The assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB) data in numerical weather prediction systems influences the state of the soil, which in turn affects the ...exchange of energy and water fluxes between the soil and the near‐surface atmosphere, with potential implications for the prediction of atmospheric variables. In this paper, the impact of assimilating SMOS TB alone or in combination with screen‐level observations and Advanced Scatterometer (ASCAT) soil moisture retrievals is assessed. Independent quality controlled insitu soil moisture observations belonging to several networks included in the International Soil Moisture Network, were used to validate the quality of both the new soil moisture analyses and the skill to predict soil moisture up to 5 days ahead. The impact on atmospheric variables is indirect and was evaluated through computation of the forecast skill at different lead times. The analysis period was selected to be around the boreal summer, a period of the year when evaporatranspiration fluxes are stronger, and when it is therefore expected that the assimilation of remote‐sensing data provides the largest impact on the state of the soil. The results show that the soil moisture state benefits from the direct assimilation of SMOS TB, especially in better representing the temporal variations of soil moisture. The skill of atmospheric variables is mainly driven by the screen‐level observations. Despite the clear benefits to the soil state, remote‐sensing data need to be used with screen‐level variables to add value to the state of the atmosphere, pointing to inconsistencies in the physical coupling between the land and near‐surface components of the ECMWF Earth system.
Bias‐corrected (BC) SMOS brightness temperature data are assimilated in the ECMWF Integrated Forecasting System. The standard deviation after BC at 40° and X polarization is shown in the figure. Several experiments are tested assimilating SMOS data in combination with 2 m air temperature, 2 m relative humidity and ASCAT soil moisture retrievals. The impact of the analyses is evaluated for both the soil moisture and atmospheric variables in the lower troposphere.
ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979-2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result ...of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources. The quality of ERA-Interim/Land is assessed by comparing with ground-based and remote sensing observations. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of site measurements. ERA-Interim/Land provides a global integrated and coherent estimate of soil moisture and snow water equivalent, which can also be used for the initialization of numerical weather prediction and climate models.
Global Level-3 surface soil moisture (SM) maps derived from the passive microwave SMOS (Soil Moisture and Ocean Salinity) observations at L-band have recently been released. In this study, a ...comparative analysis of this Level 3 product (referred to as SMOSL3) along with another Surface SM (SSM) product derived from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) at C-band is presented (this latter product is referred to as AMSRM). SM-DAS-2, a SSM product produced by the European Centre for Medium Range Weather Forecasts (ECMWF) Land Data Assimilation System (LDAS) was used to monitor both SMOSL3 and AMSRM qualities. The present study was carried out from 03/2010 to 09/2011, a period during which both SMOS and AMSR-E products were available at global scale. Three statistical metrics were used for the evaluation; the correlation coefficient (R), the Root Mean Squared Difference (RMSD), and the bias. Results were analysed using maps of biomes and Leaf Area Index (LAI). It is shown that both SMOSL3 and AMSRM captured well the spatio-temporal variability of SM-DAS-2 for most of the biomes. In terms of correlation values, the SMOSL3 product was found to better capture the SSM temporal dynamics in highly vegetated biomes (“tropical humid”, “temperate humid”, etc.) while best results for AMSRM were obtained over arid and semi-arid biomes (“desert temperate”, “desert tropical”, etc.). Finally, we showed that the accuracy of the remotely sensed SSM products is strongly related to LAI. Both the SMOSL3 and AMSRM (marginally better) SSM products correlated well with the SM-DAS-2 product over regions with sparse vegetation for values of LAI ≤1 (these regions represent almost 50% of the pixels considered in this global study). In regions where LAI>1, SMOSL3 showed better correlations with SM-DAS-2 than AMSRM: SMOSL3 had a consistent performance up to LAI=6, whereas the AMSRM performance deteriorated with increasing values of LAI. This study reveals that SMOS and AMSR-E complement one another in monitoring SSM over a wide range in conditions of vegetation density and that there are valuable satellite observed SSM data records over more than 10years, which can be used to study land–atmosphere processes.
•Sensitivity of AMSR-E and SMOS to soil moisture is compared at global scale.•Correlation metrics with a reference are evaluated for original data and anomalies.•The SMOS level 3 ascending product performs better than the descending one.•LAI is found to be a key variable to explain correlation results with the reference.•Performances of SMOS and AMSR-E are complementary depending on vegetation density.
