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README Documents

NLDAS-1 Readme (Last Update: Mar. 14, 2013)

NLDAS-2 Readme (Last Update: Mar. 12, 2014)

GLDAS-1 Readme (Last Update: May 11, 2015)

GLDAS-2 Readme (Last Update: Jul. 24, 2015)

FLDAS Readme (Last Update: Dec. 09, 2015)

NEWS WEB Climatology Version 1 Readme (Last update: Nov. 30, 2015)

GRACEDADM Readme (Last update:  May 27, 2016)

Primary References

Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J. K. Entin, J. P. Walker, D. Lohmann, and D. Toll, 2004. The Global Land Data Assimilation System, Bull. Amer. Meteor. Soc., 85(3): 381-394.

Mitchell, K.E., D. Lohmann, P.R. Houser, E.F. Wood, J.C. Schaake, A. Robock, B.A. Cosgrove, J. Sheffield, Q. Duan, L. Luo, R.W. Higgins, R.T. Pinker, J.D. Tarpley, D.P. Lettenmaier, C.H. Marshall, J.K. Entin, M. Pan, W. Shi, V. Koren, J. Meng, B.H. Ramsay, and A.A. Bailey, 2004. The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system,  J. Geophys. Res., 109, D07S90, doi:10.1029/2003JD003823.

Xia, Y., K. Mitchell, M. Ek, J. Sheffield, B. Cosgrove, E. Wood, L. Luo, C. Alonge, H. Wei, J. Meng, B. Livneh, D. Lettenmaier, V. Koren, Q. Duan, K. Mo, Y. Fan, and D. Mocko, 2012. Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res., 117, D03109, doi:10.1029/2011JD016048.

GLDAS Evaluation Studies and Other References:  http://ldas.gsfc.nasa.gov/gldas/GLDASpublications.php

NLDAS Evaluation Studies and Other References:  http://ldas.gsfc.nasa.gov/nldas/NLDASpublications.php

GRIBTAB Files

A GRIBTAB file  with ".txt" is identical to the corresponding GRIBTAB file with ".tab". Both ".txt" and ".tab" are provided for users' convenience.

NLDAS-1 Data

NLDAS-2 Data

GLDAS-1 Data

GLDAS-2 Data

  • Reprocessed GLDAS-2 data are  archived  in  self-describing  and  machine-independent  NetCDF format.

Forcing Data Sets

NLDAS Forcing Data Sets

The two phases of NLDAS (NLDAS-1 and NLDAS-2) use different sources of model data and observations to create the respective forcing data sets.

 

The chief source of NLDAS-1 forcing is the 40-km National Center for Environmental Prediction (NCEP) Eta model-based Data Assimilation System (EDAS) [Rogers et al., 1995], a continuously-cycled North American 4DDA system. Geostationary Operational Environmental Satellite (GOES)-based solar insolation [Pinker et al., 2003] provides the primary insolation forcing (shortwave down at the surface) for NLDAS-1. NLDAS-1 precipitation forcing over CONUS is anchored to NCEP's 1/4th-degree gauge-only daily precipitation analyses of Higgins et al. [2000].

The main source of NLDAS-2 forcing is the 32-km North American Regional Reanalysis (NARR) [Mesinger et al., 2006].  The NARR downward shortwave radiation field is bias-corrected to the GOES-based satellite retrievals [Pinker et al., 2003].  The total precipitation field is derived from Climate Prediction Center (CPC) daily CONUS gauge data [Higgins et al., 2000; Chen et al., 2008] (with the PRISM topographical adjustment [Daly et al., 1994]), CPC hourly CONUS/Mexico gauge data (HPD) [Higgins et al., 1996], hourly Doppler Stage II radar precipitation data [Fulton et al., 1998], half-hourly CMORPH data [Joyce et al., 2004], and 3-hourly NARR precipitation data. Reflecting the strengths of each dataset, hourly NLDAS-2 precipitation is derived by using the Doppler radar, CMORPH products, or HPD data to temporally disaggregate the daily gauge products.

For more information about the NLDAS-1 and NLDAS-2 forcing, please visit http://ldas.gsfc.nasa.gov/nldas/NLDAS1forcing.php and http://ldas.gsfc.nasa.gov/nldas/NLDAS2forcing.php

Chen, M., W. Shi, P. Xie, V.B.S. Silva, V.E. Kousky, R.W. Higgins, and J.E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation.  J. Geophys. Res. Atmos, 113(D4), doi: 10.1029/2007JD009132

Fulton, R. A., J. P. Breidenbach, D. Seo, D. A. Miller, and T. O’Bannon, 1998: The WSR-88D Rainfall Algorithm. Wea. Forecasting, 13, 377–395.

