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Noah P Molotch - One of the best experts on this subject based on the ideXlab platform.
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spatio temporal variability of Snow Water Equivalent in the extra tropical andes cordillera from distributed energy balance modeling and remotely sensed Snow cover
Hydrology and Earth System Sciences, 2016Co-Authors: Edward Cornwell, Noah P Molotch, James McpheeAbstract:Abstract. Seasonal Snow cover is the primary Water source for human use and ecosystems along the extratropical Andes Cordillera. Despite its importance, relatively little research has been devoted to understanding the properties, distribution and variability of this natural resource. This research provides high-resolution (500 m), daily distributed estimates of end-of-winter and spring Snow Water Equivalent over a 152 000 km2 domain that includes the mountainous reaches of central Chile and Argentina. Remotely sensed fractional Snow-covered area and other relevant forcings are combined with extrapolated data from meteorological stations and a simplified physically based energy balance model in order to obtain melt-season melt fluxes that are then aggregated to estimate the end-of-winter (or peak) Snow Water Equivalent (SWE). Peak SWE estimates show an overall coefficient of determination R2 of 0.68 and RMSE of 274 mm compared to observations at 12 automatic Snow Water Equivalent sensors distributed across the model domain, with R2 values between 0.32 and 0.88. Regional estimates of peak SWE accumulation show differential patterns strongly modulated by elevation, latitude and position relative to the continental divide. The spatial distribution of peak SWE shows that the 4000–5000 m a.s.l. elevation band is significant for Snow accumulation, despite having a smaller surface area than the 3000–4000 m a.s.l. band. On average, maximum Snow accumulation is observed in early September in the western Andes, and in early October on the eastern side of the continental divide. The results presented here have the potential of informing applications such as seasonal forecast model assessment and improvement, regional climate model validation, as well as evaluation of observational networks and Water resource infrastructure development.
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Snow Water Equivalent in the sierra nevada blending Snow sensor observations with Snowmelt model simulations
Water Resources Research, 2013Co-Authors: Bin Guan, Thomas H Painter, Noah P Molotch, Duane E Waliser, Steven M Jepsen, Jeff DozierAbstract:[1] We estimate the spatial distribution of daily melt-season Snow Water Equivalent (SWE) over the Sierra Nevada for March to August, 2000–2012, by two methods: reconstruction by combining remotely sensed Snow cover images with a spatially distributed Snowmelt model and a blended method in which the reconstruction is combined with in situ Snow sensor observations. We validate the methods with 17 Snow surveys at six locations with spatial sampling and with the operational Snow sensor network. We also compare the methods with NOAA's operational Snow Data Assimilation System (SNODAS). Mean biases of the methods compared to the Snow surveys are −0.193 m (reconstruction), 0.001 m (blended), and −0.181 m (SNODAS). Corresponding root-mean-square errors are 0.252, 0.205, and 0.254 m. Comparison between blended and Snow sensor SWE suggests that the current sensor network inadequately represents SWE in the Sierra Nevada because of the low spatial density of sensors in the lower/higher elevations. Mean correlation with streamflow in 19 Sierra Nevada Watersheds is better with reconstructed SWE (r = 0.91) versus blended SWE (r = 0.81), Snow sensor SWE (r = 0.85), and SNODAS SWE (r = 0.86). On the other hand, the correlation with blended SWE is generally better than with reconstructed, Snow sensor, and SNODAS SWE late in the Snowmelt season when Snow sensors report zero SWE but Snow remains in the higher elevations. Sensitivity tests indicate downwelling longwave radiation, Snow albedo, forest density, and turbulent fluxes are potentially important sources of errors/uncertainties in reconstructed SWE, and domain-mean blended SWE is relatively insensitive to the number of Snow sensors blended.
