Assimilation of MODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80‐member) atmospheric forcing that facilitates ensemble‐based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root‐mean‐square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December‐January‐February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower‐middle latitudes (23°–45°N). Only minimal modifications are made in the higher‐middle (45°–66°N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100%. In some regions it also reveals that CLM4‐modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March‐April‐May), adjustments to snow move poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

[1]  Shihyan Lee,et al.  A review of global satellite-derived snow products , 2012 .

[2]  Robert Pincus,et al.  DART/CAM: An Ensemble Data Assimilation System for CESM Atmospheric Models , 2012 .

[3]  Jeffrey P. Walker,et al.  Hydrologic Data Assimilation , 2012 .

[4]  S. Bates,et al.  The CCSM4 Ocean Component , 2012 .

[5]  Sujay V. Kumar,et al.  Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado , 2012 .

[6]  R. Koster,et al.  Assessment and Enhancement of MERRA Land Surface Hydrology Estimates , 2011 .

[7]  H. Douville,et al.  Snow contribution to springtime atmospheric predictability over the second half of the twentieth century , 2011 .

[8]  J. Slingo,et al.  Using idealized snow forcing to test teleconnections with the Indian summer monsoon in the Hadley Centre GCM , 2011 .

[9]  Zong-Liang Yang,et al.  Multisensor snow data assimilation at the continental scale: The value of Gravity Recovery and Climate Experiment terrestrial water storage information , 2010 .

[10]  Dorothy K. Hall,et al.  Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product , 2010 .

[11]  Jeffrey L. Anderson,et al.  The Data Assimilation Research Testbed: A Community Facility , 2009 .

[12]  Benjamin F. Zaitchik,et al.  Forward-Looking Assimilation of MODIS-Derived Snow-Covered Area into a Land Surface Model , 2009 .

[13]  Zong-Liang Yang,et al.  Enhancing the estimation of continental-scale snow water equivalent by assimilating MODIS snow cover with the ensemble Kalman filter , 2008 .

[14]  Christian Rocken,et al.  The COSMIC/FORMOSAT-3 Mission: Early Results , 2008 .

[15]  Günter Blöschl,et al.  Spatio‐temporal combination of MODIS images – potential for snow cover mapping , 2008 .

[16]  M. Brodzik,et al.  Improved Snow Cover Retrievals from Satellite Passive Microwave Data over the Tibet Plateau: The need for atmospheric corrections over high elevations , 2007 .

[17]  Zong-Liang Yang,et al.  An observation-based formulation of snow cover fraction and its evaluation over large North American river basins , 2007 .

[18]  D. Hall,et al.  Accuracy assessment of the MODIS snow products , 2007 .

[19]  Jeffrey L. Anderson,et al.  An adaptive covariance inflation error correction algorithm for ensemble filters , 2007 .

[20]  Philip J. Rasch,et al.  Present-day climate forcing and response from black carbon in snow , 2006 .

[21]  Charles S. Zender,et al.  Linking snowpack microphysics and albedo evolution , 2006 .

[22]  M. Clark,et al.  Snow Data Assimilation via an Ensemble Kalman Filter , 2006 .

[23]  D. Lettenmaier,et al.  Assimilating remotely sensed snow observations into a macroscale hydrology model , 2006 .

[24]  C. Zender,et al.  Snowpack radiative heating: Influence on Tibetan Plateau climate , 2005 .

[25]  Alfred T. C. Chang,et al.  Quantifying the uncertainty in passive microwave snow water equivalent observations , 2005 .

[26]  Matthew Rodell,et al.  Updating a Land Surface Model with MODIS-Derived Snow Cover , 2004 .

[27]  Konstantine P. Georgakakos,et al.  Impacts of parametric and radar rainfall uncertainty on the ensemble streamflow simulations of a distributed hydrologic model , 2004 .

[28]  G. Danabasoglu,et al.  The Community Climate System Model Version 4 , 2011 .

[29]  D. Tarboton,et al.  The application of depletion curves for parameterization of subgrid variability of snow , 2004 .

[30]  G. Liston Representing Subgrid Snow Cover Heterogeneities in Regional and Global Models , 2004 .

[31]  V. Salomonson,et al.  Estimating fractional snow cover from MODIS using the normalized difference snow index , 2004 .

[32]  M. Clark,et al.  Use of Medium-Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow , 2004 .

[33]  R. Koster,et al.  Assessing the Impact of Horizontal Error Correlations in Background Fields on Soil Moisture Estimation , 2003 .

[34]  David Robinson,et al.  Gridded North American monthly snow depth and snow water equivalent for GCM evaluation , 2003 .

[35]  N. DiGirolamo,et al.  MODIS snow-cover products , 2002 .

[36]  Jeffrey L. Anderson An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .

[37]  J. Whitaker,et al.  Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter , 2001 .

[38]  A. S. Bamzai,et al.  Relation between Eurasian Snow Cover, Snow Depth, and the Indian Summer Monsoon: An Observational Study , 1999 .

[39]  B. Brasnett,et al.  A Global Analysis of Snow Depth for Numerical Weather Prediction , 1999 .

[40]  J. Shukla,et al.  The Effect of Eurasian Snow Cover on the Indian Monsoon , 1995 .

[41]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[42]  A. Rango,et al.  Snow water equivalent estimation by microwave radiometry , 1982 .

[43]  D. Lawrence,et al.  Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model , 2011 .

[44]  J. Randerson,et al.  Technical Description of version 4.0 of the Community Land Model (CLM) , 2010 .

[45]  T. Meyers,et al.  Sensitivity of Land Surface Simulations to Model Physics, Land Characteristics, and Forcings, at Four CEOP Sites , 2007 .

[46]  V. V. Salomonsona,et al.  Estimating fractional snow cover from MODIS using the normalized difference snow index , 2004 .

[47]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[48]  A. Rango,et al.  Snow water equivalent determination by microwave radiometry , 1981 .