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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. [ABSTRACT FROM AUTHOR]

A previous study proposed an adaptive observation error inflation (AOEI) method for an ensemble Kalman filter (EnKF)-based atmospheric data assimilation system to assimilate all-sky infrared brightness temperatures. Brightness temperature differences between clear- and cloudy-sky radiances are large, and observation-minus-forecast differences (or innovations) are therefore likely to be large around boundaries between clear- and cloudy-sky regions. The AOEI method mitigates these discrepancies by adaptively inflating observation errors. Ocean frontal regions have similar characteristics to the borders between clear- and cloudy-sky regions with large innovations. Consequently, we have implemented the AOEI with an EnKF-based regional ocean data assimilation system, in which the assimilation interval is set to 1 d to utilize frequent satellite observations. We conducted sensitivity experiments to investigate the impacts of the AOEI on salinity structure, geostrophic balance, and accuracy. A control run, in which the AOEI is not applied, shows the degradation of low-salinity North Pacific Intermediate Water around the Kuroshio Extension region, where the innovation amplitude and forecast ensemble spread are large in association with the fronts and eddies. The resulting large temperature and salinity increments weaken the density stratification, leading to large vertical diffusivity. As a result, the low-salinity water in the intermediate layer is lost through strong vertical diffusion. When the AOEI is used, the salinity structure in the ocean interior is preserved because the AOEI suppresses the salinity degradation by reducing the temperature and salinity increments. We also demonstrate that the AOEI provides significant improvement of the geostrophic balance and the analysis accuracy of temperature, salinity, and surface-flow fields. [ABSTRACT FROM AUTHOR]

• An ocean forcing dataset JRA55-do significantly improves nearshore warm SST biases. • The improvement mechanisms of the warm SST biases are quantitatively investigated. • Ocean forcing datasets are essential for ocean data assimilation systems. In the previous study, the authors have produced an eddy-resolving ocean ensemble analysis product called the local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) over the western North Pacific and Maritime Continent regions using an ocean data assimilation system driven by the Japanese operational atmospheric reanalysis dataset known as the JRA-55. However, the LORA includes warm biases in sea surface temperatures (SSTs) in coastal regions during the boreal winter. In this study, we perform sensitivity experiments with atmospheric forcing using an ocean forcing dataset known as the JRA55-do, which adjusts the JRA-55 to high-quality reference datasets to reduce biases and uncertainties. The results show that the nearshore warm SST biases are significantly improved by the JRA55-do. During the boreal autumn, the improvement comes from mainly two factors: (i) enhancement of surface cooling by latent heat releases caused by removing contamination of weak winds at the land grid cells, and (ii) weakening surface heating by downward shortwave radiation through the adjustment in the JRA55-do. During the boreal winter, enhanced cooling by the analysis increments suppresses the growth of the warm SST biases when the JRA55-do is used. However, if the JRA-55 dataset is used, the adaptive observation error inflation (AOEI) scheme acts negatively to keep the nearshore SST biases in winter. Based on the innovation statistics, the AOEI inflates the observation errors when the differences between the squared observation-minus-forecast innovations and the squared forecast ensemble spreads are larger than the prescribed observation error variance, and improves the accuracy in the open ocean, especially around the frontal regions. However, when substantial warm SST biases are formed in the previous season, AOEI's observation error inflation makes the analysis increments smaller and cannot suppress the warm biases. We also validate the analysis accuracy using various data such as sea surface height and horizontal velocities and find that the JRA55-do has significant advantages. Therefore, continuous maintenance and development of ocean forcing datasets are essential for ocean modeling and data assimilation. [ABSTRACT FROM AUTHOR]

