Observing System Simulation Experiment Framework Research Articles

Synthetic Observations of the Planetary Boundary Layer from Space: A Retrieval Observing System Simulation Experiment Framework

Abstract To address critical gaps identified by the National Academies of Sciences, Engineering, and Medicine in the current Earth system observation strategy, the 2017–27 Decadal Survey for Earth Science and Applications from Space recommended incubating concepts for future targeted observables including the atmospheric planetary boundary layer (PBL). A subsequent NASA PBL Incubation Study Team Report identified measurement requirements and activities for advancing the maturity of the technologies applicable to the PBL targeted observables and their associated science and applications priorities. While the PBL is the critical layer where humans live and surface energy, moisture, and mass exchanges drive the Earth system, it is also the farthest and most inaccessible layer for spaceborne instruments. Here we document a PBL retrieval observing system simulation experiment (OSSE) framework suitable for assessing existing and new measurement techniques and determining their accuracy and improvements needed for addressing the elevated Decadal Survey requirements. In particular, the benefits of large-eddy simulation (LES) are emphasized as a key source of high-resolution synthetic observations for key PBL regimes: from the tropics, through subtropics and midlatitudes, to subpolar and polar regions. The potential of LES-based PBL retrieval OSSEs is explored using six instrument simulators: Global Navigation Satellite System–Radio Occultation, differential absorption radar, visible to shortwave infrared spectrometer, infrared sounder, Multi-angle Imaging SpectroRadiometer, and microwave sounder. The crucial role of LES in PBL retrieval OSSEs and some perspectives for instrument developments are discussed.

Assessment of Inflation Schemes on Parameter Estimation and Their Application in ENSO Prediction in an OSSE Framework

The ensemble Kalman filter is often used in parameter estimation, which plays an essential role in reducing model errors. However, filter divergence is often encountered in an estimation process, resulting in the convergence of parameters to the improper value and finally in parameter estimation failure. To alleviate this degeneration, various covariance inflation schemes have been proposed. In this study, I examined six currently used inflation schemes: fixed inflation, conditional covariance inflation, modified estimated parameter ensemble spread, relaxation-to-prior perturbations, relaxation-to-prior spread, and new conditional covariance inflation. The six schemes were thoroughly explored using the Zebiak–Cane model and the local ensemble transform Kalman filter in the observing system simulation experiment framework. Emphasis was placed on the comparison of these schemes when it came to estimating single and multiple parameters in terms of oceanic analyses and resultant El Niño–Southern Oscillation (ENSO) predictions. The results showed that the new conditional covariance inflation scheme had the best results in terms of the estimated parameters, resultant state analyses, and ENSO predictions. In addition, the results suggested that better parameter estimation yields better state simulations, resulting in improved predictions. Overall, this study provides viable information for selecting inflation schemes for parameter estimation, offering theoretical guidance for constructing operational assimilation systems.

Preparing the assimilation of the future MTG‐IRS sounder into the mesoscale numerical weather prediction AROME model

AbstractThe infrared sounder (IRS) instrument is an infrared Fourier‐transform spectrometer that will be on board the Meteosat Third Generation series of the future European Organization for the Exploitation of Meteorological Satellite's geostationary satellites. It will measure the radiance emitted by the Earth at the top of the atmosphere using 1,960 channels. The IRS will provide high spatial‐ and temporal‐frequency four‐dimensional information on atmospheric temperature and humidity, winds, clouds, and surfaces, as well as on the chemical composition of the atmosphere. The assimilation of these new observations represents a great challenge and opportunity for the improvement of numerical weather prediction (NWP) forecast skill, especially for mesoscale models such as the Applications de la Recherche à l'Opérationnel à Méso‐Echelle (AROME) at Météo‐France. The objectives of this study are to prepare for the assimilation of the IRS in this system and to evaluate its impact on the forecasts when added to the currently assimilated observations. By using an observing system simulation experiment constructed for a mesoscale NWP model. This observing system simulation experiment framework makes use of synthetic observations of both IRS and the currently assimilated observing systems in AROME, constructed from a known and realistic state of the atmosphere. The latter, called the “nature run”, is derived from a long and uninterrupted forecast of the mesoscale model. These observations were assimilated and evaluated using a 1 hr update cycle three‐dimensional variational data assimilation system over 2‐month periods, one in the summer and one in the winter. This study demonstrates the benefits that can be expected from the assimilation of IRS observations into the AROME NWP system. The assimilation of only 75 channels over oceans increases the total amount of observations used in the AROME three‐dimensional variational data assimilation system by about 50%. The IRS impact in terms of forecast scores was evaluated and compared for the summer and winter periods. The main findings are as follows: (a) over both periods the assimilation of these observations leads to statistically improved forecasts over the whole atmospheric column; (b) for the summer‐season experiment, the forecast ranges up to 48 hr are improved; (c) for the winter‐season experiment, the impact on the forecasts is globally positive but is smaller than the summer period and extends only to 24 hr. Based on these results, it is foreseen that the addition of future IRS observations in the AROME NWP systems will significantly improve mesoscale weather forecasts.

