Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments

Cho, Eunsang; Vuyovich, Carrie M.; Kumar, Sujay V.; Wrzesien, Melissa L.; Kim, Rhae Sung

doi:10.5194/tc-17-3915-2023

Cited by 4 publications

(1 citation statement)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Snow is an important freshwater resource and remains the only element in the water cycle without a dedicated spaceborne mission (Derksen et al, 2019). While surface snow depth observation networks support the generation and validation of coarse resolution (>25 km), snow water equivalent (SWE) products from passive microwave remote sensing (e.g., Luojus et al, 2021), higher spatial resolution (<500 m), sources of spatially continuous snow information are needed to meet the needs of climate services, water resource management, and environmental prediction (Garnaud et al, 2019(Garnaud et al, , 2021Kim et al, 2021;Cho et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Montpetit,

King,

Meloche

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth Observation mission. The snow scientific community has shown that Ku-Band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the Snow MicroPenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer Model (SMRT), it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-Band radar backscatter at a 100-m scale. Airborne Ku-Band data was acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using Radarsat-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil = 0.010±0.002) and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, was retrieved. Using the Geometrical Optics surface backscatter model, the retrieved effective soil permittivity increased from C-Band (εsoil = 2.47) to X-Band (εsoil = 2.61), to Ku-Band (εsoil = 2.77) for the TVC domain. Using SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-Band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-Band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve SWE at the 100 m scale.

show abstract

Section: Introductionmentioning

confidence: 99%

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Montpetit,

King,

Meloche

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation

Pflug,

Wrzesien,

Kumar

et al. 2024

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24∘×37∘ domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

show abstract

Subgridding high-resolution numerical weather forecast in the Canadian Selkirk mountain range for local snow modeling in a remote sensing perspective

Billecocq,

Langlois,

Montpetit

2024

The Cryosphere

View full text Add to dashboard Cite

Abstract. Snow water equivalent (SWE) is a key variable in climate and hydrology studies. Yet, current SWE products mask out high-topography areas due to the coarse resolution of the satellite sensors used. The snow remote sensing community is hence pushing towards active-microwave approaches for global SWE monitoring. Designing a SWE retrieval algorithm is not trivial, as multiple combinations of snow microstructure representations and SWE can yield the same radar signal. Retrieval algorithm designs are converging towards forward modeling approaches using an educated first guess on the snowpack structure. Snow highly varies in space and time, especially in mountain environments where the complex topography affects atmospheric and snowpack state variables in numerous ways. In Canada, automatic weather stations are too sparse, and high-resolution numerical weather prediction systems have a maximal resolution of 2.5 km × 2.5 km, which is too coarse to capture snow spatial variability in a complex topography. In this study, we designed a subgridding framework for the Canadian High Resolution Deterministic Prediction System (HRDPS). The native 2.5 km × 2.5 km resolution forecast was subgridded to a 100 m × 100 m resolution and used as the input for snow modeling over two winters in Glacier National Park, British Columbia, Canada. Air temperature, relative humidity, precipitation, and wind speed were first parameterized regarding elevation using six automatic weather stations. We then used Alpine3D to spatialize atmospheric parameters and radiation input accounting for terrain reflections, and we performed the snow simulations. We evaluated modeled snowpack state variables relevant for microwave remote sensing against simulated profiles generated with automatic weather station data and compared them to simulated profiles driven by raw HRDPS data. The subgridding framework improves the optical grain size bias by 18 % on average and the modeled SWE by 16 % compared to simulations driven with raw HRDPS forecasts. This work could improve the snowpack radar backscattering modeling by up to 7 dB and serves as a basis for SWE retrieval algorithms using forward modeling in a Bayesian framework.

show abstract

Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments

Cited by 4 publications

References 79 publications

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation

Subgridding high-resolution numerical weather forecast in the Canadian Selkirk mountain range for local snow modeling in a remote sensing perspective

Contact Info

Product

Resources

About