This study develops an ensemble Kalman filter (EnKF)-based regional ocean data assimilation system in which the local ensemble transform Kalman filter (LETKF) is implemented with version 1.0 of the Stony Brook Parallel Ocean Model (sbPOM) to assimilate satellite and in situ observations at a daily frequency. A series of sensitivity experiments are performed with various settings of the incremental analysis update (IAU) and covariance inflation methods, for which the relaxation-to-prior perturbations and spread (RTPP and RTPS, respectively) and multiplicative inflation (MULT) are considered. We evaluate the geostrophic balance and the analysis accuracy compared with the control experiment in which the IAU and covariance inflation are not applied. The results show that the IAU improves the geostrophic balance, degrades the accuracy, and reduces the ensemble spread, and that the RTPP and RTPS have the opposite effect. The experiment using a combination of the IAU and RTPP results in a significant improvement for both balance and analysis accuracy when the RTPP parameter is 0.8–0.9. The combination of the IAU and RTPS improves the balance when the RTPS parameter is ≤0.8 and increases the analysis accuracy for parameter values between 1.0 and 1.1, but the balance and analysis accuracy are not improved significantly at the same time. The experiments with MULT inflating the forecast ensemble spread by 5 % do not demonstrate sufficient skill in maintaining the balance and reproducing the surface flow field regardless of whether the IAU is applied or not. The 11 d ensemble forecast experiments show consistent results. Therefore, the combination of the IAU and RTPP with a parameter value of 0.8–0.9 is found to be the best setting for the EnKF-based ocean data assimilation system. [ABSTRACT FROM AUTHOR]

This study develops an ensemble Kalman filter (EnKF)-based regional ocean data assimilation system, in which the local ensemble transform Kalman filter (LETKF) is implemented with the Stony Brook Parallel Ocean Model (sbPOM) version 1.0 to assimilate satellite and in-situ observations at a daily frequency. A series of sensitivity experiments are performed with various settings of the incremental analysis update (IAU) and covariance inflation methods, for which the relaxation-to-prior perturbations and spread (RTPP and RTPS, respectively) and multiplicative inflation (MULT) are considered. We evaluate the geostrophic balance and the analysis accuracy compared with the control experiment in which the IAU and covariance inflation are not applied. The results show that the IAU improves the geostrophic balance, degrades the accuracy, and reduces the ensemble spread, and that the RTPP and RTPS have the opposite effect. The experiment using the combination of the IAU and RTPP results in significant improvement for both balance and accuracy when the RTPP parameter is 0.8-0.9. The combination of the IAU and RTPS improves the balance when the RTPS parameter is ≤ 0.8 and increases the accuracy for the parameter values between 1.0 and 1.1, but the balance and accuracy are not improved significantly at the same time. The experiments with MULT do not demonstrate sufficient skill in maintaining the balance and reproducing the surface flow field regardless of whether the IAU is applied or not. Therefore, the combination of the IAU and RTPP with the parameter of 0.8-0.9 is found to be the best setting for the EnKF-based ocean data assimilation system. [ABSTRACT FROM AUTHOR]

An objective analysis of monthly ocean subsurface temperatures from 1950 to 1998 is carried out. The analysis scheme and the results with estimated analysis errors are presented. The analysis domain is global with a horizontal grid of 1° × 1° and 14 vertical levels in the upper 500 m. Subsurface temperature observations used in the objective analysis are archived by the National Ocean Data Center of the National Oceanic and Atmospheric Administration, together with those collected through the global telecommunication system and domestic communication lines in Japan. All the observations are preprocessed by quality control and data selection procedures developed in this study. Together with these observations, threedimensional fields of the upper-ocean temperature are optimally estimated using a variational technique. To ensure smooth and continuous vertical temperature profiles, a constraint term is introduced to the cost function that is minimized in the analysis. In addition, the analysis scheme is formulated to constrain mixed layer temperatures to become close to sea surface temperatures produced by the Met Office. The three-dimensional structure of thermal anomalies is represented by the objective analysis. Interannual variations of temperature anomalies in the northern and tropical Pacific are presented and examined with the estimated errors. For the purpose of verification against independent observations of the objective analysis, dynamical heights estimated from the analyzed temperatures and climatological salinity are compared with tide gauge and sea surface height observations. An investigation of analysis errors and signal-to-noise (S-N) ratio reveals that the reliability increases in the tropical Pacific since the 1970s and the S-N ratio for seasonally averaged temperatures in a 3° latitude × 6° longitude box at 100-m depth is 2.5 in the 1990s. This is not only due to the increase in data sampling but also to an increase... [ABSTRACT FROM AUTHOR]