Assessing the Impact of the Assimilation of SWOT Observations in a Global High-Resolution Analysis and Forecasting System – Part 2: Results

A first attempt was made to quantify the impact of the assimilation of Surface Water Ocean Topography (SWOT) swath altimeter data in a global 1/12° high resolution analysis and forecasting system through a series of Observing System Simulation Experiments (OSSEs). The OSSE framework (Nature Run and Free Run) and data assimilation scheme have been described in detail in a companion article (Benkiran et al., 2021). The impact of assimilating data from SWOT and three nadir altimeters was quantified by estimating analysis and forecast error variances for sea surface height (SSH), temperature, salinity, zonal, and meridional velocities. Wave-number spectra and coherence analyses of SSH errors were also computed. SWOT data will significantly improve the quality of ocean analyses and forecasts. Adding SWOT observations to those of three nadir altimeters globally reduces the variance of SSH and surface velocities in analyses and forecasts by about 30 and 20%, respectively. Improvements are greater in high-latitude regions where space/time coverage of SWOT is much denser. The combination of SWOT data with data from three nadir altimeters provides a better resolution of wavelengths between 50 and 200 km with a more than 40% improvement outside tropical regions with respect to data from three nadir altimeters alone. The study has also highlighted that the impact of using SWOT data is likely to be very different depending on geographical areas. Constraining smaller spatial scales (wavelengths below 100 km) remains challenging as they are also associated with small time scales. Although this is only a first step, the study has demonstrated that SWOT data could be readily assimilated in a global high-resolution analysis and forecasting system with a positive impact at all latitudes and outstanding performances.

Accounting for Transport Error in Inversions: An Urban Synthetic Data Experiment.

We present and discuss the use of a high‐dimensional computational method for atmospheric inversions that incorporates the space‐time structure of transport and dispersion errors. In urban environments, transport and dispersion errors are largely the result of our inability to capture the true underlying transport of greenhouse gas (GHG) emissions to observational sites. Motivated by the impact of transport model error on estimates of fluxes of GHGs using in situ tower‐based mole‐fraction observations, we specifically address the need to characterize transport error structures in high‐resolution large‐scale inversion models. We do this using parametric covariance functions combined with shrinkage‐based regularization methods within an Ensemble Transform Kalman Filter inversion setup. We devise a synthetic data experiment to compare the impact of transport and dispersion error component of the model‐data mismatch covariance choices on flux retrievals and study the robustness of the method with respect to fewer observational constraints. We demonstrate the analysis in the context of inferring CO2 fluxes starting with a hypothesized prior in the Washington D.C. /Baltimore area constrained by a synthetic set of tower‐based CO2 measurements within an observing system simulation experiment framework. This study demonstrates the ability of these simple covariance structures to substantially improve the estimation of fluxes over standard covariance models in flux estimation from urban regions.

The importance of simulated errors in observing system simulation experiments

Observing System Simulation Experiments (OSSEs) for numerical weather prediction rely on simulated observations that should include simulated observation errors in order to realistically represent the behaviour of real data. Real observations include many types of error, such as instrument error, representativeness error, and observation operator error, with some portion of this error being correlated in time and space or possibly between data types. Data assimilation systems are designed to account for random, uncorrelated errors, but are not yet adept at handling correlated errors; as a result, the correlated errors are more readily incorporated into the analysis increment by the data assimilation system than uncorrelated errors. In this work, the role of correlated observation errors in modifying the behaviour of the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE framework is investigated. The effects on analysis increment, analysis error, forecast errors and observation impacts of including or neglecting correlated simulated errors is explored. The use of correlated observations for calibration and validation of the OSSE is also discussed.

