Impacts of snow cover fraction data assimilation on modeled energy and moisture budgets

Abstract During snow cover fraction (SCF) data assimilation (DA), the simplified observation operator and presence of cloud cover cause large errors in the assimilation results. To reduce these errors, a new snow cover depletion curve (SDC), known as an observation operator in the DA system, is statistically fitted to in situ snow depth (SD) observations and Moderate Resolution Imaging Spectroradiometer (MODIS) SCF data from January 2004 to October 2008. Using this new SDC, a two-dimensional deterministic ensemble–variational hybrid DA (2DEnVar) method of integrating the deterministic ensemble Kalman filter (DEnKF) and a two-dimensional variational DA (2DVar) is proposed. The proposed 2DEnVar is then used to assimilate the MODIS SCF into the Common Land Model (CoLM) at five sites in the Altay region of China for data from November 2008 to March 2009. The analysis performance of the 2DEnVar is compared with that of the DEnKF. The results show that the 2DEnVar outperforms the DEnKF as it effectively reduces the bias and root-mean-square error during the snow accumulation and ablation periods at all sites except for the Qinghe site. In addition, the 2DEnVar, with more assimilated MODIS SCF observations, produces more innovations (observation minus forecast) than the DEnKF, with only one assimilated MODIS SCF observation. The problems of cloud cover and overestimation are addressed by the 2DEnVar.

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Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

The Cryosphere ◽

10.5194/tc-9-1879-2015 ◽

2015 ◽

Vol 9 (5) ◽

pp. 1879-1893 ◽

Cited By ~ 11

Author(s):

K. Atlaskina ◽

F. Berninger ◽

G. de Leeuw

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Air Temperature ◽

Land Surface ◽

Surface Albedo ◽

Vegetation Index ◽

Precipitation Amount ◽

Snow Cover Fraction ◽

Air Temperatures ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

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An application of ANN for mountainous snow cover fraction mapping with MODIS and ancillary topographic data

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS ◽

10.1109/igarss.2013.6721378 ◽

2013 ◽

Author(s):

Jinliang Hou ◽

Chunlin Huang

Keyword(s):

Snow Cover ◽

Topographic Data ◽

Snow Cover Fraction

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Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

The Cryosphere ◽

10.5194/tc-11-1647-2017 ◽

2017 ◽

Vol 11 (4) ◽

pp. 1647-1664 ◽

Cited By ~ 29

Author(s):

Emmy E. Stigter ◽

Niko Wanders ◽

Tuomo M. Saloranta ◽

Joseph M. Shea ◽

Marc F. P. Bierkens ◽

...

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Snow Cover ◽

Climate Sensitivity ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snowmelt Runoff ◽

Snow Water ◽

Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

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