Characterization of Errors in a Coupled Snow Hydrology–Microwave Emission Model

2008 ◽  
Vol 9 (1) ◽  
pp. 149-164 ◽  
Author(s):  
Konstantinos M. Andreadis ◽  
Ding Liang ◽  
Leung Tsang ◽  
Dennis P. Lettenmaier ◽  
Edward G. Josberger
Keyword(s):  
Forest Cover ◽  
Microwave Radiometer ◽  
Coupled Model ◽  
Microwave Remote Sensing ◽  
Quantitative Information ◽  
Microwave Emission ◽  
Prediction Errors ◽  
Emission Model ◽  
Snow Hydrology ◽  
Infiltration Capacity

Abstract Traditional approaches to the direct estimation of snow properties from passive microwave remote sensing have been plagued by limitations such as the tendency of estimates to saturate for moderately deep snowpacks and the effects of mixed land cover within remotely sensed pixels. An alternative approach is to assimilate satellite microwave emission observations directly, which requires embedding an accurate microwave emissions model into a hydrologic prediction scheme, as well as quantitative information of model and observation errors. In this study a coupled snow hydrology [Variable Infiltration Capacity (VIC)] and microwave emission [Dense Media Radiative Transfer (DMRT)] model are evaluated using multiscale brightness temperature (TB) measurements from the Cold Land Processes Experiment (CLPX). The ability of VIC to reproduce snowpack properties is shown with the use of snow pit measurements, while TB model predictions are evaluated through comparison with Ground-Based Microwave Radiometer (GBMR), aircraft [Polarimetric Scanning Radiometer (PSR)], and satellite [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E)] TB measurements. Limitations of the model at the point scale were not as evident when comparing areal estimates. The coupled model was able to reproduce the TB spatial patterns observed by PSR in two of three sites. However, this was mostly due to the presence of relatively dense forest cover. An interesting result occurs when examining the spatial scaling behavior of the higher-resolution errors; the satellite-scale error is well approximated by the mode of the (spatial) histogram of errors at the smaller scale. In addition, TB prediction errors were almost invariant when aggregated to the satellite scale, while forest-cover fractions greater than 30% had a significant effect on TB predictions.

scholarly journals MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering

2015 ◽  
Vol 8 (8) ◽  
pp. 2611-2626 ◽  
Author(s):  
M. Proksch ◽  
C. Mätzler ◽  
A. Wiesmann ◽  
J. Lemmetyinen ◽  
M. Schwank ◽  
...  
Keyword(s):  
Microwave Remote Sensing ◽  
Horizontal Layer ◽  
Microwave Emission ◽  
Emission Model ◽  
Input Parameters ◽  
Fitting Procedures ◽  
Splitting Parameter ◽  
Set Up ◽  
Passive Measurements ◽  
Active Microwave Remote Sensing

Abstract. The Microwave Emission Model of Layered Snowpacks (MEMLS) was originally developed for microwave emissions of snowpacks in the frequency range 5–100 GHz. It is based on six-flux theory to describe radiative transfer in snow including absorption, multiple volume scattering, radiation trapping due to internal reflection and a combination of coherent and incoherent superposition of reflections between horizontal layer interfaces. Here we introduce MEMLS3&a, an extension of MEMLS, which includes a backscatter model for active microwave remote sensing of snow. The reflectivity is decomposed into diffuse and specular components. Slight undulations of the snow surface are taken into account. The treatment of like- and cross-polarization is accomplished by an empirical splitting parameter q. MEMLS3&a (as well as MEMLS) is set up in a way that snow input parameters can be derived by objective measurement methods which avoid fitting procedures of the scattering efficiency of snow, required by several other models. For the validation of the model we have used a combination of active and passive measurements from the NoSREx (Nordic Snow Radar Experiment) campaign in Sodankylä, Finland. We find a reasonable agreement between the measurements and simulations, subject to uncertainties in hitherto unmeasured input parameters of the backscatter model. The model is written in Matlab and the code is publicly available for download through the following website: http://www.iapmw.unibe.ch/research/projects/snowtools/memls.html.

