Covariance inflation plays an important role in the ensemble Kalman filter because ensemble‐based error variance is usually underestimated due to various factors such as limited size and model imperfections. Manual tuning of parameters by trial computationally expensive; therefore, several studies have proposed approaches adaptive estimation parameters. Among others, this study focuses on covariance relaxation method which realizes spatially dependent with a homogeneous parameter. This...

Abstract Japan's new geostationary satellite “Himawari‐8” enables updating precipitation and flood predictions as frequently every 10 min to capture early possible the risk associated with rapidly changing severe weather. This study focuses on advantage of frequent update by assimilating all‐sky Himawari‐8 infrared radiances in case September 2015 Kanto‐Tohoku heavy rainfall, a major flooding event Japan. The analyzed tropical cyclone representation moisture transport are improved data...

Abstract This study aims to propose two new approaches improve precipitation forecasts from numerical weather prediction (NWP) models through effective data assimilation of satellite‐derived precipitation. The is known be very difficult mainly because highly non‐Gaussian statistics variables. Following Lien et al., this addresses the non‐Gaussianity issue by applying Gaussian transformation (GT) based on empirical cumulative distribution function (CDF) We a method that constructs CDF with...

This paper is the first publication presenting predictability of record-breaking rainfall in Japan July 2018 (RJJ18), severest flood-related disaster since 1982. Of three successive precipitation stages RJJ18, this study investigates synoptic-scale third-stage using near-real-time global atmospheric data assimilation system named NEXRA. With NEXRA, intense western on 6 was well predicted 3 days advance. Comparing forecasts at different initial times revealed that rains tied to generation a...

Abstract. To date, many studies have performed numerical estimations of biomass production and agricultural water demand to understand the present future supply–demand relationship. A crop calendar (CC), which defines date or month when farmers sow harvest crops, is an essential input for estimations. This study aims a new global data set, SAtellite-derived CRop Agricultural simulations (SACRA), discuss advantages disadvantages compared existing census-based model-derived products. We...

Future river discharge in the Chao Phraya River basin was projected based on performance of multiple General Circulation Models (GCMs). We developed a bias-corrected future climate dataset termed IDD (IMPAC-T Driving Dataset) under which H08 hydrological model used to project discharge. The enabled us conduct projection that considered spread projections derived from GCMs. Multiple performance-based were obtained using correlation monsoon precipitation between GCMs and several observations....

Abstract. Model predictive control (MPC) is an optimization-based framework for linear and nonlinear systems. MPC estimates inputs by iterative optimization of a cost function that minimizes deviations from desired state while accounting costs over finite prediction horizon. This process typically involves direct computations in space through full model evaluations, making it computationally expensive high-dimensional study introduces ensemble (EnMPC), novel combines data assimilation. EnMPC...

We report that in several chaotic, high-dimensional nonlinear systems, the evolution of multiple ensembles starting from nearby initial conditions exhibits a transient low-dimensional distribution phase space. This is primarily achieved by stretching ensemble along unstable directions system's trajectories. Furthermore, we discuss potential using this to significantly reduce search space when controlling future states. As concrete example system, use Lorenz 96 model under parameter setting...

The prediction and mitigation of extreme weather events are important challenges in science society. Recently, Miyoshi colleagues introduced the control simulation experiment framework to examine controllability chaotic systems under observational uncertainty. Using this framework, they developed a method reduce Lorenz 96 model by exploiting system’s sensitivity initial conditions, guiding trajectories toward desired outcomes with small inputs (Sun et al., Nonlin. Processes...

To support disaster prevention, it is essential to know in advance when scenarios start distinguish one from the others, thus requiring development of early detection methods such separations. Ensemble prediction systems has been developed provide evolutions via their ensemble members, because future state atmosphere predicted by a single member less meaningful than estimate probability density all members. By construction, primary function an system forecasters with degree uncertainty and...

  Severe rainfall events can cause significant harm to individuals, damage infrastructure, and result in substantial economic losses. If precipitation regulation could be realized, it help mitigate the risks of disasters. However, controlling remains a formidable challenge due highly complex uncertain dynamics weather systems. To address this, we propose novel control framework for management based on numerical prediction (NWP) model applied series warm bubble experiments, where...

