluxes are different and their difference changes quickly with time. Under the second situation, there is a large change in the partitioning of available surface energy between the sensible and latent heat fluxes with the sensible heat fluxes decreasing and latent heat fluxes largely increasing, and also the fluxes difference becomes large from the two models with the largest difference appearing when the air stability change.
Under the third situation, the tests show that there is a certain deviation between the simulated and observed due to uncertainties in boundary conditions and model parameters, but the outputs still reflect the atmospheric characteristics and trends in the near-surface layer. The SCM simulated soil moisture is better than that with the LSM; for the simulation of soil temperature, both models do not have a good performance; for2-m air temperature and humidity and10-m wind, LSM cannot simulate, while SCM has a relatively good performance and the simulated temperature is best among three model states, but still has a relatively large error; the simulation of surface heat fluxes with SCM is better, however, it becomes worse when the ABL states change; LSM cannot simulate the ABL state profiles while the performance of SCM is not consistent with a better simulation of the ABL wind.
OSSE shows that the errors of estimation can be greatly reduced by assimilating the near-surface soil moisture and temperature observations, but the assimilation effects are different between the two models. Assimilating more types of observations by SCM will further improve the estimates of surface heat flux, and meanwhile effectively improve the boundary layer. More soil layers or smaller assimilation time interval can effectively increase the assimilation effect. Localization or inflation of background error covariance matrix will improve the performance of data assimilation. The sample size and its forms of background ensemble have a very large impact on the data assimilation; if the sample has a good representative, better estimates can be obtained even if the ensemble number is small. The ABL states estimated with SCM are more sensitive to the increase while are not to the decrease of observational errors in the near-surface atmospheric states.
Assimilating the real observations into SCM once every6hours can effectively improve the estimation of soil moisture profile and two mid-layer soil temperature estimates, but does not significantly improve the estimates of the near-surface soil temperature,2-m air temperature and humidity,10-m wind, surface heat flux estimates, and the ABL states. The reason is that the time interval is6hours between two successive observations so the DA frequency is small. Besides, unlike the OSSE, the prediction error is so large that seriously influences the DA effects.
In summary, the difference of soil moisture and soil temperature simulated with two models is small while that of surface heat fluxes is large which are greatly influenced by the interaction between the land and atmosphere. In the context of comparison between two-model DA performance, the soil moisture estimates by LSM is better than that by SCM but the soil temperature estimates becomes worse; on the average, the estimates of surface heat fluxes by LSM is better than that by SCM; SCM can be combined with DA method to estimate the ABL states while LSM cannot do by assimilating the near-surface atmospheric observations. However, due to large prediction error and long observational time interval, the estimates of ABL states are not good in comparison with the corresponding observations. Therefore, if the model is used to simulate and estimate the land surface states, we suggest adopting LSM; If the model is used to provide initial ABL states, we suggest using SCM.
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