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A sensitivity analysis for the horizontal localization scale is performed for a numerical weather prediction (NWP) system that uses a 30-s update to refresh a 500-m mesh with observations from a new-generation multiparameter phased array weather radar (MP-PAWR). Testing is performed using three case studies of convective weather events that occurred during August–September 2019, with the aim to determine the most suitable scale for short-range forecasting of precipitating convective systems and to better understand model behavior to a rapid update cycle. Results showed that while the model could provide useful skill at lead times up to 30 min, forecasts would consistently overestimate rainfall and were unable to outperform nowcasts performed with a simple advection model. Using a larger localization scale, e.g., 4 km, generated stronger convective and dynamical instability in the analyses that made conditions more favorable for spurious and intense convection to develop in forecasts. It was demonstrated that lowering the localization scale reduced the size of analysis increments during early cycling, limiting the buildup of these conditions. Improved representation of the localized convection in the initial conditions was suggested as an important step to mitigating this issue in the model.

We have produced an eddy-resolving local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) for the western North Pacific (WNP) and Maritime Continent (MC) regions (LORA-WNP and LORA-MC, respectively). This paper describes the system configuration and validation comparisons with Japan Coastal Ocean Predictability Experiment 2M (JCOPE2M) reanalysis and Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) observational datasets. The results show that the surface horizontal velocity in the LORA-WNP is closer to independent drifter buoy observations in the mid-latitude region, especially along the Kuroshio Extension (KE), and is less close in the subtropical region than the JCOPE2M, although the AVISO is the closest over the whole domain. The sea surface temperatures (SSTs) in the LORA-WNP correspond better to assimilated satellite observations than the JCOPE2M over most of the domain except for coastal regions. The results using an independent buoy south of the KE indicate that better fit of temperature in the LORA-WNP may be limited to the upper 300 m depth, probably because of the prescribed vertical localization cutoff length of 370 m. In the MC region, the surface velocity in the LORA-MC is closer to the independent drifter buoys in the equatorial coastal region and is less close in the offshore region than the AVISO. The SSTs in the LORA-MC correspond better to the assimilated satellite observations in the offshore region than the nearshore region. Therefore, the LORA-WNP and LORA-MC have sufficient accuracy for geoscience research applications as well as for fisheries, marine transport, and environment consultants.

This study explores coupled land-atmosphere data assimilation (DA) for improving weather and hydrological forecasts by assimilating soil moisture (SM) data. To assimilate land data with a coupled land-atmosphere model, weakly-coupled DA has been a common approach, in which land (atmospheric) data are not used to analyze atmospheric (land) model variables. This study integrates a land DA component into a global atmospheric DA system of the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF), so that we can perform strongly-coupled land-atmosphere DA experiments. We perform various types of coupled DA experiments by assimilating atmospheric observations and SM data simultaneously. The results show that analyzing atmospheric variables by assimilating SM data improves SM analysis and forecasts and mitigates a warm temperature bias in the lower troposphere where a dry SM bias exists. However, analyzing SM by assimilating atmospheric observations has detrimental impacts on SM analysis and forecasts.

This study investigates the impact of high frequency, such as 3-hourly and 1-hourly satellite microwave radiances, in global atmospheric data assimilation. To understand the impact of such a high-frequency satellite radiances data assimilation, we designed an observing system simulation experiment (OSSE) using the global NICAM-LETKF system at 56 km horizontal resolution. A free run was conducted with the NICAM model and treated as the reference (Nature) for the OSSE experiments. With the NICAM-LETKF system, we conducted five experiments, without data assimilation (NoDA), with only conventional data assimilation but not satellite radiances (NoSat), 6-hourly (6H), 3-hourly (3H), and 1-hourly (1H) satellite clear-sky radiances assimilation. The results showed that satellite microwave radiances assimilation improved the forecast of air temperature and wind over the global ocean compared to NoSat experiments. With the increase in the assimilation frequency of the satellite radiances, the air temperature and winds showed improvement in their representation over the ocean but degraded over land. Over the ocean, microwave radiances assimilation improved the typhoon eyewall wind intensities and its structure for 1H satellite radiances assimilation compared to 6H. These improvements in the wind intensities are prominent during the landfall stage of the typhoon. Forecasting landfall storms' strong winds are essential for disaster prevention and mitigation.

