Community Microwave Emission Model Research Articles

The Simulation of L-Band Microwave Emission of Frozen Soil during the Thawing Period with the Community Microwave Emission Model (CMEM)

One-third of the Earth’s land surface experiences seasonal freezing and thawing. Freezing-thawing transitions strongly impact land-atmosphere interactions and, thus, also the lower atmosphere above such areas. Observations of two L-band satellites, the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions, provide flags that characterize surfaces as either frozen or not frozen. However, both state transitions—freezing and thawing (FT)—are continuous and complex processes in space and time. Especially in the L-band, which has penetration depths of up to tens of centimeters, the brightness temperature ( T B ) may be generated by a vertically-mixed profile of different FT states, which cannot be described by the current version of the Community Microwave Emission Model (CMEM). To model such complex state transitions, we extended CMEM in Fresnel mode with an FT component by allowing for (1) a varying fraction of an open water surface on top of the soil, and (2) by implementing a temporal FT phase transition delay based on the difference between the soil surface temperature and the soil temperature at 2.5 cm depth. The extended CMEM (CMEM-FT) can capture the T B progression from a completely frozen to a thawed state of the contributing layer as observed by the L-band microwave radiometer ELBARA-III installed at the Maqu station at the northeastern margin of the Tibetan Plateau. The extended model improves the correlation between the observations and CMEM simulations from 0.53/0.45 to 0.85/0.85 and its root-mean-square-error from 32/25 K to 20/15 K for H/V-polarization during thawing conditions. Yet, CMEM-FT does still not simulate the freezing transition sufficiently.

Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the Murray–Darling basin in Australia. The hydro-meteorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010–2011). Next, SMOS brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX–CMEM model (2010–2015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX–CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM) . This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX–CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average.

Evaluation of a Microwave Emissivity Module for Snow Covered Area with CMEM in the ECMWF Integrated Forecasting System

The Community Microwave Emission Modelling platform (CMEM) has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the forward operator radiative transfer model for low frequency passive microwave brightness temperatures (TB). It is used at ECMWF for L-band TB monitoring over snow free areas. In this paper, upgrades to CMEM are presented in order to explore forward modelling in snow-covered areas for coupled land-atmosphere numerical weather prediction systems. The upgrades enable to use CMEM on an extended range of frequencies and the Helsinki University of Technology multi-layer snow emission model is implemented. Offline CMEM experiments are evaluated against AMSR2 (Advanced Microwave Scanning Radiometer 2) observations showing that simulated TB is improved when using a multi-layer snow scheme, compared to a single-layer scheme. The improvements mainly result from a better representation of snow characteristics in the multi-layer snowpack model. CMEM is also evaluated in the Integrated Forecasting System and coupled to RTTOV (Radiative Transfer for TOVS). The numerical results show improved simulated TB at low frequency V polarization over snow-covered area compared to a configuration using emissivity atlas. Degradations at frequencies higher than 20 GHz indicate that further improvements are required in the emissivity and snowpack properties modelling.

Simulation of Passive Microwave Data Toward Efficient Assimilation Over Indian Subcontinent

Brightness temperature (Tb) is sensitive to soil moisture (SM) estimates and has the advantage of increasing the spatial coverage of SM measurements. This letter focuses on the simulation of Tb from the land surface variables generated by Noah with multiparameterized (Noah-MP) and a forward observation operator, community microwave emission model (CMEM) over the Indian subcontinent with a spatial resolution of 0.25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> ×0.25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> . Traditionally, soil dielectric constant and vegetation optical depth are the most important parameters that affect the sensitivity of the top of the atmosphere Tb. Hence, the results of the simulated Tb are presented for a total of 12 configurations and the one with better result (C12) is compared against the microwave polarization difference index (MPDI)-based Tb and observed SM and ocean salinity (SMOS) Tb. The simulated SM from the land surface model (LSM) is also compared with the observed SMOS SM to examine the sensitivity of the simulated Tb with SM. The results reveal that MPDI approach has immense potential in simulating the observed Tb and can be used toward the development of an efficient Tb assimilation system.

