Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture

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Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model JIFU YIN Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China, and NOAA/NESDIS/Center for Satellite Applications and Research, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

XIWU ZHAN NOAA/NESDIS/Center for Satellite Applications and Research, College Park, Maryland

YOUFEI ZHENG Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science and Technology, Nanjing, China

JICHENG LIU NOAA/NESDIS/Center for Satellite Applications and Research, College Park, Maryland, and Department of Geography and GeoInformation Science, George Mason University, Fairfax, Virginia

LI FANG AND CHRISTOPHER R. HAIN NOAA/NESDIS/Center for Satellite Applications and Research, and Cooperative Institute for Climate and Satellites, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland (Manuscript received 25 March 2014, in final form 18 September 2014) ABSTRACT Many studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged surface-layer soil moisture fields from SBSM have a higher spatial coverage and generally agree with model simulations in the global patterns of wet and dry regions. The impacts of assimilating SMOPS blended data on model soil moisture and soil temperature are evident in both sparsely and densely vegetated areas. Temporal correlations between in situ observations and net shortwave radiation and net longwave radiation are higher with assimilating SMOPS blended product than without the data assimilation.

1. Introduction Corresponding author address: Professor Youfei Zheng, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science and Technology, 219 Ningliu Road, Pukou, Nanjing 210044, China. E-mail: [email protected] DOI: 10.1175/JHM-D-14-0070.1 Ó 2015 American Meteorological Society

The application of complex water balance formulations embedded within land surface models (LSMs) is a common approach to track soil moisture (SM), soil

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temperature (LST), and surface energy fluxes from the short to long term (Ek et al. 2003; Mo et al. 2010; Crow et al. 2012). However, these model-based estimates are subject to errors of models and their inputs of meteorological forcing data (Reichle and Koster 2004). Consequently, it is believed that a land data assimilation system that merges satellite retrievals and model estimates of soil moisture may provide optimal values of land surface state variables (Reichle and Koster 2004). In the past few years, the greater diversity of methods have made significant progress toward operational soil moisture remote sensing, which leads to the availability of several global datasets (Scipal et al. 2008), including passive and active microwave observations (Y. Y. Liu et al. 2011; Hain et al. 2012). The most extensively validated passive microwave SM retrieval datasets have been generated using brightness temperature observations. With the improved algorithms, it has been possible to derive soil moisture from existing operational passive microwave satellite systems such as the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E; Njoku et al. 2003) and WindSat (Li et al. 2010). In addition, the Soil Moisture and Ocean Salinity (SMOS) satellite launched by the European Space Agency (ESA) in November 2009 carries the low-frequency L-band (1.42 GHz) sensor, which has been continuously providing global soil moisture observations for the past several years (Y. Y. Liu et al. 2011). The active microwave satellite sensors provide useful retrievals of near-surface soil moisture observations at regional and global scales (Wagner et al. 2003; Owe et al. 2008; Y. Y. Liu et al. 2011). The European Remote Sensing Satellite–1 (ERS-1) scatterometer began its operation in 1992, ERS-2 started collecting data in March 1996, and the Advanced Scatterometer (ASCAT) on board the Meteorological Operation (MetOp) satellite program was launched in October 2006 (Y. Y. Liu et al. 2011). The soil moisture retrieval products from the available microwave remote sensing missions have offered an opportunity to improve the realism of modeled soil moisture (Parrens et al. 2014). Many studies have been done to improve the initial soil moisture state by assimilating these remotely sensed near-surface soil moisture data products (Crow and Wood 2003; Reichle and Koster 2005; Koster et al. 2009; Zhan et al. 2012; Parrens et al. 2014). However, the soil moisture data retrieved from each of these single satellite sensors vary significantly from each other (Y. Y. Liu et al. 2011). From a single satellite platform, the special coverage of the soil moisture retrievals is usually incomplete in space and time, which may have adverse influences on assimilating effect (Reichle et al. 2008). Combination of the

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products from these multiple sensors may provide better efficiency and effectiveness of their assimilation as long as the individual products are merged properly (Albergel et al. 2013). Many of the previous studies have focused on the assimilation of synthetic SM retrievals (Reichle and Koster 2005; Kumar et al. 2009; Xia et al. 2012; Hain et al. 2012; Zhan et al. 2012). However, few studies have evaluated the assimilation of multiple types of real remotely sensed SM retrievals at the global scale, and even fewer studies have evaluated the merits of assimilating a combination of the multiple sensor data products. In this paper, the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product is briefly introduced first in the next section. Then, a numerical experiment using the ensemble Kalman filter (EnKF) to assimilate SBSM in the Noah LSM is described in section 3. The results based on comparisons with SM and LST observations from the U.S. Department of Agriculture (USDA) Soil Climate Analysis Network (SCAN) and net shortwave (SWnet) and net longwave (LWnet) radiation retrieved from Surface Radiation Budget Network (SURFRAD) stations are shown in section 4, and the discussion and conclusions are provided in section 5.