Bayesian inference of spatially varying Manning’s n coefficients in an idealized coastal ocean model using a generalized Karhunen-Loève expansion and polynomial chaos

Bayesian inference with coordinate transformations and polynomial chaos for a Gaussian process with a parametrized prior covariance model was introduced in Sraj et al. (Comput Methods Appl Mech Eng 298:205–228, 2016a) to enable and infer uncertainties in a parameterized prior field. The feasibility of the method was successfully demonstrated on a simple transient diffusion equation. In this work, we adopt a similar approach to infer a spatially varying Manning’s n field in a coastal ocean model. The idea is to view the prior on the Manning’s n field as a stochastic Gaussian field, expressed through a covariance function with uncertain hyper-parameters. A generalized Karhunen-Loeve (KL) expansion, which incorporates the construction of a reference basis of spatial modes and a coordinate transformation, is then applied to the prior field. To improve the computational efficiency of the method proposed in Sraj et al. (Comput Methods Appl Mech Eng 298:205–228, 2016a), we propose to use two polynomial chaos expansions to (i) approximate the coordinate transformation and (ii) build a cheap surrogate of the large-scale advanced circulation (ADCIRC) numerical model. These two surrogates are used to accelerate the Bayesian inference process using a Markov chain Monte Carlo algorithm. Water elevation data are inverted within an observing system simulation experiment framework, based on a realistic ADCIRC model, to infer the KL coordinates and hyper-parameters of a reference 2D Manning’s field. Our results demonstrate the efficiency of the proposed approach and suggest that including the hyper-parameter uncertainties greatly enhances the inferred Manning’s n field, compared with using a covariance with fixed hyper-parameters.

Uncertainty of Observation Impact Estimation in an Adjoint Model Investigated with an Observing System Simulation Experiment

Abstract Adjoint models are often used to estimate the impact of different observations on short-term forecast skill. A common difficulty with the evaluation of short-term forecast quality is the choice of verification fields. The use of self-analysis fields for verification is typical but incestuous, and it introduces uncertainty resulting from biases and errors in the analysis field. In this study, an observing system simulation experiment (OSSE) is used to explore the uncertainty in adjoint model estimations of observation impact. The availability of the true state for verification in the OSSE framework in the form of the nature run allows calculation of the observation impact without the uncertainties present in self-analysis verification. These impact estimates are compared with estimates calculated using self-analysis verification. The Global Earth Observing System, version 5 (GEOS-5), forecast model with the Gridpoint Statistical Interpolation system is used with the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE capability. The adjoint model includes moist processes, with total wet energy selected as the norm for evaluation of observation impacts. The results show that there are measurable but small errors in the adjoint model estimation of observation impact as a result of self-analysis verification. In general, observations of temperature and winds tend to have overestimated impacts with self-analysis verification while observations of humidity and moisture-affected observations tend to have underestimated impacts. The small magnitude of the differences in impact estimates supports the robustness of the adjoint method of estimating observation impacts.

Assimilation of horizontal line‐of‐sight winds with a mesoscale EnKF data assimilation system

This paper compares the horizontal line‐of‐sight (HLOS) wind observations with a single wind component and full wind information in a limited‐area domain over Europe and the North Atlantic. The motivation for the study is the forthcoming Aeolus mission of the European Space Agency which will provide vertical profiles of HLOS winds.A new observing system simulation experiment framework was developed using the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF) data assimilation with the Weather Research and Forecasting (WRF) model at a horizontal resolution of 15 km. The 50‐member EAKF/WRF is nested in the operational 50‐member ensemble prediction system of ECMWF (ENS) using model‐level data available twice per day. The ensemble spread at lateral boundaries provided by ENS, especially in the North Atlantic, is shown to be sufficient to carry out experiments without covariance inflation.Results show that the information content of HLOS winds is on average divided linearly between the zonal and meridional wind components depending on the observation azimuth. In areas of significant covariances such as fronts in the Atlantic, multivariate covariance information provides significant useful analysis increments from the HLOS wind observations, especially if observations are aligned along the front. The application of the spatially and temporally adaptive prior inflation improved all scores compared with the case without inflation.

