A 3DVAR Land Data Assimilation Scheme: Part 2, Test with ECMWF ERA-40

A 3DVAR Land Data Assimilation Scheme: Part 2, Test with ECMWF ERA-40 Lanjun Zou 1 * a,b,c Wei Gao a,d Tongwen Wu b Xiaofeng Xu b Bingyu Du a,and James Slusser d a Sino-US Cooperative Center for Remote Sensing, Nanjing University of Information Science & Technology, Nanjing, PRC b Laboratory for Climate Studies, China Meteorological Administration, Beijing, PRC c Jiangsu Key Laboratory of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing, PRC d USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO ABSTRACT In the first part of this paper, a 3DVar Land Data Assimilation Scheme (LDAS) is presented. With virtue of this land data assimilation system, this part of the paper demonstrates the results and error analysis of assimilating air temperature data observed at various meteorological stations in China into the output of ECMWF ERA-40. The air temperature distribution of sparse observation zones is obtained, which shows the validity of the assimilation procedure. The 3DVar LDAS can greatly improve the ECMWF background estimates with the high quality observations of air temperature from the Chinese meteorological stations. By comparing the assimilated air temperature field and the ECMWF background field to the observations, the assimilation outputs have better agreement with the air temperature variation trend than the ECMWF background. Another advantage of the assimilated result is that it can describe the extreme air temperature more accurately. Keywords: land data assimilation, 3DVAR, ECMWF, air temperature 1. INTRODUCTION Data assimilation was firstly presented to solve the initial problem in numerical weather prediction (NWP), but now its applications go beyond NWP. The work of producing meteorological data sets of long time serial is crucial and meaningful to research. Some main NWP centers in the world have done excellent jobs about reanalysis, such as ECMWF ERA-40 and NCEP Reanalysis II etc. The Data Assimilation Office (DAO) at NASA's Goddard Space Flight Center also produces a global multi-year reanalysis of historical observations for the period 1980-1994 [1,2,3]. China has numerous station observed data with high quality, including air temperature, air pressure, air humidity, precipitation, wind, cloud, soil temperature, soil moisture, radiation etc., but these data have not been made best use of. Also, national climate center has developed dynamical climate model for short-time climate prediction. From the 1950s, NWP levels of China have improved greatly, but applications of observation data are quite low efficient, so it needs to develop data assimilation schemes suitable for Chinese weather and climate use [4,5,,6]. Land surface is an important part in climate research. But land surface data have great uncertainties because of their inhomogeneous characteristics, so it is hard to evaluate land data and apply them into atmospheric models. Land data assimilation is a tool that effectively combines land data of different type with different error under the consideration of background error and observation error. It became a hot research field after 1990s, though it is behind atmospheric and oceanic data assimilation. In order to obtain data sets with high quality, meeting the application needs and providing validation data for models, our goal is set to develop a land data assimilation scheme suitable to current Chinese observation status and model applications, and to carry out reanalysis work by using this data assimilation system. It will be helpful to grasp the indeed functions between land surface and atmosphere system for researchers and will bring great convenience and * Address correspondence to: wgao@uvb.nrel.colostate.edu, phone (970) 491-3609, fax (970) 491-3601,lanjunzou@gmail.com; phone: +86 10 6840-0082 Remote Sensing and Modeling of Ecosystems for Sustainability III, edited by Wei Gao, Susan L. Ustin, Proc. of SPIE Vol. 6298, 62981M, (2006) 0277-786X/06/$15 doi: 10.1117/12.679991 Proc. of SPIE Vol. 6298 62981M-1

advance to weather and climate research. ECMWF ERA-40 and Chinese station observed air temperature are assimilated as an example in this study. Other air temperature assimilation works can see Praveen and Kaleita, 2003; Keppenne and Rienecker, 2003, etc. Assimilation effect is evaluated with observation and EC background. Results show that, on the one hand, the air temperature distribution of sparse observation zones is obtained; on the other hand, assimilation can greatly improve background data with station-observed air temperature data of high quality assimilated into ERA-40 [7,8]. 2. ECMWF ERA-40 AIR TEMPERATURE ASSIMILATION TEST 2.1 ECMWF ERA-40 and Chinese station observation introduction ECMWF ERA-40 and station observed air temperature data are used in this assimilation test. The new reanalysis project ERA-40 covers the period from mid-1957 to 2001 including the earlier ECMWF reanalysis ERA-15, 1979-1993. The main objective is to promote the use of global analyses of the state of the atmosphere, land and surface conditions over the period. The three dimension variational technique will be applied using the T159L60 version of Integrated Forecasting System to produce the analyses every six hours. Analysis involves comprehensive use of satellite data, starting from the early Vertical Temperature Profile Radiometer data in 1972, then later including TOVS, SSM/I, ERS and ATOVS data. Cloud Motion Winds will be used from 1979 onwards. Most of the older observations for ERA-40 have been made available by NCAR. For more information about ECMWF reanalysis please see Uppala, et. al., 2005. Observations on 194 international exchange Chinese stations are selected. Their element factors include the daily/monthly/annual values of air temperature, air pressure, moisture, wind, precipitation and sunshine time with their high quality (CMA, 2004). 10 Daily Air Temperature of [] - [] Over China [] - [] (K) 8 6 4 2 0-2 -4 Air Temp -6 1 31 61 91 121 151 181 Stations Figure 1. Daily air temperature difference between and ECMWF 2.2 Why choose EC? The goal of our assimilation system is set to assimilate Chinese station-observation into ECMWF land surface data. But why do we choose ECMWF air temperature data to assimilate? In order to examine the difference of ECMWF and observation, EC background is interpolated to observation station without topography correction. By comparing ECMWF daily air temperature data with Chinese station daily observations, we found it is unreasonable that EC air temperature data are lower than station observations in most station positions, in some of which even lower than 10 K (See. Figure 1). By statistics of the above figure 1, the station number and the ratio to total stations (194) about the difference of observation and ECMWF interpolated in observation are listed in the following Table 1. Temp(K) <-4 [-4,-3) [-3,-2) [-2,-1) [-1,0) [0,1) [1,2) St. Number 1 1 3 10 53 70 20 Ratio (%) 0.5 0.5 1.5 5.2 27.5 36.3 10.4 [2,3) [3,4) [4,5) [5,6) [6,7) [7,8) [8,9) [9,10) 13 5 3 9 3 0 1 1 6.7 2.6 1.5 4.8 1.5 0 0.5 0.5 Table 1. Statistics on difference of ECMWF and Observation Proc. of SPIE Vol. 6298 62981M-2

