Evaluation of snow depth and snow cover represented by multiple datasets over the Tianshan Mountains: Remote sensing, reanalysis, and simulation
Evaluation of snow depth and snow cover represented by multiple datasets over the Tianshan Mountains: Remote sensing, reanalysis, and simulation
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天山多数据集积雪深度和积雪覆盖评估：遥感、再分析和模拟
DOI：
10.1002/joc.7459
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发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Lanhai Li
中科院分区：
文献类型：
--
作者：
Qian Li;Tao Yang;Lanhai Li
山地积雪在水资源和生态系统中具有重要作用。然而，现有的积雪深度产品显示出很大的不确定性，整个天山，中亚。利用ERA 5、ERA 5 ‐Land、被动微波和WRF模式降尺度模拟的天山积雪深度资料，对天山积雪深度进行了对比分析。积雪深度进行了评价，对现场观测，而积雪覆盖范围进行了评价，通过交互式多传感器冰雪测绘系统（IMS）积雪。此外，还比较了四个数据集之间与雪相关的指标，如年平均雪深和积雪天数。结果表明，雪相关指标的空间格局是相对一致的数据集，虽然存在差异的大小。此外，雪相关指标的时间变化取决于所采用的数据集。WRF模拟的雪深与实测值相比，日雪深偏差最小（平均偏差为0.12cm，均方根误差为2.08cm），与IMS相比，WRF模拟的雪深在雪网格分类方面表现最好（检测概率为0.766）。与ERA 5-Land积雪深度相比，它在年平均积雪深度和积雪覆盖天数方面的表现也优于47.2%和13.3%。这项研究突出了在表征的空间格局和时间变化的雪深产品的WRF模式模拟的基础上的信心。
Mountain snowpacks play important roles in water resource and ecological system. However, existing snow depth products show great uncertainties across the Tianshan Mountains, Central Asia. This study evaluated and compared four snow depth datasets over the Tianshan Mountains, including snow depth datasets from ERA5, ERA5‐Land, passive microwave, as well as a dynamically downscaled simulation by Weather Research and Forecasting (WRF) model. The snow depth was evaluated against in situ observations while the snow cover extent was evaluated by interactive multisensor snow and ice mapping system (IMS) snow cover. Furthermore, the snow‐related metrics, such as annual mean snow depth and snow cover days, were compared among the four datasets. The results showed that the spatial patterns of snow‐related metrics were relatively consistent among datasets, although discrepancies existed in magnitude. Additionally, the temporal variations in snow‐related metrics depended on the dataset employed. The simulated snow depth from WRF got the lowest bias in daily snow depth value against the in situ observations (mean bias = 0.12 cm, root mean square error = 2.08 cm), additionally, it had the best performance in classifying correct snow grids compared with IMS (probability of detection = 0.766). It also outperformed in annual mean snow depth and snow cover days by 47.2 and 13.3% compared with ERA5‐Land snow depth. This study highlights the confidence in characterizing both the spatial pattern and temporal variations of snow depth products based on WRF model simulation.
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影响因子：
56.9
作者：
D. Shugar;M. Jacquemart;D. Shean;S. Bhushan;K. Upadhyay;A. Sattar;W. Schwanghart;S. McBride;M. V. W. de Vries;M. Mergili;A. Emmer;C. Deschamps-Berger;M. McDonnell;R. Bhambri;S. Allen;E. Berthier;J. Carrivick;J. Clague;M. Dokukin;S. Dunning;H. Frey;S. Gascoin;U. Haritashya;C. Huggel;A. Kääb;J. Kargel;J. Kavanaugh;P. Lacroix;D. Petley;S. Rupper;M. Azam;S. Cook;A. P. Dimri;M. Eriksson;D. Farinotti;J. Fiddes;K. Gnyawali;S. Harrison;M. Jha;M. Koppes;A. Kumar;S. Leinss;U. Majeed;S. Mal;A. Muhuri;J. Noetzli;F. Paul;I. Rashid;K. Sain;J. Steiner;F. Ugalde;C. Watson;M. Westoby
通讯作者：
D. Shugar;M. Jacquemart;D. Shean;S. Bhushan;K. Upadhyay;A. Sattar;W. Schwanghart;S. McBride;M. V. W. de Vries;M. Mergili;A. Emmer;C. Deschamps-Berger;M. McDonnell;R. Bhambri;S. Allen;E. Berthier;J. Carrivick;J. Clague;M. Dokukin;S. Dunning;H. Frey;S. Gascoin;U. Haritashya;C. Huggel;A. Kääb;J. Kargel;J. Kavanaugh;P. Lacroix;D. Petley;S. Rupper;M. Azam;S. Cook;A. P. Dimri;M. Eriksson;D. Farinotti;J. Fiddes;K. Gnyawali;S. Harrison;M. Jha;M. Koppes;A. Kumar;S. Leinss;U. Majeed;S. Mal;A. Muhuri;J. Noetzli;F. Paul;I. Rashid;K. Sain;J. Steiner;F. Ugalde;C. Watson;M. Westoby
影响因子：
5.4
作者：
M. Wrzesien;T. Pavelsky;M. Durand;J. Dozier;J. Lundquist
通讯作者：
M. Wrzesien;T. Pavelsky;M. Durand;J. Dozier;J. Lundquist
基于中亚天山地区气象站长期观测的气温和降水时空变化检测
影响因子：
5.5
作者：
Xu Min;Kang Shichang;Wu Hao;Yuan Xu
通讯作者：
Yuan Xu
DOI：
--
发表时间：
2015
期刊：
--
影响因子：
--
作者：
通讯作者：
--
影响因子：
30.7
作者：
Kraaijenbrink, Philip D. A.;Stigter, Emmy E.;Immerzeel, Walter W.
通讯作者：
Immerzeel, Walter W.