2. Datasets a. SMOPS blended product There are several global soil moisture data products that have been generated from the existing satellite sensors or that will be generated from future ones. It may be more convenient to blend them together as a single data layer for assimilating them into an operational numerical weather prediction model. For this purpose, NOAA/NESDIS has developed the global SMOPS as a preprocessor of assimilating all available satellite soil moisture data products into the operational Global Forecast System (GFS) of the NOAA/NCEP/ National Weather Service. After retrieving soil moisture from satellite brightness temperature TB observations or ingesting existing soil moisture retrievals, SMOPS merges all these available soil moisture data layers within the past 6 h into a single data layer in order for the GFS to carry out the soil moisture data assimilation every 6 h. Currently, SMOPS ingests TB observations from the Naval Research Laboratory’s WindSat and soil moisture retrievals from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) ASCAT and the SMOS mission of the European Space Agency (ESA) and combines them after they are scaled to the same soil moisture climatology of the Noah LSM (Zhan et al. 2012) using the

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cumulative distribution function matching approach (Reichle and Koster 2004). To generate a merged global soil moisture data product, retrievals from WindSat are at footprint while the retrievals from SMOS and ASCAT are already gridded. Equal weights are applied when multiple retrievals are available for a single grid. From 2013, SMOPS is operational at the NESDIS Office of Satellite and Product Operations (OSPO), and all individual satellite sensor soil moisture data layers and the merged data layer are continuously available (www.ospo.noaa.gov/Products/ land/smops/index.html). The daily SBSM on a roughly global domain (from 608S, 1808 to 908N, 1808) at 25-km spatial resolution from 2008 to 2011 is employed in this paper.

assimilation (Reichle et al. 2002). The EnKF is a Monte Carlo approximation of a sequential Bayesian filtering process, which alternates between an ensemble forecast step and a state variable update step (Reichle et al. 2002). In the forecast step, an ensemble of model states is propagated forward in time using the land surface model. Here, we define the analyzed estimate at location i and time t . 0 as Ati,j 5 Fti,j 1 K(Mti,j 2 HFti,j ) ,

where j counts from 1 to the number of ensemble members N and Fti,j , Mti,j , and Ati,j are vector forecast, measurement, and analyzed estimates, respectively. The Kalman gain matrix K is given by

b. In situ measurement datasets Datasets of in situ SM and LST measurements from SCAN are collected for assessing the accuracy of Noah LSM simulations with and without the SMOPS data assimilation. Hourly SM measurements were taken with a device measuring the dielectric constant of the soil at depths of 5, 10, 20, 50, and 100 cm wherever possible (Q. Liu et al. 2011). There are a total of 179 SCAN sites that provide hourly in situ SM and LST at a depth of 10 cm that matches the 10-cm thickness of the first layer of Noah, version 3.2 (Noah3.2), implemented in Land Information System (LIS). The data from each SCAN site are extensively quality controlled using methods including automatic detection of problematic observations and visual inspection of the time series (Q. Liu et al. 2011). After quality control to the hourly data, the SCAN observations were aggregated into daily averages during the experiment period (2008–11) in this work. Radiation data are also collected for evaluation of the Noah LSM radiation simulations from seven SURFRAD stations. These stations are distributed among diverse climates that include the hot, dry desert of the southwestern United States; the moist environment of the eastern United States; and the semiarid northern plains. The sites were chosen to be regionally representative (Augustine and Hodges 2005; Augustine and Dutton 2013). The qualified minute-resolution surface SWnet and LWnet radiation were retrieved and averaged at daily temporal resolution in this paper.

3. Synthetic experiment a. Ensemble Kalman filter The EnKF can provide a flexible approach to merging the model simulations and the corresponding observations according to their error characteristics. It has been widely used as an effective technique for soil moisture

(1)

K5

t HtT Ccci i t HtT 1 Ct Hti Ccci i vvi

,

(2)

the matrix H is the measurement operator and relates t t the model state to the measurement and Ccci and Cvvi are error variances for forecast and measurement estimates, respectively. These error variances vary in time because they depend on the dynamics and all the data included in the previous updates; the Kalman gain depends on the forecast error covariances, which are obtained directly from the ensemble prior to the update (Reichle et al. 2002). Here, we used the NASA LIS and manually adjusted the variance values to make the normalized innovations satisfy the zero mean and unit standard deviation requirement for optimal assimilation results. In particular, these error variances used a constant value (3%) as what LIS examples were using. According to previous research (Reichle et al. 2002; Kumar et al. 2008), we set ensemble size of EnKF as 12 in this work.

b. Experiment overview We designed a synthetic experiment to investigate the performances of assimilating the SMOPS blended product. The basic procedure of the experiment is similar to Kumar et al. (2009, 2012), Peters-Lidard et al. (2011), and Hain et al. (2012) and is summarized as follows. 1) Based on the Noah LSM, a single realization with the best available forcing and initialization is chosen and assumed to represent the ‘‘true’’ state of the Noah model, referred to as the control run (CTR). 2) An open-loop run (OLP) is perturbed to the ‘‘truth’’ run according to Table 1, and it represents the model skill with suboptimal forcing and initialization and without the benefit of data assimilation.

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TABLE 1. Perturbation parameters for meteorological forcing inputs and for state variables. Cross correlation for forcing variable perturbations Perturbation type

Std dev

P

SW

LW

P SW LW

0.5 (mm) 0.3 (W m22) 50 (W m22)

1.0 20.8 0.5

20.8 1.0 20.5

0.5 20.5 1.0

Cross correlation for state variable perturbations Perturbation type SM1 (0–10 cm) SM2 (10–30 cm) SM3 (30–60 cm) SM4 (60–100 cm)