Impact of CYGNSS Ocean Surface Wind Speeds on Numerical Simulations of a Hurricane in Observing System Simulation Experiments

AbstractThe NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in late 2016. It will make available frequent ocean surface wind speed observations throughout the life cycle of tropical storms and hurricanes. In this study, the impact of CYGNSS ocean surface winds on numerical simulations of a hurricane case is assessed with a research version of the Hurricane Weather Research and Forecasting Model and a Gridpoint Statistical Interpolation analysis system in a regional observing system simulation experiment framework. Two different methods for reducing the CYGNSS data volume were tested: one in which the winds were thinned and one in which the winds were superobbed.The results suggest that assimilation of the CYGNSS winds has great potential to improve hurricane track and intensity simulations through improved representations of the surface wind fields, hurricane inner-core structures, and surface fluxes. The assimilation of the superobbed CYGNSS data seems to be more effective in improving hurricane track forecasts than thinning the data.

Impact of Assimilating CYGNSS Data on Tropical Cyclone Analyses and Forecasts in a Regional OSSE Framework

AbstractThe impact of assimilating ocean surface wind observations from the Cyclone Global Navigation Satellite System (CYGNSS) is examined in a high-resolution Observing System Simulation Experiment (OSSE) framework for tropical cyclones (TCs). CYGNSS is a planned National Aeronautics and Space Administration constellation of microsatellites that utilizes existing GNSS satellites to retrieve surface wind speed. In the OSSE, CYGNSS wind speed data are simulated using output from a “nature run” as truth. In a case study using the regional Hurricane Weather Research and Forecasting modeling system and the Gridpoint Statistical Interpolation data assimilation scheme, analyses of TC position, structure, and intensity, together with large-scale variables, are improved due to the assimilation of the additional surface wind data. These results indicate the potential importance of CYGNSS ocean surface wind speed data and furthermore that the assimilation of directional information would add further value to TC analyses and forecasts.

Spectral analysis of forecast error investigated with an observing system simulation experiment

The spectra of analysis and forecast error are examined using the observing system simulation experiment framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office. A global numerical weather prediction model, the Global Earth Observing System version 5 with Gridpoint Statistical Interpolation data assimilation, is cycled for 2 months with once-daily forecasts to 336 hours to generate a Control case. Verification of forecast errors using the nature run (NR) as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self-analysis verification significantly overestimates the error growth rates of the early forecast, as well as mis-characterising the spatial scales at which the strongest growth occurs. The NR-verified error variances exhibit a complicated progression of growth, particularly for low wavenumber errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realisation of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

A complementary note to ‘A lag‐1 smoother approach to system‐error estimation’: the intrinsic limitations of residual diagnostics

Recently, this author studied an approach to the estimation of system error based on combining observation residuals derived from a sequential filter and fixed lag‐1 smoother. While extending the methodology to a variational formulation, experimenting with simple models and making sure consistency was found between the sequential and variational formulations, the limitations of the residual‐based approach came clearly to the surface. This note uses the sequential assimilation application to simple nonlinear dynamics to highlight the issue. Only when some of the underlying error statistics are assumed known is it possible to estimate the unknown component. In general, when considerable uncertainties exist in the underlying statistics as a whole, attempts to obtain separate estimates of the various error covariances are bound to lead to misrepresentation of errors. The conclusions are particularly relevant to present‐day attempts to estimate observation‐error correlations from observation residual statistics. A brief illustration of the issue is also provided by comparing estimates of error correlations derived from a quasi‐operational assimilation system and a corresponding Observing System Simulation Experiments framework.

A Study of Coupling Parameter Estimation Implemented by 4D-Var and EnKF with a Simple Coupled System

Coupling parameter estimation (CPE) that uses observations to estimate the parameters in a coupled model through error covariance between variables residing in different media may increase the consistency of estimated parameters in an air-sea coupled system. However, it is very challenging to accurately evaluate the error covariance between such variables due to the different characteristic time scales at which flows vary in different media. With a simple Lorenz-atmosphere and slab ocean coupled system that characterizes the interaction of two-timescale media in a coupled “climate” system, this study explores feasibility of the CPE with four-dimensional variational analysis and ensemble Kalman filter within a perfect observing system simulation experiment framework. It is found that both algorithms can improve the representation of air-sea coupling processes through CPE compared to state estimation only. These simple model studies provide some insights when parameter estimation is implemented with a coupled general circulation model for improving climate estimation and prediction initialization.