Higgins, R.W., J.E. Janowiak, and Y.-P. Yao, 1996: A gridded hourly precipitation data base for the United States.  NCEP/Climate Prediction Center, ATLAS No. 1, U.S. Dept. of Commerce, NOAA/NWS.

Higgins, R. W., W. Shi, E. Yarosh, and R. Joyce , 2000: Improved United States Precipitation Quality Control System and Analysis. NCEP/Climate Prediction Center Atlas No. 7, NOAA, U. S. Department of Commerce.

Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydromet., 5, 487-503.

Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360.

Pinker, R. T., and 13 co-authors, 2003: Surface radiation budgets in support of the GEWEX Continental-Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res., 108(D22), 8844.

Rogers, E., D. G. Deaven, and G. S. Dimego, 1995: The Regional Analysis System for the Operational “Early” Eta Model: Original 80-km Configuration and Recent Changes. Wea. Forecasting, 10, 810–825.

GLDAS Forcing Data Sets

The GLDAS Version 1 (GLDAS-1)  simulations were forced with multiple datasets for the period of 1979 to present:

  • 1979-1993: bias-corrected European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis data [Berg et al., 2003]
  • 1994-1999: bias-corrected National Center for Atmospheric Research (NCAR) Reanalysis data [Berg et al., 2003]
  • 2000: National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields [Derber et al., 1991]
  • 2001-present: a combination of NOAA/GDAS atmospheric analysis fields, spatially and temporally disaggregated NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) [Xie and Arkin, 1996] fields, and observation-based downward shortwave and longwave radiation fields from the Air Force Weather Agency (AFWA).

The GLDAS Version 2.0 (GLDAS-2.0) simulations were forced with the Global Meteorological Forcing Data set from the Princeton University [Sheffield et al., 2006] from 1948 to 2010.

For more information on GLDAS forcing, please visit http://ldas.gsfc.nasa.gov/gldas/GLDASforcing.php.

Berg, A. A., J. S. Famiglietti, J. P. Walker, and P. R. Houser, 2003: Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes, J. Geophys. Res, 108 (D16), 4490.  

Derber, J. C., D. F. Parrish, and S. J. Lord, 1991: The new global operational analysis system at the National Meteorological Center. Weather Forecasting, 6, 538-547. 

Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-yr high-resolution global dataset of meteorological forcings for land surface modeling, J. Climate, 19 (13), 3088-3111. 

Xie P., and P. A. Arkin, 1996: Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539-2558. 

Land Surface Models

Common Land Model (CLM)

CLM was conceived at the 1998 National Center for Atmospheric Research (NCAR) Climate System Model (CSM) meeting, and it was subsequently developed by a grass-roots collaboration of scientists. CLM includes superior components from each of three contributing models: the NCAR Land Surface Model (Bonan 1998), the Biosphere-Atmosphere Transfer Scheme (Dickinson et al. 1993), and the LSM of the Institute of Atmospheric Physics of the Chinese Academy of Sciences (Dai and Zeng 1997). The model applies finite-difference spatial discretization methods and a fully implicit time integration scheme to numerically integrate the governing equations. CLM can be run as a stand-alone, 1-D column model. It is also the land model for NCAR's coupled Community Climate System Model (CCSM). CLM continues to evolve, but only proven and well-tested physical parameterizations and numerical schemes are installed in the official version of the code. LIS currently uses CLM version 2.0. For more information, see: http://www.cgd.ucar.edu/tss/clm/

Mosaic Model

Mosaic  (Koster and Suarez 1996) is a well established and theoretically sound LSM, as demonstrated by its performance in PILPS and GSWP experiments. Mosaic's physics and surface flux calculations are similar to the SiB LSM (Sellers et al., 1986). It is a stand-alone, 1-D column model that can be run both uncoupled and coupled to the atmospheric column. Mosaic was the first to treat subgrid scale variability by dividing each model grid cell into a Mosaic of tiles (after Avissar and Pielke 1989) based on the distribution of vegetation types within the cell. This capability is now available in the LIS interface for all the models it drives.