Std dev 23

23

6.00 3 10 m m 1.10 3 1024 m3 m23 6.00 3 1025 m3 m23 4.00 3 1025 m3 m23 3

3) The assimilation of the SMOPS blended product using the EnKF implemented in NASA’s LIS (Kumar et al. 2008) is implemented as the data assimilation run (DA). It is worth noting that the configurations of CTR and OLP simulations here are different from the traditional ‘‘twin experiments.’’ In this study, in situ data are used to evaluate data assimilation impact in addition to the comparison with OLP using CTR as the ‘‘truth.’’ In fact, the Gaussian random perturbation parameters with zero mean (Table 1) are chosen to perturb the model states and parameters because of the assumption of unbiased state variables in the EnKF. The rationale is based on the implicit assumption that zero-mean Gaussian noise added to the state variables and parameters in the EnKF analysis should not cause a systematic bias in model output when the ensemble mean of the perturbed state variables are compared with unperturbed state variables (Ryu et al. 2009). The CTR case is not perturbed; the simulations from the CTR case represent their ‘‘true’’ values and are used to evaluate the effect of assimilation, compensating for the lack of spatial representativeness in in situ SM validation datasets across the global domain. The in situ observational data are independent from the perturbations and data assimilations. The accuracy of the DA case against the in situ data minus that of the corresponding OLP case should be a good metric to assess the improvements resulting from the data assimilation. All simulations and assimilation runs are conducted over a gridded domain that covers basically all land of the whole globe (from 608S, 1808 to 908N, 1808) at 25-km spatial resolution. In each experiment, the model is spun up by cycling five times through the period from 1 January 2008 to 26 December 2011. All simulations are conducted over the same 4-yr period with 0.5-h timestep inputs and daily outputs, and the model in each case

SM1

SM2

SM3

SM4

1.0 0.6 0.4 0.2

0.6 1.0 0.6 0.4

0.4 06 1.0 0.6

0.2 0.4 0.6 1.0

is run with four layers with thicknesses of 10, 30, 60, and 100 cm. The Noah LSM is driven by monthly Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) albedo product (MCD43C3) and weekly Advanced Very High Resolution Radiometer (AVHRR) green vegetation fraction (GVF) at 25-km resolution. The Noah3.2 implemented in LIS, version 6.1.6, for each case is forced by the meteorological data from the Global Data Assimilation System (GDAS). The GDAS is the operational global atmospheric data assimilation system of NCEP (Derber et al. 1991). GDAS assimilates a variety of conventional meteorological data (radiosonde, buoy, ship, and airborne) and satellite-derived observations, using a four-dimensional multivariate approach, and produces operational, global analyses for four synoptic hours: 0000, 0600, 1200, and 1800 UTC (Rodell et al. 2004). In this paper, the latter 3-hourly 25-km GDAS products are used, which include precipitation P, downward shortwave radiation SW, downward longwave radiation LW, near-surface air temperature, near-surface humidity, near-surface wind, and surface pressure on the global domain.

4. Results Figure 1 shows the average daily coverage probability (ADCP) of the SBSM product on a roughly global domain over the period from 1 January 2008 to 26 December 2011. It demonstrates that ADCP of SBSM is more than 90% over the high latitudes, except Greenland, and roughly more than 0.8 over the midlatitudes. Although the ADCP is lower in low latitudes, it is basically larger than 70% and reaches 80%–90% in many regions. Figure 1 also demonstrates that SBSM retrievals are available at high usage rate on the global domain, including the regions where the vegetation is dense, namely, South America, central and southern Africa, the eastern United States, Southeast Asia, and western

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FIG. 1. ADCP of SM retrieved from SBSM over the period from 1 Jan 2008 to 26 Dec 2011.

Europe (the high GVF areas in Fig. 2). The ADCP pattern from SBSM indicated that SBSM can be available for more than 24 days over midlatitudes and more than 21 days over low latitudes in one month (30 days). But the lower ADCP can be seen in the Tibetan Plateau, where freezing of the soil limits the number of data available in winter, resulting in a lower year-round average (Reichle et al. 2004). The goal of this study is to estimate the performance of assimilating satellite soil moisture data into the Noah LSM. The compatibility of the SM retrievals with global model soil moisture is examined first for data quality. The pattern of daily averaged SBSM product and the spatial distribution of daily averaged model SM obtained from the CTR case are shown in Fig. 2. The time period is from January 2008 to December 2011. The global wetness and dryness patterns of both CTR and the SBSM product agree as well as expected. The driest places with lower GVF are in the Sahara Desert, the Arabian Peninsula, central Australia, the western United States, and northern China. The wettest regions with higher GVF are located in the tropical rain forests of South America and Africa, Southeast Asia, and the temperate and boreal forests of North America and Eurasia. The important differences between the model and satellite SM values can be seen in North America (a strong west-to-east gradient in satellite data) and north Eurasia (a strong south-tonorth gradient in model data). This implies that the satellite retrievals are drier than model in the western United States, where GVF is lower, but the model is wetter than SBSM in north Eurasia, where vegetation is dense, although the SBSM climatology are already scaled to the Noah LSM climatology globally. However, it is not possible to tell with confidence whether the retrieval algorithm or the model is more accurate than the other (Reichle et al. 2004). It is anticipated that an assessment of the assimilation results and the CTR case against the in

situ observational data can indicate whether SBSM or model simulation is closer to reality. The improvements for top layer (0–10 cm) soil moisture from assimilating SBSM over OLP are demonstrated in Fig. 3. The improvements and degradations here are based on the assumptions that the CTR case is true, as described in section 3. The areas of blue shading for the assimilation experiment show the magnitude of root-mean-square deviation (RMSD) improvement over the OLP case, while red shading denotes regions of degradations caused by the assimilation. The improvements are not only shown in lower GVF regions such as in the Sahara Desert, the Arabian Peninsula, and central Australia, but also in densely vegetated areas, namely, the tropical rain forests of South America, Africa, and Southeast Asia. The degraded regions are in the boreal forests of North America and Eurasia, where SBSM retrievals may have been influenced by both higher GVF and lower temperature. Figure 4 shows the frequency probability as a function of monthly root-mean-square error (RMSE) for top layer (0–10 cm) SM and LST between in situ observations from SCAN and the CTR case (black line), OLP case (red line), and DA case (blue line) during 2008–11 over the United States. The statistical density function of frequency probability (DFP) shifting toward the left means improved and toward the right means degraded. In low GVF (GVF , 0.3) areas, the DFP of SM for the DA case is clearly less than in the CTR and OLP cases when RMSE is larger than 0.12 m3 m23, and it shifts obviously toward the left compared to the OLP case. After assimilating SBSM, the average RMSEs for SM in CTR are reduced about 0.01 m3 m23 (11%–15%) in spring and summer, and in the OLP case they can be decreased by more than 0.02 m3 m23 (20%–25%) in summer and autumn (Fig. 5). In middle GVF

