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This study investigated the prediction skill of the Asian dust seasonal forecasting model (GloSea5-ADAM) on the Asian dust and meteorological variables related to the dust generation using hindcasts of GloSea5-ADAM for the period of 1991~2016 for East Asia. GloSea5-ADAM incorporates the dust generation algorithm of the Asian Dust and Aerosol Model (ADAM) into the Global Seasonal Forecasting System version 5 (GloSea5). The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) depending on the combination of the initial dates in the sub-seasonal scale were compared to that from synoptic observation and ERA5 reanalysis data. The evaluation criteria used Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC). The Asian dust and meteorological variables in the source region (35~44°N, 90~115°E) showed high ACC in the prediction scale within one month. The best performances for all variables showed when the use of the initial dates closest to the prediction month based on MBE, RMSE, and ACC. ACC was as high as 0.4 in Spring when using the closest two initial dates. In particular, the GloSea5-ADAM shows the best performance of Asian dust generation with an ACC of 0.60 in the occurrence frequency of Asian dust in March when using the closest initial dates for initial conditions. This result showed that the performances could be improved by adjusting the number of ensembles considering the combination of the initial date.

Understanding and quantifying air-sea exchanges of enthalpy and momentum fluxes are crucial for the advanced prediction of tropical cyclone (TC) intensity. Here, we present a new parameterization of air-sea fluxes at extreme wind speeds from 40 m s−1 to 75 m s−1, which covers the range of major TCs. Our approach assumes that the TC can reach its maximum potential intensity (MPI) if there are no influences of external forces such as vertical wind shear or other environmental constraints.This method can estimate the ratio of the enthalpy and momentum exchange coefficient (Ck/Cd) under the most intense TCs without direct flux measurements. The estimation showed that Ck/Cd increases with wind speed at extreme winds above 40 m s−1. Two types of surface layer schemes of the Hurricane Weather and Research Forecast (HWRF) were designed based on the wind speed dependency of the Ck/Cd found at high winds: (i) an increase of Ck/Cd based on decreasing Cd (Cd_DC) and (ii) an increase of Ck/Cd based on increasing Ck (Ck_IC). The modified surface layer schemes were compared to the original HWRF scheme (using nearly fixed Cd and Ck at extreme winds; CTRL) through idealized experiments and real-case predictions. The idealized experiments showed that Cd_DC reduced frictional dissipation in the air-sea interface as well as significantly reduced sea surface cooling, making the TC stronger than other schemes. As a result, Cd_DC reduced the mean absolute error and negative bias by 15.0% (21.0%) and 19.1% (32.0%), respectively, for all lead times of Hurricane Irma in 2017 (Typhoon Mangkhut in 2018) compared to CTRL. This result suggests that new parameterization of Ck/Cd with decreasing Cd at high winds can help improve TC intensity prediction, which currently suffers from underestimating the intensity of the strongest TCs.

This review summarizes experiences at operational centers to forecast tropical cyclone (TC) intensity change as presented to the International Workshop on Tropical Cyclones (IWTC-9) in Hawaii in 2018. Some operational forecast centers have been able to leverage advances in intensity guidance to increase forecast skill, albeit incrementally, while others have struggled to make any significant improvements. Rapid intensity changes continue to present major challenges to operational centers and individual difficult cases illustrate the forecasting challenges. It is noteworthy that the realization of a recommendation from IWTC-8 in 2014, to adapt guidance initially developed for the North Atlantic and North-East Pacific to other basins, has led to improved forecast skill of some agencies. Recent worldwide difficult cases are presented so that the research community can further investigate, potentially leading to improved intensity forecasts when similar cases are observed in the future. Keywords: tropical cyclone, intensity, change, rapid intensification

This review summarizes techniques used by operational centers to forecast tropical cyclone intensity change as presented to the International Workshop on Tropical Cyclones (IWTC-9) in Hawaii in 2018. Recent advances and major changes over the past four years are presented, with a special focus on forecasting rapid intensity changes. Although intensity change remains one of the most difficult aspects of tropical cyclone forecasting, objective guidance has shown some improvement. The greatest improvements are realized when consensus methods are utilized, especially those that blend statistical-dynamical based guidance with dynamical ocean-coupled regional models. These models become even more skillful when initialized with inner core observational data. Continued improvement and availability of intensity guidance along with associated forecaster training are expected to deliver forecasting improvements in the future. Keywords: tropical cyclone, intensity, change, rapid intensification

