Assimilation of remotely sensed hydrological datasets in the North American Land Data Assimilation System (NLDAS) Sujay V. Kumar 1 , Christa D. Peters-Lidard 1 , David Mocko 1 , Rolf Reichle 2 , Ben Zaitchik 3 , Yuqiong Liu 1 , Kristi Arsenault 1 , Youlong Xia 4 , Michael B. Ek 4 , George Riggs 5 , Ben Livneh 6 , Michael Cosh 7 1 – Hydrological Sciences Laboratory, NASA/GSFC, Greenbelt, MD 2 – NASA Global Modeling and Assimilation Office, Greenbelt, MD 3 – Johns Hopkins University, Baltimore, MD 4 – Environmental Modeling Center, NOAA, College Park, MD 5 – Cryospheric Sciences Branch, NASA/GSFC, Greenbelt, MD 6 – Cooperative Institute for Research in Environmental Sciences, Boulder, CO 7 – USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD
North American Land Data Assimilation System (NLDAS) NLDAS – a real-time and retrospective data assimilation system to provide accurate land surface states for NWP models and end-use applications NLDAS is a successful collaboration project that has produced nearly 34 years of hourly 1/8 th degree surface forcing and land surface model output over CONUS and parts of Canada/Mexico The new phase of the NLDAS project builds upon NLDAS phase-2 and includes the following: Assimilation of remotely sensed terrestrial hydrological datasets. New versions of the land surface models (Noah.3.3, Catchment Fortuna 2.5, VIC 4.1.1, SAC HTET/SNOW17). Conduct the simulations using a common platform using the NASA Land Information System (LIS). Comprehensive model evaluation using the Land surface Verification Toolkit (LVT). 2
Experiment Setup Model domain: Continental United States (CONUS) at 1/8 th degree spatial resolution, including parts of Canada/Mexico (25-53 ° N; 125-67 ° W) Forcing data: NLDAS-phase II (NLDAS2) meteorological forcing data. Hourly precipitation includes CPC’s daily PRISM Corrected gauge analysis, downward shortwave radiation bias-corrected using GOES SRB shortwave data, all other fields derived from the NCEP North American Regional Reanalysis (NARR) data. Land surface model: Noah LSM version 3.3 and Catchment version Fortuna 2.5, includes a 15-year spin-up, followed by a 33 Data assimilation method: Ensemble Kalman Filter (EnKF) and Ensemble Kalman Smoother (EnKS) Time period: Jan 1, 1979 to 1 Jan 2012. 3
Soil moisture data assimilation Soil moisture retrievals are available from low-frequency (C, X, and L-band) active and passive microwave data (SMMR, TMI, AMSR-E, WindSat , SMOS, SMAP, …) In the NLDAS project, we use the following product for assimilation Essential Climate Variable ( ECV ) soil moisture product (Liu et al. 2012, Wagner et al. 2012) from ESA; uses C-band scatterometers (ERS-1/2 scatterometer, METOP advanced scatterometer) and multi-frequency radiometers (SMMR, SSM/I, TMI, AMSR-E, Windsat) used in model simulations from 1979-2002 . Land Parameter Retrieval Model (LPRM; Owe et al. 2008) retrievals of AMSR-E used in model simulations from 2002 to 2011. Data flagged for light and moderate vegetation, no precipitation, no snow cover, DMSP; F8-F16 SMMR no frozen ground, no RFI are used in data SSM/I assimilation. TMI The observations are scaled to the LSM’s AMSR-E TRMM Nimbus-7 climatology using CDF matching Windsat Aqua SMOS A 12-member ensemble is used in Corriolis assimilation. METOP-A ERS-1&2 A spatially distributed observation error ASCAT standard deviation (between 0.02-0.12 SCAT m3/m3) specification was employed. SMAP 1980 1990 2000 2010 4
Snow data assimilation Multi-sensor snow products are available – snow depth retrievals from passive microwave (SMMR, SSM/I, AMSR-E) and near IR (MODIS). Passive microwave sensors can measure during cloudy and nighttime conditions, but have coarser spatial resolution, problems in areas with dense vegetation and open water. In the NLDAS project, we use three passive microwave based snow products for assimilation SMMR ( spans 1978-1987 ), SSM/I ( spans 1987-2002 ) and AMSR-E ( spans 2002-2011 ); SMMR and SSM/I retrievals are based on the Chang et al. (1987) and AMSR-E retrievals are based on the improved retrieval algorithm from Kelly et al. (2009) Remote sensing observations are bias-corrected using the Cressman method using the in-situ observations from Global Historical Climate Network (GHCN). snow depth from obs background field (from snow depth EDR) weight function, which is a function of horizontal ( ) and vertical difference ( ) A 12-member ensemble is used in assimilation. 5
