The Noah-MP land surface model was run on an equidistant cylindrical grid at a spatial resolution of 0.05 degree x 0.05 degree from 2015 to 2020. Open loop and data assimilation (with and without CDF-matching) runs were executed based on the methodology described in Ahmad et al. (2021) using MERRA2 and IMERG precipitation boundary conditions. The NetCDF files archived here were reprocessed to include the model output states discussed in the paper only. These include: 1) surface soil moisture, 2) rootzone soil moisture, 3) evapotranspiration, and 4) gross primary production.
References:
Ahmad, J. A., Forman, B. A., and Kumar, S. V. (2021), SMAP retrieval assimilation improves soil moisture estimation across irrigated areas in South Asia, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-460.The data archived here includes the NASA Noah-MP (version 4.0.1) land surface model output used in the investigation of the impact of passive microwave-based soil moisture retrieval assimilation on soil moisture estimation in South Asia (Ahmad et al., 2021). SMAP soil moisture retrievals are assimilated into the Noah-MP land surface model to improve the estimation of soil moisture and other related states. The open loop (OL) represents Noah-MP’s modeling capabilities using MERRA2 and IMERG precipitation. Two different types of data assimilation runs were executed using the MERRA2 and IMERG precipitation boundary conditions, i.e., with CDF-matching (DA-CDF) and without CDF matching (DA-NoCDF). The key findings in this paper include: 1) assimilation results without any CDF-matching yielded the lowest error in estimated soil moisture, 2) the best goodness-of-fit statistics were achieved for the IMERG-forced DA-NoCDF soil moisture experiment, 3) biases associated with unmodeled hydrologic processes such as irrigation were corrected via assimilation, and 4) the highest influence of assimilation was observed across croplands.NASA Understanding Changes in High Mountain Asia (Contract# 80NSSC2OK1531