Abstract. Prediction and mitigation of extreme weather events are important scientific societal challenges. Recently, Miyoshi Sun (2022) proposed a control simulation experiment framework that assesses the controllability chaotic systems under observational uncertainty, within this framework, et al. (2023) developed method to prevent in Lorenz 96 model. However, since their is primarily designed apply inputs all grid variables, success rate decreases approximately 60 % when applied single...

Abstract. Data assimilation (DA) has been successfully applied in paleoclimate reconstruction. DA combines model simulations and climate proxies based on their error sizes. Therefore, the information is crucial for to work optimally. However, little attention paid observation errors previous studies, especially when are assimilated directly. This study assessed feasibility of innovation statistics, a method developed numerical weather prediction, estimating reconstruction its impact skills....

Abstract. A tropical cyclone is a meteorological phenomenon that produces heavy rainfall, damaging winds, thunderstorms, storm surges, among others. This also system of complex interactions between local sea-surface temperatures vertical atmospheric conditions, such as shear and regional steering flows. single discipline cannot rise to the challenge posed by understanding mechanisms governing birth, maturity, decay cyclone. Collaborative work earth science other disciplines can address...

Evaluating impacts of observations on the skill numerical weather prediction (NWP) is important. The Ensemble Forecast Sensitivity to Observation (EFSO) provides an efficient approach diagnosing observation impacts, quantifying how much each improves or degrades a subsequent forecast with given verification reference. This study investigates sensitivity EFSO impact estimates choice reference, using global NWP system consisting Non‐hydrostatic Icosahedral Atmospheric Model (NICAM) and Local...

It is important to examine what future hydrological changes could occur as a result of climate change. In this study, we projected and their consistency under near-future end-of-21st-century in the Chao Phraya River Basin. Through simulations using output from six AOGCMs RCP 4.5 8.5 scenarios, have reached following conclusions. Our results demonstrate increase mid-rainy season precipitation climate, which necessary condition for large volume runoff late rainy season. Under all showed (>...

Abstract This study aims to improve precipitation forecasts by estimating model parameters of a numerical weather prediction with an ensemble‐based data assimilation method. We implemented the parameter estimation algorithm into global atmospheric system NICAM‐LETKF, which incorporates Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and Local Ensemble Transform Kalman Filter (LETKF). estimated globally uniform large‐scale condensation scheme known as B 1 Berry's parameterization....

Abstract This study proposes using data assimilation (DA) for climate research as a tool optimizing model parameters objectively. Mitigating radiation bias is very important change assessments with general circulation models. With the Nonhydrostatic ICosahedral Atmospheric Model (NICAM), this estimated an autoconversion parameter in large‐scale condensation scheme. We investigated two approaches to reducing bias: examining useful satellite observations estimation and exploring advantages of...

With the serial treatment of observations in ensemble Kalman filter (EnKF), assimilation order is usually assumed to have no significant impact on analysis accuracy. However, Nerger derived that analyses with different orders are if covariance localization applied observation space. This study explores whether can be optimized systematically improve estimates. A mathematical demonstration a simple two-dimensional case indicates cause analyses, although differences two magnitude smaller than...

Abstract Over the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, nowcasting based on spatiotemporal extrapolation tends to provide a better forecast at shorter lead times with much less computation. Therefore, merging from NWP and systems would be viable approach quantitative (QPF). Although optimal weights between are usually defined as global constant, vary in space, particularly for QPF. This study proposes method...

The predefined threshold is one of the main fundamental and methodological challenges underlying uncertainty delayed release existing burned area (BA) products. To improve accuracy timeliness BA mapping, a tuning-free moderate-scale detection algorithm (AT&RF algorithm) was proposed to carry out rapid mapping for immediate post-fire assessment. A case study carried at Chornobyl Exclusion Zone (ChEZ) assess performance algorithm. evaluation results indicated that successfully detected in 2015...