Abstract Improving the predictability of sudden local severe weather is a grand challenge for numerical prediction. Recently, capability geostationary satellites to observe infrared radiances has been significantly improved, and it expected that “Big Data” from new generation could contribute improving convective predictability. We examined potential impacts assimilating frequent observations satellite, Himawari‐8, on implemented real‐data experiment in which Himawari‐8 all‐sky...

This study examines the advantages of infrared all‐sky radiance (ASR) assimilation over traditional clear‐sky (CSR) using a mesoscale LETKF data system. To effectively assimilate ASR from Himawari‐8 geostationary satellite, cloud‐dependent quality‐control procedure and an observation error model were developed. A single humidity band to be assimilated thinning distance determined based on statistics. The operational pre‐processing parameter settings, such as errors adaptive bias correction...

Abstract Despite the importance of 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 capacity to positively impact weather seasonal forecasting. We develop a new LDAS can improve skill an ecohydrological model simulate simultaneously surface moisture, by assimilating passive microwave observations are sensitive both terrestrial biomass. This first...

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

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

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

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

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

The performance of land surface models (LSMs) significantly affects the understanding atmospheric and related processes. Many LSMs' soil vegetation parameters were unknown so that it is crucially important to efficiently optimize them. Here I present a globally applicable computationally efficient method for parameter optimization uncertainty assessment LSM by combining Markov Chain Monte Carlo (MCMC) with machine learning. First, performed long-term (decadal scales) ensemble simulation LSM,...

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

Ensemble forecasting is a powerful tool for supporting informed decision-making in managing multi-hazard risks associated with tropical cyclones (TCs). Although TC ensemble forecasts are widely used operational numerical weather prediction systems, their potential disaster and management has not been fully exploited. Here we propose novel, efficient, practical method to extract meaningful Multi-Hazard Worst Case Scenarios (MHWCS) from large forecast of 1000-members. We performed the...

Despite the critical need for accurate flood prediction, water resource management, and climate impact planning, many regions—particularly in Asia, Africa, South America—face a significant lack of river discharge observation. Although numerous hydrological machine learning models have been proposed, it is still grand challenge to achieve rainfall-runoff modeling which accurate, interpretable, computationally cheap even under conditions with limited observation data. We...

Ensemble forecasting is a powerful tool for supporting informed decision-making in managing multi-hazard risks associated with tropical cyclones (TCs). While TC ensemble forecasts are widely utilized operational numerical weather prediction systems, their potential disaster remains underutilized. Here we propose novel, efficient, and practical method to extract meaningful worst case scenarios (MHWCS) from large forecast of 1000-members the first time. We perform simulation Hagibis (2019)...

Abstract Understanding tropical cyclone (TC) risk is crucial for societal resilience and aligns with the United Nations Sustainable Development Goals. Although analyzing ranking historical TCs helps assess their associated risk, an optimal method to combine multiple factors into a single measure still unclear. This makes it challenging disaster practitioners objectively overall from TCs. We address this gap by employing Pareto optimality—a novel approach evaluate rank meteorological, hazard,...

Multi-hazard events resulting from tropical cyclones (TC) involve the simultaneous or sequential occurrence of various destructive disasters, including storm surges, heavy rainfall, and strong winds, which collectively pose significant threats to society (Needham et al., 2015; Takagi 2022). While ensemble forecasts are widely employed in TC prediction, their potential for comprehensively assessing multi-hazard scenarios has been ignored (Titley 2019). In this study, we introduce Pareto...

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

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

This study integrates ecohydrological vegetation and multi-sector multi-region economic growth models to evaluate the impacts of drought on markets value water. The values several parameters agricultural production function are identified by applying leaf area indices that simulated model, AgriCLVDAS. three-sector three-region closed-economy model with functions both irrigable rainfed farmland as well stochastic process precipitation availability river water formulated analyze rent GDP in...

Abstract Although the interactions between soil moisture (SM) and vegetation dynamics have been extensively investigated, most of previous findings are derived from satellite-observed and/or model-simulated SM data, which inevitably include multiple sources error. With effort many field workers researchers in in-situ measurement data integration, it is now possible to obtain integrated dataset global range. Here we used International Soil Moisture Network analyze anomaly correlation leaf...

Abstract Ensemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used operational numerical weather prediction systems, their potential for disaster has not been maximized. Here we present novel, efficient, and practical method utilize large forecast 1,000 members analyze surge scenarios toward effective decision such as evacuation planning issuing warnings. We perform...

Uncertainty in surface soil roughness strongly degrades the performance of moisture (SSM) and vegetation water content (VWC) retrieval from passive microwave observations. This paper proposes an algorithm to objectively determine parameter radiative transfer model by fusing optical satellite It is then demonstrated a semiarid situ observation site. The correction this new positively impacted SSM (root-mean-square error reduced 0.088 0.070) VWC Advanced Microwave Scanning Radiometer 2...

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 understanding interactions, methodological development model–data in socio-hydrology its infancy. Here we propose applying sequential assimilation, which has been widely used geoscience, to a model. We developed particle filtering adopted flood risk model performed an idealized system...

Abstract Coupled data assimilation (CDA) has been attracting researchers' interests to improve Earth system modeling. The CDA methods are classified into two: weakly coupled (wCDA), which considers cross‐compartment interaction only in a forecast phase, and strongly (sCDA), additionally uses other compartment's information an analysis phase. Although sCDA can theoretically provide better estimates than wCDA since fully inter‐compartment covariances, the potential of practice is still debate....

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

Abstract. Prediction of spatiotemporal chaotic systems is important in various fields, such as numerical weather prediction (NWP). While data assimilation methods have been applied NWP, machine learning techniques, reservoir computing (RC), recently recognized promising tools to predict systems. However, the sensitivity skill machine-learning-based imperfectness observations unclear. In this study, we evaluate RC with noisy and sparsely distributed observations. We intensively compare...