Floods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding. [ABSTRACT FROM AUTHOR]

This study compares three TMI rainfall datasets generated by two versions of NASA's Goddard Profiling algorithm (GPROF2010 and GPROF2017) and JAXA's Global Satellite Mapping of Precipitation algorithm (GSMaP) over land, coast, and ocean. We use TRMM precipitation radar observations as the reference, and also include CloudSat cloud profiling radar (CPR) observations as the reference over ocean. First, the dynamic thresholds for rainfall detection used by GSMaP and GPROF2017 have better detection capability, indicating by larger Heidke skill score (HSS) values, compared with GPROF2010 over both land and coast. Over ocean, all three datasets have very similar HSS regardless of including CPR observations. Next, intensity analysis shows that no single dataset performs the best according to all three statistical metrics (correlation, root-mean-square error, and relative bias), except that GSMaP performs the best for stratiform precipitation over coast, and GPROF2017 performs the best for convective precipitation over ocean, based on all three metrics. Finally, an error decomposition analysis shows that the total error and its three components have very different characteristics over several regions among these three datasets. For example, the positive total error in GPROF2010 and GSMaP is primarily caused by the positive hit bias over central Africa, while the false bias in GPROF2017 is largely responsible for this positive total error. For future algorithm development, results from this study imply that a convective–stratiform separation technique may be necessary to reduce the large underestimation for convective rain intensity. [ABSTRACT FROM AUTHOR]

A rain-gauge-adjusted algorithm for global satellite mapping of precipitation (GSMaP) that estimates the surface precipitation rate with resolutions of 0.1° and 1 h over the globe is described herein. Precipitation is one of the most important parameters of the earth’s system, and its global distribution and changes are essential data for modeling the water cycle, maintaining ecosystems, increasing agricultural production, improving weather forecasting precision, and implementing flood warning systems. In the Global Precipitation Measurement project, integrated products of high-resolution mapping of precipitation, obtained from microwave measurements made by a constellation satellite and infrared radiometers in geostationary orbit, are developed and supplied to the public. However, these high-resolution products, such as GSMaP_MVK, sometimes underestimate surface precipitation, introducing large errors into hydrological modeling. This paper combines the global gauge data set with GSMaP_MVK, using a new algorithm [gauge-adjusted GSMaP (GSMaP_Gauge)], described and evaluated herein using local radar and rain-gauge data sets. This algorithm outperforms other GSMaP products in all validation tests. [ABSTRACT FROM AUTHOR]

A multi-scale three-dimensional variational (MS-3DVAR) scheme is developed to assimilate high-resolution Himawari-8 sea surface temperature (SST) data for the first time into an operational ocean nowcast/forecast system targeting the North Western Pacific, JCOPE2. MS-3DVAR improves representation of the Kuroshio path south of Japan, its associated sea level variations, and temperature/salinity profiles south of Japan, the Kuroshio/Oyashio mixed water region, and the Japan Sea as compared to those of the products by the traditional single-scale 3DVAR. Validation results demonstrate that MS-3DVAR well assimilates the sparsely distributed in situ temperature and salinity profiles data by spreading the information over the large scale and by representing the detailed information near the measurement points. MS-3DVAR succeeds to assimilate the Himawari-8 SST product without noisy features caused by the cloud effects. We also find that MS-3DVAR is more effective for estimating oceanic conditions in regions with smaller mesoscale variability including the mixed water region and Japan Sea than in south of Japan. [ABSTRACT FROM AUTHOR]

