Microstructure representation of snow in coupled snowpack and microwave emission models
Microstructure representation of snow in coupled snowpack and microwave emission models
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DOI：
10.5194/tc-11-229-2017
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
M. Sandells;R. Essery;N. Rutter;L. Wake;L. Leppänen;J. Lemmetyinen
中科院分区：
文献类型：
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作者：
M. Sandells;R. Essery;N. Rutter;L. Wake;L. Leppänen;J. Lemmetyinen
抽象的。这是首次在一个通用的建模框架中包含多种雪演化和微波发射耦合模型的研究，目的是概括雪演化模型预测的积雪微结构与雪发射模型模拟的亮温观测所需的微结构之间的联系。由63个Jules调查模型积雪模拟、3个微结构演化函数和7个微波发射模型组态组成的1323个系综成员模拟了18.7 GHz和36.5 GHz的亮温。来自芬兰索丹基拉北极研究中心的两年气象数据被用来在2011-2012年和2012-2013年冬季驱动该模型。结果表明，SNTHERM模拟的雪粒直径太大(平均误差为0.12~0.16 mm)，而MOSY和SNICAR的微结构演化函数模拟的雪粒直径太小(平均误差−为0.16~−0.24 mm；SNICAR的平均误差为−0.14~−0.18 mm)。没有一种模型(HUT、MEMLS或DMRT-ML)在所有频率和极化情况下都能提供一致的良好匹配。对于特定的频率和偏振，一个季节的平均亮度温度偏差的最小绝对值在0.7到6.9K之间。为了比较积雪模型微结构和排放模型微结构之间的兼容性，提出了积雪微结构的最佳比例因子。比例因子在SNTHERM-经验MEMLS模型组合(2011-2012年)和DMRT-ML与摩西微结构相结合(2012-2013年)之间变化。微结构模型之间比例因子的差异通常大于微波发射模型之间的差异，这表明在积雪-微波模式耦合系统中，更准确的模拟将主要通过改进积雪微结构表示法，其次是改进发射模型。当Jules调查模式集合应用于2011-2012年季节的摩西微结构和经验MEMLS发射模式时，积雪模式中的其他积雪参数，主要是致密化，导致在36.5 GHz H-Poll和V-Poll的平均亮度温差分别为11K和18K。积雪参数化的影响随着微波散射的增加而增加。在设计积雪质量反演系统和微波资料同化系统时，应考虑积雪微结构与微波辐射模式的一致性，以及积雪增密算法的选择。
Abstract. 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 Sodankyla 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 −0.24 mm for MOSES and −0.14 to −0.18 mm 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 11 K at 36.5 GHz H-pol and 18 K 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.