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reconstructing Snow Water Equivalent in the rio grande headWaters using remotely sensed Snow cover data and a spatially distributed Snowmelt model
Hydrological Processes, 2009Co-Authors: Noah P MolotchAbstract:Snow covered area (SCA) observations from the Landsat Enhanced Thematic Mapper (ETM+) were used in combination with a distributed Snowmelt model to estimate Snow Water Equivalent (SWE) in the headWaters of the Rio Grande basin (3,419 km2) - a spatial scale that is an order of magnitude greater than previous reconstruction model applications. In this reconstruction approach, modeled Snowmelt over each pixel is integrated over the time of ETM+ observed Snow cover to estimate SWE. Considerable differences in the magnitude of SWE were simulated during the study. Basin-wide mean SWE was 2·6 times greater in April 2001 versus 2002. Despite these climatological differences, the model adequately recovered SWE at intensive study areas (ISAs); mean absolute SWE error was 23% relative to observed SWE. Reconstruction model SWE errors were within one standard deviation of the mean observed SWE over 37 and 55% of the four 16-km2 intensive field campaign study sites in 2001 and 2002, respectively; a result comparable to previous works at much smaller scales. A key strength of the technique is that spatially distributed SWE estimates are not dependent upon ground-based observations of SWE. Moreover, the model was relatively insensitive to the location of forcing observations relative to commonly used statistical SWE interpolation models. Hence, the reconstruction technique is a viable approach for obtaining high-resolution SWE estimates at larger scales (e.g. >1000 km2) and in locations where detailed hydrometeorological observations are scarce. Copyright © 2009 John Wiley & Sons, Ltd.
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estimating the distribution of Snow Water Equivalent using remotely sensed Snow cover data and a spatially distributed Snowmelt model a multi resolution multi sensor comparison
Advances in Water Resources, 2008Co-Authors: Noah P Molotch, Steven A MargulisAbstract:Abstract Time series of fractional Snow covered area (SCA) estimates from Landsat Enhanced Thematic Mapper (ETM+), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) data were combined with a spatially distributed Snowmelt model to reconstruct Snow Water Equivalent (SWE) in the Rio Grande headWaters (3419 km 2 ). In this reconstruction approach, modeled Snowmelt over each pixel is integrated during the period of satellite-observed Snow cover to estimate SWE. Due to underestimates in Snow cover detection, maximum basin-wide mean SWE using MODIS and AVHRR were, respectively, 45% and 68% lower than SWE estimates obtained using ETM+ data. The mean absolute error (MAE) of SWE estimated at 100-m resolution using ETM+ data was 23% relative to observed SWE from intensive field campaigns. Model performance deteriorated when MODIS (MAE = 50%) and AVHRR (MAE = 89%) SCA data were used. Relative to differences in the SCA products, model output was less sensitive to spatial resolution (MAE = 39% and 73% for ETM+ and MODIS simulations run at 1 km resolution, respectively), indicating that SWE reconstructions at the scale of MODIS acquisitions may be tractable provided the SCA product is improved. When considering tradeoffs between spatial and temporal resolution of different sensors, our results indicate that higher spatial resolution products such as ETM+ remain more accurate despite the lower frequency of acquisition. This motivates continued efforts to improve MODIS Snow cover products.
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estimating the spatial distribution of Snow Water Equivalent in an alpine basin using binary regression tree models the impact of digital elevation data and independent variable selection
Hydrological Processes, 2005Co-Authors: Noah P Molotch, Roger C Bales, M T Colee, Jeff DozierAbstract:Regression tree models have been shown to provide the most accurate estimates of distributed Snow Water Equivalent (SWE) when intensive field observations are available. This work presents a comparison of regression tree models using different source digital elevation models (DEMs) and different combinations of independent variables. Different residual interpolation techniques are also compared. The analysis was performed in the 19Ð 1k m 2 Tokopah Basin, located in the southern Sierra Nevada of California. Snow depth, the dependent variable of the statistical models, was derived from three Snow surveys (April, May and June 1997), with an average of 328 depth measurements per survey. Estimates of distributed SWE were derived from the product of the Snow depth surfaces, the average Snow density (54 measurements on average) and the fractional Snow covered area (obtained from the Landsat Thematic Mapper and the Airborne Visible/Infrared Imaging Spectrometer). Independent variables derived from the standard US Geological Survey DEM yielded the lowest overall model deviance and lowest error in Snow depth prediction. Simulations using the Shuttle Radar Topography Mission DEM and the National Elevation Dataset DEM were improved when northness was substituted for solar radiation in five of six cases. Co-kriging with maximum upwind slope and elevation proved to be the best method for distributing residuals for April and June, respectively. Inverse distance weighting was the best residual distribution method for May. Copyright 2004 John Wiley & Sons, Ltd.