In the near future, the SMOS (Soil Moisture and Ocean Salinity) mission will provide global maps of surface soil moisture (SM). The SMOS baseline payload is an L-band (1.4 GHz) two dimensional ...interferometric microwave radiometer which will provide multi-angular and dual-polarization observations. In the framework of the ground segment activities for the SMOS mission an operational SMOS Level 2 Soil Moisture algorithm was developed. The principle of the algorithm is to exploit multi-angular data in order to retrieve simultaneously several surface parameters including soil moisture and vegetation characteristics. The algorithm uses an iterative approach, minimizing a cost function computed from the differences between measured and modelled brightness temperature (
T
B) data, for all available incidence angles.
In the algorithm, the selected forward model is the so-called L-MEB (L-band Microwave Emission of the Biosphere) model which was the result of an extensive review of the current knowledge of the microwave emission of various land covers. This model is a key element in the SMOS L2 algorithm and could be used in future assimilation studies. There is thus a strong need for a reference study, describing the model and its implementation. In order to address these needs a detailed description of soil and vegetation modelling in L-MEB is given in this study. In a second step, the use of L-MEB in soil moisture retrievals is evaluated for several experimental data sets over agricultural crops. Calibrations of the soil and vegetation L-MEB parameters are investigated for corn, soybean and wheat. Over the different experiments, very consistent results are obtained for each vegetation type in terms of calibration and soil moisture retrievals.
An analysis is carried out for two hydrologically contrasting but thermodynamically similar areas on the Tibetan Plateau, to evaluate soil moisture analysis based on the European Centre for ...Medium‐Range Weather Forecasts (ECMWF) previous optimum interpolation scheme and the current point‐wise extended Kalman filter scheme. To implement the analysis, this study used two regional soil moisture and soil temperature networks (i.e., Naqu and Maqu) on the Tibetan Plateau. For the cold‐semiarid Naqu area, both ECMWF soil moisture analyses significantly overestimate the regional soil moisture in the monsoon seasons. For the cold‐humid Maqu network area, the ECMWF products have comparable accuracy as reported by previous studies in the humid monsoon period. The comparisons were made among the liquid soil moisture analysis from ECMWF, the ground station's measurements and the satellite estimates from the Advanced Scatterometer sensor. The results show reasonable performances of the ECMWF soil moisture analyses (i.e., both optimum interpolation and extended Kalman filter products) and the Advanced Scatterometer level 2 products, when compared to the in situ measurements.
Key Points
ECMWF's land analyses overestimate soil moisture in cold‐semiarid area
In cold‐humid area, ECMWF's soil moisture analyses are reasonable
The liquid only SM analysis outperforms total SM analysis
The West African monsoon interacts strongly with the land surface, yet knowledge of these interactions is severely limited by the lack of observations of surface energy fluxes. Within the framework ...of the AMMA project, three eddy covariance flux stations were installed to sample the three main surface types near Hombori (Mali) in the central Sahel at 15.3°N, and a fourth station was installed near Bamba in the northern Sahel at 17.1°N to sample semi-desert conditions. Observed land types near Hombori comprised a grassland growing on sandy soil (near the village of Agoufou), a flooded forest in a clay-soil depression (Kelma), and a bare rocky soil (Eguerit). The energy balance closure at the grassland site was satisfactory, but less so at the flooded forest site. Surface water heat storage during the flood and advection probably were responsible for most of the imbalance.
The daily sensible heat flux (
H) was fairly constant throughout the year at Bamba and Eguerit, with only a slight increase during the monsoon season corresponding to increased net radiation. By contrast, the seasonal cycle of the grassland site was marked, with
H decreasing during the monsoon season from 70
W
m
−2 in May to 20
W
m
−2 in August. The flooded woodland exhibited the strongest contrast between the dry and wet seasons, with daily sensible heat flux close to zero during the flood. During the peak monsoon season, the two vegetated sites had the highest net radiation and the lowest sensible heat flux, as a consequence of the strong evapotranspiration rates caused by both high soil moisture availability and high leaf area index. Lateral fluxes of water were found to be strong drivers of inter-site sensible and latent heat fluxes variability, with water leaving bare rocky soils as surface runoff and ending in the clay depressions (e.g., Kelma), whereas the sandy soils were locally endorheic, with most of the rainfall being rapidly returned to the atmosphere.
