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 Microstructure representation of snow in coupled snowpack and microwave emission models

2017 ◽  
Vol 11 (1) ◽  
pp. 229-246 ◽  
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 ◽  
Emission Models ◽  
The Impact

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 polarisations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarisation ranged from 0.7 to 6.9 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 3.3 for DMRT-ML in conjunction with MOSES 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 at 36.5 GHz H-pol and 18 K at V-pol when the Jules Investigation Model ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011–2012 season. The impact of snowpack parameterisation increases as the microwave scattering increases. 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 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 SMOS brightness temperature assimilation into the Community Land Model

2017 ◽  
Vol 21 (11) ◽  
pp. 5929-5951 ◽  
Author(s):  
Dominik Rains ◽  
Xujun Han ◽  
Hans Lievens ◽  
Carsten Montzka ◽  
Niko E. C. Verhoest
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Extreme Event ◽  
Root Zone ◽  
Incident Angle ◽  
Grid Cell ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Layers ◽  
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.

scholarly journals Modeling time series of microwave brightness temperature at Dome C, Antarctica, using vertically resolved snow temperature and microstructure measurements

2011 ◽  
Vol 57 (201) ◽  
pp. 171-182 ◽  
Author(s):  
Ludovic Brucker ◽  
Ghislain Picard ◽  
Laurent Arnaud ◽  
Jean-Marc Barnola ◽  
Martin Schneebeli ◽  
...  
Keyword(s):  
Time Series ◽  
Grain Size ◽  
Brightness Temperature ◽  
Near Infrared ◽  
Microwave Emission ◽  
Dense Medium ◽  
Emission Model ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Size Profile

AbstractTime series of observed microwave brightness temperatures at Dome C, East Antarctic plateau, were modeled over 27 months with a multilayer microwave emission model based on dense-medium radiative transfer theory. The modeled time series of brightness temperature at 18.7 and 36.5 GHz were compared with Advanced Microwave Scanning Radiometer–EOS observations. The model uses in situ high-resolution vertical profiles of temperature, snow density and grain size. The snow grain-size profile was derived from near-infrared (NIR) reflectance photography of a snow pit wall in the range 850–1100 nm. To establish the snow grain-size profile, from the NIR reflectance and the specific surface area of snow, two empirical relationships and a theoretical relationship were considered. In all cases, the modeled brightness temperatures were overestimated, and the grain-size profile had to be scaled to increase the scattering by snow grains. Using a scaling factor and a constant snow grain size below 3 m depth (i.e. below the image-derived snow pit grain-size profile), brightness temperatures were explained with a root-mean-square error close to 1 K. Most of this error is due to an overestimation of the predicted brightness temperature in summer at 36.5 GHz.

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 Antarctic Snowmelt Detected by Diurnal Variations of AMSR-E Brightness Temperature

2018 ◽  
Vol 10 (9) ◽  
pp. 1391 ◽  
Author(s):  
Lei Zheng ◽  
Chunxia Zhou ◽  
Ruixi Liu ◽  
Qizhen Sun
Keyword(s):  
Brightness Temperature ◽  
Structural Changes ◽  
Ice Sheet ◽  
Microwave Emission ◽  
Emission Model ◽  
Antarctic Ice Sheet ◽  
Earth Observing System ◽  
Air Temperatures ◽  
Wet Snow ◽  
The Antarctic

Antarctic surface snowmelt is sensitive to the polar climate. The ascending and descending passes of the Advanced Microwave Scanning Radiometer for Earth Observing System Sensor (AMSR-E) observed the Antarctic ice sheet in the afternoon (the warmest period) and at midnight (a cold period), enabling us to make full use of the diurnal amplitude variations (DAV) in brightness temperature (Tb) to detect snowmelt. The DAV in vertically polarized 36.5 GHz Tb (DAV36V) is extremely sensitive to liquid water and can reduce the effects of the structural changes in snowpacks during melt seasons. A set of controlled experiments based on the microwave emission model of layered snow (MEMLS) were conducted to study the changes of the vertically polarized 36.5 GHz Tb (Δ36V) during the transitions from dry to wet snow regimes. Results of the experiments suggest that 9 K can be used as a DAV36V threshold to recognize snowmelt. The analyses of snowmelt suggest that the Antarctic ice sheet began to melt in November and became almost completely frozen in late March of the following year. The total cumulative melt area from 2002 to 2011 was 2.44 × 106 km2, i.e., 17.58% of the Antarctic ice sheet. The annual cumulative melt area showed considerable fluctuations, with a significant (above 90% confidence level) drop of 5.24 × 104 km2/year in the short term. Persistent snowmelt (i.e., melt that continues for at least three days) detected by AMSR-E and hourly air temperatures (Tair) were very consistent. Though melt seasons became longer in the western Antarctic Peninsula and the Shackleton Ice Shelf, Antarctica was subjected to considerable decreases in duration and melting days in stable melt areas, i.e., −0.64 and −0.81 days/year, respectively. Surface snowmelt in Antarctica decreased temporally and spatially from 2002 to 2011.

scholarly journals Influence of meter-scale wind-formed features on the variability of the microwave brightness temperature around Dome C in Antarctica

2013 ◽  
Vol 7 (4) ◽  
pp. 3675-3716 ◽  
Author(s):  
G. Picard ◽  
A. Royer ◽  
L. Arnaud ◽  
M. Fily
Keyword(s):  
Numerical Simulations ◽  
Brightness Temperature ◽  
High Sensitivity ◽  
Microwave Emission ◽  
Emission Model ◽  
Density Profiles ◽  
Microwave Brightness Temperature ◽  
Snow Properties ◽  
The Difference ◽  
Microwave Radiometers