STUDY ON THE ESTIMATION OF MICROWAVE LAND SURFACE EMISSIVITY OVER EAST ASIA

Estimation of the land surface emissivity is crucial to evaluate the boundary conditions of atmospheric modeling data assimilation. Brightness temperatures observed by satellite passive microwave radiometer give information on emission forma raindrops and scattering by frozen particles. By applying radiative transfer model of the atmosphere, we can estimate the atmospheric hydrological process (i. e., rainfall, snowfall, water vapor, cloud water, etc.). However it is difficult to estimate microwave land surface emissivity at higher frequencies over land. Because land use, land cover and surface wetness over land are various and heterogeneous by comparing with over ocean. Therefore, we aim to estimate the microwave land surface emissivity over East Asia by using observed data and numerical models. Firstly, we investigate the characteristics of land surface emissivity derived from SSM/I, AMSR-E and AMSR2 over Asia and the relationship between land surface emissivity and land hydrological variables based on satellite data sets and numerical models. Secondary, we estimate the land surface emissivity by applying the Community Microwave Emission Model (CMEM) and Community Radiative Transfer Model (CRTM).Simulation results shows the estimated LSE is overestimated and its variability is large by comparing the observed data.

SMOS brightness temperature assimilation into the Community Land Model

Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010–2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The long-term assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.

Validation of the Soil Moisture Active Passive (SMAP) satellite soil moisture retrieval in an Arctic tundra environment

AbstractThis study examines the Soil Moisture Active Passive soil moisture product on the Equal Area Scalable Earth‐2 (EASE‐2) 36 km Global cylindrical and North Polar azimuthal grids relative to two in situ soil moisture monitoring networks that were installed in 2015 and 2016. Results indicate that there is no relationship between the Soil Moisture Active Passive (SMAP) Level‐2 passive soil moisture product and the upscaled in situ measurements. Additionally, there is very low correlation between modeled brightness temperature using the Community Microwave Emission Model and the Level‐1 C SMAP brightness temperature interpolated to the EASE‐2 Global grid; however, there is a much stronger relationship to the brightness temperature measurements interpolated to the North Polar grid, suggesting that the soil moisture product could be improved with interpolation on the North Polar grid.

Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture

In the present study, soil moisture assimilation is conducted over the Indian subcontinent, using the Noah Land Surface Model (LSM) and the Soil Moisture Operational Products System (SMOPS) observations by utilizing the Ensemble Kalman Filter. The study is conducted in two stages involving assimilation of soil moisture and simulation of brightness temperature (Tb) using radiative transfer scheme. The results of data assimilation in the form of simulated Surface Soil Moisture (SSM) maps are evaluated for the Indian summer monsoonal months of June, July, August, September (JJAS) using the Land Parameter Retrieval Model (LPRM) AMSR-E soil moisture as reference. Results of comparative analysis using the Global land Data Assimilation System (GLDAS) SSM is also discussed over India. Data assimilation using SMOPS soil moisture shows improved prediction over the Indian subcontinent, with an average correlation of 0.96 and average root mean square difference (RMSD) of 0.0303 m3/m3. The results are promising in comparison with the GLDAS SSM, which has an average correlation of 0.93 and average RMSD of 0.0481 m3/m3. In the second stage of the study, the assimilated soil moisture is used to simulate X-band brightness temperature (Tb) at an incidence angle of 55° using the Community Microwave Emission Model (CMEM) Radiative transfer Model (RTM). This is aimed to study the sensitivity of the parameterization scheme on Tb simulation over the Indian subcontinent. The result of Tb simulation shows that the CMEM parameterization scheme strongly influences the simulated top of atmosphere (TOA) brightness temperature. Furthermore, the Tb simulations from Wang dielectric model and Kirdyashev vegetation model shows better similarity with the actual AMSR-E Tb over the study region.

