600 Comparison and Evaluation of the Noah 3.6 and Noah-MP Skin Temperature Products as Candidate Variables for Assimilation of Remotely Sensed Measurements

Tuesday, 14 January 2020
Hall B (Boston Convention and Exhibition Center)
John B. Eylander, U.S. Army Corps of Engineers, Hanover, NH

A number of studies conducted over the past decade have demonstrated mixed results when assimilating satellite land surface skin temperature (LST) into both land surface models and surface energy balance models. While the methods to ingest remotely sensed LST in land models or surface energy balance models have varied between variational assimilation methods (Boni et al. 2001; Basilovich et al. 2007; Balsamo et al. 2007; Xu et al. 2014; Pinjosovsky et al. 2017) and assimilation via an ensemble Kalman filter (Reichle et al. 2010; Ghent et al. 2010; Draper et al. 2014; Fang et al. 2018); impact of the LST assimilation success depends heavily on the configuration of the land model and/or the method utilized to update the model state with the remotely sensed LST variable. This project is focused on evaluating the computation of LST in two specific versions of the Noah land surface model, Noah 3.6 and Noah-MP, and identifying a model configuration, parameter, or version best suited for being updated via remotely sensed measurements. This presentation will describe how skin temperature is computed in the Noah version 3.6 and Noah-MP, evaluate the respective results when executed using the same input data as part of the US Air Force configuration of the NASA Land Information System, and compare the model results against surface observations of LST obtained from ARM Southern Great Plains and SURFRAD sites and satellite measurements obtained from the NASA International Satellite Cloud Climatology Project H Version (Young et al. 2018).

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Draper, C., R. Reichle, G. De Lannoy, and B. Scarino, 2014: A Dynamic Approach to Addressing Observation-Minus-Forecast Bias in a Land Surface Skin Temperature Data Assimilation System. J. Hydrometeorol., https://doi.org/10.1175/jhm-d-14-0087.1.

Fang, L., X. Zhan, C. R. Hain, J. Yin, J. Liu, and M. A. Schull, 2018: An assessment of the impact of land thermal infrared observation on regional weather forecasts using two different data assimilation approaches. Remote Sens., doi:10.3390/rs10040625.

Ghent, D., J. Kaduk, J. Remedios, J. Ardö, and H. Balzter, 2010: Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter. J. Geophys. Res. Atmos., doi:10.1029/2010JD014392.

Pinjosovsky, H. S. B., S. Thiria, C. Ottlé, J. Brajard, F. Badran, and P. Maugis, 2017: Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6. Geosci. Model Dev., https://doi.org/10.5194/gmd-10-85-2017.

Reichle, R. H., S. V. Kumar, S. P. P. Mahanama, R. D. Koster, and Q. Liu, 2010: Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models. J. Hydrometeorol., https://doi.org/10.1175/2010JHM1262.1.

Young, A. H., K. R. Knapp, A. Inamdar, W. Hankins, and W. B. Rossow, 2018: The International Satellite Cloud Climatology Project, H-Series Climate Data Record Product, Earth System Science Data, 10, 583-593, https://doi.org/10.5194/essd-10-583-2018

Xu, T., S. M. Bateni, S. Liang, D. Entekhabi, and K. Mao, 2014: Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from Geostationary Operational Environmental Satellites.J. Geophys. Res., doi:10.1002/2014JD021814.

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