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The prevalent soil moisture probe algorithms are based on a polynomial function that does not account for the variability in soil organic matter. Users are expected to choose a model before application: either a model for mineral soil or a model for organic soil. Both approaches inevitably suffer from limitations with respect to estimating the volumetric soil water content in soils with a wide range of organic matter content. In this study, we propose a new algorithm based on the idea that the amount of soil organic matter (SOM) is related to major uncertainties in the in situ soil moisture data obtained using soil probe instruments. To test this theory, we derived a multiphase inversion algorithm from a physically based dielectric mixing model capable of using the SOM amount, performed a selection process from the multiphase model outcomes, and tested whether this new approach improves the accuracy of soil moisture (SM) data probes. The validation of the proposed new soil probe algorithm was performed using both gravimetric and dielectric data from the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12). The new algorithm is more accurate than the previous soil-probe algorithm, resulting in a slightly improved correlation (0.824 to 0.848), 12 % lower root mean square error (RMSE; 0.0824 to 0.0727 cm 3 cm -3), and 95 % less bias (-0.0042 to 0.0001 cm 3 cm -3). These results suggest that applying the new dielectric mixing model together with global SOM estimates will result in more reliable soil moisture reference data for weather and climate models and satellite validation. [ABSTRACT FROM AUTHOR]

We developed the height correction algorithm based on the Monin–Obukhov similarity theory for application in Weather Research and Forecasting (WRF) model outputs to correct the 10-m height definition and match weather station observations. Thirty simulations were conducted for April 2012 to validate the height correction algorithm against observations such as the automated surface observing system and automatic weather stations. The WRF model simulations were constructed with two nested domains with horizontal grid spacings of 10 and 1 km. The root mean square error of the diagnosed 10-m wind speed after applying the height correction algorithm was consistently reduced compared to the one before applying the algorithm over the innermost domain during the analysis period. The impact of the height correction on diagnosed wind speed was greater when the environment was unstable and the magnitude of the roughness length was large. Evaluation showed that a strong synoptic forcing condition results in a significant reduction in the bias of wind speed. • Developed the height correction algorithm based on Monin–Obukhov similarity theory. • The 10-m height definition in the model is corrected to match the height of observation. • Alleviated positive bias in wind speed of weather research and forecasting model. [ABSTRACT FROM AUTHOR]