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

2009 ◽  
Vol 10 (1) ◽  
pp. 213-226 ◽  
Author(s):  
Matthias Drusch ◽  
Thomas Holmes ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo
Keyword(s):  
Soil Moisture ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Space Station ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Database ◽  
Brightness Temperatures ◽  
L Band ◽  
Rms Errors

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.

scholarly journals Multimodel Estimation of Snow Microwave Emission during CLPX 2003 Using Operational Parameterization of Microphysical Snow Characteristics

2008 ◽  
Vol 9 (6) ◽  
pp. 1491-1505 ◽  
Author(s):  
Rafał Wójcik ◽  
Konstantinos Andreadis ◽  
Marco Tedesco ◽  
Eric Wood ◽  
Tara Troy ◽  
...  
Keyword(s):  
Grain Size ◽  
Brightness Temperature ◽  
Land Surface ◽  
Bayesian Model Averaging ◽  
Grain Morphology ◽  
Microwave Emission ◽  
Growth Equation ◽  
Emission Model ◽  
Infiltration Capacity ◽  
Brightness Temperatures

Abstract Existing forward snow emission models (SEMs) are limited by knowledge of both the temporal and spatial variability of snow microphysical parameters, with grain size being the most difficult to measure or estimate. This is due to the sparseness of in situ data and the lack of simple operational parameterizations for the evolution of snowpack properties. This paper compares snow brightness temperatures predicted by three SEMs using, as inputs, predicted snowpack characteristics from the Variable Infiltration Capacity (VIC) model. The latter is augmented by a new parameterization for the evolution of snow grain morphology and density. The grain size dynamics are described using a crystal growth equation. The three SEMs used in the study are the Land Surface Microwave Emission Model (LSMEM), the Dense Media Radiative Transfer (DMRT) model, and the Microwave Emission Model of Layered Snowpacks (MEMLS). Estimated brightness temperature is validated against the satellite [Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E)] data at two sites from the Cold Land Processes Experiment (CLPX), conducted in Colorado in the winter of 2003. In addition, a merged multimodel estimate, based on Bayesian model averaging, is developed and compared to the measured brightness temperatures. The advantages of the Bayesian approach include the increase in the mean prediction accuracy as well as providing a nonparametric estimate of the error distributions for the brightness temperature estimates.

scholarly journals Contribution of Snow-Melt Water to the Streamflow over the Three-River Headwater Region, China

2021 ◽  
Vol 13 (8) ◽  
pp. 1585
Author(s):  
Sisi Li ◽  
Mingliang Liu ◽  
Jennifer C. Adam ◽  
Huawei Pi ◽  
Fengge Su ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Influence Factor ◽  
Hydrological Cycle ◽  
Microwave Radiometer ◽  
Hydrological Regime ◽  
Rate Of Change ◽  
Snow Hydrology ◽  
Infiltration Capacity ◽  
Vic Model

Snowmelt water is essential to the water resources management over the Three-River Headwater Region (TRHR), where hydrological processes are influenced by snowmelt runoff and sensitive to climate change. The objectives of this study were to analyse the contribution of snowmelt water to the total streamflow (fQ,snow) in the TRHR by applying a snowmelt tracking algorithm and Variable Infiltration Capacity (VIC) model. The ratio of snowfall to precipitation, and the variation of the April 1 snow water equivalent (SWE) associated with fQ,snow, were identified to analyse the role of snowpack in the hydrological cycle. Prior to the simulation, the VIC model was validated based on the observed streamflow data to recognize its adequacy in the region. In order to improve the VIC model in snow hydrology simulation, Advanced Scanning Microwave Radiometer E (ASMR-E) SWE product data was used to compare with VIC output SWE to adjust the snow parameters. From 1971 to 2007, the averaged fQ,snow was 19.9% with a significant decreasing trend over entire TRHR (P<0.05).The influence factor resulted in the rate of change in fQ,snow which were different for each sub-basin TRHR. The decreasing rate of fQ,snow was highest of 0.24%/year for S_Lantsang, which should be due to the increasing streamflow and the decreasing snowmelt water. For the S_Yangtze, the increasing streamflow contributed more than the stable change of snowmelt water to the decreasing fQ,snow with a rate of 0.1%/year. The April 1 SWE with the minimum value appearing after 2000 and the decreased ratio of snowfall to precipitation during the study period, suggested the snow solid water resource over the TRHR was shrinking. Our results imply that the role of snow in the snow-hydrological regime is weakening in the TRHR in terms of water supplement and runoff regulation due to the decreased fQ,snow and snowfall.