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FIG. 2. Average (top) SM retrieved from SBSM, (middle) simulated SM in CTR case, and (bottom) daily GVF from 1 Jan 2008 to 26 Dec 2011.

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FIG. 3. RMSD-DA minus RMSD-OLP for SM (m3 m23) for 0–10 cm. RMSD-DA is the RMSD between the DA and CTR cases. RMSD-OLP indicates RMSD between the OLP and CTR cases. The blue (red) color indicates pixels where the DA case decreased (increased) RMSD compared to the OLP case, but white is insignificantly changed pixels.

(0.7 . GVF $ 0.3) regions, the DFP of SM for DA can be seen much more when RMSE is below 0.05 m3 m23, which leads to RMSEs for CTR and OLP cases that are reduced more than 0.01 m3 m23 (14%–28%) in all seasons except in winter. In high GVF areas (GVF $ 0.7), with data assimilation, it clearly shifts toward the left compared to CTR, which leads to a 0.01 m3 m23 (10%– 15%) decrement of RMSEs for CTR in all seasons. However, compared to the OLP case, the improvements are insignificant with the slight degradations shown in summer and autumn. In Fig. 4, we also find that the DFPs of SM for the DA case shift obviously toward the left in the growing season (April–October) and throughout the year, which leads to a 0.01 m3 m23 (13%– 15%) decrement of RMSEs for CTR for the whole year, a 0.01 m3 m23 (about 13%) decrement for the OLP case in spring, and a more than 0.02 m3 m23 (about 25%) decrement in summer and autumn (Fig. 5). Particularly, the order of magnitude (0.01–0.02 m3 m23) is small, but the statistical results show that the improvements of the DA case in comparison with both CTR and OLP cases are all significant (the credibility level is 0.01 and the sample size is 1455 since there are 1455 days from 1 January 2008 to 26 December 2011). In Figs. 4 and 5, we can also see that DFPs of LST for the DA, OLP, and CTR cases are all slightly changed with RMSE increasing in low GVF areas, which leads to the modest performances (all of three cases) of model LST and slight impact of assimilation in all seasons. The improved phenomenon is shown in middle and high GVF areas. The DFPs for the DA, OLP, and CTR cases under both middle and high GVF conditions can mainly be seen when RMSEs are below 8 K with some slight

fluctuations. Compared to the OLP case, model LST in the DA case shifts clearly toward the left in both middle and high GVF areas. In particular, in spring and autumn, RMSEs of the OLP case are reduced by about 0.6 K (9%–13%) in middle GVF areas and 0.5 K (6%–8%) in densely vegetated areas. In summer, the improvements are 1.4 K (26%) in high GVF areas and 1 K (17%) in middle GVF areas. The DFP patterns of LST in both growing season and throughout the year are similar, with data assimilation performing better in growing season. In winter, however, both the model SM and LST are improved insignificantly. Figure 6 shows time series of U.S. domain–averaged SM and LST RMSE (with respect to the SCAN in situ observations) for the 0–10-cm surface soil layer in the CTR (black line), OLP (red line), and DA (blue line) simulations from 1 January 2008 to 26 December 2011. For surface-layer SM, spatially averaged RMSE for the DA, CTR, and OLP cases are 0.074, 0.087 (17.6% increase versus DA), and 0.087 m3 m23 (17.6% increase versus DA), respectively. In addition, spatially averaged RMSE values are also computed for surface-layer LST. In particular, the RMSE for the DA, CTR, and OLP cases are 7.73, 7.75 (0.3% increase versus DA), and 7.95 K (2.9% increase versus DA), respectively. Figure 7 demonstrates the model SWnet and LWnet radiation as a function of in situ observations from seven SURFRAD stations over the period from 1 January 2008 to 26 December 2011. The correlation coefficients R between model SWnet and in situ observations are all more than 0.9 (sample size is 10 185, 1455 days for 7 sites) and standard deviations (std dev) are limited to below 2.35 W m22, which implies that the Noah model has

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FIG. 4. The U.S. domain–averaged frequency probability as a function of monthly RMSE for 0–10-cm (left) SM and (right) LST between in situ observations and each of three cases during 2008–11 for (from top to bottom) low GVF (GVF , 0.3), middle GVF (0.7 . GVF $ 0.3), high GVF (GVF $ 0.7), growing season (April–October), and throughout the year.

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FIG. 5. The U.S. domain seasonal average of monthly RMSE for 0–10-cm (left) SM and (right) LST between in situ observations from SCAN and each of the three cases during 2008–11. Monthly RMSE for the sites (from top to bottom) under low GVF, middle GVF, high GVF, and throughout the year.