An Observing System Simulation Experiments framework based on the ensemble square root Kalman Filter for evaluating the concentration of chlorophyll a by multi-source data: A case study in Taihu Lake

Data assimilation is a method to produce a description of the system state, as accurately as possible, under the control of observations by using all the available information and by taking into account the observation and model errors. We developed a framework for Observing System Simulation Experiments (OSSEs) based on the ensemble square root Klaman filter (EnSRF) technique, and the framework could assimilate two data sets of chlorophyll a retrieved from Environmental Satellite 1 (HJ-1) and moderate resolution imaging spectro-radiometer (MODIS) onboard the Terra platform, separately. We assumed that one of the retrieved results was the proxy “truth value” and the other one contained errors. Based on EnSRF technique, combined with the three dimensional numerical model of wind-driven circulation and pollutant transportation in a large-scale lake, we investigated the potential impact of location distributions of simulated observation stations in Taihu Lake (China) on the performance of data assimilation. In addition, the effectiveness of this method for evaluation and prediction of the concentration of chlorophyll a was validated. The results showed that the location of simulated observation stations not only influenced the accuracy of evaluating and forecasting results, but also the performance of data assimilation. We also discuss the impact of assimilation time and background error on the results. This study demonstrated that this method of data assimilation is effective for evaluation and prediction of the concentration of chlorophyll a in highly turbid case 2 waters.

The SPRINTARS version 3.80/4D-Var data assimilation system: development and inversion experiments based on the observing system simulation experiment framework

Abstract. We present an aerosol data assimilation system based on a global aerosol climate model (SPRINTARS – Spectral Radiation-Transport Model for Aerosol Species) and a four-dimensional variational data assimilation method (4D-Var). Its main purposes are to optimize emission estimates, improve composites, and obtain the best estimate of the radiative effects of aerosols in conjunction with observations. To reduce the huge computational cost caused by the iterative integrations in the models, we developed an offline model and a corresponding adjoint model, which are driven by pre-calculated meteorological, land, and soil data. The offline and adjoint model shortened the computational time of the inner loop by more than 30%. By comparing the results with a 1 yr simulation from the original online model, the consistency of the offline model was verified, with correlation coefficient R > 0.97 and absolute value of normalized mean bias NMB < 7% for the natural aerosol emissions and aerosol optical thickness (AOT) of individual aerosol species. Deviations between the offline and original online models are mainly associated with the time interpolation of the input meteorological variables in the offline model; the smaller variability and difference in the wind velocity near the surface and relative humidity cause negative and positive biases in the wind-blown aerosol emissions and AOTs of hygroscopic aerosols, respectively. The feasibility and capability of the developed system for aerosol inverse modelling was demonstrated in several inversion experiments based on the observing system simulation experiment framework. In the experiments, we used the simulated observation data sets of fine- and coarse-mode AOTs from sun-synchronous polar orbits to investigate the impact of the observational frequency (number of satellites) and coverage (land and ocean), and assigned aerosol emissions to control parameters. Observations over land have a notably positive impact on the performance of inverse modelling as compared with observations over ocean, implying that reliable observational information over land is important for inverse modelling of land-born aerosols. The experimental results also indicate that information that provides differentiations between aerosol species is crucial to inverse modelling over regions where various aerosol species coexist (e.g. industrialized regions and areas downwind of them).

An Observing System Simulation Experiment for the Impact of MTG Candidate Infrared Sounding Mission on Regional Forecasts: System Development and Preliminary Results

An Observing System Simulation Experiment (OSSE) was designed and developed to assess the potential benefit of the Infrared Sounding on the Meteosat Third Generation (MTG-IRS) geostationary meteorological satellite system to regional forecasts. In the proposed OSSE framework, two different models, namely, the MM5 and WRF models, were used in a nature run and data assimilation experiments, respectively, to reduce the identical twin problem. The 5-day nature run, which included three convective storms that occurred during the period from 11 to 16 June 2002 over US Great Plains, was generated using MM5 with a 4 km. The simulated “conventional” observations and MTG-IRS retrieved temperature and humidity profiles, produced from the nature run, were then assimilated into the WRF model. Calibration experiments showed that assimilating real or simulated “conventional” observations yielded similar error statistics in analyses and forecasts, indicating that the developed OSSE system worked well. On average, the MTG-IRS retrieved profiles had positive impact on the analyses and forecasts. The analyses reduced the errors not only in the temperature and the humidity fields but in the horizontal wind fields as well. The forecast skills of these variables were improved up to 12 hours. The 18 h precipitation forecast accuracy was also increased.