A Selected Multi-model CONsensus (SMCON) technique was developed and verified for the tropical cyclone track forecast in the western North Pacific. The SMCON forecasts were produced by averaging numerical model forecasts showing low 70% latest 6 h prediction errors among 21 models. In the homogeneous comparison for 54 tropical cyclones in 2013 and 2014, the SMCON improvement rate was higher than the other forecasts such as the Non-Selected Multi-model CONsensus (NSMCON) and other numerical models (i.e., GDAPS, GEPS, GFS, HWRF, ECMWF, ECMWF_H, ECMWF_EPS, JGSM, TEPS). However, the SMCON showed lower or similar improvement rate than a few forecasts including ECMWF_EPS forecasts at 96 h in 2013 and at 72 h in 2014 and the TEPS forecast at 120 h in 2013. Mean track errors of the SMCON for two year were smaller than the NSMCON and these differences were 0.4, 1.2, 5.9, 12.9, 8.2 km at 24-, 48-, 72-, 96-, 120-h respectively. The SMCON error distributions showed smaller central tendency than the NSMCON`s except 72-, 96-h forecasts in 2013. Similarly, the density for smaller track errors of the SMCON was higher than the NSMCON`s except at 72-, 96-h forecast in 2013 in the kernel density estimation analysis. In addition, the NSMCON has lager range of errors above the third quantile and larger standard deviation than the SMCON`s at 72-, 96-h forecasts in 2013. Also, the SMCON showed smaller bias than ECMWF_H for the cross track bias. Thus, we concluded that the SMCON could provide more reliable information on the tropical cyclone track forecast by reflecting the real-time performance of the numerical models.

In this study a superensemble was constructed and assessed to examine its applicability to the tropical cyclone track forecasts in the western North Pacific. The data used for this study were outputs of 20 tropical cyclone forecast models and analyzed tropical cyclone tracks by the Korea Meteorological Administration from 2011 to 2013. The annual mean track errors were analyzed at the 24-, 48-, 72-, 96- and 120-h periods for 2012 and 2013, and the superensemble forecasts showed lower annual errors than the simple mean consensus (using 20 numerical models), ECMWF_TIGG, and GFS. The superensemble track errors for individual tropical cyclone cases were lower than the simple mean consensus over 60% of the total cases, and lower than the best-performing model over 50% of the total cases for the 24-, 48-, and 72-h forecast periods. In the track error distribution, the superensemble had lower density for relatively large errors than the simple mean consensus, and higher density for smaller errors than single models. When the results are combined, the probability of the superensemble yielding lower errors than the simple mean consensus and single models becomes high, which indicates that the superensemble can serve as an objective reference for the tropical cyclone track forecasts.

Evaluation of predictability of numerical models for tropical cyclone track was performed using along-and cross-track component. The along-and cross-track bias were useful indicators that show the numerical models predictability associated with cause of errors. Since forecast errors, standard deviation and consistency index of along-track component were greater than those of cross-track component, there was some rooms for improvement in alongtrack component. There was an overall slow bias. The most accurate model was JGSM for 24hour forecast and ECMWF for 48~96-hour forecast in direct position error, along-track error and cross-track error. ECMWF and GFS had a high variability for 24-hour forecast. The results of predictability by track type showed that most significant errors of tropical cyclone track forecast were caused by the failure to estimate the recurvature phenomenon.

In this study, a tropical cyclogenesis detection system, Tropical Cyclone Analysis & Forecast (TCAF), was evaluated with an operational numerical model of the Korea Meteorological Administration (KMA). The tracking performance was compared with the result with the ECMWF model input field (TCAF-ECMWF). In order to improve the performance, different tracking time at an interval of 6 hours were investigated. The lowest false alarm rate and the highest hit rate (correct detection) were achieved at 06 hour after the initial tracking time. The tracking performance was also tested on two typhoons in 2013, LEEPI (1304) and DANAS (1324). The results showed that the TCAF-ECMWF detected tropical depressions 72 hours before the formation of the typhoon DANAS, which is a 12-hour earlier detection compared with the current performance with the use of KMA's numerical weather prediction (NWP) model data. So, it is expected that TC genesis detection could be improved by determining an optimal tracking time and by using more accurate NWP model data. Keywords: tropical cyclone, genesis detection, numerical weather prediction

Typhoon Sanba was selected for describing the Korea Meteorological Administration (KMA) Global Data Assimilation Prediction System (GDAPS) model bias tendency in forecast for the interaction between mid-latitude trough and movement speed of typhoon. We used the KMA GDAPS analyses and forecasts initiated 00 UTC 15 September 2012 from the historical typhoon record using Typhoon Analysis and Prediction System (TAPS) and Combined Meteorological Information System-3 (COMIS-3). Sea level pressure fields illustrated a development of the low level mid-latitude cyclogenesis in relation to Jet Maximum at 500 hPa. The study found that after Sanba entered the mid-latitude domain, its movement speed was forecast to be accelerated. Typically, Snaba interacted with mid-latitude westerlies at the front of mid-latitude trough. This event occurred when the Sanba was nearing recurvature at 00 and 06 UTC 17 September. The KMA GDAPS sea level pressure forecasts provided the low level mid-latitude cyclone that was weaker than what it actually analyzed in field. As a result, the mid-latitude circulations affecting on Sanba`s movement speed was slower than what the KMA GDAPS actually analyzed in field. It was found that these circulations occurred due to the weak mid-tropospheric jet maximum at the 500 hPa. In conclusion, the KMA GDAPS forecast tends to slow a bias of slow movement speed when Sanba interacted with the mid-latitude trough.