Abstract. Space-borne passive microwave radiometers are widely used to retrieve information in snowy regions by exploiting the high sensitivity of microwave emission to snow properties. For the Antarctic Plateau, many studies presenting retrieval algorithms or numerical simulations have assumed, explicitly or not, that the subpixel-scale heterogeneity is negligible and that the retrieved properties were representative of whole pixels. In this paper, we investigate the spatial variations of brightness temperature over a range of a few kilometers in the Dome C area. Using ground-based radiometers towed by a vehicle allowing measurements with meter resolution, we collected brightness temperature transects at 11, 19 and 37 GHz at horizontal and vertical polarizations. The most remarkable observation was a series of regular undulations of the signal with a significant amplitude of up to 10 K at 37 GHz and a quasi-period of 30–50 m. In contrast, the variability at longer length scales seemed to be weak in the investigated area and the mean brightness temperature was close to AMSR-E and WindSat satellite observations for all the frequencies and polarisations. To establish a link between the snow characteristics and undulation-scale variations of microwave emission, we collected detailed snow grain size and density profiles to run the DMRT-ML microwave emission model at two points where opposite extrema of brightness temperature were observed. The numerical simulations revealed that the difference in density of the upper first meter of the snowpack explained most of the brightness temperature variations. In addition, we found in the field that these variations of density were linked to the hardness of the snowpack. Areas of hard snow – probably formed by the wind – were clearly visible and covered as much as 39% of the investigated area. Their brightness temperature was higher than in normal areas. This result implied that the microwave emission measured by satellites over Dome C is more complex than expected and very likely depends on the areal proportion of the two different types of areas having distinct snow properties.

scholarly journals Microwave emission from density-stratified Antarctic firn at 6 cm wavelength

1996 ◽  
Vol 42 (140) ◽  
pp. 63-76 ◽  
Author(s):  
Richard D. West ◽  
Dale P. Winebrenner ◽  
Leung Tsang ◽  
Helmut Rott
Keyword(s):  
Brightness Temperature ◽  
Depth Resolution ◽  
Density Fluctuations ◽  
Microwave Emission ◽  
Estimation Methods ◽  
Emission Model ◽  
Model Predictions ◽  
Input Parameters ◽  
Limited Length ◽  
Model Tuning

AbstractPrevious observations have shown spatial covariances between microwave emission from Antarctic firn at 6 cm wavelength, physical firn temperature and firn-density stratification. Such observations motivate us to understand the physics underlying such covariances and, based on that understanding, to develop estimation methods for firn in which density, and therefore dielectric permittivity, varies randomly in discrete layers with mean thicknesses on the order of centimeters. The model accounts for depth profiles of the physical temperature, mean density and variance of random density fluctuations from layer to layer. We also present a procedure to estimate emission-model input parameters objectively from in situ density-profile observations, as well as uncertainties in the input parameters and corresponding uncertainties in theoretical brightness-temperature predictions. We compare emission-model predictions with ground-based observations at four diverse sites in Antarctica which span a range of accumulation rates and other parameters. We use coincident characterization data to estimate model inputs. At two sites, layered-medium emission-model predictions based on the most probable input parameters (i.e. with no model tuning) agree with observations to within 3.5% for incidence angles≤50°. Corresponding figures for the other two sites are 7.5% and 10%. However, uncertainties in the input parameters are substantial due to the limited length and depth resolution of the characterization data. Uncertainties in brightness-temperature predictions are correspondingly substantial. Thus brightness-temperature predictions for the last-mentioned sites based on only slightly less probable input parameters are also in close agreement with observations. The significance of agreements and discrepancies could be clarified using characterization measurements with finer depth resolution.

scholarly journals Microwave emission from density-stratified Antarctic firn at 6 cm wavelength

1996 ◽  
Vol 42 (140) ◽  
pp. 63-76 ◽  
Author(s):  
Richard D. West ◽  
Dale P. Winebrenner ◽  
Leung Tsang ◽  
Helmut Rott
Keyword(s):  
Brightness Temperature ◽  
Depth Resolution ◽  
Density Fluctuations ◽  
Microwave Emission ◽  
Estimation Methods ◽  
Emission Model ◽  
Model Predictions ◽  
Input Parameters ◽  
Limited Length ◽  
Model Tuning

AbstractPrevious observations have shown spatial covariances between microwave emission from Antarctic firn at 6 cm wavelength, physical firn temperature and firn-density stratification. Such observations motivate us to understand the physics underlying such covariances and, based on that understanding, to develop estimation methods for firn in which density, and therefore dielectric permittivity, varies randomly in discrete layers with mean thicknesses on the order of centimeters. The model accounts for depth profiles of the physical temperature, mean density and variance of random density fluctuations from layer to layer. We also present a procedure to estimate emission-model input parameters objectively from in situ density-profile observations, as well as uncertainties in the input parameters and corresponding uncertainties in theoretical brightness-temperature predictions. We compare emission-model predictions with ground-based observations at four diverse sites in Antarctica which span a range of accumulation rates and other parameters. We use coincident characterization data to estimate model inputs. At two sites, layered-medium emission-model predictions based on the most probable input parameters (i.e. with no model tuning) agree with observations to within 3.5% for incidence angles≤50°. Corresponding figures for the other two sites are 7.5% and 10%. However, uncertainties in the input parameters are substantial due to the limited length and depth resolution of the characterization data. Uncertainties in brightness-temperature predictions are correspondingly substantial. Thus brightness-temperature predictions for the last-mentioned sites based on only slightly less probable input parameters are also in close agreement with observations. The significance of agreements and discrepancies could be clarified using characterization measurements with finer depth resolution.

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