scholarly journals An initial assessment of SMAP soil moisture retrievals using high-resolution model simulations and in situ observations

2016 ◽  
Vol 43 (18) ◽  
pp. 9662-9668 ◽  
Author(s):  
Ming Pan ◽  
Xitian Cai ◽  
Nathaniel W. Chaney ◽  
Dara Entekhabi ◽  
Eric F. Wood
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Initial Assessment ◽  
Model Simulations ◽  
Resolution Model ◽  
High Resolution Model ◽  
In Situ Observations

scholarly journals Tuning and assessment of the HYCOM-NORWECOM V2.1 modeling system

2014 ◽  
Vol 7 (6) ◽  
pp. 8399-8432 ◽  
Author(s):  
A. Samuelsen ◽  
C. Hansen ◽  
H. Wehde
Keyword(s):  
High Resolution ◽  
Arctic Ocean ◽  
Basic Research ◽  
Grazing Rate ◽  
The Arctic ◽  
Atlantic Sector ◽  
In Situ Data ◽  
Resolution Model ◽  
High Resolution Model

Abstract. The HYCOM-NORWECOM modeling system is used both for basic research and as a part of the forecasting system for the Arctic Marine Forecasting Centre through the MyOcean project. Here we present a revised version of this model. The present model, as well as the sensitivity simulations leading up to this version, has been compared to a dataset of in-situ measurements of nutrient and chlorophyll from the Norwegian Sea and the Atlantic sector of the Arctic Ocean. The revisions having most impact included adding diatoms to the diet of micro-zooplankton, increasing micro-zooplankton grazing rate and decreased silicate-to-nitrate ratio in diatoms. Model runs are performed both with a coarse- (~50 km) and higher-resolution (~15 km) model configuration, both covering the North Atlantic and Arctic Ocean. While the new model formulation improves the results in both the coarse- and high-resolution model, the nutrient bias is smaller in the high-resolution model, probably as a result of the better resolution of the main processes and with that improved circulation. The final revised version delivers satisfactory results for all three nutrients as well as improved result for chlorophyll in terms of the annual cycle amplitude. However, for chlorophyll the correlation with in-situ data remains relatively low. Besides the large uncertainties associated with observational data this is possibly caused by the fact that constant C / N and Chl / N ratios are implemented in the model.

scholarly journals Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

2021 ◽  
Author(s):  
Yaoping Wang ◽  
Jiafu Mao ◽  
Mingzhou Jin ◽  
Forrest M. Hoffman ◽  
Xiaoying Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Earth System Model ◽  
System Model ◽  
Surface Model ◽  
Earth System ◽  
Model Simulations ◽  
The Earth ◽  
Wide Range ◽  
In Situ Observations

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling/retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multi-layer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5° resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations and the latest gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Assessed against in situ observations, the global mean bias of the synthesized SM data ranged from −0.044 to 0.033 m3/m3, root mean squared error from 0.076 to 0.104 m3/m3, and Pearson correlation from 0.35 to 0.67. The merged SM datasets also showed the ability to capture historical large-scale drought events and physically plausible global sensitivities to observed meteorological factors. Three of the new SM products, produced by applying any of the three merging methods onto the source datasets excluding the Earth system models, were finally recommended for future applications because of their better performances than the Earth system model–dependent merged estimates. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

scholarly journals Heat and moisture budgets from airborne measurements and high-resolution model simulations

2012 ◽  
Vol 117 (1-2) ◽  
pp. 47-61 ◽  
Author(s):  
Stefan Zacharias ◽  
Mark Reyers ◽  
Joaquim G. Pinto ◽  
Jan H. Schween ◽  
Susanne Crewell ◽  
...  
Keyword(s):  
High Resolution ◽  
Model Simulations ◽  
Airborne Measurements ◽  
Resolution Model ◽  
High Resolution Model ◽  
Heat And Moisture

scholarly journals ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

2018 ◽  
Vol 22 (6) ◽  
pp. 3515-3532 ◽  
Author(s):  
Clement Albergel ◽  
Emanuel Dutra ◽  
Simon Munier ◽  
Jean-Christophe Calvet ◽  
Joaquin Munoz-Sabater ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Model Simulations ◽  
In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

scholarly journals Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

2021 ◽  
Author(s):  
Guokun Lyu ◽  
Nuno Serra ◽  
Meng Zhou ◽  
Detlef Stammer
Keyword(s):  
High Resolution ◽  
Arctic Ocean ◽  
Sea Level ◽  
The Arctic ◽  
Sea Level Variability ◽  
Model Simulations ◽  
Marginal Seas ◽  
Resolution Model ◽  
High Resolution Model ◽  
Canadian Basin

Abstract. Two high-resolution model simulations are used to investigate the spatio-temporal variability of the Arctic Ocean sea level. The model simulations reveal barotropic sea level variability at periods < 30 days, which is strongly captured by bottom pressure observations. The seasonal sea level variability is driven by volume ex-changes with the Pacific and Atlantic Oceans and the redistribution of the water by the wind. Halosteric effects due to river runoff and evaporation minus precipitation (EmPmR), ice melting/formation also contribute in the marginal seas and seasonal sea ice extent regions. In the central Arctic Ocean, especially the Canadian Basin, the decadal halosteric effect dominates sea level variability. Satellite altimetric observations and Gravity Re-covery and Climate Experiment (GRACE) measurements could be used to infer freshwater content changes in the Canadian Basin at periods longer than one year. The increasing number of profiles seems to capture fresh-water content changes since 2007, encouraging further data synthesis work with a more complicated interpola-tion method. Further, in-situ hydrographic observations should be enhanced to reveal the freshwater budget and close the gaps between satellite altimetry and GRACE, especially in the marginal seas.

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