This paper summarizes results of a survey of the operational status of tropical cyclone (TC) forecast verification in the western North Pacific region, covering the Members of the ESCAP/WMO Typhoon Committee, RSMC Tokyo - Typhoon Center, and JTWC. Results of the survey indicate that significant efforts have been focused on the verification of TC forecast guidance by operational forecast agencies in the region, either individually or through international cooperation. A variety of TC forecast verification products are available to the forecasters in either real time or in post-season through multiple means, with the main focus on the deterministic track and intensity forecasts. Only a few verification products are available for probabilistic forecasts. Verification of TC precipitation and high wind forecasts are also lacking sufficient attention in the region. It would be helpful in the future to focus more attention toward these aspects and incorporate methods that will provide a greater depth of information regarding the TC forecast performance. Keywords: Tropical cyclone, forecast, verification

Hepatitis C virus (HCV) is an important human pathogen because it causes chronic hepatitis, liver cirrhosis, and hepatoma. Interferon (IFN)-λ1, which inhibits viral replication, and natural killer (NK) cells, which kill virus-infected cells, would be important in the innate immunity to HCV infection. Although it cannot directly stimulate NK cells because of the absence of its receptors on NK cells, IFN-λ1 is able to bind the receptors on HCV-infected hepatocytes. This study investigated, therefore, whether the stimulation of HCV-infected hepatocytes by IFN-λ1 treatment modulated NK cell function. Cell-culture-generated HCV virions, hepatoma cell lines, such as Huh7.5 and HCV-core replicon Huh7 cells, and primary human NK cells were employed. IFN-λ1 responses and NK cell function were measured by qPCR, ELISA, western blot, migration assay, and flow cytometry. IFN-λ1 effectively reduced HCV replication and increased the expression of interferon-stimulated genes (ISGs), such as MX1 and IFIT1, in HCV-infected cells and replicon cells. In addition, IFN-λ1 enhanced the transcription of chemokines such as IP-10 and IL-8. In particular, IP-10 secretion by HCV-infected Huh7.5 cells and HCV-core replicon Huh7 cells was significantly increased after IFN-λ1 treatment. Although the concentration of IP-10 secreted by HCV-infected Huh7.5 cells in vitro was insufficient to elicit considerable migration of NK cells, recombinant IP-10 could induce migration of NK cells ex vivo. In conclusion, IFN-λ1 enhanced IP-10 secretion by HCV-infected cells and IP-10 could modulate NK cell migration. These results suggested that IFN-λ1 would indirectly regulate the chemotaxis of NK cells through augmenting IP-10 secretion by HCV-infected cells.

This study introduces an unequally weighted technique for Multi-model Ensemble (MME) forecasting for western North Pacific Tropical Cyclone (TC) tracks. Weights are calculated by partial least square regression, and members are selected by paired t-test. The performances for shorter forecast time ranges, such as 24, 48 and 72 h, are examined in order to improve the MME model, in which the weights for members are equally assigned. For longer forecast time ranges, such as 96 and 120 h, weights for MME members are thought to be less reliable, since the modeling is more likely to be influenced by the climate variability in the data period. A combination of both techniques for the shorter and the longer forecast time ranges is suggested as an alternative MME forecast procedure in operational meteorological agencies.

The main difficulty in constructing palpation simulators is to compute and to generate stable and realistic haptic feedback without vibration. When a user haptically interacts with highly non-homogeneous soft tissues through a palpation simulator, a sudden change of stiffness in target tissues causes unstable interaction with the object.We propose a model consisting of a virtual adjustable damper and an energy measuring element. The energy measuring element gauges energy which is stored in a palpation simulator and the virtual adjustable damper dissipates the energy to achieve stable haptic interaction.To investigate the haptic behavior of the proposed method, impulse and continuous inputs are provided to target tissues. If a haptic interface point meets with the hardest portion in the target tissues modeled with a conventional method, we observe unstable motion and feedback force. However, when the target tissues are modeled with the proposed method, a palpation simulator provides stable interaction without vibration.The proposed method overcomes a problem in conventional haptic palpation simulators where unstable force or vibration can be generated if there is a big discrepancy in material property between an element and its neighboring elements in target tissues.

[1] This paper presents a statistical model to forecast seasonal tropical cyclone activity. In order to give a comprehensive view of seasonal tropical cyclone activity, we include not only the number of total tropical cyclones but also the number of typhoons and the NTA (Normalized Typhoon Activity) index as the predictands. The model is based on a multiple linear regression model in which the final predictors are selected with respect to minimizing the prediction error rather than simply fitting with past data. The model is expanded into ensemble prediction by considering the uncertainty of the single and deterministic forecast. The probability forecast based on the ensemble model shows reasonably good skill with respect to reliability and relative operating characteristics.