Overview of the north American land data assimilation system (NLDAS)

Youlong Xia, Brian A. Cosgrove, Michael B. Ek, Justin Sheffield, Lifeng Luo, Eric F. Wood, Kingtse Mo, Kenneth Mitchell, Dag Lohmann, Paul Houser, John Schaake, Alan Robock, Brian Cosgrove, Qingyun Duan, Lifeng Luo, R. Wayne Higgins, Rachel Pinker, J. Dan Tarpley, Dennis Lettenmaier, Curtis MarshallJared Entin, Ming Pan, Wei Shi, Victor Koren, Jesse Meng, Bruce Ramsay, Andrew Bailey, Charles Alonge, Jiarui Dong, Yun Fan, Kintse Mo, Ben Livneh, David Mocko, Helin Wei, Andy Wood, Youlong Xia

Research output: Chapter in Book/Report/Conference proceedingChapter

10 Scopus citations

Abstract

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), together with its NOAA Climate Program Office (CPO) Climate Prediction Program of the Americas (CPPA) partners, have established a North American Land Data Assimilation System (NLDAS). The system runs multiple land surface models (LSMs) over the Continental United States (CONUS) to generate long-term hourly, 1/8th degree hydrological and meteorological products. NLDAS was initiated in 1998 as a collaborative project between NOAA, NASA, and several universities to improve the generation of initial land surface conditions for numerical weather prediction models. The first phase of NLDAS (NLDAS-1, 1998-2005) centered on the construction of the overall NLDAS system and on the assessment of the ability of the four NLDAS LSMs to accurately simulate water fluxes, energy fluxes, and state variables. These LSMs included the Noah, Mosaic, Sacramento Soil Moisture Accounting (SAC-SMA), and Variable Infiltration Capacity (VIC) models. Building on the results of NLDAS-1, the project entered into a second phase (NLDAS-2, 2006-present) which has included upgraded forcing data and LSMs, model intercomparison studies, real-time monitoring of extreme weather events, and seasonal hydrologic forecasts. NLDAS-1 and NLDAS-2 have also spurred and supported other modeling activities, including high-resolution 1 km land surface modeling and the establishment of regional and global land data assimilation systems. NLDAS-2 operates on both a real-time monitoring mode and an ensemble seasonal hydrologic forecast mode. In the monitoring mode, land states (soil moisture and snow water equivalent) and water fluxes (evaporation, total runoff, and streamflow) from real-time LSM executions are depicted as anomalies and percentiles with respect to their own modelbased climatology. One key application of the real-time updates is for drought monitoring over the CONUS, and NLDAS supports both NOAA Climate Prediction Center (CPC) and US National Integrated Drought Information System (NIDIS) drought monitoring activities. The uncoupled ensemble seasonal forecast mode generates downscaled ensemble seasonal forecasts of surface forcing based on a climatological Ensemble Stream flow Prediction (ESP) type approach, a method utilizing CPC Official Seasonal Climate Outlooks, and a third approach using NCEP Climate Forecast System (CFS) ensemble dynamical model predictions. The three sets of forcing ensembles are then used to drive a chosen LSM (currently VIC) in seasonal forecast mode over 14 large river basins that together span the CONUS domain. One-to six-month ensemble seasonal forecast products such as air temperature, precipitation, soil moisture, snowpack, total runoff, evaporation, and stream flow are derived using each forecasting approach. The anomalies and percentiles of the predicted products and the drought probability forecast based on the predicted total column soil moisture for each forcing approach can be used for the purpose of drought prediction over the CONUS, and provide key support for NIDIS and CPC drought forecast efforts.

Original languageEnglish (US)
Title of host publicationLand Surface Observation, Modeling and Data Assimilation
PublisherWorld Scientific Publishing Co.
Pages337-378
Number of pages42
ISBN (Electronic)9789814472616
ISBN (Print)9789814472609
DOIs
StatePublished - Jan 1 2013

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)

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