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FIG. 6. Based on the SCAN in situ observations, the averaged time series for 0–10-cm (left) SM and (right) LST RMSE for each of the three cases during 2008–11.

great capability to capture the variation trend of SWnet. Compared to the OLP (R 5 0.913, std dev 5 2.304) and CTR cases (R 5 0.914, std dev 5 2.302), DA (R 5 0.914, std dev 5 2.298) can slightly improve the results. However, the model SWnet values are smaller than the observations according to the regression analysis. The R value for LWnet is enhanced clearly in the DA case (R 5 0.732) compared to the CTR (R 5 0.705) and the OLP cases (R 5 0.702), and the std dev is reduced from more than 1.2 W m22 in CTR (1.201 W m22) and OLP (1.232 W m22) to 1.164 W m22 with assimilating SBSM.

5. Discussion and summary Our analysis is based on assimilating daily SMOPS blended soil moisture (SBSM) retrievals from January 2008 to December 2011. The SMOPS blended product that combines soil moisture (SM) products from WindSat, ASCAT, and SMOS has a high coverage probability (more than 21 days in low latitudes, more than 24 days in midlatitudes, and roughly full cover in high latitudes in 30 days) on the global domain. The average daily coverage probability (ADCP) for SMOS, ASCAT, WindSat, and SMOPS blended soil moisture product is shown in Fig. 8. The ADCP is largely increased by SBSM product in comparison with SMOS, ASCAT, and WindSat over high latitude, midlatitude, low latitude, and global domains. In particular, the global domain–averaged ADCP for SBSM, SMOS, ASCAT, and WindSat are 84.2%, 34.0% (43.8% reduction versus SBSM), 66.5% (21.0% reduction), and 73.2% (10.5% reduction), respectively. The largest increase can be seen in low-latitude areas, where the domain-averaged ADCP for SBSM, SMOS, ASCAT, and WindSat are 79.2%, 39.3% (50.4% reduction versus SBSM), 55.0% (30.6% reduction), and 55.5% (29.9% reduction), respectively. Data assimilation methods (including the EnKF) are designed to correct random errors in the model background and assume that model and observations are climatologically unbiased. Such climatological

biases must be addressed as part of the assimilation experiment. Based on the a priori scaling method of Reichle and Koster (2004), in this study, the observations are scaled to the model’s climatology so that the cumulative distribution functions (CDFs) of the observations and the model match. Both the satelliteretrieved and model SM rely on independent observations. We have compared the rescaled global SM data from SMOPS blended product and that from Noah LSM integrations of observed antecedent meteorological forcing. The Student’s t test is used to examine the statistical significance. The significant relationships (credit level is 0.01; sample sizes are more than 8401; correlation coefficients are bigger than 0.36) can be seen in both dry (0–0.1 m3 m23 level in Table 2) and wet areas ($0.3 m3 m23 level in Table 2). The differences of their average values are small (below 0.03 m3 m23) for each of the four levels except in wet areas. The statistical results demonstrate that these two datasets agree with each other well in the global patterns of wet and dry regions. However, there are also significant differences between model and satellite SM that can be seen in North America (a strong west-to-east gradient in satellite data) and north Eurasia (a strong south-to-north gradient in model data). According to the synthetic experiment, the improvements of the DA case on model SM in comparison with the CTR case can exhibit the quality of SMOPS blended product, which is a crucial role that has a positive/negative impact on assimilating results. Yet, the results of assimilation (DA) and CTR cases examined by in situ observations (Figs. 4, 5) imply that the SMOPS blended product is closer to the true climatology. According to the assumptions of experiment, we estimated the improvements for the top layer (0–10 cm) of assimilating SMOPS blended product on model SM. After data assimilation, the RMSE of OLP is reduced in both low and high green vegetation fraction (GVF) areas. In particular, the improvements are also seen in

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FIG. 7. The model (left) SWnet (filled circles) and (right) LWnet (open circles) radiation as a function of in situ observations retrieved from seven SURFRAD stations over the period from 1 Jan 2008 to 26 Dec 2011. The sample size is 10 185 (1455 days for 7 sites) for (top) CTR case (black), (middle) OLP case (red), and (bottom) DA case (blue). The green line from the lower-left corner to the upper-right corner indicates the regression curve. The brown diagonal line represents the model values and in situ observations are same. The abbreviation SD indicates std dev.

the tropical rain forests of South America, Africa, and Southeast Asia. However, the degradations are shown in the boreal forests of North America and Eurasia, where they are influenced by both higher GVF and lower temperature. In the DA case, the model simulation performs well in high GVF areas but modestly in winter in Fig. 5, which may indicate that the degradations are mainly from the noise caused by lower temperatures. In essence, special preprocesses for a soil moisture product should be made according to Kumar et al. (2009).

However, the special preprocesses were not used in this paper, because our goal here is to estimate the performance of assimilating the ‘‘original’’ SBSM data into a land surface model. The larger improvements of DA on top layer (0–10 cm) soil moisture are observed in spring, summer, and autumn. In particular, in low GVF areas (GVF , 0.3), the RMSE between observational soil moisture from SCAN and SM in the CTR case is reduced 11%–15% in spring and summer, and the RMSE for the CTR case can be decreased by 20%–25%

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FIG. 8. ADCP for SMOS, ASCAT, WindSat, and SBSM products over high, mid-, and low latitudes and the global domain.