Estimation of surface carbon fluxes with an advanced data assimilation methodology

We perform every 6 h a simultaneous data assimilation of surface CO2 fluxes and atmospheric CO2 concentrations along with meteorological variables using the Local Ensemble Transform Kalman Filter (LETKF) within an Observing System Simulation Experiments framework. In this paper, we focus on the impact of advanced variance inflation methods and vertical localization of column CO2 data on the analysis of CO2. With both additive inflation and adaptive multiplicative inflation, we are able to obtain encouraging multiseasonal analyses of surface CO2 fluxes in addition to atmospheric CO2 and meteorological analyses. Furthermore, we examine strategies for vertical localization in the assimilation of simulated CO2 from GOSAT that has nearly uniform sensitivity from the surface to the upper troposphere. Since atmospheric CO2is forced by surface fluxes, its short‐term variability should be largest near the surface. We take advantage of this by updating observed changes only into the lower tropospheric CO2 rather than into the full column. This results in a more accurate analysis of CO2 in terms of both RMS error and spatial patterns. Assimilating synthetic CO2ground‐based observations and CO2 retrievals from GOSAT and AIRS with the enhanced LETKF, we obtain an accurate estimation of the evolving surface fluxes even in the absence of any a priori information. We also test the system with a longer assimilation window and find that a short window with an efficient treatment for wind uncertainty is beneficial to flux inversion. Since this study assumes a perfect forecast model, future research will explore the impact of model errors.

Development and validation of observing‐system simulation experiments at NASA's Global Modeling and Assimilation Office

AbstractInitial design and validation of baseline Observing System Simulation Experiments (OSSEs) at NASA's Global Modeling and Assimilation Office (GMAO) are described. The OSSEs mimic the procedures used to analyze global observations for specifying states of the atmosphere. As simulations, however, OSSEs are not only confined to already existing observations and they provide a perfect description of the true state being analyzed. These two properties of the simulations can be exploited to improve both existing and envisioned observing systems and the algorithms to analyze them. Preliminary to any applications, however, the OSSE framework must be adequately validated.This first version of the simulated observations is drawn from a 13 month simulation of nature produced by the European Center for Medium‐Range Weather Forecasts. These observations include simulated errors of both instruments and representativeness. Since the statistics of analysis and forecast errors are partially determined by these observational errors, their appropriate modelling can be crucial for validating the realism of the OSSE. That validation is performed by comparing the statistics of the results of assimilating these simulated observations for one summer month compared with the corresponding statistics obtained from assimilating real observations during the same time of year. The assimilation system is the three‐dimensional variational analysis (GSI) scheme used at both the National Centers for Environmental Prediction and GMAO. Here, only statistics concerning observation innovations or analysis increments within the troposphere are considered for the validation.In terms of the examined statistics, the OSSE is validated remarkably well, even with some simplifications currently employed. In order to obtain this degree of success, it was necessary to employ horizontally correlated observation errors for both atmospheric motion vectors and some satellite observed radiances. The simulated observations with added observation errors appear suitable for some initial OSSE applications.

Improving Spaceborne Radiometer Soil Moisture Retrievals With Alternative Aggregation Rules for Ancillary Parameters in Highly Heterogeneous Vegetated Areas

Retrieving soil moisture from spaceborne microwave radiometer observations often requires ancillary parameters such as surface vegetation opacity or vegetation water content (VWC). The conventional approach for deriving representative footprint- scale values of these parameters is to simply average the corresponding parameters of high-resolution pixels over the entire radiometer footprint. However, previous work has shown that simple averaging of ancillary parameter values may result in biased soil moisture retrievals from spaceborne radiometers. This letter uses a synthetic observing-system simulation experiment framework to theoretically demonstrate that ancillary VWC or canopy-opacity parameter values obtained with an alternative aggregation rule may significantly reduce the magnitude of soil moisture retrieval errors for highly heterogeneous vegetated areas.