Research relative to angular distribution of snow reflectance/snow cover characterization and microwave emission

Remote sensing has been applied in recent years to monitoring snow cover properties for applications in hydrologic and energy balance modeling. In addition, snow cover has been recently shown to exert a considerable local influence on weather variables. Of particular importance is the potential of sensors to provide data on the physical properties of snow with high spatial and temporal resolution. Visible and near-infrared measurements of upwelling radiance can be used to infer near-surface properties through the calculation of albedo. Microwave signals usually come from deeper within the snow pack and thus provide depth-integrated information, which can be measured through clouds and does not relay on solar illumination.Fundamental studies examining the influence of snow properties on signals from various parts of the electromagnetic spectrum continue in part because of the promise of new remote sensors with higher spectral and spatial accuracy. Information in the visible and near-infrared parts of the spectrum comprise nearly all available data with high spatial resolution. Current passive microwave sensors have poor spatial resolution and the data are problematic where the scenes consist of mixed landscape features, but they offer timely observations that are independent of cloud cover and solar illumination

Development and evaluation of an advanced microwave radiance data assimilation system for estimating snow water storage at the continental scale

Snow cover modulates the Earth's surface energy and water fluxes, and snowmelt runoff is the principal source of water for humans and ecosystems in many of the middle to high latitudes in the Northern Hemisphere. Understanding spatial and temporal variation in snowpack is crucial for climate studies and water resource management and thus the climate and hydrological research communities have invested in improving large-scale snow estimates. This dissertation aims to develop an advanced snow radiance assimilation (RA) system to improve continental-scale snow water storage estimates. The RA system is comprised of the Community Land Model version 4 (CLM4) (for snow energy and mass balance modeling), radiative transfer models (RTMs) (for brightness temperature estimates), and the Data Assimilation Research Testbed (DART) (for ensemble-based data assimilation). Two snowpack RTMs, the Microwave Emission Model for Layered Snowpacks (MEMLS) and the Dense Media Radiative Transfer--Multi Layers model (DMRT-ML), are used to simulate T[subscript B] of a multi-layered snowpack. Through an error characterization study, this dissertation presents that the correlations between snow water equivalent (SWE) error and brightness temperature (T[subscript B]) error and subsequent RA performance in estimating snow are significantly affected by all physical properties of soil and snow involved in estimating T[subscript B]. Based on the error characterization results, it is hypothesized that the continental-scale RA performance in estimating snow water storage can be improved by simultaneously updating all model physical states and parameters determining T[subscript B] based on a rule, in which prior estimates are updated depending on their correlations with a prior T[subscript B]. The results of a series of RA experiments show that the improved continental-scale snow estimates are obtained by applying the hypothesis. This dissertation also shows that further improvement of the performance of the RA system can be achieved, especially for vegetated areas, by assimilating the best-performing frequency channels (i.e., 18.7 and 23.8 GHz) and by considering the vegetation single scattering albedo to represent the vegetation effect on T[subscript B] at the top of the atmosphere.Geological Science

Derivation and evaluation of a new extinction coefficient for use with the n-HUT snow emission model

In this study, snow slab data collected from the Arctic Snow Microstructure Experiment were used in conjunction with a six-directional flux coefficient model to calculate individual slab absorption and scattering coefficients. These coefficients formed the basis for a new semiempirical extinction coefficient model, using both frequency and optical diameter as input parameters, along with the complex dielectric constant of snow. Radiometric observations, at 18.7, 21.0, and 36.5 GHz at both horizontal polarization (H-Pol) and vertical polarization (V-Pol), and snowpit data collected as part of the Sodankylä Radiometer Experiment were used to compare and contrast the simulated brightness temperatures produced by the multi-layer Helsinki University of Technology snow emission model, utilizing both the original empirical model and the new semiempirical extinction coefficient model described here. The results show that the V-Pol RMSE and bias values decreased when using the semiempirical extinction coefficient; however, the H-Pol RMSE and bias values increased on two of the lower microwave bands tested. The unbiased RMSE was shown to decrease across all frequencies and polarizations when using the semiempirical extinction coefficient

Simulation of the Microwave Emission of Multi-layered Snowpacks Using the Dense Media Radiative Transfer Theory: the DMRT-ML Model

DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1-200 GHz similar to those acquired routinely by spacebased microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large icesheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python