Chris Derksen - One of the best experts on this subject based on the ideXlab platform.
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GlobSnow v3.0 Northern Hemisphere Snow Water Equivalent dataset
'Springer Science and Business Media LLC', 2021Co-Authors: Kari Luojus, Juha Lemmetyinen, Chris Derksen, Jouni Pulliainen, Lawrence Mudryk, Matias Takala, Colleen Mortimer, Mikko Moisander, Mwaba Hiltunen, Tuomo SmolanderAbstract:Measurement(s) Snow • Snow mass • Snow Water Equivalent Technology Type(s) satellite imaging Sample Characteristic - Environment cryosphere Sample Characteristic - Location Northern Hemisphere Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.1433661
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retrieval of effective correlation length and Snow Water Equivalent from radar and passive microwave measurements
Remote Sensing, 2018Co-Authors: Juha Lemmetyinen, Chris Derksen, Helmut Rott, G Macelloni, Joshua King, Martin Schneebeli, Andreas Wiesmann, Leena Leppanen, Anna Kontu, Jouni PulliainenAbstract:Current methods for retrieving SWE (Snow Water Equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of Snow microstructural properties from the total Snow mass, and signal saturation when Snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of Snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of Snow microstructure, we derive an effective correlation length for the Snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry Snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of Snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of Snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical Snow models, is necessary to further improve retrieval skill, in particular for Snow regimes with larger temporal variability in Snow microstructure and a more pronounced layered structure.
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Validation of physical model and radar retrieval algorithm of Snow Water Equivalent using SnowSAR data
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017Co-Authors: Jiyue Zhu, Juha Lemmetyinen, Chris Derksen, Chuan Xiong, Shurun Tan, Leung Tsang, Joshua KingAbstract:We validate an absorption based radar retrieval algorithm of Snow Water Equivalent (SWE) using X- and Ku-band backscatter with airborne SAR data. The bicontinuous dense media radiative transfer (Bic-DMRT) model is first applied to generate a look-up table of Snow properties against backscattering at X- and Ku-bands. In the retrieval algorithm, the background scattering is subtracted from the total scattering giving the volume scattering of Snow. With the look-up table, we generate regression equations between multiple and single scattering and correlations between the scattering albedo and optical thickness at the two bands. With these relationships and the volume scattering of the Snowpack, the best solution for the radar observation is found using a priori constrained least-squares cost function. Next, the absorption loss of the Snowpack is derived from the solution, which is directly proportional to the SWE. We have applied the algorithm to airborne SAR observations from Finland and Canada. The retrieval algorithm is shown to be effective, achieving root mean square error (RMSE) of ~19 mm for both SnowSAR data, which is smaller than the 20mm RMSE requirement of SCLP.
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characterization of northern hemisphere Snow Water Equivalent datasets 1981 2010
Journal of Climate, 2015Co-Authors: Lawrence Mudryk, Chris Derksen, Paul J Kushner, Ross BrownAbstract:AbstractFive, daily, gridded, Northern Hemisphere Snow Water Equivalent (SWE) datasets are analyzed over the 1981–2010 period in order to quantify the spatial and temporal consistency of satellite retrievals, land surface assimilation systems, physical Snow models, and reanalyses. While the climatologies of total Northern Hemisphere Snow Water mass (SWM) vary among the datasets by as much as 50%, their interannual variability and daily anomalies are comparable, showing moderate to good temporal correlations (between 0.60 and 0.85) on both interannual and intraseasonal time scales. Wintertime trends of total Northern Hemisphere SWM are consistently negative over the 1981–2010 period among the five datasets but vary in strength by a factor of 2–3. Examining spatial patterns of SWE indicates that the datasets are most consistent with one another over boreal forest regions compared to Arctic and alpine regions. Additionally, the datasets derived using relatively recent reanalyses are strongly correlated with ...