The authors improve the high-resolution Global Satellite Mapping of Precipitation (GSMaP) product for Typhoon Morakot (2009) over Taiwan by using an orographic/nonorographic rainfall classification scheme. For the estimation of the orographically forced upward motion used in the orographic/nonorographic rainfall classification scheme, the optimal horizontal length scale for averaging the elevation data is examined and found to be about 50 km. It is inferred that as the air ascends en masse on the horizontal scale, it becomes unstable and convection develops. The orographic/nonorographic rainfall classification scheme is extended to the GSMaP algorithm for all passive microwave radiometers in orbit, including not just microwave imagers but also microwave sounders. The retrieved rainfall rates, together with infrared images, are used for the high-resolution rainfall products, which leads to much better agreement with rain gauge observations. [ABSTRACT FROM AUTHOR]

The adequacy of hourly rainfall sampling was examined in terms of the detection of diurnal variations using 8 yr (1998–2005) of data observed by the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) satellite. It was found that the monthly and hourly rain samples for each 0.2° grid point over the 8-yr period are composed of multiple precipitation systems. In this study, a “3-h-significant diurnal peak” was defined as the time of maximum rainfall with consecutive positive anomalies for more than 3 h. The fraction of the analyzed area with a 3-h-significant diurnal peak increased annually and accounted for 43% of the total global tropics at 0.2° resolution over the 8-yr period. The diurnal signature over Tibet and the Amazon showed a high degree of spatial uniformity (at >10° scale). The degree of similarity and locations of the regional diurnal characteristics are described in terms of seasonal variations and at multiple resolutions based on spatial uniformity. For example, uniform early-afternoon peaks generally appear over the coastal land and areas of high relief, whereas a seasonally invariant early-afternoon peak over the low-lying Amazon basin is recognized as a regional characteristic. In areas of coastal ocean, early-morning peaks appear in certain regions such as the area surrounding the so-called Maritime Continent and the area off the west coast of Mexico. These peaks are distinct from the global characteristics of late-morning rainfall maxima recorded over most coastal oceans and early-morning peaks recorded over open ocean. The results are also compared with those derived from TRMM Microwave Imager (TMI) data. In addition to obtaining a coherent signal, regional differences in the timing of maximum rainfall over the Tibetan Plateau were addressed; this discrepancy is attributed to limitations of the scattering algorithm used for TMI data in terms of detecting shallow convection and screening cold surfaces. [ABSTRACT FROM AUTHOR]

This paper documents the production and validation of retrieved rainfall data obtained from satellite-borne microwave radiometers by the Global Satellite Mapping of Precipitation (GSMaP) Project. Using various attributes of precipitation derived from Tropical Rainfall Measuring Mission (TRMM) satellite data, the GSMaP has implemented hydrometeor profiles derived from Precipitation Radar (PR), statistical rain/no-rain classification, and scattering algorithms using polarization-corrected temperatures (PCTs) at 85.5 and 37 GHz. Combined scattering-based surface rainfalls are computed depending on rainfall intensities. PCT85 is not used for stronger rainfalls, because strong depressions of PCT85 are related to tall precipitation-top heights. Therefore, for stronger rainfalls, PCT37 is used, with PCT85 used for weaker rainfalls. With the suspiciously strong rainfalls retrieved from PCT85 deleted, the combined rainfalls correspond well to the PR rain rates over land. The GSMaP algorithm for the TRMM Microwave tmager (TMI) is validated using the TRMM PR, ground radar [Kwajalein (KWAJ) radar and COBRA], and Radar Automated Meteorological Data Acquisition System (AMeDAS) precipitation analysis (RA). Monthly surface rainfalls retrieved from six microwave radiometers (GSMaP̱MWR) are compared with the gauge-based dataset. Rain rates retrieved from the TMI (GSMaP̱TMI) are in better agreement with the PR estimates over land everywhere except over tropical Africa in the boreal summer. Validation results of the KWAJ radar and COBRA show a good linear relationship for instantaneous rainfall rates, while validation around Japan using the RA shows a good relationship in the warm season. Poor results, connected to weak- precipitation cases, are found in the cold season around Japan. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is an indispensable means of measuring and monitoring precipitation on a global scale. The Tropical Rainfall Measuring Mission (TRMM) is continuing to make significant progress in helping the global features of precipitation to be understood, particularly with the help of a pair of spaceborne microwave sensors, the TRMM Microwave Imager (TMI) and precipitation radar (PR). The TRMM version-5 standard products, however, are known to have a systematic inconsistency in mean monthly rainfall. To clarify the origin of this inconsistency, the authors investigate the zonal mean precipitation and the regional trends in the hydrometeor profiles in terms of the precipitation water content (PWC) and the precipitation water path (PWP) derived from the TMI profiling algorithm (2A12) and the PR profile (2A25). An excess of PR over TMI in near-surface PWC is identified in the midlatitudes (especially in winter), whereas PWP exhibits a striking excess of TMI over PR around the tropical rainfall maximum. It is shown that these inconsistencies arise from TMI underestimating the near-surface PWC in midlatitude winter and PR underestimating PWP in the Tropics. This conclusion is supported by the contoured-frequency-by-altitude diagrams as a function of PWC. Correlations between rain rate and PWC/ PWP indicate that the TMI profiling algorithm tends to provide a larger rain rate than the PR profile under a given PWC or PWP, which exaggerates the excess by TMI and cancels the excess by PR through the conversion from precipitation water to rain rate. As a consequence, the disagreement in the rainfall products between TMI and PR is a combined result of the intrinsic bias originating from the different physical principles between TMI and PR measurements and the purely algorithmic bias inherent in the conversion from precipitation water to rain rate. [ABSTRACT FROM AUTHOR]