Snow Properties Retrieval Using Passive Microwave Observations

Seasonal snow cover, the second-largest component of the cryosphere, is crucial in controlling the climate system, through its important role in modifying Earth’s albedo. The temporal variability of snow extent and its physical properties in the seasonal cycle also make up a significant element to the cryospheric energy balance. Thus, seasonal snowcover should be monitored not only for its climatological impacts but also for its rolein the surface-water supply, ground-water recharge, and its insolation properties at local scales. Snowpack physical properties strongly influence the emissions from the substratum, making feasible snow property retrieval by means of the surface brightness temperature observed by passive microwave sensors. Depending on the observing spatial resolution, the time series records of daily snow coverage and a snowpacks most-critical properties such as the snow depth and snow water equivalent (SWE) could be helpful in applications ranging from modeling snow variations in a small catchment to global climatologic studies. However, the challenge of including spaceborne snow water equivalent (SWE) products in operational hydrological and hydroclimate modeling applications is very demanding with limited uptake by these systems. Various causes have been attributed to this lack of up-take but most stem from insufficient SWE accuracy. The root causes of this challenge includes the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process that are caused by uncertainties with the forward emission modeling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of the whole range of retrieval methodologies can provide the clarity needed to move the thinking forward in this important field. Following a review on snow depth and SWE retrieval methods using passive microwave remote sensing observations, this research employs a forward emission model to simulate snowpacks emission and compare the results to the PM airborne observations. Airborne radiometer observations coordinated with ground-based in-situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to the volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory forMulti Layered (DMRT-ML) snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. The DMRT-ML was parameterized with the in-situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. Snow depth retrieval from passive microwave observations without a-priori information is a highly underdetermined system. An accurate estimate of snow depth necessitates a-priori information of snowpack properties, such as grain size, density, physical temperature and stratigraphy, and, very importantly, a minimization of this a prior information requirement. In previous studies, a Bayesian Algorithm for Snow Water Equivalent (SWE) Estimation (BASE) have been developed, which uses the Monte Carlo Markov Chain (MCMC) method to estimate SWE for taiga and alpine snow from 4-frequency ground-based radiometer Tb. In our study, BASE is used in tundra snow for datasets of 464 footprints inthe Eureka region coupled with airborne passive microwave observations—the same fieldstudy that forward modelling was evaluated. The algorithm searches optimum posterior probability distribution of snow properties using a cost function between physically based emission simulations and Tb observations. A two-layer snowpack based on local snow cover knowledge is assumed to simulate emission using the Dense Media Radiative Transfer-Multi Layered (DMRT-ML) model. Overall, the results of this thesis reinforce the applicability of a physics-based emission model in SWE retrievals. This research highlights the necessity to consider the two-part emission characteristics of a slab-dominated tundra snowpack and suggests performing inversion in a Bayesian framework

PASSIVE MICROWAVE SATELLITE SNOW OBSERVATIONS FOR HYDROLOGIC APPLICATIONS

Melting snow provides an essential source of water in many regions of the world and can also contribute to devastating, wide-scale flooding. The objective of this research was to investigate the potential for passive microwave remotely sensed data to characterize snow water equivalent (SWE) and snowmelt across diverse regions and snow regimes to improve snowmelt runoff estimation. The first step was to evaluate the current, empirically-based passive microwave SWE products compared to NOAA’s operational SWE estimates from SNODAS across 2100 watersheds over eight years. The best agreement was found within basins in which maximum annual SWE is less than 200 mm, and forest fraction is less than 20%. Next, a sensitivity analysis was conducted to evaluate the microwave signal response to spatially distributed wet snow using a loosely-coupled snow-emission model. The results over an area approximately the size of a microwave pixel found a near-linear relationship between the microwave signal response and the percent area with wet snow present. These results were confirmed by evaluating actual wet snow events over a nine year period, and suggest that the microwave response provides the potential basis for disaggregating melting snow within a microwave pixel. Finally, a similar sensitivity analysis conducted in six watersheds with diverse landscapes and snow conditions confirmed the relationship holds at a basin scale. The magnitude of the microwave response to wet snow was compared to the magnitude of subsequent discharge events to determine if an empirical relation exists. While positive increases in brightness temperature (TB) correspond to positive increases in discharge, the magnitude of those changes is poorly correlated in most basins. The exception is in basins where snowmelt runoff typically occurs in one event each spring. In similar basins, the microwave response may provide information on the magnitude of spring runoff. Methods to use these findings to improve current snow and snow melt estimation as well as future research direction are discussed