in summer and autumn. As expected, the improvements are also shown in middle (0.7 . GVF $ 0.3) and high (GVF $ 0.7) GVF areas. The similarly improved patterns are also observed in model soil temperature of the surface layer, but to a lesser extent. The good performances of assimilating SMOPS blended soil moisture data on both model SWnet and LWnet radiations are found here with decreased standard deviations and increased correlation coefficients, but the model SWnet radiations are smaller than the in situ observations. The accuracy of model root-zone soil moisture is very important to estimate model skill in a data assimilation system. But data assimilation techniques rely on the inherent surface–root zone connection to propagate surface information to deeper soil layers. Depending on the surface–root zone (vertical) coupling strength of the LSM, the information from surface observations is vertically propagated differently for each LSM during data assimilation. Thus, the potential of surface soil moisture assimilation to improve root-zone information is largely related to land surface model structure (Kumar et al. 2009). Our goal is to estimate the performance of assimilating SBSM data into the Noah land surface model. Here, model surface-layer (0–10 cm) soil moisture, soil temperature, net shortwave radiation, and net longwave radiation with assimilating SMOPS blended data are assessed to demonstrate the benefit from data assimilation. Based on the in situ observations, the good performance of model root-zone soil moisture with

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assimilating SMOPS blended data is demonstrated in another manuscript (Yin et al. 2014). Our analysis of assimilating SMOPS blended data demonstrates insignificant improvements for model soil moisture and soil temperature in winter, because the noises generated by snow and the frozen soil have negative influences on assimilating soil moisture (Kumar et al. 2009). The larger improvements are shown in middle GVF areas since: 1) the presence of dense vegetation in wet regions makes satellite remote sensing of soil moisture impossible sometimes and 2) in dry regions the high level of noise in the satellite data drowns out the typically small variability signal (Koster et al. 2000; Koster and Suarez 2003, Reichle et al. 2004). The large improvements of assimilating single satellite retrievals on land surface model skill have been shown in previous works (Crow and Wood 2003; Reichle and Koster 2005; Koster et al. 2009; Zhan et al. 2012; Parrens et al. 2014). In particular, with respect to in situ SM observations, the correlation coefficient (RMSE) is significantly increased (decreased) by the assimilation of either passive or active microwave SM data retrieved from single sensor (Owe et al. 2008; Hain et al. 2012; Draper et al. 2012; Maggioni et al. 2013; Kumar et al. 2014). Yet, the maximum accuracy and coverage is recommended that active and passive microwave observations should be assimilated together (Draper et al. 2012). In these works, some preprocessing prior to assimilation was made to reduce the influence of potential bias from these single satellite SM products on assimilating effects (Owe et al. 2008; Hain et al. 2012; Draper et al. 2012), which can be degraded by the assimilation case without benefit of quality control of satellite SM data (Li et al. 2011). Here, assimilating the merged product of SM retrievals from WindSat, ASCAT, and SMOS did not require quality control, while significant improvements are found. In fact, the science of data assimilation and the key to success are largely in the accurate specification of the assimilating satellite product (Reichle 2008; Maggioni et al. 2013). Nevertheless, the advantages of SMOPS blended product compared to other observational data are beyond the scope in this paper, and we will focus on the topic using in situ observations in another paper.

TABLE 2. Model SM values in the CTR case, which are divided into four levels, in comparison with SBSM product on global domain. SM in CTR case (m3 m23)

Avg SM in CTR case (m3 m23)

Avg SBSM (m3 m23)

R

Sample size

0–0.1 0.1–0.2 0.2–0.3 $0.3

0.087 0.144 0.223 0.276

0.056 0.160 0.251 0.345

0.67 0.42 0.36 0.42

8401 55 181 80 260 88 816

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In summary, we obtain four key results. 1) The SMOPS blended product has a high coverage capability, and the surface-layer soil moisture climatologies of SMOPS blended retrievals and model results agree on the global domain. 2) Even so, there are important differences between model and satellite soil moisture that can be seen in North America and north Eurasia, but based on the examination by in situ observations, we can find that the SMOPS blended product is closer to the true climatology. 3) The improvements of assimilating SMOPS blended data on model soil moisture and soil temperature can be seen not only in low and middle GVF areas, but also in high GVF areas, and a better performance is shown in middle GVF areas. 4) Temporal correlations between in situ observations and SWnet/LWnet are stronger with assimilating SMOPS blended product than without the benefit of data assimilation. Additional future work that could potentially lead to additional improvements of data assimilation includes the following. 1) The observational data coverage is incomplete in space and time, which will have adverse influences on the assimilating effect if the retrieved data are masked out too much. But if they are not well controlled in quality, the signals that are not properly handled from snow, frozen soil, and dense vegetation can give rise to an unwanted influence on the SM retrievals and have a negative effect on the performance of data assimilation. Based on the in situ observations on a large-scale domain, the quality control of satellite data in soil moisture assimilation is expected to be developed. 2) According to the regression analysis, the model SWnet values are smallerthan the observations. In fact, surface albedo is a crucial parameter (Cescatti et al. 2012) in determining the magnitude of energy fluxes in the soil–plant–atmosphere continuum (Chapin et al. 2008). The monthly surface albedo used in this paper is near–real time, but higher-resolution (weekly–daily) albedo may potentially lead to additional improvements on Noah3.2 model simulations. On 28 October 2011, the Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite was successfully launched into a circular, near-polar orbit with an altitude of 824 km above the Earth (Weng et al. 2012). Suomi-NPP can provide daily albedo at about 750-m spatial resolution. The temporal resolution here is monthly, but it is expected to improve by assimilating the higher temporal– and higher spatial–resolution albedo.

