Yohei Sawada

Abstract Improving the predictability of sudden local severe weather is a grand challenge for numerical weather prediction. Recently, the capability of geostationary satellites to observe infrared radiances has been significantly improved, and it is expected that the “Big Data” from the new generation geostationary satellites could contribute to improving convective predictability. We examined the potential impacts of assimilating frequent infrared observations from a new generation geostationar

Abstract Despite the importance of the coupling between vegetation dynamics and root‐zone soil moisture in land‐atmosphere interactions, there is no land data assimilation system (LDAS) that currently addresses this issue, limiting the capacity to positively impact weather and seasonal forecasting. We develop a new LDAS that can improve the skill of an ecohydrological model to simulate simultaneously surface soil moisture, root‐zone soil moisture, and vegetation dynamics by assimilating passive

Abstract To improve the skill of reproducing land‐atmosphere interactions in weather, seasonal, and climate prediction systems, it is necessary to simulate correctly and simultaneously the surface soil moisture (SSM) and terrestrial biomass in land surface models. Despite the performance of hydrological and ecosystem models depends highly on parameter calibration, a method for parameter estimation in ungauged areas has yet to be established. We develop an autocalibration system that can simultan

We present a field-verified algorithm for retrieving vegetation water content (VWC), which is the mass of water in vegetation tissue per ground area, using observed microwave brightness temperatures (TBs). We can use 6.925and 10.65-GHz microwave observations to minimize the species dependence of the relationship between vegetation optical depth (VOD) and VWC. Then, we can easily estimate the VWC after obtaining the VOD. Although the VOD retrieved at these frequencies is highly affected by uncert

Abstract Despite the importance of the ecological and agricultural aspects of severe droughts, no drought monitoring and prediction framework based on a land data assimilation system (LDAS) has been developed to monitor and predict vegetation dynamics in the middle of droughts. In this study, we applied a LDAS that can simulate surface soil moisture, root‐zone soil moisture, and vegetation dynamics to the Horn of Africa drought in 2010–2011 caused by the precipitation deficit in two consecutive

Despite the importance of the interaction between soil moisture and vegetation dynamics to understand the complex nature of drought, few land reanalyses explicitly simulate vegetation growth and senescence. In this study, I provide a new land reanalysis which explicitly simulates the interaction between sub-surface soil moisture and vegetation dynamics by the sequential assimilation of satellite microwave brightness temperature observations into a land surface model (LSM). Assimilating satellite

Abstract Drought severely damages water and agricultural resources, and both hydrological and ecological responses are important for its understanding. First, precipitation deficit induces soil moisture deficiency and high plant water stress causing agricultural droughts. Second, hydrological drought characterized by deficit of river discharge and groundwater follows agricultural drought. However, contributions of vegetation dynamics to these processes at basin scale have not been quantified. To

Abstract. To improve the efficiency of flood early warning systems (FEWS), it is important to understand the interactions between natural and social systems. The high level of trust in authorities and experts is necessary to improve the likeliness of individuals to take preparedness actions responding to warnings. Despite many efforts to develop the dynamic model of human and water in socio-hydrology, no socio-hydrological models explicitly simulate social collective trust in FEWS. Here, we deve

Abstract The prolonged Millennium drought in southeast Australia (2001–2009) provides a unique opportunity to analyze the responses of a semiarid ecosystem to severe droughts. In this paper, we analyzed vegetation dynamics in the Millennium drought using visible/infrared observations, passive microwave observations, and a simple ecohydrological model. The satellite observations indicated that the ecosystem maintained its greenness in the Millennium drought, although the total aboveground biomass

Abstract We examine the potential of assimilating river discharge observations into the atmosphere by strongly coupled river‐atmosphere ensemble data assimilation. The Japan Meteorological Agency's Non‐Hydrostatic atmospheric Model (JMA‐NHM) is first coupled with a simple rainfall‐runoff model. Next, the local ensemble transform Kalman filter is used for this coupled model to assimilate the observations of the rainfall‐runoff model variables into the JMA‐NHM model variables. This system makes it

Abstract. In socio-hydrology, human–water interactions are simulated by mathematical models. Although the integration of these socio-hydrological models and observation data is necessary for improving the understanding of human–water interactions, the methodological development of the model–data integration in socio-hydrology is in its infancy. Here we propose applying sequential data assimilation, which has been widely used in geoscience, to a socio-hydrological model. We developed particle fil

Agricultural drought monitoring and prediction technology are urgently needed. We applied an ecohydrological land data assimilation system (LDAS), which can simulate soil moisture and leaf area index (LAI) by data assimilation of microwave brightness temperature into a land surface model (LSM), to monitor and predict agricultural droughts in North Africa. We successfully monitor nationwide crop failures, which are characterized by the declines of the nationwide wheat production, in Morocco, Alge

Uncertainty in surface soil roughness strongly degrades the performance of surface soil moisture (SSM) and vegetation water content (VWC) retrieval from passive microwave observations. This paper proposes an algorithm to objectively determine the surface soil roughness parameter of the radiative transfer model by fusing microwave and optical satellite observations. It is then demonstrated in a semiarid in situ observation site. The roughness correction of this new algorithm positively impacted t

Abstract. In socio-hydrology, human-water interactions are simulated by mathematical models. Although the integration of these socio-hydrologic models and observation data is necessary to improve the understanding of the human-water interactions, the methodological development of the model-data integration in socio-hydrology is in its infancy. Here we propose to apply sequential data assimilation, which has been widely used in geoscience, to a socio-hydrological model. We developed particle filt

In this paper, we implemented the in-situ observation of surface soil moisture (SSM), vegetation water content (VWC), and microwave brightness temperatures. By analyzing this in-situ observation dataset and the numerical simulation, we investigated the source of the uncertainty of the current algorithms for Advanced Microwave Scanning Radiometer for Earth observation system (AMSR-E) and AMSR2 to retrieve SSM and vegetation dynamics. Our findings are: (1) the microwave radiative transfer at C-ban