We have developed an ocean state nowcast/forecast system (JCOPE-T DA) that targets the coastal waters around Japan and assimilates daily remote sensing and in situ data. The ocean model component is developed based on the Princeton Ocean Model with a generalized sigma coordinate and calculates oceanic conditions with a 1/36-degree (2–3 km) resolution and an hourly result output interval. To effectively represent oceanic phenomena with a spatial scale smaller than 100 km, we adopted a data assimilation scheme that explicitly separates larger and smaller horizontal scales from satellite sea surface temperature data. Our model is updated daily through data assimilation using the latest available remote-sensing data. Here we validate the data assimilation products of JCOPE-T DA using various kinds of in situ observational data. This validation proves that the JCOPE-T DA model output outperforms those of a previous version of JCOPE-T, which is based on nudging the values of temperature and salinity toward those provided by a different coarse grid data-assimilated model JCOPE2M. Parameter sensitivity experiments show that the selection of horizontal scale separation parameters considerably affects the representation of sea surface temperature. Additional experiments demonstrate that the assimilation of daily-updated satellite sea surface temperature data actually improves the model's efficiency in representing typhoon-induced disturbances of sea surface temperature on a time scale of a few days. Assimilation of additional in situ data, such as temperature/salinity/ocean current information, further improves the model's ability to represent the ocean currents near the coast accurately. [ABSTRACT FROM AUTHOR]

This paper describes a quasi-physical method (the Q-method) for determining the sea surface temperatures (SSTs). The Q-method is a coefficient-based technique developed for processing the multiband infrared (IR) data of the geostationary Himawari-8 satellite. We applied the Q-method to the split-window data from the Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission-Climate (GCOM-C) satellite. A comparison of the determined SGLI SSTs and buoy data shows a bias with a robust standard deviation of −0.097 K and 0.28 K in the daytime and −0.18 K and 0.28 K at night, respectively. Meanwhile, high biases of nearly −0.5 K were calculated for SSTs at and around 305 K. A residuals analysis suggests that the high negative bias is caused by insufficient information on the atmospheric correction brought by split-window data. This paper discusses the physical and mathematical background of the Q-method and compares it with another coefficient-based physical scheme. • Quasi-physical determination of sea surface temperature. • Physical and mathematical background of the quasi-physical determination. • Accuracy of quasi-physically determined sea surface temperature. • Result comparison with another coefficient-based physical scheme. • Physical background for the high bias. [ABSTRACT FROM AUTHOR]

