scholarly journals Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

2021 ◽  
Vol 13 (10) ◽  
pp. 2012
Author(s):  
Yue Yu ◽  
Jinmei Pan ◽  
Jiancheng Shi
Keyword(s):  
Process Model ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Single Layer ◽  
Microwave Emission ◽  
Emission Model ◽  
Snow Layer ◽  
Optimal Method ◽  
Microstructure Parameter ◽  
Multiple Layer

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (TB) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow TB from the snow’s surface was compared with the bulk TB of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk TB error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk TB of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm.

scholarly journals Validation of the SNTHERM Model Applied for Snow Depth, Grain Size, and Brightness Temperature Simulation at Meteorological Stations in China

2020 ◽  
Vol 12 (3) ◽  
pp. 507
Author(s):  
Tao Chen ◽  
Jinmei Pan ◽  
Shunli Chang ◽  
Chuan Xiong ◽  
Jiancheng Shi ◽  
...  
Keyword(s):  
Grain Size ◽  
Brightness Temperature ◽  
Grain Growth ◽  
Snow Depth ◽  
Process Model ◽  
Microwave Emission ◽  
The Tibetan Plateau ◽  
Emission Model ◽  
Vertical Polarization ◽  
Modeling Tools

Validation of the snow process model is an important preliminary work for the snow parameter estimation. The snow grain growth is a continuous and accumulative process, which cannot be evaluated without comparing with the observations in snow season scale. In order to understand the snow properties in the Asian Water Tower region (including Xinjiang province and the Tibetan Plateau) and enhance the use of modeling tools, an extended snow experiment at the foot of the Altay Mountain was designed to validate and improve the coupled physical Snow Thermal Model (SNTHERM) and the Microwave Emission Model of Layered Snowpacks (MEMLS). By matching simultaneously the observed snow depth, geometric grain size, and observed brightness temperature (TB), with an RMSE of 1.91 cm, 0.47 mm, and 4.43 K (at 36.5 GHz, vertical polarization), respectively, we finalized the important model coefficients, which are the grain growth coefficient and the grain size to exponential correlation length conversion coefficients. When extended to 102 meteorological stations in the 2008–2009 winter, the SNTHERM predicted the daily snow depth with an accuracy of 2–4 cm RMSE, and the coupled SNTHERM-MEMLS model predicted the satellite-observed TB with an accuracy of 13.34 K RMSE at 36.5 GHz, vertical polarization, with the fractional snow cover considered.

scholarly journals Complete subset averaging with many instruments

2020 ◽  
Author(s):  
Seojeong Lee ◽  
Youngki Shin
Keyword(s):  
Demand Function ◽  
Simulation Experiment ◽  
Mean Squared Error ◽  
Asymptotic Optimality ◽  
Weighted Average ◽  
Subset Size ◽  
Extensive Simulation ◽  
Squared Error ◽  
Many Instruments ◽  
Class Of Estimators

Summary We propose a two-stage least squares (2SLS) estimator whose first stage is the equal-weighted average over a complete subset with k instruments among K available, which we call the complete subset averaging (CSA) 2SLS. The approximate mean squared error (MSE) is derived as a function of the subset size k by the Nagar (1959) expansion. The subset size is chosen by minimising the sample counterpart of the approximate MSE. We show that this method achieves asymptotic optimality among the class of estimators with different subset sizes. To deal with averaging over a growing set of irrelevant instruments, we generalise the approximate MSE to find that the optimal k is larger than otherwise. An extensive simulation experiment shows that the CSA-2SLS estimator outperforms the alternative estimators when instruments are correlated. As an empirical illustration, we estimate the logistic demand function in Berry et al. (1995) and find that the CSA-2SLS estimate is better supported by economic theory than are the alternative estimates.

scholarly journals Evaluation of a plot-scale methane emission model using eddy covariance observations and footprint modelling