An attempt was made to scale the sensible heat flux up to the scale of the AMMA northern super-site (60
km
×
60
km), following a simple scaling scheme, which accounted for the contrasting surface types and water regimes. The super-site average sensible heat flux proved to be close to the grassland sensible heat flux, in part because grassland occupies 55% of the area. A strong spatial variability was caused by the difference in water regime and vegetation type, at a scale large enough to potentially influence the atmospheric properties such as the boundary layer.
Soil Moisture Analyses at ECMWF Albergel, C.; de Rosnay, P.; Balsamo, G. ...
Journal of hydrometeorology,
10/2012, Letnik:
13, Številka:
5
Journal Article
Recenzirano
Odprti dostop
In situ soil moisture from 117 stations across the world and under different biome and climate conditions are used to evaluate two soil moisture products from the European Centre for Medium-Range ...Weather Forecasts (ECMWF)—namely, the operational analysis and the interim reanalysis ECMWF Re-Analysis Interim (ERA-Interim). ECMWF’s operational Integrated Forecasting System (IFS) is based on a continuous effort to improve the analysis and modeling systems, resulting in frequent updates (a few times a year). The ERA-Interim reanalysis is produced by a fixed IFS version (for the main component of the atmospheric model and data assimilation). It has the advantage of being consistent over the whole period from 1979 onward and by design, reanalysis products are more suitable than their operational counterparts for use in climate studies. Although the two analyses show good skills in capturing surface soil moisture variability, they tend to overestimate soil moisture, particularly for dry land. Over the 2008–10 period, averaged statistical scores (correlation, bias, and root-mean-square difference) are 0.70, −0.081 m³ m−3, and 0.113 m³ m−3for the operational product and 0.63, −0.079 m³ m−3, and 0.121 m³ m−3for ERA-Interim. Compared to the scheme used in ERA-Interim, the current model used in the IFS has an improved match to soil moisture that is attributed to recent changes in the IFS. Indeed, major upgrades recently implemented in the operational land surface analysis and modeling system improve the surface and the root-zone soil moisture analyses.
This paper presents the future European Centre for Medium‐Range Weather Forecasts soil moisture analysis system based on a point‐wise Extended Kalman Filter (EKF). The performance of the system is ...evaluated against the current operational Optimal Interpolation (OI) system. Both systems use proxy observations, i.e., 2 m air temperature and relative humidity. The spatial structure of the analysis increments obtained from both analyses are comparable. However, the EKF‐based increments are generally higher for the top soil layers then for the bottom layer. This gradient better reflects the underlying hydrological processes in that the strongest interaction between soil moisture and bare soil evaporation and transpiration through vegetation should occur in top layers where most of the roots are located. The impact on the forecast skill, e.g., air temperature at 2 m and 500 hPa height, is neutral. The new EKF surface analysis system offers a range of further development options for the exploitation of satellite observations for the initialization of the land surface in Numerical Weather Prediction.
The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, is the European Space Agency's (ESA) second Earth Explorer Opportunity mission. The scientific objectives of the SMOS ...mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide information on vegetation, in particular plant available water and water content in a canopy, drought index and flood risks, surface ocean winds in storms, freeze/thaw state and sea ice and its effect on ocean–atmosphere heat fluxes and dynamics affecting large-scale processes of the Earth's climate system.
Significant progress has been made over the course of the now 6-year life time of the SMOS mission in improving the ESA provided level 1 brightness temperature and level 2 soil moisture and sea surface salinity data products. The main emphasis of this paper is to review the status of the mission and provide an overview and performance assessment of SMOS data products, in particular with a view towards operational applications, and using SMOS products in data assimilation.
SMOS is in excellent technical condition with no limiting factors for operations beyond 2017. The instrument performance fulfils the requirements. The radio-frequency interference (RFI) contamination originates from man-made emitters on ground, operating in the protected L-band and adding signal to the natural radiation emitted by the Earth. RFI has been detected worldwide and has been significantly reduced in Europe and the Americas but remains a constraint in Asia and the Middle East. The mission's scientific objectives have been reached over land and are approaching the mission objectives over ocean.
This review paper aims to provide an introduction and synthesis to the papers published in this RSE special issue on SMOS.
•SMOS is in excellent technical conditions.•No technical limits exist to operate the mission beyond 2017.•New data products for operational users have been included in the SMOS portfolio.•SMOS data are already used in data assimilation and operational forecasting systems.•SMOS observed inter-annual changes have great potential for climate research.
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