scholarly journals CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data

2013 ◽  
Vol 17 (2) ◽  
pp. 783-793 ◽  
Author(s):  
T. Y. Lakhankar ◽  
J. Muñoz ◽  
P. Romanov ◽  
A. M. Powell ◽  
N. Y. Krakauer ◽  
...  
Keyword(s):  
Grain Size ◽  
Field Experiment ◽  
Brightness Temperature ◽  
Snow Depth ◽  
Winter Season ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Substantial Impact ◽  
Brightness Temperatures ◽  
Snowpack Properties

Abstract. The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during January–March 2011 at the research site of the National Weather Service office, Caribou, ME, USA. In this experiment dual-polarized microwave (37 and 89 GHz) observations were accompanied by detailed synchronous observations of meteorology and snowpack physical properties. The objective of this long-term field experiment was to improve understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to improve retrievals of snow cover properties from satellite observations. In this paper we present an overview of the field experiment and comparative preliminary analysis of the continuous microwave and snowpack observations and simulations. The observations revealed a large difference between the brightness temperature of fresh and aged snowpack even when the snow depth was the same. This is indicative of a substantial impact of evolution of snowpack properties such as snow grain size, density and wetness on microwave observations. In the early spring we frequently observed a large diurnal variation in the 37 and 89 GHz brightness temperature with small depolarization corresponding to daytime snowmelt and nighttime refreeze events. SNTHERM (SNow THERmal Model) and the HUT (Helsinki University of Technology) snow emission model were used to simulate snowpack properties and microwave brightness temperatures, respectively. Simulated snow depth and snowpack temperature using SNTHERM were compared to in situ observations. Similarly, simulated microwave brightness temperatures using the HUT model were compared with the observed brightness temperatures under different snow conditions to identify different states of the snowpack that developed during the winter season.

scholarly journals CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data

2012 ◽  
Vol 9 (7) ◽  
pp. 8105-8136
Author(s):  
T. Lakhankar ◽  
J. Muñoz ◽  
P. Romanov ◽  
A. M. Powell ◽  
N. Krakauer ◽  
...  
Keyword(s):  
Grain Size ◽  
Field Experiment ◽  
Brightness Temperature ◽  
Winter Season ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Substantial Impact ◽  
Brightness Temperatures ◽  
Snow Conditions ◽  
Snowpack Properties

Abstract. The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during winter 2011 at the research site of the National Weather Service office, Caribou ME, USA. In this ground experiment, dual polarized microwave (37 and 89 GHz) observations are conducted along with detailed synchronous observations of snowpack properties. The objective of this long term field experiment is to improve our understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to improve retrievals of snow cover properties from satellite observations in the microwave spectral range. In this paper, we presented the overview of field experiment and preliminary analysis of the microwave observations for the first year of experiment along with support observations of the snowpack properties obtained during the 2011 winter season. SNTHERM and HUT (Helsinki University of Technology) snow emission model were used to simulate snowpack properties and microwave brightness temperatures respectively. Simulated brightness temperatures were compared with observed brightness temperature from radiometer under different snow conditions. On the time series, large difference in the brightness temperature were observed for fresh compared to aged snow even under the same snow depth, suggesting a substantial impact of other parameters such as: snow grain size and density on microwave observations. A large diurnal variation in the 37 and 89 GHz brightness temperature with small depolarization factor was observed due to cold nights and warm days, which caused a cycling between wet snow and ice-over-snow states during the early spring. Scattering analysis of microwave brightness temperatures from radiometers were performed to distinguished different snow conditions developed through the winter season.

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 Estimation of Snow Depth over the Qinghai-Tibetan Plateau Based on AMSR-E and MODIS Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1989 ◽  
Author(s):  
Liyun Dai ◽  
Tao Che ◽  
Hongjie Xie ◽  
Xuejiao Wu
Keyword(s):  
Tibetan Plateau ◽  
Brightness Temperature ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Snow Depth ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
New Method ◽  
Retrieval Algorithm ◽  
Mountainous Areas