scholarly journals MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering

2015 ◽  
Vol 8 (3) ◽  
pp. 2605-2652 ◽  
Author(s):  
M. Proksch ◽  
C. Mätzler ◽  
A. Wiesmann ◽  
J. Lemmetyinen ◽  
M. Schwank ◽  
...  
Keyword(s):  
Microwave Remote Sensing ◽  
Horizontal Layer ◽  
Microwave Emission ◽  
Emission Model ◽  
Input Parameters ◽  
Fitting Procedures ◽  
Splitting Parameter ◽  
Set Up ◽  
Passive Measurements ◽  
Active Microwave Remote Sensing

Abstract. The Microwave Emission Model of Layered Snowpacks (MEMLS) was originally developed for microwave emissions of snowpacks in the frequency range 5–100 GHz. It is based on six-flux theory to describe radiative transfer in snow including absorption, multiple volume scattering, radiation trapping due to internal reflection and a combination of coherent and incoherent superposition of reflections between horizontal layer interfaces. Here we introduce MEMLS3&amp;a, an extension of MEMLS, which includes a backscatter model for active microwave remote sensing of snow. The reflectivity is decomposed into diffuse and specular components. Slight undulations of the snow surface are taken into account. The treatment of like and cross polarization is accomplished by an empirical splitting parameter q. MEMLS3&amp;a (as well as MEMLS) is set up in a way that snow input parameters can be derived by objective measurement methods which avoids fitting procedures of the scattering efficiency of snow, required by several other models. For the validation of the model we have used a combination of active and passive measurements from the NoSREx campaign in Sodankylä, Finland. We find a reasonable agreement between the measurements and simulations, subject to uncertainties in hitherto unmeasured input parameters of the backscatter model. The model is written in MATLAB and the code is publicly available for download through the following website: http://www.iapmw.unibe.ch/research/projects/snowtools/memls.html.

Correcting for the influence of frozen lakes in satellite microwave radiometer observations through application of a microwave emission model

2011 ◽  
Vol 115 (12) ◽  
pp. 3695-3706 ◽  
Author(s):  
Juha Lemmetyinen ◽  
Anna Kontu ◽  
Juha-Petri Kärnä ◽  
Juho Vehviläinen ◽  
Matias Takala ◽  
...  
Keyword(s):  
Microwave Radiometer ◽  
Microwave Emission ◽  
Emission Model ◽  
Satellite Microwave

Retrieval of basic soil physical properties by assimilating radiometry observations in the community land surface model

2020 ◽  
Author(s):  
Hong Zhao ◽  
Yijian Zeng ◽  
Bob Su ◽  
Xujun Han
Keyword(s):  
Soil Moisture ◽  
Soil Properties ◽  
Land Surface ◽  
Land Surface Model ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Surface Model ◽  
Emission Model ◽  
The Impact ◽  
Parameter Retrieval

&lt;p&gt;Accurate basic soil properties information is fundamental for obtaining reliable soil moisture using land surface models. In view of the passive microwave remote sensing, basic soil properties have an impact on soil dielectric constant, together with soil moisture and temperature. The common link enables to use coupled land surface model with microwave emission model for retrieving basic soil properties in space, especially in remote areas such as the third pole region. The Maqu site in the eastern Tibetan Plateau, including ELBARA-III radiometry observations, was taken as the case. This paper employed an improved observation operator&amp;#8212; a discrete scattering-emission model of L-band radiometry with an air-to-soil transition model embedded in, which considers both geometric and dielectric roughness impacts from heterogeneous topsoil structure on surface emission. Community Land Model 4.5 together with Local Ensemble Transform Kalman Filter algorithm were used by mean of the Open Source Multivariate Land Data Assimilation Framework. The retrieved basic soil properties were compared to in situ measurements, as well as the update soil moisture and temperature and energy fluxes. The impacts from surface roughness consideration and polarization configuration on parameter retrieval were also evaluated. To gain an insight on the impact from time interval of observations on parameter retrieval, results using observations at SMAP descending and ascending time were discussed.&lt;/p&gt;

scholarly journals Effects of Various parameters on Radiative Transfer Theory Based Microwave Emission Model

