scholarly journals Soil moisture initialization for climate prediction: Characterization of model and observation errors

2005 ◽  
Vol 110 (D13) ◽  
Author(s):  
Wenge Ni-Meister
Keyword(s):  
Soil Moisture ◽  
Climate Prediction ◽  
Soil Moisture Initialization

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

Statistical characterization of the spatial variability of soil moisture in a cutover peatland

2004 ◽  
Vol 18 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Richard M. Petrone ◽  
J. S. Price ◽  
S. K. Carey ◽  
J. M. Waddington
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Statistical Characterization

A NOVEL NMR INSTRUMENT FOR CHARACTERIZATION OF SOIL MOISTURE

2013 ◽  
Author(s):  
David Walsh ◽  
Elliot Grunewald ◽  
Hong Zhang
Keyword(s):  
Soil Moisture

scholarly journals Characterization of Rape Field Microwave Emission and Implications to Surface Soil Moisture Retrievals

2012 ◽  
Vol 4 (1) ◽  
pp. 247-270 ◽  
Author(s):  
Florian Schlenz ◽  
Joachim Fallmann ◽  
Philip Marzahn ◽  
Alexander Loew ◽  
Wolfram Mauser
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Microwave Emission ◽  
Surface Soil Moisture

Multi-scale assimilation of SMAP data: comparison between land surface and land surface-subsurface model

2020 ◽  
Author(s):  
Haojin Zhao ◽  
Roland Baatz ◽  
Carsten Montzka ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Simulation Model ◽  
Land Surface ◽  
Cosmic Ray ◽  
Remotely Sensed ◽  
Surface Model ◽  
Systematic Bias

<p>Soil moisture plays an important role in the coupled water and energy cycles of the terrestrial system. However, the characterization of soil moisture at the large spatial scale is far from trivial. To cope with this challenge, the combination of data from different sources (in situ measurements by cosmic ray neutron sensors, remotely sensed soil moisture and simulated soil moisture by models) is pursued. This is done by multiscale data assimilation, to take the different resolutions of the data into account. A large number of studies on the assimilation of remotely sensed soil moisture in land surface models has been published, which show in general only a limited improvement in the characterization of root zone soil moisture, and no improvement in the characterization of evapotranspiration. In this study it was investigated whether an improved modelling of soil moisture content, using a simulation model where the interactions between the land surface, surface water and groundwater are better represented, can contribute to extracting more information from SMAP data. In this study over North-Rhine-Westphalia, the assimilation of remotely sensed soil moisture from SMAP in the coupled land surface-subsurface model TSMP was tested. Results were compared with the assimilation in the stand-alone land surface model CLM. It was also tested whether soil hydraulic parameter estimation in combination with state updating could give additional skill compared to assimilation in CLM stand-alone and without parameter updating. Results showed that modelled soil moisture by TSMP did not show a systematic bias compared to SMAP, whereas CLM was systematically wetter than TSMP. Therefore, no prior bias correction was needed in the data assimilation. The results illustrate how the difference in simulation model and parameter estimation result in significantly different estimated soil moisture contents and evapotranspiration.  </p>

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