Akira Yamazaki

The stratospheric quasi-biennial oscillation (QBO) is predictable many months in advance, making it a potential source of seasonal predictability. However, identifying its impacts on the troposphere remains challenging because QBO-related signals are often masked by the stronger influence of the El Niño–Southern Oscillation (ENSO). Statistical detection is difficult because of QBO–ENSO aliasing, whereas idealized numerical model experiments often lack direct observational support. To address this issue, we present a novel framework, termed the Adaptive Signal Isolation Hindcast (ASH). For each forecast case, two hindcast experiments are performed with either the observed QBO phase or the opposite phase imposed during model integration. By differencing the resulting forecasts, the QBO response can be separated from non-QBO variability while allowing direct comparison with observations for each forecast case. Using this framework, we identify an Indian Ocean-sector pathway through which the QBO influences the Asian summer monsoon and the Walker circulation during boreal midsummer. During the westerly QBO phase, enhanced shear instability associated with the monsoon easterly jet allows warm lower-stratospheric anomalies to extend downward over the Indian Ocean–Maritime Continent region. The resulting upper-tropospheric warming is redistributed toward the off-equatorial regions through a Matsuno–Gill-type response, increasing atmospheric stability and weakening the large-scale monsoon circulation. In regions influenced by this mechanism, forecasts with the observed QBO phase exhibit improved agreement with the reanalysis. These findings highlight the importance of improving QBO representation in numerical models and leveraging QBO information in seasonal forecasting, particularly when atmospheric predictability driven by sea surface temperature anomalies is limited.

Storm‐resolving models have the potential to outperform conventional models with a horizontal resolution of 150 km in simulating global atmospheric circulation by resolving deep convection and gravity waves. Recent studies have suggested that the long‐standing “too zonal and too equatorward” storm‐track bias in climate models can be mitigated by enhancing the horizontal resolution. This study investigates the horizontal structures of storm tracks using multiple global storm‐resolving models with a resolution of 5 km or finer under a realistic land‐sea configuration focusing on the winter of 2020. Storm‐resolving models substantially mitigate the long‐standing storm‐track biases in the lower‐tropospheric North Atlantic, but appear to introduce a northward‐displacement bias in the upper troposphere. In contrast, the long‐standing biases remain in the North Pacific, with only marginal improvements. These results highlight the importance of considering regional and vertical characteristics of storm tracks to reduce their biases in climate models.

To improve the forecast accuracy of numerical weather prediction, it is essential to obtain better initial conditions by combining simulations and available observations via data assimilation. It has been known that a part of observations degrade the forecast accuracy. Detecting and discarding such detrimental observations via proactive quality control (PQC) could improve the forecast accuracy. However, conventional methods for diagnosing observation impacts require future observations as a reference state and PQC cannot be real‐time in general. This study proposes using machine learning (ML) trained by a time series of analyses to obtain a reference state without future observations and enable real‐time ML‐based PQC. This study presents proof‐of‐concept using a low‐dimensional dynamical system. The results indicate that ML‐based and model‐based estimates of observation impacts are generally consistent. Furthermore, ML‐based real‐time PQC successfully improves the forecast accuracy compared to a baseline experiment without PQC.

Ensemble dynamical downscaling experiments were performed to investigate the influence of East Siberian blocking on a heavy snowfall event that occurred over Fukui City, Japan, in early February 2018 and was associated with the development of the Japan Sea Polar airmass Convergence Zone (JPCZ). The downscaling experiments simulated the enhancement of the East Asian cold air stream and its flow along two routes: the western route, which runs from the Eurasian Continent via the Yellow Sea and the Korean Peninsula; and the northern route, which originates in the Sea of Okhotsk and runs via the northern Japan Sea. As a result, the location and strength of the simulated JPCZ in the downscaling experiments are consistent with those in the Japanese regional reanalysis dataset. For the sensitivity experiments, the blocking that develops over East Siberia just prior to the formation of the JPCZ was removed, and the results indicate that the East Siberian blocking contributes significantly to JPCZ development by enhancing the East Asian cold air stream along the western route. Additional data analyses based on the 20-year reanalysis revealed that East Siberian blocking can enhance both the western and northern routes of the cold air streams.

This study conducts a thorough investigation into the behaviors of analysis ensemble spreads linked to stratospheric sudden warming (SSW) events. A stratosphere-resolving ensemble data assimilation system is used here to document the evolution of analysis spread leading up to a pair of warming events. Precursory signals of the increased ensemble spreads were found a few days prior to two SSW events that occurred during December 2018 and August–September 2019 in the Northern and Southern Hemispheres, respectively. The signals appeared in the upper and middle stratosphere and did not appear at lower heights. When the signals appeared, it was found that both tendency by forecast and analysis increment in a forecast-analysis (data assimilation) cycle simultaneously became large. An empirical orthogonal function analysis showed that the dominant structures of the precursory signals were equivalent barotropic and were 90° out-of-phase with the analysis ensemble-mean field. Over the same period, the upper and middle stratosphere became more susceptible to barotropic instability than in their previous states. We conclude that the differing growth of barotropically unstable modes across ensemble members can amplify spread during the lead-up to SSW events. Significance Statement: Winds in the winter stratospheric polar vortex are typically westerly. Occasionally, however, warming over the pole leads to a reversal of the flow through a process known as stratospheric sudden warming. These events are difficult to predict even in state-of-the-art analysis and forecasting systems. In this study, we identify a precursor signal in the form of increased ensemble spread that appears to originate from differing realizations of growing barotropic modes across the ensemble. This signal could serve as a useful forecasting tool by enhancing situational awareness in the lead-up to potential stratospheric sudden warming events.