Ensemble-based estimates of error covariances suffer from limited ensemble size due to computational restrictions in data assimilation systems for numerical weather prediction. Localization of error covariances can mitigate sampling errors and is crucial for ensemble-based data assimilation. However, finding optimal localization methods, functions, or scales is challenging. We present a new approach to derive an empirical optimal localization (EOL) from a large ensemble dataset. The EOL allows for a better understanding of localization requirements and can guide toward improved localization.Our study presents EOL estimates using 40-member subsamples assuming a 1000-member ensemble covariance as truth. The EOL is derived from a 5-day training period. In the presentation, we cover both model and observation space vertical localization and discuss: vertical error correlations and EOL estimates for different variables and settings; the effect of the EOL compared to common localization approaches, such as distance-dependent localization with a Gaspari-Cohn function; and vertical localization of infrared and visible satellite observations in the context of observation space localization. Proper observation space localization of error covariances between non-local satellite observations and state space is non-trivial and still an open research question. First, we evaluate requirements for optimal localization for different variables and spectral channels. And secondly, we investigate the situation dependence of vertical localization in convection-permitting NWP simulations, which suggests an advantage of using adaptive situation-dependent localization approaches.

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 mid-latitudes 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 beneficial observation impact signals. The accumulated AMSU-Aobservation impactwas 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.

This presentation provides an overall summary of the project PREVENIR, an international cooperation project between Argentina and Japan since 2022 for five years under the Science and Technology Research Partnership for Sustainable Development (SATREPS) program jointly funded by the Japan International Cooperation Agency (JICA) and the Japan Science and Technology Agency (JST). The main goal is to develop an impact-based early warning system for heavy rains and urban floods designed for highly vulnerable urban basins in Buenos Aires Province and in Córdoba Province, Argentina. PREVENIR takes advantage of leading research on simulations and Big Data Assimilation (BDA) with the Japan’s flagship supercomputer “Fugaku” and its predecessor “K” and develops a total package for disaster prevention, namely, monitoring, quantitative precipitation estimates (QPE), nowcasting, BDA and NWP, hydrological model prediction, warning communications, public education, and capacity building. Here, the Japanese leading institutions in the scientific research and operational services, i.e., RIKEN, Osaka University, the International Centre for Water Hazard and Risk Management (ICHARM), and the Japan Meteorological Agency (JMA) closely work with the Argentinian counterparts, i.e., the National Meteorological Service, the National Water Institute, and the National Research Council of Argentina under the strong support of JICA, JST, and Argentinian Foreign Affairs Ministry. Heavy rains and urban floods are important global issues under the changing climate. The total package for disaster prevention will be the first of its kind in Argentina and will provide useful tools and recommendations for the implementation of similar systems in other parts of the world., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

In numerical weather prediction (NWP), sensitivity to initial conditions brings chaotic behaviors and an intrinsic limit to predictability, but it also implies an effective control in which a small control signal grows rapidly to make a substantial difference. The Observing Systems Simulation Experiment (OSSE) is a well-known approach to study predictability, where “nature” is synthesized by an independent NWP model run. In this study, we extend the OSSE and design the control simulation experiment (CSE), where we apply a small signal to control “nature”. Idealized experiments with the Lorenz-63 three-variable system show that we can control “nature” to stay in a chosen regime without shifting to the other, i.e., in a chosen wing of Lorenz's butterfly attractor, by adding small perturbations to “nature”. Using longer-lead-time forecasts, we achieve more effective control with a perturbation size of less than only 3 % of the observation error. We anticipate our idealized CSE to be a starting point for a realistic CSE using the real-world NWP systems, toward possible future applications to reduce weather disaster risks. The CSE may be applied to other chaotic systems beyond NWP.

This article reviews the first version of the NICAM–LETKF JAXA Research Analysis (NEXRA), a weather research analysis system, which was developed from the fusion of the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the local ensemble transform Kalman filter (LETKF) using various observational data, such as NCEP PREPBUFR, radiances from Advanced Microwave Sounding Unit (AMSU)-A, and precipitation obtained from Global Satellite Mapping of Precipitation (GSMaP). The development of NEXRA started in 2017 as a collaboration between the Japan Aerospace Exploration Agency (JAXA), the Atmosphere and Ocean Research Institute at the University of Tokyo (AORI), and the RIKEN Center for Computational Science (R-CCS). NEXRA routinely provides 2-D graphical weather information on the web site for general users and ensemble analysis data for research purposes. Routine operation of the first version of NEXRA started on the JAXA Supercomputer System Generation 2 (JSS2) in June 2018 and ended in February 2021 when JSS2 ceased operation. Routine operation of the second version of NEXRA (NEXRA_2.0) started in April 2021 on the JAXA Supercomputer System Generation 3 (JSS3). This article reviews the development and operation of the first version of NEXRA and describes the use of the ensemble analysis data produced by NEXRA as the atmospheric initial conditions for predictability research in the analysis of three events: the record-breaking rainfall in Japan that occurred in July 2018, Typhoon Krosa in August 2019, and the rapid intensification of Typhoon Haishen in August 2020. Finally, some details of the collaborations between NEXRA and other projects and of the development of the current version of NEXRA (NEXRA_2.0) are given, and a future vision for NEXRA is outlined.