Improving microwave brightness temperature predictions based on Bayesian model averaging ensemble approach

The choices of the parameterizations for each component in a microwave emission model have significant effects on the quality of brightness temperature (Tb) simulation. How to reduce the uncertainty in the Tb simulation is investigated by adopting a statistical post-processing procedure with the Bayesian model averaging (BMA) ensemble approach. The simulations by the community microwave emission model (CMEM) coupled with the community land model version 4.5 (CLM4.5) over mainland China are conducted by the 24 configurations from four vegetation opacity parameterizations (VOPs), three soil dielectric constant parameterizations (SDCPs), and two soil roughness parameterizations (SRPs). Compared with the simple arithmetical averaging (SAA) method, the BMA reconstructions have a higher spatial correlation coefficient (larger than 0.99) than the C-band satellite observations of the advanced microwave scanning radiometer on the Earth observing system (AMSR-E) at the vertical polarization. Moreover, the BMA product performs the best among the ensemble members for all vegetation classes, with a mean root-mean-square difference (RMSD) of 4K and a temporal correlation coefficient of 0.64.

Calibration to improve forward model simulation of microwave emissivity at GPM frequencies over the U.S. Southern Great Plains.

Better estimation of land surface microwave emissivity promises to improve over-land precipitation retrievals in the GPM era. Forward models of land microwave emissivity are available but have suffered from poor parameter specification and limited testing. Here, forward models are calibrated and the accompanying change in predictive power is evaluated. With inputs (e.g., soil moisture) from the Noah land surface model and applying MODIS LAI data, two microwave emissivity models are tested, the Community Radiative Transfer Model (CRTM) and Community Microwave Emission Model (CMEM). The calibration is conducted with the NASA Land Information System (LIS) parameter estimation subsystem using AMSR-E based emissivity retrievals for the calibration dataset. The extent of agreement between the modeled and retrieved estimates is evaluated using the AMSR-E retrievals for a separate 7-year validation period. Results indicate that calibration can significantly improve the agreement, simulating emissivity with an across-channel average root-mean-square-difference (RMSD) of about 0.013, or about 20% lower than if relying on daily estimates based on climatology. The results also indicate that calibration of the microwave emissivity model alone, as was done in prior studies, results in as much as 12% higher across-channel average RMSD, as compared to joint calibration of the land surface and microwave emissivity models. It remains as future work to assess the extent to which the improvements in emissivity estimation translate into improvements in precipitation retrieval accuracy.

Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations

AbstractThe Community Land Model (CLM) includes a large variety of parameterizations, also for flow in the unsaturated zone and soil properties. Soil properties introduce uncertainties into land surface model predictions. In this paper, soil moisture and soil properties are updated for the coupled CLM and Community Microwave Emission Model (CMEM) by the Local Ensemble Transform Kalman Filter (LETKF) and the state augmentation method. Soil properties are estimated through the update of soil textural properties and soil organic matter density. These variables are used in CLM for predicting the soil moisture retention characteristic and the unsaturated hydraulic conductivity, and the soil texture is used in CMEM to calculate the soil dielectric constant. The following scenarios were evaluated for the joint state and parameter estimation with help of synthetic L‐band brightness temperature data assimilation: (i) the impact of joint state and parameter estimation; (ii) updating of soil properties in CLM alone, CMEM alone or both CLM and CMEM; (iii) updating of soil properties without soil moisture update; (iv) the observation localization of LETKF. The results show that the characterization of soil properties through the update of textural properties and soil organic matter density can strongly improve with assimilation of brightness temperature data. The optimized soil properties also improve the characterization of soil moisture, soil temperature, actual evapotranspiration, sensible heat flux, and soil heat flux. The best results are obtained if the soil properties are updated only. The coupled CLM and CMEM model is helpful for the parameter estimation. If soil properties are biased, assimilation of soil moisture data with only state updates increases the root mean square error for evapotranspiration, sensible heat flux, and soil heat flux.