2014 ◽  
Vol 11 (17) ◽  
pp. 4651-4664 ◽  
Author(s):  
A. Budishchev ◽  
Y. Mi ◽  
J. van Huissteden ◽  
L. Belelli-Marchesini ◽  
G. Schaepman-Strub ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Methane Emission ◽  
Average Method ◽  
Temporal Dynamics ◽  
Weighted Average ◽  
Ambient Atmosphere ◽  
Emission Model ◽  
Closed Chamber ◽  
Weighted Average Method ◽  
Emission Models

Abstract. Most plot-scale methane emission models – of which many have been developed in the recent past – are validated using data collected with the closed-chamber technique. This method, however, suffers from a low spatial representativeness and a poor temporal resolution. Also, during a chamber-flux measurement the air within a chamber is separated from the ambient atmosphere, which negates the influence of wind on emissions. Additionally, some methane models are validated by upscaling fluxes based on the area-weighted averages of modelled fluxes, and by comparing those to the eddy covariance (EC) flux. This technique is rather inaccurate, as the area of upscaling might be different from the EC tower footprint, therefore introducing significant mismatch. In this study, we present an approach to validate plot-scale methane models with EC observations using the footprint-weighted average method. Our results show that the fluxes obtained by the footprint-weighted average method are of the same magnitude as the EC flux. More importantly, the temporal dynamics of the EC flux on a daily timescale are also captured (r2 = 0.7). In contrast, using the area-weighted average method yielded a low (r2 = 0.14) correlation with the EC measurements. This shows that the footprint-weighted average method is preferable when validating methane emission models with EC fluxes for areas with a heterogeneous and irregular vegetation pattern.

scholarly journals High-Resolution Biomass Burning Aerosol Transport Simulations in the Tropics

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 91
Author(s):  
Nurzahziani ◽  
Chinnawat Surussavadee ◽  
Thanchanok Noosook
Keyword(s):  
High Resolution ◽  
Biomass Burning ◽  
Mean Squared Error ◽  
Dry Mass ◽  
Northern Region ◽  
Emission Model ◽  
Aerosol Transport ◽  
Monthly Average ◽  
Biomass Burning Aerosol ◽  
The Tropics

This study evaluates the performance of the Weather Research and Forecasting Model with Chemistry (WRF-Chem) for simulating biomass burning aerosol transport at high resolution in the tropics using two different biomass burning emission inventories. Hourly, daily, and monthly average PM10 dry mass concentrations at 5 km resolution—simulated separately using the Brazilian Biomass Burning Emission Model (WRF-3BEM) and the Fire Inventory from NCAR (WRF-FINN) and their averages (WRF-AVG) for 3 months from February to April—are evaluated, using measurements from ground stations distributed in northern Thailand for 2014 and 2015. Results show that WRF-3BEM agrees well with observations and performs much better than WRF-FINN and WRF-AVG. WRF-3BEM simulations are almost unbiased, while those of WRF-FINN and WRF-AVG are significantly overestimated due to significant overestimates of FINN emissions. WRF-3BEM and the measured monthly average PM10 concentrations for all stations and both years are 89.22 and 87.20 μg m−3, respectively. The root mean squared error of WRF-3BEM simulated monthly average PM10 concentrations is 72.00 and 47.01% less than those of WRF-FINN and WRF-AVG, respectively. The correlation coefficient of WRF-3BEM simulated monthly PM10 concentrations and measurements is 0.89. WRF-3BEM can provide useful biomass burning aerosol transport simulations for the northern region of Thailand.

scholarly journals Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment

2018 ◽  
Vol 10 (9) ◽  
pp. 1451 ◽  
Author(s):  
Alexandre Roy ◽  
Marion Leduc-Leballeur ◽  
Ghislain Picard ◽  
Alain Royer ◽  
Peter Toose ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Frozen Soil ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Emission Model ◽  
Canadian Prairie ◽  
Seasonal Snow ◽  
L Band

Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the TB simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured TB at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed TB at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature.

