Overview of the North American Land Data Assimilation System (NLDAS)

Xia, Youlong; Ek, Michael; Sheffield, Justin; Liu, Luo; Wood, Eric F.; Mo, Kingtse C.; Mitchell, Kenneth E.; Lohmann, Dag; Houser, Paul R.; Schaake, John C.; Robock, Alan; Cosgrove, B.; Duan, Qingyun; Higgins, R. Wayne; Pinker, R. T.; Tarpley, J. Dan; Lettenmaier, Dennis P.; Marshall, Curtis H.; Entin, Jared; Pan, Ming; Shi, Wei; Koren, Victor; Meng, Jesse; Ramsay, Bruce H.; Bailey, Andrew; Alonge, Charles; Dong, Jing; Fan, Yun; Mo, K. C.; Livneh, Ben; Mocko, David M.; Wei, Helin; Wood, A. W.

doi:10.1142/9789814472616_0011

Cited by 32 publications

(39 citation statements)

References 45 publications

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“…As an example, X might be soil moisture simulated by a model at a finite resolution, and Z might be in situ soil moisture data, effectively at a point scale. Or X and Z could represent different physical variables altogether; perhaps X is soil moisture simulated on a 1/8° grid (e.g., Xia et al, ), and Z is leaf area index as estimated from remote sensing (e.g., Knyazikhin et al, ). Nevertheless, there is some amount of information shared between the modeled data, the retrievals, and the in situ data, and this total amount of information can, in principle, be extracted by a hypothetically perfect DA algorithm in the analysis vector X + .…”

Section: Theorymentioning

confidence: 99%

The Efficiency of Data Assimilation

Nearing

Yatheendradas

Crow

et al. 2018

Water Resources Research

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Data assimilation is the application of Bayes' theorem to condition the states of a dynamical systems model on observations. Any real‐world application of Bayes' theorem is approximate, and therefore, we cannot expect that data assimilation will preserve all of the information available from models and observations. We outline a framework for measuring information in models, observations, and evaluation data in a way that allows us to quantify information loss during (necessarily imperfect) data assimilation. This facilitates quantitative analysis of trade‐offs between improving (usually expensive) remote sensing observing systems versus improving data assimilation design and implementation. We demonstrate this methodology on a previously published application of the ensemble Kalman filter used to assimilate remote sensing soil moisture retrievals from Advanced Microwave Scattering Radiometer for Earth (AMSR‐E) into the Noah land surface model.

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Section: Theorymentioning

confidence: 99%

The Efficiency of Data Assimilation

Nearing

Yatheendradas

Crow

et al. 2018

Water Resources Research

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“…Model evaluation studies (Pan et al, ; Robock et al, ; Sheffield et al, ) during the first phase of the NLDAS project (NLDAS‐1) were primarily focused on evaluating the model outputs against available reference measurements. The deficiencies in individual model formulations identified in these studies led to Phase 2 of the NLDAS project, where model parameterizations and boundary condition inputs were improved (Xia et al, ). Though the model evaluations conducted in NLDAS‐2 (Xia et al, ) indicate greater level of agreement between the constituent models relative to those in NLDAS‐1, significant intermodel differences were also observed, particularly for cold season and subsurface hydrologic processes.…”

Section: Introductionmentioning

confidence: 99%

Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

Kumar

Wang

Mocko

et al. 2017

Water Resources Research

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Multimodel ensembles are often used to produce ensemble mean estimates that tend to have increased simulation skill over any individual model output. If multimodel outputs are too similar, an individual LSM would add little additional information to the multimodel ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. The article presents a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multimodel ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/upgraded models that are under consideration for the next phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture, snow water equivalent, and terrestrial water storage. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer time scales. Trade‐offs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity‐accuracy space compared to the new LSMs. The results of the article indicate that simultaneous consideration of model similarity and accuracy at the relevant time scales is necessary in the development of multimodel ensemble.

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“…Over the past decade or so, the NLDAS team has not only created high‐quality atmospheric forcing data to drive LSMs but also collected substantial observational data sets to build necessary tools for evaluating the accuracy of surface and subsurface energy/water fluxes that LSMs produce. These efforts have resulted in the NLDAS test bed [ Xia et al, ], which includes the following four LSMs: the community Noah LSM (Noah) [ Ek et al, ], the Mosaic LSM (Mosaic) [ Koster and Suarez , ], the Sacramento Soil Moisture Accounting model [ Burnash et al, ], and the Variable Infiltration Capacity (VIC) model [ Liang et al, ]. However, the current system does not yet include those models that incorporate recent developments in the land model community, such as the Community Land Model version 4 (CLM4) [ Lawrence et al, ] and the multiparameterization options version of the Noah model (Noah‐MP) [ Niu et al, ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of simulated water balance from Noah, Noah‐MP, CLM, and VIC over CONUS using the NLDAS test bed

et al. 2014

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This study assesses the hydrologic performance of four land surface models (LSMs) for the conterminous United States using the North American Land Data Assimilation System (NLDAS) test bed. The four LSMs are the baseline community Noah LSM (Noah, version 2.8), the Variable Infiltration Capacity (VIC, version 4.0.5) model, the substantially augmented Noah LSM with multiparameterization options (hence Noah-MP), and the Community Land Model version 4 (CLM4). All four models are driven by the same NLDAS-2 atmospheric forcing. Modeled terrestrial water storage (TWS), streamflow, evapotranspiration (ET), and soil moisture are compared with each other and evaluated against the identical observations. Relative to Noah, the other three models offer significant improvements in simulating TWS and streamflow and moderate improvements in simulating ET and soil moisture. Noah-MP provides the best performance in simulating soil moisture and is among the best in simulating TWS, CLM4 shows the best performance in simulating ET, and VIC ranks the highest in performing the streamflow simulations. Despite these improvements, CLM4, Noah-MP, and VIC exhibit deficiencies, such as the low variability of soil moisture in CLM4, the fast growth of spring ET in Noah-MP, and the constant overestimation of ET in VIC.

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Overview of the North American Land Data Assimilation System (NLDAS)

Cited by 32 publications

References 45 publications

The Efficiency of Data Assimilation

The Efficiency of Data Assimilation

Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

Assessment of simulated water balance from Noah, Noah‐MP, CLM, and VIC over CONUS using the NLDAS test bed

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