Civil & Environmental Engineering Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1657

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Start date: 2020Subject: search.filters.subject.soil moisture

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    Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America
    (MDPI, 2020-09-22) Park, Jongmin; Forman, Barton A.; Reichle, Rolf H.; De Lannoy, Gabrielle; Tarik, Saad B.
    L-band brightness temperature (𝑇𝑏) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in 𝑇𝑏 observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, 𝑇𝑏 estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily 𝑇𝑏 observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed 𝑇𝑏 were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed 𝑇𝑏 with a relatively low, domain-average bias (0.79 K (ascending) and −2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled 𝑇𝑏 due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.
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    SMAP soil moisture assimilated Noah-MP model output
    (2021) Ahmad, Jawairia; Forman, Bart; Kumar, Sujay
    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.