Based on a study of Tb spectra, we proposed a new snow depth retrieval algorithm for mountainous deep snow using airborne multifrequency PM radiance observation. In contrast to previous snow depth estimations using satellite PM radiance assimilation, the newly- proposed method utilized a single flight observation and deployed the snow hydrologic models as a basis for a “snapshot” retrieval algorithm. This method is promising since the satellite-based retrieval methods have difficulties to estimate snow depth due to their coarse resolution and computational effort. Our approach consists of a particle filter using combinations of multiple PM frequencies and multi-layer snow physical model (i.e., Crocus) to resolve melt-refreeze crusts. Results showed that there was a significant improvement over the prior snow depth estimates and the capability to reduce the prior snow depth biases. When applying our snow depth retrieval algorithm using a combination of four PM frequencies (10.7-, 18.7-, 37.0-, and 89.0 GHz), the root mean square error (RMSE) values were reduced by 62% at the snow depth transects sites where forest density was less than 5% despite deep snow conditions. This method displayed a higher sensitivity to different combinations of frequencies, model stratigraphy (i.e. different number of layering scheme for snow physical model) and estimation methods (particle filter and Kalman filter) except the forest cover density and precipitation bias. The prior RMSE values at the forest-covered areas were reduced by 27 - 41% even in the presence of forest cover.

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.

Improving the estimation of snow water equivalent (SWE) in the Sierra Nevada is critical for the water resources management in California. In this study, we carried out an experiment to estimate SWE in the Upper Kern Basin, Sierra Nevada, by assimilating AMSR-E observed brightness temperatures (Tb) into a coupled hydrology and radiative transfer model using an ensemble Kalman batch reanalysis. The data assimilation framework merges the complementary SWE information from modeling and observations to improve SWE estimates. The novelty of this assimilation study is that both the modeling and the radiance data processing were specifically improved to provide more information about SWE. With the enhanced SWE signals in both simulations and observations, the batch reanalysis stands a better chance of successfully improving the SWE estimates. The modeling was at a very high resolution (90m) and spanned a range of mountain environmental factors to better characterize the effects of the mountain environment on snow distribution and radiance emission. We have developed a dynamic snow grain size module to improve the radiance modeling during the intense snowfall events. The AMSR-E 37GHz V-pol observed Tb was processed at its native footprint resolution at ~100 square km. In the batch assimilation, the model predicted the prior SWE and Tb; the prior estimate of an entire year was then updated by the dry-season observations at one time. One advantage of this is that the prior SWE of a certain period is updated using the observations both before and after this period, which takes advantage of the temporally continuous signal of the seasonal snow accumulation in the observations. We found the posterior SWE estimates showed improved accuracy and robustness. During the study period of 2003 to 2008, at point-scale, the average bias of the six-year April 1st SWE was reduced from -0.17 m to -0.02m, the average temporal SWE RMSE of the snow accumulation season decreased by 51.2%. The basin-scale results showed that the April 1st SWE bias reduced from -0.17m to -0.11m, and the temporal SWE RMSE of the accumulation season decreased by 23.6%.

Proceedings of the NATO Advanced Study Institute, Acquafredda, Maratea, Italy from 19 May to 1 June 2002