From the above table, around two thirds of the EC air temperature data are lower than station observations, thus, it is urgently necessary to assimilate station observations with EC air temperature data and topography correction is needed in assimilation. 2.3 Air temperature test over China Chinese air temperature observation is assimilated with ERA-40 background and analysis field is improved. Results are in the followings (See Figure. 2, 3, 4). Figure 2. Station observed air temperature (K) Figure 3. EC background air temperature (K) Assimilation is meanly 2K (up to K) higher than EC background in Tibet; it is closer to the observations on nearby stations than ERA-40 background. Researches on air temperature of Tibetan Plateau demonstrate climate change (Chen et al, 2003; Yeh and Wu, 1998; Yanai et al, 1992). Assimilated low value centers occur on North-East China, which is similar to observation, but low value centers are not clearly seen on ERA-40. In East China, such as Jiangsu, Zhejiang and Anhui, especially Hubei, ERA-40 background is among 296-K, while observation is larger than K, some stations are even high to 303K. Air temperature comes to -302K on most of these areas after assimilation. It is quite reasonable with observation. In the West China, Xinjiang, low value center of background is 292K, while nearby observation is 303-305K. Low value center comes to 296K after assimilation, greatly reducing the difference with observation. Assimilation puts station-observed air temperature data into ERA-40, and also the air temperature distribution of sparse observation areas is obtained, which is useful to research on these areas. Figure 4. Assimilated air temperature (K) Proc. of SPIE Vol. 6298 62981M-3

3. COMPARISON BETWEEN ASSIMILATION AND ECMWF ERA-40 BACKGROUND In order to evaluate the effect of assimilation analysis, assimilation and EC ERA-40 background are interpolated to observation station respectively without topography correction. Interpolation error may be related to observation positions, background grids longitudes and latitudes, grid distances. We compare the difference of assimilation and EC ERA-40 background with observation. The followings list the comparative results of six stations including Beijing, Nanjing, Lanzhou, Haikou, Lhasa and Urmqi (See Figure 5). grasps the extreme air temperature more accurately. 310 Air Temperature of Beijing in 1997 305 Air Temperature of Nanjing in 1997 305 265 Air Temperature of Lanzhou in 1997 Air Temperature of Lhasa in 1997 304 302 298 296 294 292 288 286 306 Air Temperature of Haikou in 1997 284 310 Air Temperature of Urmqi in 1997 265 255 250 Fig. 5. Comparison of Observation, EC ERA-40 background and Assimilation on Chinese six stations From the above figures, in low altitude areas, including Beijing and Nanjing, ECMWF reanalysis is close to the station observation, while in high altitude areas, including Lanzhou and Lhasa, ECMWF reanalysis is much lower than station observation, for example, EC ERA-40 is about 5K lower in Lanzhou and 10K lower in Lhasa than station-observation. Proc. of SPIE Vol. 6298 62981M-4

