Le Duc

Experiments with two ensemble systems of resolutions 10 km (MF10km) and 2 km (MF2km) were designed to examine the value of cloud-resolving ensemble forecast in predicting precipitation on small spatio-temporal scales. Since the verification was performed on short-term precipitation at high resolution, uncertainties from small-scale processes caused the traditional verification methods to be inconsistent with the subjective evaluation. An extended verification method based on the Fractions Skill

Abstract. The Nash–Sutcliffe efficiency (NSE) is a widely used score in hydrology, but it is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for the NSE from the viewpoint of signal progressing. Thus, a simulation is viewed as a received signal containing a wanted signal (observations) contaminated by an unwanted signal (noise). This vie

Forecast performances of the July 2020 Kyushu heavy rain have been revisited with the aim of improving the forecasts for this event. While the Japan Meteorological Agency's (JMA) deterministic forecasts were relatively good, the JMA's ensemble forecasts somehow missed this event. Our approach is to introduce flow-dependence into assimilation by running a 1000-member local ensemble transform Kalman filter (LETKF1000) to extract more information from observations and to better quantify forecast un

Abstract We propose a unifying theory for covariance inflation (CI) in the Ensemble Kalman Filter (EnKF) that encompasses all existing CI methods and can explain many open problems in CI. Each CI method is identified with an inflation function that alters analysis perturbations through their singular values. Inflation functions are usually considered as functions of singular values of background or analysis perturbations. However, we have shown that it is more fruitful if inflation functions are

Data assimilation experiments on Myanmar tropical cyclone (TC), Nargis, using the Local Ensemble Transform Kalman Filter (LETKF) method and the Japan Meteorological Agency (JMA) non-hydrostatic model (NHM) were performed to examine the impact of LETKF on analysis performance in real cases. Although the LETKF control experiment using NHM as its driving model (NHM–LETKF) produced a weak vortex, the subsequent 3-day forecast predicted Nargis’ track and intensity better than downscaling from JMA’s g

Verification in the presence of observation errors is approached from the Bayesian point of view. Like data assimilation (DA), Bayesian verification is shown to have a robust foundation established by Bayesian inference. Together, DA and Bayesian verification form two difference levels of Bayesian inference. Evaluation of a model is equivalent to inference on the plausibility of this model given observations. Relative performances between different models are measured by ratios of posterior plau

Abstract. The Nash-Sutcliffe efficiency (NSE) is a widely used score in hydrology but is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. Thus, a forecast is viewed as a received signal containing a wanted signal (observations) contaminated by an unwanted signal (noise). This view underlin

It is well-known in rainfall ensemble forecasts that ensemble means suffer substantially from the diffusion effect resulting from the averaging operator. Therefore, ensemble means are rarely used in practice. The use of the arithmetic average to compute ensemble means is equivalent to the definition of ensemble means as centers of mass or barycenters of all ensemble members where each ensemble member is considered as a point in a high-dimensional Euclidean space. This study uses the limitation o

Abstract Part 1 of this study shows the existence of a unifying theory that encompasses all exiting covariance inflation (CI) methods under a framework of inflation functions operating on the eigenvalues of the ensemble transform matrix. Given a vast space of potential inflation functions, a natural question is how to choose the optimal one. This part 2 shows how adaptive inflation can be implemented in the context of inflation functions to estimate inflation functions on‐the‐fly from observatio

Abstract In the hybrid variational–ensemble data assimilation schemes preconditioned on the square root of background covariance , is a linear map from the model space to a higher-dimensional space. Because of the use of the nonsquare matrix , the transformed cost function still contains the inverse of . To avoid this inversion, all studies have used the diagonal quadratic form of the background term in practice without any justification. This study has shown that this practical cost function be

In the ensemble transform Kalman filter (ETKF), an ensemble transform matrix (ETM) is a matrix that maps background perturbations to analysis perturbations. All valid ETMs are shown to be the square roots of the analysis error covariance in ensemble space that preserve the analysis ensemble mean. ETKF chooses the positive symmetric square root Ts as its ETM, which is justified by the fact that Ts is the closest matrix to the identity I in the sense of the Frobenius norm. Besides this minimum nor

Abstract In sensitivity analysis, ensemble sensitivity is defined as the regression coefficients resulting from a simple linear regression of changes of a response function on initial perturbations. One of the interpretations for ensemble sensitivity considers this a simplified version of regression-based adjoint sensitivity called univariate ensemble sensitivity whose derivation involves the so-called diagonal approximation. This approximation, which replaces the analysis error covariance matri

<strong class="journal-contentHeaderColor">Abstract.</strong> The Nash-Sutcliffe efficiency (NSE) is a widely used score in hydrology but is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. Thus, a forecast is viewed as a received signal containing a wanted signal (observations) contaminat

<strong class="journal-contentHeaderColor">Abstract.</strong> The Nash-Sutcliffe efficiency (NSE) is a widely used score in hydrology but is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. Thus, a forecast is viewed as a received signal containing a wanted signal (observations) contaminat

The Nash-Sutcliffe efficiency (NSE) is a widely used score in hydrology but is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. Thus, a forecast is viewed as a received signal containing a wanted signal (observations) contaminated by an unwanted signal (noise). This view underlines an impo