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.

scholarly journals Dirty H2 Molecular Clusters as the DIB Sources: Spectroscopic and Physical Properties

2013 ◽  
Vol 9 (S297) ◽  
pp. 378-380
Author(s):  
L. S. Bernstein ◽  
F. O. Clark ◽  
D. K. Lynch
Keyword(s):  
Single Molecule ◽  
Molecular Clouds ◽  
Rotational Temperature ◽  
Single Layer ◽  
Molecular Clusters ◽  
Microwave Emission ◽  
Giant Molecular Clouds ◽  
Induced Dipole ◽  
Spectral Profiles ◽  
Uv Photons

AbstractWe propose that the diffuse interstellar bands (DIBs) arise from absorption lines of electronic transitions in molecular clusters primarily composed of a single molecule, atom, or ion (“seed”), embedded in a single-layer shell of H2 molecules (Bernstein et al. 2013). Less abundant variants of the cluster, including two seed molecules and/or a two-layer shell of H2 molecules may also occur. The lines are broadened, blended, and wavelength-shifted by interactions between the seed and surrounding H2 shell. We refer to these clusters as CHCs (Contaminated H2 Clusters). CHC spectroscopy matches the diversity of observed DIB spectral profiles, and provides good fits to several DIB profiles based on a rotational temperature of 10 K. CHCs arise from ~cm-sized, dirty H2 ice balls, called CHIMPs (Contaminated H2 Ice Macro-Particles), formed in cold, dense, Giant Molecular Clouds (GMCs), and later released into the interstellar medium (ISM) upon GMC disruption. Attractive interactions, arising from Van der Waals and ion-induced dipole potentials, between the seeds and H2 molecules enable CHIMPs to attain cm-sized dimensions. When an ultraviolet (UV) photon is absorbed in the outer layer of a CHIMP, it heats the icy matrix and expels CHCs into the ISM. While CHCs are quickly destroyed by absorbing UV photons, they are replenished by the slowly eroding CHIMPs. Since CHCs require UV photons for their release, they are most abundant at, but not limited to, the edges of UV-opaque molecular clouds, consistent with the observed, preferred location of DIBs. An inherent property of CHCs, which can be characterized as nanometer size, spinning, dipolar dust grains, is that they emit in the radio-frequency region. Thus, CHCs offer a natural explanation to the anomalous microwave emission (AME) feature in the ~10-100 GHz spectral region.

scholarly journals Microstructure representation of snow in coupled snowpack and microwave emission models

2016 ◽  
Author(s):  
Melody Sandells ◽  
Richard Essery ◽  
Nick Rutter ◽  
Leanne Wake ◽  
Leena Leppänen ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Microstructure Evolution ◽  
Microwave Emission ◽  
Model Systems ◽  
Research Centre ◽  
Emission Model ◽  
Mean Error ◽  
Scale Factors ◽  
Wide Range ◽  
Emission Models

Abstract. This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland were used to drive the model over the 2011–2012 and 2012–2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error −0.16 to −0.24 mm for MOSES, and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS or DMRT-ML) provided a consistently good fit across all frequencies and polarizations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarization ranged from 0.9 to 7.2 K. Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERM-Empirical MEMLS model combination (2011–2012), and 5.0 or greater when considering non-sticky particles in DMRT-ML in conjunction with MOSES or SNICAR microstructure (2012–2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack-microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K when the JIM ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011–2012 season. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

scholarly journals Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yunfeng Wu ◽  
Xin Luo ◽  
Fang Zheng ◽  
Shanshan Yang ◽  
Suxian Cai ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Function Approximation ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Radial Basis Function Networks ◽  
Data Sets ◽  
Radial Basis ◽  
Synthetic Function ◽  
Adaptively Adjusting

This paper presents a novel adaptive linear and normalized combination (ALNC) method that can be used to combine the component radial basis function networks (RBFNs) to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error) and the better fidelity (characterized by normalized correlation coefficient) of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

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