This study examined the regional performance of a data assimilation (DA) system that couples the particle filter and the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM). This DA system optimizes model parameters of dormancy and photosynthetic rate, which are sensitive to phenology in the SEIB-DGVM, by assimilating satellite-observed leaf area index (LAI). The experiments without DA overestimated LAIs over Siberia relative to the satellite-observed LAI, whereas the DA system successfully reduced the error. DA provided improved analyses for the LAI and other model variables consistently, with better match with satellite observed LAI and with previous studies for spatial distributions of the estimated tree LAI, gross primary production (GPP), and above ground biomass. Most remarkably, the spatial distribution of tree LAI was estimated separately from undergrowth LAI because the SEIB-DGVM simulated the vertical structure of forest explicitly, and because satellite-observed LAI provided information on the onset and the end of the leaf season of tree and undergrowth, respectively. The DA system also provided the spatial distribution of the model parameters for tree separately from those of undergrowth. DA experiments started dormancy of trees more than a month earlier than the default phenology model and resulted in a decrease of the GPP.

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-hour forecasts, and how long the 6-hour 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-hour observation impacts obtained by the data denials and EFSO could reasonably estimate the observation impacts by the data denials on short-range (6-hour to 2-day) forecasts. Furthermore, during the medium-range (4-day to 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 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., ひとつひとつの観測データが気象予測に与える影響を簡易に評価する手法を確認 --北極の観測データは7日先の北米気象予測の改善に貢献することも明らかに--. 京都大学プレスリリース. 2021-04-30.

Numerical simulation of Venus’ atmosphere is useful to understand the dynamics of the system. The Venus atmospheric data assimilation system “ALEDAS-V” (Sugimoto et al. 2017) based on the Venus atmospheric general circulation model “AFES-Venus” (Sugimoto et al. 2014) has been used to simulate Venus’ atmosphere and generated some key phenomena such as the super-rotation, baroclinic waves, and thermal tides. To further understand the dynamics of Venus’ atmosphere, Bred Vectors (BV) are computed with the AFES-Venus model. This method can identify different growing modes of the system and has been used to study the dynamics of the Earth (Toth and Kalnay 1993, 1997) and Martian atmospheres (Greybush et al. 2013). However, to our knowledge, there has been no similar study on Venus’ atmosphere. To conduct the breeding cycle, we first produced a five (Earth) year free run of the AFES-Venus model initialized from an idealized zonal wind profile. Next, the forecast states on January 01 and August 25 in the 5th Earth year are used as the initial conditions for the control run and the perturbed run, respectively. These two initial conditions have the same sub-solar positions. To emphasize the active dynamics, the BV norm is defined by the temperature norm from the 60 km to 80 km altitudes, weighted by pressure and latitude. For the breeding cycle, a rescaling norm and rescaling interval are specified. During the breeding cycle, at every rescaling interval (including the initial time), if the norm is bigger than the specified rescaling norm, the BV is rescaled to the rescaling norm. Different combinations of the parameters are tested. The BV amplitude generally remains stable throughout the whole year without significant seasonal variability (Figure 1), which is different from the Martian atmosphere. The growth rate of the BV amplitude can represent the characteristics of the instabilities. It is calculated by taking the natural logarithm of the ratio of the BV amplitude at the end of the time interval (before rescaling) to the amplitude at the beginning of the interval. It is then converted to the daily growth rate by dividing the ratio of the time interval to one day. When the rescaling norm is smaller or the rescaling interval is shorter, the average growth rate is higher (Figure 2). Further BV analysis will be conducted such as analyzing the BV structure by taking composite mean along the super-rotation and conducting BV breeding cycle without thermal tides. These results will be useful to understand the dynamics of the thermal tide, baroclinic waves, super-rotation, and other important features of Venus atmosphere. Figure 1. The bred vector amplitude (K2 ) time evolution from the experiments with different rescaling days (1, 2, 5, 10, 20) and the same rescaling norm (10 K2 ). Figure 2. The average daily growth rate of the bred vector amplitude (K2 ) for different combination of rescaling norm and rescaling days. The average is taken from February to December. Reference Greybush, S. J., E. Kalnay, M. J. Hoffman, and R. J. Wilson, 2013: Identifying Martian atmospheric instabilities and their physical origins using bred vectors. Q. J. R. Meteorol. Soc., 139, 639–653, https://doi.org/10.1002/qj.1990. Sugimoto, N., M. Takagi, and Y. Matsuda, 2014: Baroclinic instability in the Venus atmosphere simulated by GCM. J. Geophys. Res. Planets, 119, 1950–1968, https://doi.org/10.1002/2014JE004624. ——, A. Yamazaki, T. Kouyama, H. Kashimura, T. Enomoto, and M. Takagi, 2017: Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere. Sci. Rep., 7, 9321, https://doi.org/10.1038/s41598-017-09461-1. Toth, Z., and E. Kalnay, 1993: Ensemble Forecasting at NMC: The Generation of Perturbations. Bull. Am. Meteorol. Soc., 74, 2317–2330, https://doi.org/10.1175/1520-0477(1993)0742.0.CO;2. ——, and ——, 1997: Ensemble Forecasting at NCEP and the Breeding Method. Mon. Weather Rev., 125, 3297–3319, https://doi.org/10.1175/1520-0493(1997)1252.0.CO;2.