2017 ◽  
Vol 27 (1) ◽  
pp. 95-107
Author(s):  
Mazen E. Assiri Mazen E. Assiri
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Stem Volume ◽  
Transfer Model ◽  
Emission Model ◽  
Radiative Transfer Theory ◽  
Transfer Theory

This paper outlines research that is currently being carried out to model the interaction of electromagnetic radiation with earth and atmosphere. Among many others, passive microwave (PM) imagery represents a useful source of data for mapping Earth features. Since, signal of a microwave radiometer is composed of surface and atmospheric contributions, for proper interpretation of the data these effects should be quantified. This research presents analysis of radiative transfer model contributors, which include; the ground based parameters, forest area, water area, and meteorological parameters. The principal objective of this study is to analyze the degree to which brightness temperature can be affected by various earth and atmospheric features. A sensitivity analysis is performed to test the contributing effects of various parameters in radiative transfer theory based microwave emission model. The results of the study show that soil temperature and forest stem volume are the main contributing parameters in estimating brightness temperature values. The results further show that both the earthly located features and atmospheric parameters are important factors that must be taken into account in the development and application of radiative transfer theory based models

MODELING X-BAND MICROWAVE RADIATION OF THE ARCTIC SEAS BASED ON SATELLITE OBSERVATIONS: TAKING INTO ACCOUNT A MEASUREMENT ANGLE

2021 ◽  
pp. 69-77
Author(s):  
E. V. Zabolotskikh ◽  
◽  
B. Chapron ◽  
◽  
Keyword(s):  
Wind Speed ◽  
Microwave Radiation ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Microwave Emission ◽  
The Arctic ◽  
Observation Angle ◽  
Emission Model ◽  
Arctic Seas ◽  
X Band

The ocean X-band microwave emission model for modeling measurements of satellite radiometers over the cold Arctic seas at an observation angle of 65° is proposed. The model is based on the experimental geophysical model function (GMF) of microwave emission dependence on surface wind speed for an angle of 55°, that was developed from the AMSR2 (Advanced Microwave Scanning Radiometer 2) measurements and the two-scale theory of the ocean microwave radiation. The experimental GMF is derived from the comparison of AMSR2 measurements over the Arctic seas with surface wind speeds retrieved from these data. The model is limited by wind speed of 15 m/s and does not take into account the foam emission. The model allows discriminating between longwave and shortwave wind-induced microwave radiation and using the presented approach to proceed to the observation angle of the MTVZA-GYa (temperature and humidity atmospheric sounding unit) microwave radiometer on board the Meteor-M Russian polar orbiting satellites.

scholarly journals Simulation of microwave emission from physically modeled snowpacks

2000 ◽  
Vol 31 ◽  
pp. 397-405 ◽  
Author(s):  
Andreas Wiesmann ◽  
Charles Fierz ◽  
Christian Mätzler
Keyword(s):  
Microwave Radiation ◽  
Meteorological Data ◽  
High Sensitivity ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Detailed Knowledge ◽  
Emission Model ◽  
Snowpack Properties ◽  
Good Agreement

AbstractDetailed knowledge of snowpack properties is crucial for the interpretation and modeling of thermal microwave radiation. Here we use two well-known snow models, Crocus and SNTHERM, to obtain snow profiles from meteorological data. These profiles are compared with pit profiles and used as input to the Microwave Emission Model of Layered Snowpacks (MEMLS) for the simulation of microwave radiation. The snow-profile data can be applied almost directly. Adaptation is needed only in the conversion of the grain-size used in the snow models to the correlation length used in the emission model; it is based on empirical fits. The resulting emissivities are compared with in situ microwave measurements. The computed snow depths are in good agreement with observations. Comparison of selected profiles shows that Crocus is in good agreement with the pit profile, but the density of simulated melt-freeze crusts is underestimated. The SNTHERM profiles show no such crusts, and the density deviates from the pit profiles. The computed temporal behavior of the snowpack emissivity is reasonable. Comparison of selected situations with in situ measurements indicates good agreement. However, the polarization difference tends to be underestimated because of inaccuracies in the simulation of density profiles. The results show the potential of combined snow-physical and microwave-emission models for understanding snow signatures and for developing snow algorithms for microwave remote sensing. Based on the frequency-selective penetration and on the high sensitivity to snow texture, density and wetness, microwave radiometry can offer a new dimension to snow physics. Potential applications are described.

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