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coupling the Snow thermodynamic model Snowpack with the microwave emission model of layered Snowpacks for subarctic and arctic Snow Water Equivalent retrievals
Water Resources Research, 2012Co-Authors: Alexandre Langlois, Alain Royer, Chris Derksen, Benoit Montpetit, Florent Dupont, Kalifa GoitaAbstract:[1] Satellite-passive microwave remote sensing has been extensively used to estimate Snow Water Equivalent (SWE) in northern regions. Although passive microwave sensors operate independent of solar illumination and the lower frequencies are independent of atmospheric conditions, the coarse spatial resolution introduces uncertainties to SWE retrievals due to the surface heterogeneity within individual pixels. In this article, we investigate the coupling of a thermodynamic multilayered Snow model with a passive microwave emission model. Results show that the Snow model itself provides poor SWE simulations when compared to field measurements from two major field campaigns. Coupling the Snow and microwave emission models with successive iterations to correct the influence of Snow grain size and density significantly improves SWE simulations. This method was further validated using an additional independent data set, which also showed significant improvement using the two-step iteration method compared to standalone simulations with the Snow model.
Steven A Margulis - One of the best experts on this subject based on the ideXlab platform.
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examination of the impacts of vegetation on the correlation between Snow Water Equivalent and passive microwave brightness temperature
Remote Sensing of Environment, 2017Co-Authors: Shanshan Cai, Michael Durand, Steven A MargulisAbstract:Abstract Snow accumulation, ablation, and runoff in mountainous areas are critical components of the hydrologic cycle, but are poorly known. Passive microwave (PM) measurements are sensitive to Snow Water Equivalent (SWE), even in mountain regions, but vegetation masks the microwave signals and reduces this sensitivity. This study examines how the PM Snow signal is affected by the forest density in fourteen basins in the Sierra Nevada, USA, and in a series of sixteen subsets of the Kern basin that have varied vegetation density. 36.5 GHz vertical polarization brightness temperature (Tb) time series for each basin were produced from the spaceborne AMSR-E operational period (Water year (WY) 2003 to WY2011). For each basin, the coefficient of determination (R2) between the annual minimum Tb and the concurrent SWE was calculated to evaluate the sensitivity of the PM to SWE. The relationship between the R2 values and the forest density was then analyzed to assess how vegetation affect the SWE information in the observed Tb. Mean forest coverage from MODIS was used to represent forest density. The R2 between the annual minimum Tb and concurrent SWE was > 0.6 for three of the basins. Consistent with previous studies, WY2006 demonstrated anomalous Tb values for many basins, apparently due to anomalous warm winter rainfall. Excluding WY2006, R2 is significantly higher in all basins: eight of fourteen basins have R2 > 0.6. For basins with average elevation > 2500 m, SWE correlates well with Tb. The R2 decreases monotonically with decreasing elevation. Basin elevation and forest cover are highly correlated in the Sierra; a basin elevation of 2500 m generally coincides with forest cover of 20%. A total of 42% of Sierra Nevada has
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estimating the distribution of Snow Water Equivalent using remotely sensed Snow cover data and a spatially distributed Snowmelt model a multi resolution multi sensor comparison
Advances in Water Resources, 2008Co-Authors: Noah P Molotch, Steven A MargulisAbstract:Abstract Time series of fractional Snow covered area (SCA) estimates from Landsat Enhanced Thematic Mapper (ETM+), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) data were combined with a spatially distributed Snowmelt model to reconstruct Snow Water Equivalent (SWE) in the Rio Grande headWaters (3419 km 2 ). In this reconstruction approach, modeled Snowmelt over each pixel is integrated during the period of satellite-observed Snow cover to estimate SWE. Due to underestimates in Snow cover detection, maximum basin-wide mean SWE using MODIS and AVHRR were, respectively, 45% and 68% lower than SWE estimates obtained using ETM+ data. The mean absolute error (MAE) of SWE estimated at 100-m resolution using ETM+ data was 23% relative to observed SWE from intensive field campaigns. Model performance deteriorated when MODIS (MAE = 50%) and AVHRR (MAE = 89%) SCA data were used. Relative to differences in the SCA products, model output was less sensitive to spatial resolution (MAE = 39% and 73% for ETM+ and MODIS simulations run at 1 km resolution, respectively), indicating that SWE reconstructions at the scale of MODIS acquisitions may be tractable provided the SCA product is improved. When considering tradeoffs between spatial and temporal resolution of different sensors, our results indicate that higher spatial resolution products such as ETM+ remain more accurate despite the lower frequency of acquisition. This motivates continued efforts to improve MODIS Snow cover products.