The main reason may result in that observation altitude is not equivalent to EC ERA-40. So it needs to correct the observation air temperature in assimilation with EC ERA-40. However, it shows that assimilation procedure greatly improves the air temperature analysis and makes it closer to observation in high altitude areas. In addition, the trend of ECMWF air temperature interpolated to observation is coincide with that of observed air temperature, but ERA-40 does not accurately reflect the extreme air temperature in the year. For example, the difference of ERA-40 and station observation is larger than 5K on Lanzhou. The results with station observed air temperature assimilated into EC background indicate that assimilation describe the extreme air temperature more accurately. It is clear that assimilation can greatly enhance analysis by using station air temperature of high quality. Its effects are on two parts, one is that the trend of analysis after assimilation is closer to observation; the other is that assimilation analysis The main reason contributing to great air temperature difference on Tibetan Plateau may be the terrain of high altitude. Owing to the large gradient of terrain on Tibetan Plateau, it is not ensured that neighbored grids are on the same high altitude when interpolating grids to station points, so interpolation will inevitably carry in some errors without terrain correction. Meanwhile, sparse observation station is another reason contributing to interpolation error. So, the results may be better if terrain correction of air temperature is done and more observations are used. T T a 1 a o 2 b 1 b o 2 Similar to the concept of standard deviation, we present st ( xi xi ) and st ( xi xi ) as T i 1 T i 1 mean state of the difference between assimilation and background to observation, valuating the mean difference magnitude of assimilation and background around observation, where T is the time interval on a certain station and now is taken as 365 days. The following table 2 lists the difference of assimilation and background to observation on Beijing, Nanjing, Lanzhou, Haikou, Lhasa and Urmqi. Stations Beijing Nanjing Lanzhou Haikou Lhasa Urmqi ASS- (K) 1.61 0.69 3.98 0.54 0.51 1.41 BACK-(K) 3.01 0.96 5.14 0.67 8.23 2.35 Table 2. Standard deviation of Assimilation and Background to Observation The above table illustrates that assimilation can markedly improve analysis field, especially on the west high altitude areas such as Lanzhou and Lhasa. N N a 1 a o 2 b 1 b o 2 Similarly, we present sn ( xi xi ) and sn ( xi xi ) as the difference of assimilation and N i 1 N i 1 background to observation on some time over a region, valuating how much the assimilation and background come close to observation, where N is the number of station observations available on during some time over a region and now is taken as 193. The difference of assimilation and background to observation is showed in the following Figure 6. 11 10 9 8 7 Error of Background and Assimilatiion Interpolated in ERR 6 5 4 3 2 1 Figure 6. Difference of EC Background and Assimilation to Observation The mean difference of assimilation to observation is 1.52 on the year over the region of China, much lower than that of ECMWF ERA-40 background to observation, 2.65. This result also demonstrates the remarkable effect of assimilation over China. Proc. of SPIE Vol. 6298 62981M-5

Additionally, the observed air temperature on Longhua/Shanghai (No. 58367, 121.4 E, 31.1 N) without assimilating is selected as reference to valuate the effect of assimilation. The following Figure 7 shows the comparison of assimilation, background and observation. From figure 7, the difference of ECMWF background to observation is large to 1.47K on May to Aug. (121-243 rd day) on Longhua/Shanghai, while the difference reduces to 1.28K after assimilation, that is enhancing air temperature 0.2K. Assimilation is effective to improve background; however it is not greatly remarkable because a lot of observations are already assimilated into ECMWF Reanalysis. 305 Air Temperature of Longhua(Shanghai) in 1997 Figure 7. Observed, EC background and assimilated air temperature comparison on Lonhua/Shanghai 4. SUMMARY AND CONCLUDING REMARKS In order to improve the application efficiency of station-observed data into the model and illuminating uncertainties of land data for their inhomogeneous characteristics, we develop a 3DVAR Land Data Assimilation Scheme. In this part of the paper, Chinese station observed air temperature data are assimilated into ECMWF ERA-40 as a test and comparison is made between assimilation and EC background to observation. Firstly, after assimilation, the air temperature distribution of sparse observation areas is obtained. Secondly, by comparing assimilation and EC ERA-40 background to observation, the assimilation can greatly improve analysis with station-observed air temperature of high quality assimilated into EC background., which shows the validity of the assimilation procedure. Acknowledgements ECMWF ERA-40 data used in this study have been provided by ECMWF. Authors would like to express their thanks to Dr. Zaizhi Wang for discussions about the 3DVAR scheme and Dr. Xiwu Zhan for his helpful suggestions about LDAS and comments about this paper. This work was supported by,usda UV-B Monitoring and Research Program under a grant from USDA CSREES( 2005-34263-14), Project 973 (2004CB418303, 2006CB403605), National Natural Science Foundation of China (40471097). REFERENCES 1. Chen B, Chao W C, Liu X., 2003, Enhanced climate warming in the Tibetan Plateau due to doubling CO 2 : a model study. Climate Dynamics, 2, 401-413. 2. China Meteorological Administration, 2004, Compilation of 30-year conventional surface climate data and their statistics, Standards press of China, Beijing. 3. Christian L. Keppenne and Michele M. Rienecker, 2003, Assimilation of temperature into an isopycnal ocean general circulation model using a parallel ensemble Kalman filter, Journal of Marine Systems, vol. 40-41,363-380. 4. Kalnay, E., M. Kanamitsu, R. etc, 1996, The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-471. 5. Praveen Kumar and Amy L. Kaleita, 2003, Assimilation of near-surface temperature using extended Kalman filter, Advances in Water Resources, 26(1), 79-93. 6. Uppala, S.M., Kållberg, P.W., Simmons, A.J,etc, 1997, Reanalysis of historical observations and its role in the Proc. of SPIE Vol. 6298 62981M-6

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