Terrestrial Water Storage (TWS) data assimilation Data assimilation scheme: Ensemble Kalman Smoother (EnKS) modified to accommodate monthly terrestrial water storage anomalies from GRACE. DA-TWS employs the assimilation of 1 ° x 1 ° resolution gridded TWS anomaly fields directly, without first averaging them over large river basins. Assimilation updates the model soil moisture and snow states. Animation of monthly GRACE terrestrial water storage anomaly fields. A water storage anomaly is defined here as a GRACE measures changes in total terrestrial water deviation from the long-term mean total terrestrial water storage, including groundwater, soil moisture, storage at each location. snow, and surface water. 6
Evaluation of NLDAS outputs Soil moisture: USDA Soil Climate Analysis Network (SCAN); 123 stations chosen after careful quality control (used for evaluations between 2000-2011) Four USDA ARS experimental watersheds (“ CalVal ” sites) (used for evaluations between 2001-2011) Streamflow: Gauge measurements from unregulated USGS streamflow stations (1981-2011) and naturalized streamflow data used in Koster et al. (2010) (1979-2011). Snow depth: Global Historical Climate Network (GHCN) – used for evaluations between 1979-2012. Canadian Meteorological Center (CMC) daily snow depth analysis – used for evaluations between 1998-2012. Snow Data Assimilation System (SNODAS) products from the National Operational Hydrologic Remote Sensing Center (NOHRSC) – used for evaluations between 2003/10 – 2012) All model verifications and analysis generated using the Land surface Verification Toolkit (LVT; Kumar et al. 2012) 7
Soil moisture DA: evaluation of soil moisture fields ARS CalVal Open loop (no LPRM DA (surface soil DA) moisture) Anomaly R 0.84 +/- 0.02 0.86 +/- 0.02 Anomaly RMSE (m3/m3) 0.021 +/- 0.001 0.019 +/- 0.001 ubRMSE (m3/m3) 0.024 +/- 0.002 0.022 +/- 0.002 SCAN (surface Open loop (no LPRM DA soil moisture) DA) Statistically significant Anomaly R 0.67 +/- 0.02 0.67 +/- 0.02 improvements in surface soil moisture and root zone soil Anomaly RMSE (m3/m3) 0.037+/- 0.002 0.036 +/- 0.002 moisture as a result of soil ubRMSE (m3/m3) 0.043 +/- 0.003 0.041 +/- 0.003 moisture DA Anomaly R increases, Anomaly SCAN (root zone Open loop (no LPRM DA RMSE reduces and unbiased soil moisture) DA) RMSE reduces with soil Anomaly R 0.60 +/- 0.02 0.59 +/- 0.02 moisture assimilation. Anomaly RMSE (m3/m3) 0.032 +/- 0.002 0.030 +/- 0.002 ubRMSE (m3/m3) 0.041 +/- 0.003 0.039 +/- 0.003 8
Soil moisture DA: Evaluation of streamflow The improvements are expressed using an Normalized Information Contribution (NIC) metric that measures the skill improvement from DA as a fraction of the maximum possible skill improvement NIC_RMSE NIC_R Overall improvements in all skill metrics (RMSE, R and NSE) are observed in streamflow estimates after data assimilation Skill improvements from soil moisture assimilation are mostly over parts of the Mississippi, Missouri and NIC_NSE Arkansas-Red basins and parts of Southeastern U.S. 9
Snow DA: Evaluation of snowdepth Open loop SNOW-DA CMC SNODAS (no DA) RMSE 174.0 +/- 8 114.0+/- 8 158.0+/-8 154.0+/- 8 (mm) Bias (mm) -84.1+/- 8 -31.6 +/- 8 -66.0+/- 8 33.9 +/- 8 Selected GHCN stations RMSE Bias The assimilation of gauge-corrected snow EDR provides improvements to the snow depth fields, primarily over the peak winter and spring melt periods. The gauge corrected snow EDR data shows similar skill to that of CMC and SNODAS. 10
Snow DA: Evaluation of streamflow The improvements are expressed using an Normalized Information Contribution (NIC) metric that measures the skill improvement from DA as a fraction of the maximum possible skill improvement NIC_RMSE NIC_RMSE NIC_R NIC_R Overall degradation in RMSE and NSE are observed in streamflow estimates after data assimilation Most improvements are observed over the Missouri and Upper Mississippi basins, degradations over Northwest and Colorado headwater basins. NIC_NSE NIC_NSE
Soil moisture and snow DA: Evaluation of streamflow at major basin outlets Streamflow is compared at major NIC_RMSE basin outlets against naturalized streamflow data (used in Koster et al. 2010, Nat. Geosc.) FTP GAR MUS WIL ICE RAN NIC_R GRE SBB POT DEL GUN UPM OHI RAL LEE PUE RED ALA AP A Snow DA improvements noted over high latitude basins (Green river, Musselshel, NIC_NSE Snake River, Missouri River) Soil moisture DA improvements over Apalachicola, Alabama, Willamette, Missouri at Garrison Degradations observed over the largest basins (Ohio, Upper Mississippi). 12
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