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feasibility test of multifrequency radiometric data assimilation to estimate Snow Water Equivalent
Journal of Hydrometeorology, 2006Co-Authors: Michael Durand, Steven A MargulisAbstract:Abstract A season-long, point-scale radiometric data assimilation experiment is performed in order to test the feasibility of Snow Water Equivalent (SWE) estimation. Synthetic passive microwave observations at Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) frequencies and synthetic broadband albedo observations are assimilated simultaneously in order to update Snowpack states in a land surface model using the ensemble Kalman filter (EnKF). The effects of vegetation and atmosphere are included in the radiative transfer model (RTM). The land surface model (LSM) was given biased precipitation to represent typical errors introduced in modeling, yet was still able to recover the true value of SWE with a seasonally integrated rmse of only 2.95 cm, despite a Snow depth of around 3 m and the presence of liquid Water in the Snowpack. This ensemble approach is ideal for investigating the complex theoretical relationships between the Snowpack proper...
Jouni Pulliainen - One of the best experts on this subject based on the ideXlab platform.
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GlobSnow v3.0 Northern Hemisphere Snow Water Equivalent dataset
'Springer Science and Business Media LLC', 2021Co-Authors: Kari Luojus, Juha Lemmetyinen, Chris Derksen, Jouni Pulliainen, Lawrence Mudryk, Matias Takala, Colleen Mortimer, Mikko Moisander, Mwaba Hiltunen, Tuomo SmolanderAbstract:Measurement(s) Snow • Snow mass • Snow Water Equivalent Technology Type(s) satellite imaging Sample Characteristic - Environment cryosphere Sample Characteristic - Location Northern Hemisphere Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.1433661
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Impact of dynamic Snow density on GlobSnow Snow Water Equivalent retrieval accuracy
'Copernicus GmbH', 2021Co-Authors: P. Venäläinen, Juha Lemmetyinen, Jouni Pulliainen, Kari Luojus, Mikko Moisander, Matias TakalaAbstract:Snow Water Equivalent (SWE) is an important variable in describing global seasonal Snow cover. Traditionally, SWE has been measured manually at Snow transects or using observations from weather stations. However, these measurements have a poor spatial coverage, and a good alternative to in situ measurements is to use spaceborne passive microwave observations, which can provide global coverage at daily timescales. The reliability and accuracy of SWE estimates made using spaceborne microwave radiometer data can be improved by assimilating radiometer observations with weather station Snow depth observations as done in the GlobSnow SWE retrieval methodology. However, one possible source of uncertainty in the GlobSnow SWE retrieval approach is the constant Snow density used in modelling emission of Snow. In this paper, three versions of spatially and temporally varying Snow density fields were implemented using Snow transect data from Eurasia and Canada and automated Snow observations from the United States. Snow density fields were used to post-process the baseline GlobSnow v.3.0 SWE product. Decadal Snow density information, i.e. fields where Snow density for each day of the year was taken as the mean calculated for the corresponding day over 10 years, was found to produce the best results. Overall, post-processing GlobSnow SWE retrieval with dynamic Snow density information improved overestimation of small SWE values and underestimation of large SWE values, though underestimation of SWE values larger than 175 mm was still significant.
