Evaluating impacts of observations on the skill of numerical weather prediction (NWP) is important. The Ensemble Forecast Sensitivity to Observation (EFSO) provides an efficient approach to diagnosing observation impacts, quantifying how much each observation improves or degrades a subsequent forecast with a given verification reference. This study investigates the sensitivity of EFSO impact estimates to the choice of the verification reference, using a global NWP system consisting of the Non‐hydrostatic Icosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). The EFSO evaluates observation impacts with the moist total energy norm and with recently proposed observation‐based verification metrics. The results show that each type of observation mainly contributes to the improvement of forecast departures of the observed variable maybe due to the limitation of localization in the EFSO. The EFSO overestimates the fraction of beneficial observations when verified with subsequent analyses, especially for shorter lead times such as 6 h. We may avoid this overestimation to some extent by verifying with observations, analyses from other data assimilation (DA) systems, or analyses of an independent run with the same DA system. In addition, this study demonstrates two important issues possibly leading to overestimating observation impacts. First, observation impacts would be overestimated if we apply relaxation‐to‐prior methods to the initial conditions of the ensemble forecasts in the EFSO; therefore, the ensemble forecasts in the EFSO should be independent of the ensemble forecasts in the DA cycle. Second, deterministic baseline forecasts of the EFSO, which represent the forecast without DA, should be initialized by the ensemble mean of the first guess at the analysis time, not by the previous analysis.

Over the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, precipitation nowcasting based on spatiotemporal extrapolation tends to provide a better precipitation forecast at shorter lead times with much less computation. Therefore, merging the precipitation forecasts from the NWP and extrapolation systems would be a viable approach to quantitative precipitation forecast (QPF). Although the optimal weights between the NWP and extrapolation systems are usually defined as a global constant, the weights would vary in space, particularly for global QPF. This study proposes a method to find the optimal weights at each location using the local threat score (LTS), a spatially localized version of the threat score. We test the locally optimal weighting with a global NWP system composed of the local ensemble transform Kalman filter and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM-LETKF). For the extrapolation system, the RIKEN’s global precipitation nowcasting system called GSMaP_RNC is used. GSMaP_RNC extrapolates precipitation patterns from the Japan Aerospace Exploration Agency (JAXA)’s Global Satellite Mapping of Precipitation (GSMaP). The benefit of merging in global precipitation forecast lasts longer compared to regional precipitation forecast. The results show that the locally optimal weighting is beneficial.

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The ensemble Kalman Filter (EnKF) and the 4D variational method (4DVar) are the most commonly used filters and smoothers in atmospheric science. These methods typically approximate prior densities using a Gaussian and solve a linear system of equations for the posterior mean and covariance. Therefore, strongly nonlinear model dynamics and measurement operators can lead to bias in posterior estimates. To improve the performance in nonlinear regimes, minimization of the 4DVar cost function typically follows multiple sets of iterations, known as an “outer loop”, which helps reduce bias caused by linear assumptions. Alternatively, “iterative ensemble methods” follow a similar strategy of periodically re-linearizing model and measurement operators. These methods come with different, possibly more appropriate, assumptions for drawing samples from the posterior density, but have seen little attention in numerical weather prediction (NWP) communities. Lastly, particle filters (PFs) present a purely Bayesian filtering approach for state estimation, which avoids many of the assumptions made by the above methods. Several strategies for applying localized PFs for NWP have been proposed very recently. The current study investigates intrinsic limitations of current data assimilation methodology for applications that require nonlinear measurement operators. In doing so, it targets a specific problem that is relevant to the assimilation of remotely-sensed measurements, such as radar reflectivity and all-sky radiances, which pose challenges for Gaussian-based data assimilation systems. This comparison includes multiple data assimilation approaches designed recently for nonlinear/non-Gaussian applications, as well as those currently used for NWP.