Observations of the Venus Climate Orbiter “Akatsuki” provide us with horizontal distributions of the horizontal winds derived from cloud tracking of the Ultraviolet Imager (UVI) and of temperature observed by the Longwave Infrared Camera (LIR). However, these observations are limited in altitude, local time (day or night side), and frequency. Then it is difficult to elucidate the general circulation of the Venus atmosphere, including various temporal and spatial scales, only from observations. In this study, we produced a Venus dataset (analysis) that has high temporal and spatial resolutions by assimilating horizontal winds derived by the Akatsuki observations. At the top of the cloud layer of Venus, there are planetary-scale atmospheric waves that are excited by the solar heating and move with the sun, called the thermal tides. In this presentation, we focused on thermal tides to verify the analysis. We use the Venus atmospheric data assimilation system “ALEDAS-V" (Sugimoto et al., 2017) [1] for assimilation and the Venus atmospheric general circulation model “AFES-Venus" (Sugimoto et al., 2014) [2] for ensemble forecasts. AFES-Venus is a full nonlinear dynamical GCM on the assumption of hydrostatic balance, designed for the Venus atmosphere. ALEDAS-V uses the Local Ensemble Transform Kalman Filter, and is the first data assimilation system for the Venus atmosphere. We assimilated the cloud top (~70km) zonal and meridional winds obtained by tracking morphology, using Akatsuki UVI data (Horinouchi et al., 2021) [3] from September 1st to December 31st, 2018. The assimilation data (analysis) from October 1st to November 30th, 2018, is analyzed, because the root-mean-square-deviations (RMSD) from FR (free run; the case without data assimilation) are stable. Figures (a) and (d) show the observed zonal and meridional winds, respectively. The zonal wind has a local minimum near 11 LT (local time) around the equator (Figure a). The meridional wind is the weakest at the equator and increases with latitude, and the amplitude is maximum around noon (Figure d) in the local time direction. Note that these winds obtained from observations exist only the dayside equatorward of 50° latitudes (Figure a and d). Figures (b) and (e) show the deviations from the zonal means of zonal and meridional winds at an altitude of 70 km in the FR, respectively. For zonal wind, diurnal (zonal wavenumber 1) and semidiurnal (zonal wavenumber 2) tides are dominant at latitudes poleward and equatorward of 30, respectively (Figure b). The zonal wind deviation has a local minimum at 14-15LT, which is ~ 2 hours behind the observation (Figures a and b). The meridional wind deviation is polar and equatorial on the dayside and nightside, respectively (Figure e), and this distribution is consistent with Akatsuki's observation (Figure d). Figures (c) and (f) show the zonal and meridional winds as a result of assimilation, respectively. The zonal wind in the equatorial region have a local minimum near 11 LT. The assimilation improved the semidiurnal tide closer to the observations (Figures a and c). The meridional wind is not so different from FR. This is probably because FR was originally very similar to observations (Figures d and f). These results are consistent with a previous study by Sugimoto et al. (2019) [4]. In addition, while the observed winds exist only on the dayside, the results of assimilation show that the horizontal winds field is modified significantly even on the nightside. It is suggested that spatially limited data assimilation can improve the general circulation of GCM. In the future work, we are planning to release the assimilation dataset as the “objective analysis data” of Venus for the first time in the world. [1] Sugimoto, N., et al. Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere. Scientific Reports 7(1), 9321 (2017). [2] Sugimoto, N., et al. Baroclinic instability in the Venus atmosphere simulated by GCM. J. Geophys. Res. Planets 119, 1950–1968 (2014). [3] Horinouchi, T., et al. Venus Climate Orbiter Akatsuki Cloud Motion Vector Data Set v1.0, JAXA Data Archives and Transmission System (2021). [4] Sugimoto, N., et al. Impact of data assimilation on thermal tides in the case of Venus Express wind observation. Geophys. Res. Lett. 46, 4573–4580 (2019).