Decadal climate variations in the Atlantic Ocean found in observational fields and a coupled general circulation model (CGCM) are investigated. In particular, physical processes responsible for the phase reversal are compared. Observed and modeled decadal variations have dominant periodicities around 12.3 and 9.9 yr, respectively. Both variations show similar spatial features: sea surface temperature (SST) anomalies in the western subtropical Atlantic sandwiched by those with opposite sign to the north and south, and a dipole of sea level pressure anomalies, which resemble the North Atlantic Oscillation. Their temporal evolutions are, however, different from each other, suggestive of different dynamics of the oscillation. In the observations, SST and surface-layer (0-- 100 m) temperature anomalies move eastward from the subtropical western Atlantic to the European coast along the Gulf Stream. Northward propagation of SST anomalies are also found along the western boundaries including the Gulf of Mexico. A budget analysis for the temperature equation shows that these features are the manifestation of the advection of SST anomalies by the mean current, which acts to switch one phase of the oscillation to another. Anomalous gyre intensity appears to have little contribution to the phase switching process of the near-surface variability, although the influence of the anomalous gyre is found in the lower subsurface up to 500 m. In contrast, SST anomalies in the CGCM are more strongly tied with subsurface temperature anomalies that propagate westward, consistent with a slow gyre adjustment by the baroclinic Rossby wave propagation. The wave-induced advection acts to change the phase of SST as well as the subsurface temperature anomalies in the model. Subduction of temperature anomalies is found to occur on decadal timescales both in the observation and in the model over the eastern basin where the winter mixed layer is deepened, although the consequence. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) measurements from the geostationary satellite Himawari-8 Advanced Himawari Imager (AHI) are compared with in situ skin SSTs derived from shipboard Infrared SST Autonomous Radiometers (ISAR) in the Australian region. The mean bias and standard deviation of the differences between Himawari-8 AHI and ISAR skin SST of best quality are 0.09 K and 0.30 K, with total matchups numbering 2701. Shipboard bulk SST measurements at depths between around 7.1 and 9.9 meters are compared with the matchups in a case study. Analyses show significant differences between skin and bulk SST measurements of maximum value 2.23 K under conditions of high diurnal warming. The results also demonstrate that Himawari-8 AHI skin SST with high temporal resolution has the ability to accurately measure diurnal warming events. [ABSTRACT FROM AUTHOR]

Today's Earth (TE) is near real time global terrestrial analysis system of hydrological quantities jointly developed by JAXA and U-Tokyo. As operational application of this system, JAXA and U-Tokyo have developed "TE-Japan," which is a regional high-resolution hydrological forecasting system. From TE-Japan, hydrological quantities including flooded area, river flow rate, etc. are forecasted at about 1 km resolution over whole Japan every 3 hours at nearly real time, up to 39 hours ahead. As atmospheric forcing data, it uses Radar-Amedas precipitation analysis and MSM-GPV numerical weather forecast of Japan Meteorological Agency. In this study, prediction skill of river flow rate and flooded area was tested for 2018 Japan floods in July. Due to the influence of Typhoon Prapiroon, it brought a large amount of rainfall throughout the western Japan from 5th to 8th July 2018. In the Takahashi River (Okayama prefecture), Mabi Town was flooded around the confluence point with the Oda River, around 0 o'clock on July 7, mainly due to the breakdown of the left bank of the Oda River. As a result, several dozen people were killed. In Hiji River, Ozu city was inundated near the confluence point with the Yagi River, around 9 o'clock on July 7 due to the overflow from the temporary fixed bank around the confluence point. There was no direct death caused by the flooding on the contrary.Our analysis showed that TE-Japan could predict the risk of the above two inundations from more than 24 hours ahead the floods, respectively. Currently, the official operational flood forecast of Japanese MLIT tries to issue warnings and evacuation orders at most three hours ahead of flooding. However, there may be some cases that the warning is not so helpful because it is issued in the middle of severe situation and/or during mid night. Our result indicates that it is possible to predict floods for more than ten to twenty hours ahead. This would give plenty of time for citizen to protect themselves and it helps mitigating damage. [ABSTRACT FROM AUTHOR]