Search

Core IdeasIn the proposed model, the wilting point and porosity are a function of organic matter.In organic‐rich soil, the model improves accuracy of the microwave radiative transfer model.The model is applicable for both portable and satellite soil moisture sensors. Most dielectric mixing models have been developed for mineral soils without extensive consideration of organic matter (OM). In addition, when used for in situ measurement, most of these models focus only on the real part of the effective dielectric constant without the corresponding imaginary part. Organic matter fractions in soils are found globally (57%), with an especially significant amount in the boreal region (17%). Without proper consideration of OM in dielectric mixing models and subsequent microwave radiative transfer modeling, brightness temperature (TB) calculations may be erroneous. This would lead to uncertainties in the estimation of higher level products, such as soil moisture retrievals from portable soil moisture sensors (e.g., time‐domain reflectometers) or passive microwave sensors onboard the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Microwave Scanning Radiometer (AMSR2) satellites. We incorporated OM into a dielectric mixing model by adjusting the wilting point and porosity according to the OM content, i.e., the effective soil dielectric constant decreases with higher OM due to a decrease in the fraction of free water and an increase in bound water. With the proposed soil parameters in the dielectric mixing model, high levels of OM increase the TB for a specific soil moisture by decreasing the microwave effective dielectric constant. The simulated TB better reproduced SMAP‐observed TB (11% in RMSE) through the improvement of the effective dielectric constant (40% reduction in RMSE). We anticipate that the application of our approach can improve microwave‐based surface soil moisture retrievals in areas with high OM. [ABSTRACT FROM AUTHOR]

For regional weather forecasts and climate predictions, it is important to determine the optimal domain size, location, and top height. A wide model domain can be chosen to avoid noises from lateral boundaries but this can include the Tibetan Plateau and areas of northern Manchuria to the Kamchatka Peninsula in Northeast Asia. This study shows that topographic regions around the Tibetan Plateau and warm pool areas over the Manchuria in an extended model domain may have harmful effects on the accuracy of short‐ to medium‐range regional predictions on the downwind side in spring. The inaccuracy is related to model errors due to steep terrain regions in the Tibetan Plateau and cold bias in the lower stratosphere north of Manchuria. Well‐designed spectral nudging over the eastern flank of the Tibetan Plateau and the use of a higher model top are found to improve regional predictions for Northeast Asia in spring by effectively eliminating errors associated with steep topography and temperature biases in the upper troposphere and lower stratosphere, respectively. Our findings suggest possible ways to mitigate biases due to steep mountains and upper‐level processes in regional modeling. We discuss the role of our method in terms of uncertainties in regional weather forecasts and climate predictions. [ABSTRACT FROM AUTHOR]