Control Simulation Experiment (CSE) is a recently developed approach to investigate the controllability of dynamical systems, extending the well-known Observing Systems Simulation Experiment (OSSE) in meteorology. For effective control of chaotic dynamical systems, it is essential to exploit the high sensitivity to initial conditions for dragging a system away from an undesired regime by applying minimal perturbations. In this study, we design a CSE for reducing the number of extreme events in the Lorenz-96 model. The 40 variables of this model represent idealized meteorological quantities evenly distributed on a latitude circle. The reduction of occurrence of extreme events over 100 years runs of the model is discussed as a function of the parameters of the CSE: the ensemble forecast length for detecting extreme events in advance, the magnitude and localization of the perturbations, and the quality and coverage of the observations. The design of the CSE is aimed at reducing weather extremes when applied to more realistic weather prediction models.

Over the last years, climate monitoring and operational weather forecasts have become an important topic for the renewable energy sector. An effective operation of national, and in the case of EU international, power generation aims to find the right balance between the minimization of CO2 emission and reduction of energy costs. In Germany, a considerable part of the electricity generation comes from wind. Therefore, an accurate forecast of low-level wind is essential to predict the generation of electrical power produced by wind parks. This enables timely adjustments of the conventional power plants. Currently, short-term low-level wind forecasts have considerable uncertainties. One of the cost-effective solutions to improve low-level wind forecasts is the assimilation of new observations into numerical weather prediction models. Even though in the last decade, the number of remote-sensing sites has been continuously growing, the coverage is far from being optimal to achieve significant improvement in the short-term wind forecast. However, before building new large networks of ground-based instruments it is important to estimate in advance which instruments to install, what effect to expect, and what spatial density of the distributed instruments should be.Ground-based instruments that can provide valuable information for low-level wind forecasts are Doppler lidars. In this study, we focus on the estimation of the potential impact of Doppler lidars for short-term low-level wind forecasts essential for sustainable energy applications. The potential impact is analyzed using the ensemble sensitivity analysis (ESA) [1]. ESA is based on the Ensemble Transform Kalman Filter and allows us to investigate how the assimilation of hypothetical Doppler lidars can reduce the wind forecast variance. The impact of a Doppler lidar network was analyzed with respect to surface measurements operationally assimilated by national weather services. We investigated the sensitivity of the obtained results to the number of Doppler lidars in the network, the number of altitude layers observed by Doppler lidars, and forecast lead time. Our analysis is based on a 1000-member ensemble simulation for the urban and highly populated Rhein-Ruhr area and surrounding regions [2]. The simulation uses a full-physics non-hydrostatic regional model (SCALE-RM) and covers a two-week time period in May/June 2016.This work has been conducted in the framework of the Hans-Ertel-Centre for Weather Research funded by the German Federal Ministry for Transportation and Digital Infrastructure (grant number BMVI/DWD 4818DWDP5A). This online publication is based upon work within the COST Action CA18235 supported by COST (European Cooperation in Science and Technology). We acknowledge RIKEN for providing the SCALE-RM model data.References [1] Ancell, B., and G. J. Hakim, 2007: Comparing adjoint-and ensemble-sensitivity analysis with applications to observation targeting, MWR., doi.org/10.1175/2007MWR1904.1. [2] Necker, T., et al, 2020: A convective-scale 1000-member ensemble simulation and potential applications. QJRMS, doi.org/10.1002/qj.3744.