Benchmarking of L-band soil microwave emission models

A first step before assimilating Soil Moisture and Ocean Salinity (SMOS) L-band brightness temperatures (Tb) over land is to couple land surface models (LSMs) to microwave emission models. In this study, the Interactions between Soil Biosphere and Atmosphere (ISBA) LSM is coupled to the Community Microwave Emission Modelling platform (CMEM). Simulations of Tb are performed over a 3-yr period (2003–2005) for a bare soil field in southwestern France, at the Surface Monitoring Of the Soil Reservoir EXperiment (SMOSREX) experimental site. Both ISBA and CMEM present several options for the representation of the soil moisture and soil temperature profiles. Simplified 2-layer simulations are compared with more detailed multilayer simulations. In the 2-layer simulations, the soil is divided into two layers (a thin surface layer and a bulk reservoir), and Fresnel laws are used in CMEM to model the smooth surface emissivity. In the multilayer simulations, the ISBA soil diffusion scheme is used (with 11 soil layers represented) together with either the Wilheit or Fresnel option of CMEM. The Tb simulations are compared to the Tb ground observations available for the SMOSREX site, at H and V polarizations and at different angles, and the impact of soil roughness is assessed. It is shown that taking surface soil moisture into account in the calculation of soil roughness improves the representation of the seasonal cycle and increases the correlation for all the versions of CMEM. The Tb derived from the most complex multilayer simulations correlate slightly better to the observations than the Tb derived from the 2-layer model (r=0.84 and r=0.82, respectively, for the pooled dataset). This is partly due to a better representation of the soil moisture profile. Finally, the multilayer model is used to investigate the L-band sampling depth for contrasting soil texture profiles. For a variety of soil textures, it is found that Tb are mainly driven by the top 15cm soil layer.

Assimilation of microwave brightness temperature in a land data assimilation system with multi‐observation operators

AbstractA radiative transfer model (RTM) that provides a link between model states and satellite observations (e.g., brightness temperature) can act as an observation operator in land data assimilation to directly assimilate brightness temperatures. In this study, a microwave Land Data Assimilation System (LDAS) was developed with three RTMs (The radiative transfer model for bare field (QH), land emissivity model (LandEM), and Community Microwave Emission Model (CMEM)) as its multi‐observation operators (LDAS‐MO). Assimilation experiments using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) satellite brightness temperature data from July 2005 to December 2008 were then conducted to investigate the impact of the RTMs on the assimilated results over China. It was found that the assimilated volumetric soil‐water content using each of the three observation operators improved the estimation of soil moisture content in the top soil layer (0–10 cm), with reduced root mean square errors (RMSEs), and increased correlation coefficients with field observations (OBS) as compared to a control run with no assimilation for the absence of frozen or snow‐covered conditions. The assimilated soil moisture for the QH model, which was more sensitive to dry soil than the other models, produced closer correlations with OBS in arid and semi‐arid regions while smaller RMSEs were observed for LandEM. CMEM agreed most closely with OBS over the middle and lower reaches of the Yangtze River due to its better simulation of the brightness temperature over densely vegetated areas. To improve assimilation accuracy, a Bayesian model averaging (BMA) scheme for the LDAS‐MO was developed. The BMA scheme was found to significantly enhance assimilation capability producing the soil moisture analysis, showing the lowest RMSEs and highest correlations with OBS over all areas. It was demonstrated that the BMA scheme with LDAS‐MO has the potential to estimate soil moisture with high accuracy.

A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data

Abstract. In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010, the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification, while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (a few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m3 m−3 to 0.087 m3 m−3 when using the new formulation in offline experiments, and from 0.110 m3 m−3 to 0.088 m3 m−3 in operations. It also improves correlations. Additionally, the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiments with the new formulation present drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature.

Sensitivity of L-band NWP forward modelling to soil roughness

This article investigates the sensitivity of the European Centre for Medium-Range Weather Forecasts (ECMWF) simulated L-band brightness temperatures (TB) in response to different soil roughness parameterizations. To this end, the ECMWF operational conditions during the year 2004 have been used to force the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) coupled to the Community Microwave Emission Model (CMEM). The coupled HTESSEL-CMEM system is then run at five different incident angles (20°, 30°, 40°, 50° and 60°) and for five soil roughness parameterizations available in CMEM. The performance of the simulated TB is analysed at ground point scale over the Surface Monitoring Of Soil Reservoir Experiment (SMOSREX) site in south-west of France. For this particular data set, both ground-based vertical profile of soil moisture and L-band radiometric observations are available for evaluation of the ECMWF forecast over the nearest grid box. In particular, the results show that the simple Choudhury parameterization best fits the observations for both horizontal (H-pol) and vertical (V-pol) polarization and for most of the incidence angles tested. The best forward modelling configuration is at for the V-pol, with coefficient of determination between modelled and observed TB of 82.9% and root mean squared error of 7.9 K. The sensitivity of the L-band TB errors to the empirical soil roughness parameter is also investigated. Strong sensitivity to this parameter is shown, mainly at H-pol for the least rough surfaces. The investigation carried out in this article gives an insight into the soil roughness model to be used in the operational configuration of the CMEM L-band forward operator, for future assimilation of the Soil Moisture and Ocean Salinity (SMOS) satellite data of the European Space Agency.

