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Multi-Sensor Snow Data Assimilation

Multi-Sensor Snow Data Assimilation. Matt Rodell 1 , Zhong-Liang Yang 2 , Ben Zaitchik 3 , Ed Kim 1 , and Rolf Reichle 1. 1 NASA Goddard Space Flight Center 2 The University of Texas at Austin 3 Johns Hopkins University. Project Summary. Title: Multi-Sensor Snow Data Assimilation

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Multi-Sensor Snow Data Assimilation

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  1. Multi-Sensor Snow Data Assimilation Matt Rodell1, Zhong-Liang Yang2, Ben Zaitchik3, Ed Kim1, and Rolf Reichle1 1NASA Goddard Space Flight Center 2The University of Texas at Austin 3Johns Hopkins University

  2. Project Summary • Title: Multi-Sensor Snow Data Assimilation • Problem Statement: MODIS, AMSR-E, and GRACE all provide observations that are relevant to snow water equivalent mapping, each with significant advantages and disadvantages. • Hypothesis: Global fields of SWE can be produced with greater accuracy than previously seen by simultaneously assimilating MODIS snow cover, AMSR-E SWE, and GRACE terrestrial water storage observations within a sophisticated land surface model. • Team: • NASA/GSFC: Matt Rodell (PI), Ed Kim, Rolf Reichle • U. Texas: Liang Yang (Co-PI) • Johns Hopkins: Ben Zaitchik • Timeline: Just getting started

  3. Radiation Soil Moisture Vegetation SWE, Ice, Rainfall Snow Cover GRACE GRACE provides changes in total water storage, which are dominated by SWE at high latitudes and altitudes, but at very coarse resolution Remote Sensing of Snow Aqua: MODIS, AMSR-E Terra: MODIS MODIS provides high resolution snow cover data but not SWE; AMSR-E provides SWE data but with significant errors where snow is deep or wet

  4. SW RADIATION PRECIPITATION MODIS SNOW COVER Data Integration within a Land Data Assimilation System (LDAS) INTERCOMPARISON and OPTIMAL MERGING of global data fields Satellite data products used to PARAMETERIZE and FORCE land surface models within LIS ASSIMILATION of satellite based land surface state fields (snow, soil moisture, etc.) Ground-based observations used to EVALUATE model output SNOW WATER EQUIVALENT

  5. 21Z 17 January 2003 MODIS Snow Cover (%) Observed SWE (mm) IMS Snow Cover Observations Midwestern United States Mosaic LSM Control Run SWE (mm) Assimilated SWE (mm) SWE Change (mm) Noah LSM MODIS Snow Cover Assimilation • MODIS snow cover fields used to update the Mosaic and Noah LSMs within GLDAS/LIS • Models fill spatial and temporal gaps in data, provide continuity and quality control • Assimilated output agrees more closely with IMS snow cover fields (top middle) and ground observations (top right, bottom) • Assimilated output contains more information (~hourly SWE) than MODIS (~daily snow cover) Mosaic LSM Based on Rodell and Houser, J. Hydromet., 2004. Noah LSM

  6. snow water equivalent, mm Sep-05 Jan-06 May-06 Sep-06 Jan-07 May-07 Advanced Rule-Based MODIS Snow Cover Assimilation • Foreward-looking “pull” algorithm • Assesses MODIS snow cover observation 24-72 hours ahead • Adjusts temperature to steer the simulation towards the observation • Generates additional snowfall if necessary • Improves accuracy while minimizing water imbalance Zaitchik and Rodell, J. Hydromet., 2009 Matt Rodell NASA GSFC

  7. GRACE Data Assimilation GRACE water storage, mm January-December 2003 loop Model assimilated water storage, mm January-December 2003 loop Monthly anomalies (deviations from the 2003 mean) of terrestrial water storage (sum of groundwater, soil moisture, snow, and surface water) as an equivalent layer of water. Updated from Zaitchik, Rodell, and Reichle, J. Hydromet., 2008. To scales needed for water resources and agricultural applications From scales useful for water cycle and climate studies…

  8. GRACE Data Assimilation Model separates SWE, soil moisture, and groundwater; GRACE ensures accuracy. r = 0.59 RMS = 23.5 Catchment LSM TWS Mississippi River basin r = 0.69 RMS = 18.7 GRACE-Assimilation TWS Zaitchik, Rodell, and Reichle, J. Hydrometeorology, 2008. From a global, integrated observation To application-specific water storage components

  9. NCAR Community Land Model (CLM4) Co-Chairs: David Lawrence (NCAR), Zong-Liang Yang (Univ of Texas at Austin)

  10. Multi-layer Snow Model in Community Land Model Sensible heat flux and latent heat flux ­ LW ¯ , SW , SW ¯ LW ­ Conductive heat flux across layer q q T , , , z + + + + snl 1 liq , snl 1 ice , snl 1 snl 1 Top layer (up to 5 layer) Layer 2, 3, … q q T , , , z Layer 1 0 liq , 0 ice , 0 0 Ground Snowpack heat diffusion Surface energy balance System function

  11. Retrieving Snow Mass Using GRACE & CLM Niu, Seo, Yang, et al., 2007, GRL

  12. Data Distribution from GES DISC http://disc.gsfc.nasa.gov/hydrology • Data available in GRIB and NetCDF formats • Supports on-the-fly subsetting (right) • Full documentation • Quick look maps soon to be available • Supports a growing number of national and international hydrometeorological investigations and water resources applications

  13. Project Summary • Title: Multi-Sensor Snow Data Assimilation • Problem Statement: MODIS, AMSR-E, and GRACE all provide observations that are relevant to snow water equivalent mapping, each with significant advantages and disadvantages. • Hypothesis: Global fields of SWE can be produced with greater accuracy than previously seen by simultaneously assimilating MODIS snow cover, AMSR-E SWE, and GRACE terrestrial water storage observations within a sophisticated land surface model. • Team: • NASA/GSFC: Matt Rodell (PI), Ed Kim, Rolf Reichle • U. Texas: Liang Yang (Co-PI) • Johns Hopkins: Ben Zaitchik • Timeline: Just getting started

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