3) In previous studies, the significant improvements of assimilating AMSR-E (particularly in sparsely vegetation areas) on land surface model skill are claimed (i.e., Owe et al. 2008; Hain et al. 2012) with some preprocessed priori. In fact, the AMSR-E observations at moderately low frequencies are very sensitive to land water in various forms, such as soil moisture, vegetation water content, and snow and are little affected by atmosphere conditions (Du and Liu 2013). However, the current SBSM data do not combine the signals from AMSR sensors that have greater spatial resolution and repeat frequency, because AMSR is based on essentially the same frequency as C band (ASCAT) and its soil moisture signal will still be weaker than that obtained with the L-band sensor (SMOS; Kerr et al. 2001, 2012; Reichle et al. 2004). Merging more satellite products (i.e., AMSR-E) to further enhance SMOPS blended product performances is also an important additional future work, which is expected to obtain the further improvements of assimilating SMOPS blended data on land surface model skills. Acknowledgments. This work was supported by a grant from NOAA JPSS Proving Ground and Risk Reduction (PGRR) Program and Graduate Education Innovation Project in Jiangsu Province (CXZZ12_0499). We thank Gabrielle De Lannoy for her efforts on qualitycontrolled SCAN data and Wei Guo for providing the weekly GVF data. We are also grateful to the anonymous reviewers for helping to significantly improve the quality of the manuscript. REFERENCES Albergel, C., and Coauthors, 2013: Skill and global trend analysis of soil moisture from reanalyses and microwave remote sensing. J. Hydrometeor., 14, 1259–1277, doi:10.1175/ JHM-D-12-0161.1. Augustine, J. A., and G. B. Hodges, 2005: An update on SURFRAD—The GCOS Surface Radiation budget network for the continental United States. J. Atmos. Oceanic Technol., 22, 1460–1472, doi:10.1175/JTECH1806.1. ——, and E. G. Dutton, 2013: Variability of the surface radiation budget over the United States from 1996 through 2011 from high-quality measurements. J. Geophys. Res. Atmos., 118, 43– 53, doi:10.1029/2012JD018551. Cescatti, A., and Coauthors, 2012: Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. Remote Sens. Environ., 121, 323–334, doi:10.1016/j.rse.2012.02.019. Chapin, F. S., J. T. Randerson, A. D. McGuire, J. A. Foley, and C. B. Field, 2008: Changing feedbacks in the climate–biosphere system. Front. Ecol. Environ., 6, 313–320, doi:10.1890/080005. Crow, W. T., and E. Wood, 2003: The assimilation of remotely sensed soil brightness temperature imagery into a land surface

930


model using ensemble Kalman filtering: A case study based on ESTAR measurements during SGP97. Adv. Water Resour., 26, 137–149, doi:10.1016/S0309-1708(02)00088-X. ——, S. V. Kumar, and J. D. Bolten, 2012: On the utility of land surface models for agricultural drought monitoring. Hydrol. Earth Syst. Sci. Discuss., 9, 5167–5193, doi:10.5194/hessd-9-5167-2012. Derber, J. C., D. F. Parrish, and S. J. Lord, 1991: The new global operational analysis system at the National Meteorological Center. Wea. Forecasting, 6, 538–547, doi:10.1175/ 1520-0434(1991)006,0538:TNGOAS.2.0.CO;2. Draper, C. S., R. H. Reichle, G. J. M. De Lannoy, and Q. Liu, 2012: Assimilation of passive and active microwave soil moisture retrievals. Geophys. Res. Lett., 39, L04401, doi:10.1029/2011GL050655. Du, J., and Q. Liu, 2013: Satellite detection of spatial distribution and temporal changes of surface soil moisture at Three Gorges Dam region from 2003 to 2011. Earth Interact., 17, doi:10.1175/ 2012EI000479.1. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296. Hain, C. R., W. T. Crow, M. C. Anderson, and J. R. Mecikalski, 2012: An ensemble Kalman filter dual assimilation of thermal infrared and microwave satellite observations of soil moisture into the Noah land surface model. Water Resour. Res., 48, W11517, doi:10.1029/2011WR011268. Kerr, Y. H., P. Waldteufel, J.-P. Wigneron, J.-M. Martinuzzi, J. Font, and M. Berger, 2001: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens., 39, 1729–1735, doi:10.1109/ 36.942551. ——, and Coauthors, 2012: The SMOS soil moisture retrieval algorithm. IEEE Trans. Geosci. Remote Sens., 50, 1384–1403, doi:10.1109/TGRS.2012.2184548. Koster, R. D., and M. J. Suarez, 2003: Impact of land surface initialization on seasonal precipitation and temperature prediction. J. Hydrometeor., 4, 408–423, doi:10.1175/1525-7541(2003)4,408: IOLSIO.2.0.CO;2. ——, ——, and M. Heiser, 2000: Variance and predictability of precipitation at seasonal-to-interannual timescales. J. Hydrometeor., 1, 26–46, doi:10.1175/1525-7541(2000)001,0026: VAPOPA.2.0.CO;2. ——, Z. Guo, R. Yang, P. A. Dirmeyer, K. Mitchell, and M. J. Puma, 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 4322–4325, doi:10.1175/ 2009JCLI2832.1. Kumar, S. V., R. H. Reichle, C. D. Peters-Lidard, R. D. Koster, X. Zhan, W. T. Crow, J. B. Eylander, and P. R. Houser, 2008: A land surface data assimilation framework using the land information system: Description and applications. Adv. Water Resour., 31, 1419–1432, doi:10.1016/j.advwatres.2008.01.013. ——, ——, R. D. Koster, W. T. Crow, and C. D. Peters-Lidard, 2009: Role of subsurface physics in the assimilation of surface soil moisture observations. J. Hydrometeor., 10, 1534–1547, doi:10.1175/2009JHM1134.1. ——, ——, K. W. Harrison, C. D. Peters-Lidard, S. Yatheendradas, and J. A. Santanello, 2012: A comparison of methods for a priori bias correction in soil moisture data assimilation. Water Resour. Res., 48, W03515, doi:10.1029/2010WR010261. ——, and Coauthors, 2014: Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation. J. Hydrometeor., 15, 2446–2469, doi:10.1175/JHM-D-13-0132.1.