Snow cover over the Qinghai-Tibetan Plateau (QTP) plays an important role in climate, hydrological, and ecological systems. Currently, passive microwave remote sensing is the most efficient way to monitor snow depth on global and regional scales; however, it presents a serious overestimation of snow cover over the QTP and has difficulty describing patchy snow cover over the QTP because of its coarse spatial resolution. In this study, a new spatial dynamic method is developed by introducing ground emissivity and assimilating the snow cover fraction (SCF) and land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive snow depth at an enhanced spatial resolution. In this method, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) brightness temperature and MODIS LST are used to calculate ground emissivity. Additionally, the microwave emission model of layered snowpacks (MEMLS) is applied to simulate brightness temperature with varying ground emissivities to determine the key coefficients in the snow depth retrieval algorithm. The results show that the frozen ground emissivity presents large spatial heterogeneity over the QTP, which leads to the variation of coefficients in the snow depth retrieval algorithm. The overestimation of snow depth is rectified by introducing the ground emissivity factor at 18 and 36 GHz. Compared with in situ observations, the snow cover accuracy of the new method is 93.9%, which is better than the 60.2% accuracy of the existing method (old method) which does not consider ground emissivity. The bias and root-mean-square error (RMSE) of snow depth are 1.03 cm and 7.05 cm, respectively, for the new method; these values are much lower than the values of 6.02 cm and 9.75 cm, respectively, for the old method. However, the snow cover accuracy with depths between 1 and 3 cm is below 60%, and snow depths greater than 25 cm are underestimated in Himalayan mountainous areas. In the future, the snow cover identification algorithm should be improved to identify shallow snow cover over the QTP, and topography should be considered in the snow depth retrieval algorithm to improve snow depth accuracy in mountainous areas.

scholarly journals Multi-temporal microwave satellite observation of snowpacks

1993 ◽  
Vol 17 ◽  
pp. 155-160 ◽  
Author(s):  
Anthony Wankiewicz
Keyword(s):  
Grain Size ◽  
Winter Season ◽  
Satellite Observation ◽  
Satellite Observations ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Multi Temporal ◽  
Snowpack Properties ◽  
Simulated Time ◽  
Simulated Time Series

Microwave brightness temperatures from snowpacks are simulated with a multiple-scattering model using observed hydrometeorological variables at three target areas on the Canadian plains. Comparison of model microwave emissions with those observed from the Nimbus 7 satellite allows the derivation of the snowpack properties of grain-size and microwave absorption. A simulated time series of microwave brightness temperature is produced for the winter season of 1884—85, for assessing the utility of multi-temporal satellite observations for snowpack monitoring.

scholarly journals Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

2019 ◽  
Vol 11 (24) ◽  
pp. 3037
Author(s):  
Lingjia Gu ◽  
Xintong Fan ◽  
Xiaofeng Li ◽  
Yanlin Wei
Keyword(s):  
Brightness Temperature ◽  
Snow Depth ◽  
Northeast China ◽  
Mean Squared Error ◽  
Microwave Remote Sensing ◽  
Snow Accumulation ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Lookup Table ◽  
Snow Season

At present, passive microwave remote sensing is the most efficient method to estimate snow depth (SD) at global and regional scales. Farmland covers 46% of Northeast China and accurate SD retrieval throughout the whole snow season has great significance for the agriculture management field. Based on the results of the statistical analysis of snow properties in Northeast China from December 2017 to January 2018, conducted by the China snow investigation project, snow characteristics such as snow grain size (SGS), snow density, snow thickness, and temperature of the layered snowpack were measured and analyzed in detail. These characteristics were input to the microwave emission model of layered snowpacks (MEMLS) to simulate the brightness temperature (TB) time series of snow-covered farmland in the periods of snow accumulation, stabilization, and ablation. Considering the larger SGS of the thick depth hoar layer that resulted in a rapid decrease of simulated TBs, effective SGS was proposed to minimize the simulation errors and ensure that the MEMLS can be correctly applied to satellite data simulation. Statistical lookup tables (LUTs) for MWRI and AMSR2 data were generated to represent the relationship between SD and the brightness temperature difference (TBD) at 18 and 36 GHz. The SD retrieval results based on the LUT were compared with the actual SD and the SD retrieved by Chang’s algorithm, Foster’s algorithm, the standard MWRI algorithm, and the standard AMSR2 algorithm. The results demonstrated that the proposed algorithm based on the statistical LUT achieved better accuracy than the other algorithms due to its incorporation of the variation in snow characteristics with the age of snow cover. The average root mean squared error of the SD for the whole snow season was approximately 3.97 and 4.22 cm for MWRI and AMSR2, respectively. The research results are beneficial for monitoring SD in the farmland of Northeast China.