Evaluation of the Community Microwave Emission Model Coupled with the Community Land Model over East Asia

The Community Microwave Emission Model (CMEM) developed by the European Centre for Me- dium-Range Weather Forecasts (ECMWF) can provide a link between surface states and satellite observations and simulate the passive microwave brightness temperature of the surface at low frequencies (from 1 GHz to 20 GHz). This study evaluated the performance of the CMEM cou- pled with the Community Land Model (CLM) (CMEM- CLM) using C-band (6.9 GHz) microwave brightness temperatures from the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E) over East Asia. Preliminary results support the argument that the simulated brightness temperatures of CMEM-CLM from July 2005 to June 2010 are comparable to AMSR-E observational data. CMEM-CLM performed better for vertical polarization, for which the root mean square error was approximately 15 K, compared to over 30 K for horizontal polarization. An evaluation performed over seven sub-regions in China indicated that CMEM-CLM was able to capture the temporal evolution of C-band brightness temperatures well, and the best correlation with AMSR-E appeared over western Northwest China (over 0.9 for vertical polarization). However, larger biases were found over southern North China and the middle and lower reaches of the Yangtze River.

AMMA Land Surface Model Intercomparison Experiment coupled to the Community Microwave Emission Model: ALMIP‐MEM

This paper presents the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Models Intercomparison Project (ALMIP) for Microwave Emission Models (ALMIP‐MEM). ALMIP‐MEM comprises an ensemble of simulations of C‐band brightness temperatures over West Africa for 2006. Simulations have been performed for an incidence angle of 55°, and results are evaluated against C‐band satellite data from the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR‐E). The ensemble encompasses 96 simulations, for 8 Land Surface Models (LSMs) coupled to 12 configurations of the Community Microwave Emission Model (CMEM). CMEM has a modular structure which permits combination of several parameterizations with different vegetation opacity and soil dielectric models. ALMIP‐MEM provides the first intercomparison of state‐of‐the‐art land surface and microwave emission models at regional scale. Quantitative estimates of the relative importance of land surface modeling and radiative transfer modeling for the monitoring of low‐frequency passive microwave emission on land surfaces are obtained. This is of high interest for the various users of coupled land surface microwave emission models. Results show that both LSMs and microwave model components strongly influence the simulated top of atmosphere (TOA) brightness temperatures. For most of the LSMs, the Kirdyashev opacity model is the most suitable to simulate TOA brightness temperature in best agreement with the AMSR‐E data. When this best microwave modeling configuration is used, all the LSMs are able to reproduce the main temporal and spatial variability of measured brightness temperature. Averaged among the LSMs, correlation is 0.67 and averaged normalized standard deviation is 0.98.

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

A Global Simulation of Microwave Emission: Error Structures Based on Output From ECMWF's Operational Integrated Forecast System

The European Centre for Medium-range Weather Forecasts (ECMWF) brightness will use temperatures from the soil moisture and ocean salinity mission to analyze root zone soil moisture through a variational data assimilation system. The first guess is obtained from numerical weather prediction (NWP) model fields, an auxiliary database, and a land surface microwave emission model. In this paper, we present the community microwave emission model and research the first-guess errors in L-band brightness temperatures. An error propagation study is performed on errors introduced through: (1) uncertainties in the parameterizations of the radiative transfer model; (2) auxiliary geophysical quantities for the radiative transfer computations; and (3) an imperfect NWP model. It is found that the vegetation and dielectric models introduce uncertainties with a difference of up to 25 K between models. However, the biggest error in brightness temperature is likely related to the use of an auxiliary vegetation database, which results in differences of -20 to +20 K in our simulations. These potential errors are in many regions higher than the variance in brightness temperatures related to an imperfect NWP model.