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retrieval of effective correlation length and Snow Water Equivalent from radar and passive microwave measurements
Remote Sensing, 2018Co-Authors: Juha Lemmetyinen, Chris Derksen, Helmut Rott, G Macelloni, Joshua King, Martin Schneebeli, Andreas Wiesmann, Leena Leppanen, Anna Kontu, Jouni PulliainenAbstract:Current methods for retrieving SWE (Snow Water Equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of Snow microstructural properties from the total Snow mass, and signal saturation when Snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of Snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of Snow microstructure, we derive an effective correlation length for the Snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry Snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of Snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of Snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical Snow models, is necessary to further improve retrieval skill, in particular for Snow regimes with larger temporal variability in Snow microstructure and a more pronounced layered structure.
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evaluation of passive microwave brightness temperature simulations and Snow Water Equivalent retrievals through a winter season
Remote Sensing of Environment, 2012Co-Authors: Chris Derksen, Juha Lemmetyinen, Jouni Pulliainen, Alexandre Langlois, Peter Toose, Nick Rutter, Mark C FullerAbstract:Abstract Plot-scale brightness temperature (T B ) measurements at 6.9, 19, 37, and 89 GHz were acquired in forest, open, and lake environments near Churchill, Manitoba, Canada with mobile sled-based microwave radiometers during the 2009–2010 winter season. Detailed physical Snow measurements within the radiometer footprints were made to relate the microwave signatures to the seasonal evolution of the Snowpack, and provide inputs for model simulations with the Helsinki University of Technology (HUT) Snow emission model. Large differences in depth, density, and grain size were observed between the three land cover types. Plot-scale simulations with the HUT model showed a wide range in simulation accuracy between sites and frequencies. In general, model performance degraded when the effective grain size exceeded 2 mm and/or there was an ice lens present in the pack. HUT model performance improved when simulations were run regionally at the satellite scale (using three proportional land cover tiles: open, forest, and lake) and compared to Advanced Microwave Scanning Radiometer (AMSR-E) measurements. Root mean square error (RMSE) values ranged from approximately 4 to 16 K depending on the frequency, polarization, and land cover composition of the grid cell. Snow Water Equivalent (SWE) retrievals produced using forward T B simulations with the HUT model in combination with AMSR-E measurements produced RMSE values below 25 mm for the intensive study area. Retrieval errors exceeded 50 mm when the scheme was applied regionally.
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estimating northern hemisphere Snow Water Equivalent for climate research through assimilation of space borne radiometer data and ground based measurements
Remote Sensing of Environment, 2011Co-Authors: Matias Takala, Juha Lemmetyinen, Chris Derksen, Jouni Pulliainen, Kari Luojus, J P Karna, J Koskinen, Bojan BojkovAbstract:Abstract The key variable describing global seasonal Snow cover is Snow Water Equivalent (SWE). However, reliable information on the hemispheric scale variability of SWE is lacking because traditional methods such as interpolation of ground-based measurements and stand-alone algorithms applied to space-borne observations are highly uncertain with respect to the spatial distribution of Snow mass and its evolution. In this paper, an algorithm assimilating synoptic weather station data on Snow depth with satellite passive microwave radiometer data is applied to produce a 30-year-long time-series of seasonal SWE for the northern hemisphere. This data set is validated using independent SWE reference data from Russia, the former Soviet Union, Finland and Canada. The validation of SWE time-series indicates overall strong retrieval performance with root mean square errors below 40 mm for cases when SWE
Juha Lemmetyinen - One of the best experts on this subject based on the ideXlab platform.