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 Multi-temporal microwave satellite observation of snowpacks

1993 ◽  
Vol 17 ◽  
pp. 155-160 ◽  
Author(s):  
Anthony Wankiewicz
Keyword(s):  
Grain Size ◽  
Winter Season ◽  
Satellite Observation ◽  
Satellite Observations ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Multi Temporal ◽  
Snowpack Properties ◽  
Simulated Time ◽  
Simulated Time Series

Microwave brightness temperatures from snowpacks are simulated with a multiple-scattering model using observed hydrometeorological variables at three target areas on the Canadian plains. Comparison of model microwave emissions with those observed from the Nimbus 7 satellite allows the derivation of the snowpack properties of grain-size and microwave absorption. A simulated time series of microwave brightness temperature is produced for the winter season of 1884—85, for assessing the utility of multi-temporal satellite observations for snowpack monitoring.

scholarly journals On direct passive microwave remote sensing of sea spray aerosol production

2014 ◽  
Vol 14 (21) ◽  
pp. 11611-11631 ◽  
Author(s):  
I. B. Savelyev ◽  
M. D. Anguelova ◽  
G. M. Frick ◽  
D. J. Dowgiallo ◽  
P. A. Hwang ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Brightness Temperature ◽  
Microwave Remote Sensing ◽  
Ocean Surface ◽  
Breaking Waves ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Sea Spray ◽  
Sea Spray Aerosol ◽  
Microwave Emissions

Abstract. This study addresses and attempts to mitigate persistent uncertainty and scatter among existing approaches for determining the rate of sea spray aerosol production by breaking waves in the open ocean. The new approach proposed here utilizes passive microwave emissions from the ocean surface, which are known to be sensitive to surface roughness and foam. Direct, simultaneous, and collocated measurements of the aerosol production and microwave emissions were collected aboard the FLoating Instrument Platform (FLIP) in deep water ~ 150 km off the coast of California over a period of ~ 4 days. Vertical profiles of coarse-mode aerosol (0.25–23.5 μm) concentrations were measured with a forward-scattering spectrometer and converted to surface flux using dry deposition and vertical gradient methods. Back-trajectory analysis of eastern North Pacific meteorology verified the clean marine origin of the sampled air mass over at least 5 days prior to measurements. Vertical and horizontal polarization surface brightness temperature were measured with a microwave radiometer at 10.7 GHz frequency. Data analysis revealed a strong sensitivity of the brightness temperature polarization difference to the rate of aerosol production. An existing model of microwave emission from the ocean surface was used to determine the empirical relationship and to attribute its underlying physical basis to microwave emissions from surface roughness and foam within active and passive phases of breaking waves. A possibility of and initial steps towards satellite retrievals of the sea spray aerosol production are briefly discussed in concluding remarks.

scholarly journals Arctic sea ice signatures: L-Band brightness temperature sensitivity comparison using two radiation transfer models

2016 ◽  
Author(s):  
Friedrich Richter ◽  
Matthias Drusch ◽  
Lars Kaleschke ◽  
Nina Maaß ◽  
Xiangshan Tian-Kunze ◽  
...  
Keyword(s):  
Sea Ice ◽  
Brightness Temperature ◽  
Initial Conditions ◽  
Winter Season ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
Brightness Temperatures ◽  
Ice Coverage ◽  
L Band ◽  
Transfer Models

Abstract. Sea ice is a crucial component for short-, medium- and long term numerical weather predictions. Most importantly changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-Band brightness temperatures from ESA's first Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to estimate mean thin sea ice thicknesses. Potentially, these measurements can be assimilated in forecasting systems to constrain the ice analysis leading to more accurate initial conditions and subsequently more accurate forecasts. As a first step, we use two different radiative transfer models as forward operators to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. We identify one model to be favorable for brightness temperature assimilation purposes in the ORAP5 setup. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes most pronounced in the marginal ice zone where new sea ice is formed. There we observe largest differences of more than 20 Kelvin over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yield high potential for forecasting models to correct for uncertainties in sea ice thicknesses of less than 0.5 meter and caution that uncertainties in sea ice fractional coverage may induce large errors.

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