National Centers for Environmental Prediction/Oregon State University/Air Force/Hydrologic Research Lab (Noah) Model

The community Noah LSM was developed beginning in 1993 through a collaboration of investigators from public and private institutions, spearheaded by the National Centers for Environmental Prediction (Chen et al. 1996; Koren et al. 1999). Noah is a stand-alone, 1-D column model which can be executed in either coupled or uncoupled mode.  The model applies finite-difference spatial discretization methods and a Crank-Nicholson time-integration scheme to numerically integrate the governing equations of the physical processes of the soil-vegetation-snowpack medium. Noah has been used operationally in NCEP models since 1996, and it continues to benefit from a steady progression of improvements (Betts et al. 1997; Ek et al. 2003). For more information, go to: http://www.emc.ncep.noaa.gov/mmb/gcp/noahlsm/Noah_LSM_USERGUIDE_2.7.1.htm

Variable Infiltration Capacity (VIC) Model

VIC (Liang et al. 1994; Liang et al. 1996) was originally developed in early 90’s and is maintained and upgraded at the University of Washington.  The model focuses on runoff processes that are represented by the variable infiltration curve, a parameterization of sub-grid variability in soil moisture holding capacity, and nonlinear baseflow.  VIC is a stand-alone, 1-D column model that is run uncoupled.  Various simulation modes are available including, water balance, energy balance, frozen soil, and other special cases.  This macro-scale hydrology model is used extensively in research over the watersheds in the U.S. as well as globally (e.g. Liang et al. 1998; Hamlet et al. 1999; Nijssen et al. 2001). For more information, see: http://www.hydro.washington.edu/Lettenmaier/Models/VIC/VIChome.html

Avissar, R. and R.A. Pielke, A parameterization of heterogeneous land-surface for atmospheric numerical models and its impact on regional meteorology. Mon. Wea. Rev., 117:2113-2136, 1989.

Betts, A., F. Chen, K. Mitchell, and Z. Janjic, Assessment of the land surface and boundary layer models in two operational versions of the NCEP Eta model using FIFE data. Mon.Wea. Rev., 125, 2896-2916, 1997.

Bonan, G.B., The land surface climatology of the NCAR Land Surface Model coupled to the NCAR Community Climate Model. J. Climate, 11, 1307-1326, 1998.

Chen, F., K. Mitchell, J. Schaake, Y. Xue, H. Pan, V. Koren, Y. Duan, M. Ek, and A. Betts, Modeling of land-surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res.,101 (D3), 7251-7268, 1996.

Dai, Y., and Q. Zeng, 1997: A land surface model (IAP94) for climate studies, Part I: Formulation and validation in off-line experiments. Advances in Atmos. Sci., 14, 443-460.

Dickinson, R. E., A. Henderson-Sellers, and P. J. Kennedy, Biosphere–Atmosphere Transfer Scheme (BATS) version 1e as coupled to the NCAR Community Climate Model. NCAR Tech. Note NCAR/TN-387+STR, 72 pp., 1993.

Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res., 108(D22), 8851, doi:10.1029/2002JD003296, 2003.

Hamlet, A.F. and D.P. Lettenmaier, Effects of Climate Change on Hydrology and Water Resources in the Columbia River Basin, Am. Water Res. Assoc., 35(6), 1597-1623, 1999.

Koren, V., J. Schaake, K. Mitchell, Q. Y. Duan, F. Chen, and J. M. Baker, A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J. Geophys. Res.,104, 19569-19585, 1999.

Koster, R. D., and M. J. Suarez, Energy and Water Balance Calculations in the Mosaic LSM. NASA Technical Memorandum 104606, 9, 76 pp., 1996.

Kumar, S. V., C. D. Peters-Lidard, Y. Tian, P. R. Houser, J. Geiger, S. Olden, L. Lighty, J. L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E. F. Wood, and J. Sheffield, Land Information System - An interoperable framework for high resolution land surface modeling, Environ. Modelling and Software, 21, 1402-1415, 2006.

Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415-14,428, 1994.

Liang, X., D. P. Lettenmaier, E. F. Wood, One-dimensional Statistical Dynamic Representation of Subgrid Spatial Variability of Precipitation in the Two-Layer Variable Infiltration Capacity Model, J. Geophys. Res., 101(D16) 21,403-21,422, 1996.

Liang, X., E. F. Wood, D. Lohmann, D.P. Lettenmaier, and others, The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase-2c Red-Arkansas River Basin Experiment: 2. Spatial and Temporal Analysis of Energy Fluxes, J. Global and Planetary Change, 19, 137-159, 1998.

Nijssen, B.N., R. Schnur and D.P. Lettenmaier, Global retrospective estimation of soil moisture using the VIC land surface model, 1980-1993, J. Clim. 14, 1790-1808. , 2001.

Sellers, P. J., Y. Mintz, and A. Dalcher, 1986: A simple biosphere model (SiB) for use within general circulation models. J. Atmos. Sci., 43: 505-531.

Publications and Presentations

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