SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

Picard, Ghislain; Sandells, Melody; Löwe, Henning

doi:10.5194/gmd-11-2763-2018

Cited by 89 publications

(91 citation statements)

References 119 publications

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“…Physically-based forward models that rely on a good understanding of the electromagnetic interactions with the snowpack are best suited to obtain consistent retrievals of snow macrophysical and microphysical properties or for data assimilation in NWP systems. The new generation of snow forward models, such as the Snow Microwave Radiative Transfer (SMRT), account for multi-layer snow emission and backscatter [118]. Using satellite data from microwave sensors with appropriate multi-layer forward modeling and retrieval approaches for the snow microwave properties characterisation is of great interest to support physically based multi-layer snow model developments.…”

Section: Seasonal Snow Covermentioning

confidence: 99%

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

et al. 2018

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In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

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Section: Seasonal Snow Covermentioning

confidence: 99%

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

et al. 2018

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“…In order to fully retrieve snow depth over MYI our results suggest that more research is required to investigate the influence of MYI on GR(19/7) , and detailed measurements of MYI properties and knowledge about its influence on the snow/ice emissivity are needed. The latter could be obtained from microwave emission models (e.g., Dupont et al, ; Picard et al, ; Tonboe, ) if they can be used in conjunction with ice core measurements that include measurements of MYI density and salinity as well as the size distribution of the included air bubbles in MYI and coincident snow property measurements. While a lot of MYI ice core data exist for late winter and spring (e.g., Shokr & Sinha, ), MYI samples from early winter season are rare.…”

Section: Discussionmentioning

confidence: 99%

Snow Depth Retrieval on Arctic Sea Ice From Passive Microwave Radiometers—Improvements and Extensions to Multiyear Ice Using Lower Frequencies

et al. 2018

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Snow on sea ice influences the Arctic energy and heat budgets and is therefore important for Arctic climate studies. Methods to derive snow depth based on satellite-borne microwave radiometer observations have existed since the 1990s. However, in the Arctic the most widely used algorithm can only be applied over first-year ice (FYI) and does not make use of the lower frequencies, which are available since 2002.Here we present three improvements to the current passive microwave snow depth retrieval: (a) We derive new coefficients based on a regression analysis using 5 years of Operation IceBridge airborne snow depth measurements. (b) We extend the algorithm to take advantage of the lower frequency channel at 7 GHz. (c) We consider an extension of the snow depth retrieval to multiyear ice (MYI) during spring. Our results show that the gradient ratio, GR(19/7) is most suited for deriving snow over both Arctic FYI (R =°À0.73) and MYI (R = À0.57). An evaluation of the new retrieval with Operation IceBridge snow depth measurements from March and April 2015 shows a good agreement over FYI (difference = À2.1 cm; 93% of the differences below 5 cm). Over MYI the difference is À4.0 cm and 56% of the differences are below 5 cm, that is, the root mean square distance (RMSD) is larger over MYI than over FYI. We demonstrate for the first time that spring snow depth measurements can be derived from passive microwave observations over both FYI and MYI.Plain Language Summary Snow on Arctic sea ice plays an important role in the Arctic climate system. It reflects the majority of the incoming solar radiation and isolates the sea ice from warm air in summer. However, the vast area and the extreme weather conditions make it difficult to monitor changes in snow depth during the Arctic winter. In this study, we develop a retrieval for snow depth on Arctic sea ice based on satellite observations. The advantages of satellite observations are that they provide daily coverage of the whole Arctic. We compare satellite observations in the microwave regime to airborne snow depth measurements, obtained in March and April from 2009 to 2015. We find a good agreement between changes in the signal observed by the satellite and changes in the measured snow depth when the ice is smooth. Over multiyear ice, ice that has survived at least one summer melt and that is often rough, the agreement is not as good. We demonstrate for the first time that spring snow depth estimations for the whole sea ice covered Arctic can be derived from satellite observations. While a clear signal can be found for changes in sea ice area, changes in snow depth on sea ice or snowfall are hard to quantify. In situ measurements and reanalysis data suggest a decline in summer snowfall (June to August) over the sea ice covered Arctic (Screen & Simmonds, 2012). Webster et al. (2014) analyzed spring ROSTOSKY ET AL. 7120

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“…The performance of radiative transfer models, such as MEMLS3&a, should be assessed in non-idealized situations, such as those in this study. Future developments could involve the use of alternative observation operators, making it possible to explore other theoretical approaches to microwave radiative transfer [53].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Sub-Kilometric Numerical Simulations of C-Band Radar Backscatter over the French Alps against Sentinel-1 Observations

et al. 2018

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This study compares numerical simulations and observations of C-band radar backscatter in a wide region (2300 km 2 ) in the Northern French Alps. Numerical simulations were performed using a model chain composed of the SAFRAN meteorological reanalysis, the Crocus snowpack model and the radiative transfer model Microwave Emission Model for Layered Snowpacks (MEMLS3&a), operating at a spatial resolution of 250-m. The simulations, without any bias correction, were evaluated against 141 Sentinel-1 synthetic aperture radar observation scenes with a resolution of 20 m over three snow seasons from October 2014 to June 2017. Results show that there is good agreement between observations and simulations under snow-free or dry snow conditions, consistent with the fact that dry snow is almost transparent at C-band. Under wet snow conditions, although the changes in time and space are well correlated, there is a significant deviation, up to 5 dB, between observations and simulations. The reasons for these discrepancies were explored, including a sensitivity analysis on the impact of the liquid water percolation scheme in Crocus. This study demonstrates the feasibility of performing end-to-end simulations of radar backscatter over extended geographical region. This makes it possible to envision data assimilation of radar data into snowpack models in the future, pending that deviations are mitigated, either through bias corrections or improved physical modeling of both snow properties and corresponding radar backscatter.

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SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

Cited by 89 publications

References 119 publications

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

Snow Depth Retrieval on Arctic Sea Ice From Passive Microwave Radiometers—Improvements and Extensions to Multiyear Ice Using Lower Frequencies

Evaluation of Sub-Kilometric Numerical Simulations of C-Band Radar Backscatter over the French Alps against Sentinel-1 Observations

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