The local ensemble transform Kalman filter (LET-KF) is implemented with the Weather Research and Forecasting (WRF) model, and real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies. The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble members improves the analyses consistently.

We investigated the horizontal resolution dependence of atmospheric radionuclide (Cs-137) simulations of the Fukushima nuclear accident on March 15, 2011. We used Eulerian and Lagrangian transport models with low-(15-km), medium-(3-km), and high-(500-m) resolutions; both models were driven by the same meteorological analysis that was prepared by our data assimilation system (NHM-LETKF) for each horizontal resolution. This preparation was necessary for the resolution-dependent investigation, excluding any interpolation or averaging of meteorological fields. In the results, the 15-km grid analysis could not reproduce Fukushima's mountainous topography in detail, and consequently failed to depict a complex wind structure over mountains and valleys. In reality, the Cs-137 plume emitted from the Fukushima Daiichi Nuclear Power Plant (FDNPP) was mostly blocked by Mt. Azuma and other mountains along the Naka-dori valley after crossing over Abukuma Mountains on March 15, 2011. However, the 15-km grid simulations could not represent the blockage of the Cs-137 plume, which unnaturally spread through the Naka-dori valley. In contrast, the 3-km and 500-m grid simulations produced very similar Cs-137 concentrations and depositions, and successfully produced the plume blockage and deposition along the Naka-dori valley. In conclusion, low-resolution (15-km grid or greater) atmospheric models should be avoided for assessing the Fukushima nuclear accident when a regional analysis is needed. Meanwhile, it is reasonable to use 3-km grid models instead of 500-m grid models due to their similarities and the high computational burden of 500-m grid model simulations.

This study seeks to improve forecasts of local severe weather events through data assimilation and ensemble forecasting approaches using the local ensemble transform Kalman filter (LETKF) implemented with the Japan Meteorological Agency's nonhydrostatic model (NHM). The newly developed NHM-LETKF contains an adaptive inflation scheme and a spatial covariance localization scheme with physical distance, and it permits a one-way nested analysis in which a finer-resolution LETKF is conducted by using the output of an outer model. These new features enhance the potential of the LETKF for convective-scale events. The NHM-LETKF was applied to a local severe rainfall event in Japan during 2012. Comparison of the root-meansquare errors between the model first guess and analysis showed that the system assimilated observations appropriately. Analysis ensemble spreads indicated a significant increase around the time torrential rainfall occurred, implying an increase in the uncertainty of environmental fields. Forecasts initialized with LETKF analyses successfully captured intense rainfalls, suggesting that the system could work effectively for local severe weather events. Investigation of probabilistic forecasts by ensemble forecasting indicated that this could become a reliable data source for decision making in the future. A one-way nested data assimilation scheme was also tested. The results demonstrated that assimilation with a finer-resolution model improved the precipitation forecasting of local severe weather conditions.

This study examines the advantages of infrared all-sky radiance (ASR) assimilation over traditional clear-sky radiance (CSR) assimilation using a mesoscale LETKF data assimilation system. To effectively assimilate ASR data from the Himawari-8 geostationary satellite, a cloud-dependent quality-control procedure and an observation error model were developed. A single humidity band to be assimilated and thinning distance were determined based on observation error statistics. The operational pre-processing and parameter settings, such as observation errors and an adaptive bias correction for CSR, were incorporated into the LETKF data assimilation system. A comparison of the impacts of assimilating ASRs and CSRs was accomplished using single-cycle and 10-day cycle assimilation experiments. Study results revealed that ASR assimilation provided a higher degree of improvement in the first-guess fit for conventional observations and satellite retrievals with respect to temperature, moisture and wind. Furthermore, ASR assimilation displayed a more stable improvement in the prediction of a severe rainfall event because it has more universal data coverage than CSR. Adaptive bias correction schemes with two different sets of predictors for ASRs were tested and revealed the difficulty in extracting additional information for the assimilation of no-bias corrected ASR due to complicated bias factors. This was in contrast to the CSR assimilation, where bias correction had a positive impact. KEYWORDS all-sky infrared radiance, data assimilation, Himawari-8 1 Q J R Meteorol Soc. 2019;145:745-766.wileyonlinelibrary.com/journal/qj

Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing and “big data” from the next-generation phased array weather radar that produces a high-resolution 3-dimensional rain distribution every 30 s—two orders of magnitude more data than the currently used parabolic-antenna radar. This “big data assimilation” system refreshes 30-min forecasts every 30 s, 120 times more rapidly than the typical hourly updated systems operated at the world’s weather prediction centers. A real high-impact weather case study shows encouraging results of the 30-s-update big data assimilation system.

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 geostationary satellite, Himawari‐8, on convective predictability. We implemented the real‐data experiment in which Himawari‐8 all‐sky moisture‐sensitive infrared radiances of band 8 (6.2 μm) and band 10 (7.3 μm) were assimilated into the high‐resolution (2 km) limited area model, Japan Meteorological Agency's Non‐Hydrostatic Model, every 10 min by the Local Ensemble Transform Kalman Filter. The frequent infrared observations from Himawari‐8 improve the analysis and forecast of isolated convective cells and sudden local severe rainfall induced by weak large‐scale forcing. The results imply that satellite data assimilation can contribute to better forecasting severe weather events in smaller spatiotemporal scales than the previous studies.

On 28 July, 2008, a local heavy rainfall occurred over the Hokuriku and Kinki districts, in central Japan. A stationary front existed near the heavy rainfall areas, and the atmospheric instability was increased by the inflow of the upper-level cold air.The operational mesoscale model (MSM) of the Japan Meteorological Agency (JMA) could not predict the heavy rainfall. The comparison of distributions of precipitable water vapor (PWV) between the initial condition of the MSM and the Global Positioning System (GPS) revealed that the MSM initial condition underestimated water vapor in the heavy rainfall areas because of a positional lag of a low-level convergence zone.Data assimilation experiments of GPS-derived PWV were conducted with JMA's mesoscale 4 dimensional variational assimilation system (Meso 4D-Var). The PWV derived from the nationwide ground GPS network (GPS Earth Observation NETwork: GEONET) improved both the northward position error of the low-level convergence zone and the forecast of the observed rainfall. Moreover, further improvements were obtained when the PWV derived from the GPS stations of the International GNSS Service in East Asia were added.

The regional model‐based Mesoscale Ensemble Prediction System (MEPS) has been operational since June 2019 at the Japan Meteorological Agency (JMA). The primary objective of the newly operational MEPS is to provide uncertainty information for JMA's operational regional model, Mesoscale Model (MSM), which provides information to support disaster prevention and aviation safety. This article describes MEPS in detail and discusses issues to be addressed in the future. For effective evaluation of uncertainties in MSM, the forecast model in MEPS is configured in the same way as that in MSM, except for the initial and lateral boundary conditions. Initial perturbations for all 20 ensemble runs are generated by a linear combination of singular vectors (SVs) with three different spatial and temporal resolutions, with the aim of capturing multi‐scale uncertainties in the initial conditions simultaneously. The SVs from a global model are also used as lateral boundary perturbations to ensure consistency between the initial and boundary conditions of each ensemble member. The verification results showed that MEPS achieved the expected performance of an ensemble prediction system: the ensemble mean outperformed the control forecast with a good spread–skill relationship; moreover, the skill scores of probabilistic precipitation forecasts were evaluated as valid for rainfall of up to 30 mm·(3 hr)−1. In an additional experiment conducted without using the two smaller‐scale initial perturbations, the skill was substantially reduced compared with that of the original MEPS, especially for larger precipitation thresholds. Therefore, the smaller‐scale perturbations were essential to capture uncertainties associated with local heavy rainfall events.