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Li, B., D. Toll, X. Zhan, and B. Cosgrove, 2011: Improving simulated soil moisture fields through assimilation of AMSR-E soil moisture retrievals with an ensemble Kalman filter and a mass conservation constraint. Hydrol. Earth Syst. Sci. Discuss., 8, 8131–8171, doi:10.5194/hessd-8-8131-2011. Li, L., and Coauthors, 2010: WindSat global soil moisture retrieval and validation. IEEE Trans. Geosci. Remote Sens., 48, 2224– 2241, doi:10.1109/TGRS.2009.2037749. Liu, Q., and Coauthors, 2011: The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. J. Hydrometeor., 12, 750–765, doi:10.1175/JHM-D-10-05000.1. Liu, Y. Y., R. M. Parinussa, W. A. Dorigo, R. A. M. De Jeu, W. Wagner, A. I. J. M. van Dijk, M. F. McCabe, and J. P. Evans, 2011: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol. Earth Syst. Sci., 15, 425–436, doi:10.5194/ hess-15-425-2011. Maggioni, V., R. H. Reichle, and E. N. Anagnostou, 2013: The efficiency of assimilating satellite soil moisture retrievals in a land data assimilation system using different rainfall error models. J. Hydrometeor., 14, 368–374, doi:10.1175/JHM-D-12-0105.1. Mo, K. C., L. N. Long, Y. Xia, S. K. Yang, J. E. Schemm, and M. B. Ek, 2010: Drought indices based on the Climate Forecast System Reanalysis and ensemble NLDAS. J. Hydrometeor., 12, 185–210, doi:10.1175/2010JHM1310.1. Njoku, E. G., T. J. Jackson, V. Lakshmi, T. K. Chan, and S. V. Nghiem, 2003: Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens., 41, 215–229, doi:10.1109/ TGRS.2002.808243. Owe, M., R. de Jeu, and T. Holmes, 2008: Multisensor historical climatology of satellite-derived global land surface moisture. J. Geophys. Res., 113, F01002, doi:10.1029/2007JF000769. Parrens, M., J.-F. Mahfouf, A. L. Barbu, and J.-C. Calvet, 2014: Assimilation of surface soil moisture into a multilayer soil model: Design and evaluation at local scale. Hydrol. Earth Syst. Sci., 18, 673–689, doi:10.5194/hess-18-673-2014. Peters-Lidard, C. D., S. V. Kumar, D. M. Mocko, and Y. Tian, 2011: Estimating evapotranspiration with land data assimilation systems. Hydrol. Processes, 25, 3979–3992, doi:10.1002/ hyp.8387. Reichle, R. H., 2008: Data assimilation methods in the Earth sciences. Adv. Water Resour., 31, 1411–1418, doi:10.1016/ j.advwatres.2008.01.001. ——, and R. D. Koster, 2004: Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31, L19501, doi:10.1029/2004GL020938. ——, and ——, 2005: Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model. Geophys. Res. Lett., 32, L02404, doi:10.1029/2004GL021700. ——, D. B. Mclaughlin, and D. Entekhabi, 2002: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Wea. Rev., 130, 103–114, doi:10.1175/1520-0493(2002)130,0103: HDAWTE.2.0.CO;2. ——, R. D. Koster, J. Dong, and A. A. Berg, 2004: Global soil moisture from satellite observations, land surface models, and ground data: Implications for data assimilation. J. Hydrometeor., 5, 430–442, doi:10.1175/1525-7541(2004)005,0430: GSMFSO.2.0.CO;2. ——, W. T. Crow, R. D. Koster, H. O. Sharif, and S. P. P. Mahanama, 2008: Contribution of soil moisture retrievals to land data assimilation products. Geophys. Res. Lett., 35, L01404, doi:10.1029/2007GL031986.

APRIL 2015

YIN ET AL.

Rodell, M., and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381–394, doi:10.1175/ BAMS-85-3-381. Ryu, D., W. T. Crow, X. Zhan, and T. J. Jackson, 2009: Correcting unintended perturbation biases in hydrologic data assimilation. J. Hydrometeor., 10, 734–750, doi:10.1175/2008JHM1038.1. Scipal, K., T. Holmes, R. de Jeu, V. Naeimi, and W. Wagner, 2008: A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophys. Res. Lett., 35, L24403, doi:10.1029/2008GL035599. Wagner, W., K. Scipal, C. Pathe, D. Gerten, W. Lucht, and B. Rudolf, 2003: Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. J. Geophys. Res., 108, 4611, doi:10.1029/2003JD003663. Weng, F., X. Zou, X. Wang, S. Yang, and M. D. Goldberg, 2012: Introduction to Suomi national polar-orbiting

931

partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications. J. Geophys. Res., 117, D19112, doi:10.1029/ 2012JD018144. Xia, Y., and Coauthors, 2012: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117, D03109, doi:10.1029/2011JD016048. Yin, J., X. Zhan, Y. Zheng, J. Liu, C. Hain, and L. Fang, 2014: Impact of quality control of satellite soil moisture data on their assimilation into land surface model. Geophys. Res. Lett., 41, 7159–7166, doi:10.1002/2014GL060659. Zhan, X., W. Zheng, J. Meng, J. Dong, and M. Ek, 2012: Impact of SMOS soil moisture data assimilation on NCEP-GFS forecasts. Geophys. Res. Abstr., 14, EGU2012–12724-1.