This work assesses the contribution of assimilating AMSU-A satellite-based radiance measurements to a global data assimilation system based on an atmospheric general circulation model and the local ensemble transform Kalman filter (LETKF). The radiance measurements were from three channels that are sensitive to the upper troposphere and lower stratosphere. The contribution of these measurements, or AMSU-A observation impact, was estimated both through ensemble-based forecast sensitivity to observations (EFSO) and observing system experiments (OSEs). Two streams of data-denial experiments for the AMSU-A observations were performed for about one month during winter in each hemisphere. The OSEs quantified the accumulated observation impact by cycling (repeating) data denials: including AMSU-A observations reduced the total observation impact for all observations of each data assimilation cycle. In contrast, EFSO estimated AMSU-A to increase the total observation impact. The opposing effects were attributed to the accumulated observation impact in the OSEs; the accumulation could stabilize the data assimilation cycles. In both experiments, the accumulated observation impact of AMSU-A was strongest in the upper troposphere, particularly in the austral midlatitudes where westerly jets exist and observations of other types are sparse. EFSO also assessed AMSU-A to have the most beneficial observation impact in similar locations. The AMSU-A observation impact tended to accumulate just downstream of where EFSO estimated the beneficial observation impact signals. The accumulated AMSU-A observation impact was tied to dynamic processes in the upper-tropospheric and general stratospheric circulation. Therefore, EFSO helps estimate the beneficial distributions of AMSU-A accumulated observation impact by considering their dynamical propagation. Significance Statement: The Advanced Microwave Sounding Unit-A (AMSU-A) satellite radiance assimilation technique was successfully integrated into the Atmospheric General Circulation Model for the Earth Simulator (AFES)–LETKF data assimilation system. We conducted OSEs and used EFSO to assess the AMSU-A observation impact. The two estimation methods identified opposite observation impacts due to the cycling (repeating) OSEs of the AMSU-A observations. We interpreted the causes of the opposite estimations. However, even for the cycling OSEs, EFSO appeared to help estimate distributions of the accumulated observation impact. It is important to consider the dynamical propagation of accumulated observation impact in general circulation.

Typhoon Faxai hit Japan in 2019 and severely damaged the Tokyo metropolitan area. To mitigate such damages, a good track forecast is necessary even before the typhoon formation. To investigate the predictability of the genesis and movement of a precursor vortex and its relationship with the synoptic‐scale flow, 100‐member ensemble simulations of Typhoon Faxai were performed using a 14‐km mesh global nonhydrostatic atmospheric model, which started from 16 different initial days (i.e., 1,600 members in total). The results show that the model could predict an enhanced risk of a Faxai‐like vortex heading toward Japan 2 weeks before landfall, which was up to 70%. The reason for the enhancement was a rapid increase in the members reproducing a precursor vortex from 15 to 12 days before landfall in Japan. In addition, the upper‐tropospheric vortex played an essential role in the track simulation of Faxai.

An ensemble-based forecast sensitivity to observations (EFSO) diagnosis has been implemented in an atmospheric general circulation model–ensemble Kalman filter data assimilation system to estimate the impacts of specific observations from the quasi-operational global observing system on weekly short-range forecasts. It was examined whether EFSO reasonably approximates the impacts of a subset of observations from specific geographical locations for 6-h forecasts, and how long the 6-h observation impacts can be retained during the 7-day forecast period. The reference for these forecasts was obtained from 12 data-denial experiments in each of which a subset of three radiosonde observations launched from a geographical location was excluded. The 12 locations were selected from three latitudinal bands comprising (i) four Arctic regions, (ii) four midlatitude regions in the Northern Hemisphere, and (iii) four tropical regions during the Northern Hemisphere winter of 2015/16. The estimated winter-averaged EFSO-derived observation impacts well corresponded to the 6-h observation impacts obtained by the data denials and EFSO could reasonably estimate the observation impacts by the data denials on short-range (from 6 h to 2 day) forecasts. Furthermore, during the medium-range (4–7 day) forecasts, it was found that the Arctic observations tend to seed the broadest impacts and their short-range observation impacts could be projected to beneficial impacts in Arctic and midlatitude North American areas. The midlatitude area was located just downstream of dynamical propagation from the Arctic toward the midlatitudes. Results obtained by repeated Arctic data-denial experiments were found to be generally common to those from the non-repeated experiments.

We investigated the accuracy of operational medium-range ensemble forecasts for 29 Atlantic hurricanes between 2007 and 2019. Upper-level troughs with strong wind promoted northward movement of hurricanes over the mid-latitudes. For hurricanes with upper-level troughs, relatively large errors in the prediction of troughs result in large ensemble spreads, which result in failure to forecast hurricane track. In contrast, for hurricanes without upper-level troughs, mean central position errors are relatively small in all operational forecasts because of the absence of upper-level strong wind around troughs over the mid-latitudes. Hurricane Irma in September 2017 was accompanied by upper-level strong wind around a trough; errors and ensemble spreads for the predicted upper-level trough are small, contributing to smaller errors and small ensemble spreads in the predicted tracks of Irma. Our observing system experiment reveals that inclusion of additional Arctic radiosonde observation data obtained from research vessel Mirai in 2017 improves error and ensemble spread in upper-level trough with strong wind at initial time for forecast, increasing the accuracy of the forecast of the track of Irma in 2017.