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

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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