The influence of winter time boreal forest tree transmissivity on tree emission and passive microwave snow observations

Forest cover significantly attenuates natural upwelling ground microwave emission from seasonal terrestrial snow. This presents a major challenge for the accurate retrieval of snow from airborne or spaceborne passive microwave (PM) observations. Forest transmissivity is a key parameter describing tree emission because not only does it influence the proportion of sub-canopy upwelling microwave emission penetrating through the forest canopy, it also controls the forest thermal emission. Hence, it is a very important parameter for correcting the influence of forests on spaceborne or airborne observations of the Earth’s land surface. Under sub-zero temperatures, vegetation water content can be frozen influencing the microwave transmissivity of trees. Yet this phenomenon has not been verified through experimentation leaving significant uncertainty in tree emission modelling and spaceborne microwave observations. Therefore, a season-long experiment was designed to study this phenomenon. Ground-based radiometer observations of tree emission, spaceborne observations of forest emission, and model simulations of canopy emission were conducted during this experiment. Based on this experiment, the influence of physical temperature on tree transmissivity was verified, and a model developed to quantitatively describe this temperature-transmissivity relationship. An evaluation of this temperature-transmissivity relationship was conducted showing that both ground-based and spaceborne observations of tree emission are significantly influenced by this phenomenon. Furthermore, passive microwave spaceborne snow retrievals in forested regions are influenced by this phenomenon. Finally, an approach to reduce the influence of the temperature-transmissivity relationship on passive microwave spaceborne snow retrievals is demonstrated

Consistency in the AMSR-E snow products: groundwork for a coupled snowfall and SWE algorithm

2019 Fall.Includes bibliographical references.Snow is an important wintertime property because it is a source of freshwater, regulates land-atmosphere exchanges, and increases the surface albedo of snow-covered regions. Unfortunately, in-situ observations of both snowfall and snow water equivalent (SWE) are globally sparse and point measurements are not representative of the surrounding area, especially in mountainous regions. The total amount of land covered by snow, which is climatologically important, is fairly straightforward to measure using satellite remote sensing. The total SWE is hydrologically more useful, but significantly more difficult to measure. Accurately measuring snowfall and SWE is an important first step toward a better understanding of the impacts snow has for hydrological and climatological purposes. Satellite passive microwave retrievals of snow offer potential due to consistent overpasses and the capability to make measurements during the day, night, and cloudy conditions. However, passive microwave snow retrievals are less mature than precipitation retrievals and have been an ongoing area of research. Exacerbating the problem, communities that remotely sense snowfall and SWE from passive microwave sensors have historically operated independently while the accuracy of the products has suffered because of the physical and radiometric dependency between the two. In this study, we assessed the relationship between the Northern Hemisphere snowfall and SWE products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). This assessment provides insight into regimes that can be used as a starting point for future improvements using coupled snowfall and SWE algorithm. SnowModel, a physically-based snow evolution modeling system driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, was employed to consistently compare snowfall and SWE by accounting for snow evolution. SnowModel has the ability to assimilate observed SWE values to scale the amount of snow that must have fallen to match the observed SWE. Assimilation was performed using AMSR-E, Canadian Meteorological Centre (CMC) Snow Analysis, and Snow Data Assimilation System (SNODAS) SWE to infer the required snowfall for each dataset. Observed AMSR-E snowfall and SWE were then compared to the MERRA-2 snowfall and SnowModel-produced SWE as well as SNODAS and CMC inferred snowfall and observed SWE. Results from the study showed significantly different snowfall and SWE bias patterns observed by AMSR-E. Specifically, snowfall was underestimated nearly globally and SWE had pronounced regions of over and underestimation. Snowfall and SWE biases were found to differ as a function of surface temperature, snow class, and elevation

NASA sea ice and snow validation plan for the Defense Meteorological Satellite Program special sensor microwave/imager

This document addresses the task of developing and executing a plan for validating the algorithm used for initial processing of sea ice data from the Special Sensor Microwave/Imager (SSMI). The document outlines a plan for monitoring the performance of the SSMI, for validating the derived sea ice parameters, and for providing quality data products before distribution to the research community. Because of recent advances in the application of passive microwave remote sensing to snow cover on land, the validation of snow algorithms is also addressed

Microstructure representation of snow in coupled snowpack and microwave emission models

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 0.24mm for MOSES and 0.14 to 0.18mm 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 11K at 36.5 GHz H-pol and 18K 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