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GlobSnow v3.0 Northern Hemisphere Snow Water Equivalent dataset
'Springer Science and Business Media LLC', 2021Co-Authors: Kari Luojus, Juha Lemmetyinen, Chris Derksen, Jouni Pulliainen, Lawrence Mudryk, Matias Takala, Colleen Mortimer, Mikko Moisander, Mwaba Hiltunen, Tuomo SmolanderAbstract:Measurement(s) Snow • Snow mass • Snow Water Equivalent Technology Type(s) satellite imaging Sample Characteristic - Environment cryosphere Sample Characteristic - Location Northern Hemisphere Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.1433661
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Impact of dynamic Snow density on GlobSnow Snow Water Equivalent retrieval accuracy
'Copernicus GmbH', 2021Co-Authors: P. Venäläinen, Juha Lemmetyinen, Jouni Pulliainen, Kari Luojus, Mikko Moisander, Matias TakalaAbstract:Snow Water Equivalent (SWE) is an important variable in describing global seasonal Snow cover. Traditionally, SWE has been measured manually at Snow transects or using observations from weather stations. However, these measurements have a poor spatial coverage, and a good alternative to in situ measurements is to use spaceborne passive microwave observations, which can provide global coverage at daily timescales. The reliability and accuracy of SWE estimates made using spaceborne microwave radiometer data can be improved by assimilating radiometer observations with weather station Snow depth observations as done in the GlobSnow SWE retrieval methodology. However, one possible source of uncertainty in the GlobSnow SWE retrieval approach is the constant Snow density used in modelling emission of Snow. In this paper, three versions of spatially and temporally varying Snow density fields were implemented using Snow transect data from Eurasia and Canada and automated Snow observations from the United States. Snow density fields were used to post-process the baseline GlobSnow v.3.0 SWE product. Decadal Snow density information, i.e. fields where Snow density for each day of the year was taken as the mean calculated for the corresponding day over 10 years, was found to produce the best results. Overall, post-processing GlobSnow SWE retrieval with dynamic Snow density information improved overestimation of small SWE values and underestimation of large SWE values, though underestimation of SWE values larger than 175 mm was still significant.
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retrieval of effective correlation length and Snow Water Equivalent from radar and passive microwave measurements
Remote Sensing, 2018Co-Authors: Juha Lemmetyinen, Chris Derksen, Helmut Rott, G Macelloni, Joshua King, Martin Schneebeli, Andreas Wiesmann, Leena Leppanen, Anna Kontu, Jouni PulliainenAbstract:Current methods for retrieving SWE (Snow Water Equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of Snow microstructural properties from the total Snow mass, and signal saturation when Snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of Snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of Snow microstructure, we derive an effective correlation length for the Snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry Snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of Snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of Snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical Snow models, is necessary to further improve retrieval skill, in particular for Snow regimes with larger temporal variability in Snow microstructure and a more pronounced layered structure.
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Validation of physical model and radar retrieval algorithm of Snow Water Equivalent using SnowSAR data
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017Co-Authors: Jiyue Zhu, Juha Lemmetyinen, Chris Derksen, Chuan Xiong, Shurun Tan, Leung Tsang, Joshua KingAbstract:We validate an absorption based radar retrieval algorithm of Snow Water Equivalent (SWE) using X- and Ku-band backscatter with airborne SAR data. The bicontinuous dense media radiative transfer (Bic-DMRT) model is first applied to generate a look-up table of Snow properties against backscattering at X- and Ku-bands. In the retrieval algorithm, the background scattering is subtracted from the total scattering giving the volume scattering of Snow. With the look-up table, we generate regression equations between multiple and single scattering and correlations between the scattering albedo and optical thickness at the two bands. With these relationships and the volume scattering of the Snowpack, the best solution for the radar observation is found using a priori constrained least-squares cost function. Next, the absorption loss of the Snowpack is derived from the solution, which is directly proportional to the SWE. We have applied the algorithm to airborne SAR observations from Finland and Canada. The retrieval algorithm is shown to be effective, achieving root mean square error (RMSE) of ~19 mm for both SnowSAR data, which is smaller than the 20mm RMSE requirement of SCLP.
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estimating Snow Water Equivalent with backscattering at x and ku band based on absorption loss
Remote Sensing, 2016Co-Authors: Yurong Cui, Juha Lemmetyinen, Chuan Xiong, Jiancheng Shi, Lingmei Jiang, Bin Peng, Tianjie ZhaoAbstract:Snow Water Equivalent (SWE) is a key parameter in the Earth’s energy budget and Water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the Snow medium. Several proposed models have described Snow as a collection of sphere- or ellipsoid-shaped ice particles embedded in air, while the microstructure of Snow is, in reality, more complex. Natural Snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs Snow microstructure more similar to real Snow and then simulates the Snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of Snow volume backscattering is proposed. A relationship between Snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009–2010 and 19.70 mm for the winter of 2010–2011.
