A study to compare Dvorak parameters (T-number and CI-number) for tropical cyclones over the western North Pacific in both JMA and JTWC datasets from 1987 to 2006 is presented to show if there is a difference between two datasets. The study shows that the Dvorak parameters by JTWC are generally higher than these by JMA during the period of 1992 1997 and 2002 2005. The major reasons for stronger cases in JTWC are "faster intensification before the mature stage and slow /delayed start of weakening after the mature stage".
Life histories of low-level misocyclones, one of which corresponded to a tornado vortex within a winter storm in the Japan Sea coastal region on 1 December 2007, were observed from close range by X-band Doppler radar of the East Japan Railway Company. Continuous plan position indicator (PPI) observations at 30-s intervals at the low-elevation angle revealed at least four cyclonic misocyclones within the head of the comma-shaped echo of the vortical disturbance under winter monsoon conditions. The meso-b-scale vortical disturbance developed within the weak frontal zone at the leading edge of cold-air outbreaks.High-resolution observation of misocyclones revealed the detailed structures of these misocyclones and their temporal evolution. As the parent storm evolved, a low-level convergence line was observed at the edge of the easternmost misocyclone. This convergence line was considered to be important for the initiation and development of the misocyclones and the tornado through vortex stretching. The strongest misocyclone gradually intensified as its diameter contracted until landfall, and then began to dissipate soon after landfall. The temporal evolution of the misocyclones through landfall is discussed.Surface wind and pressure variations suggested a cyclonic vortex passage, which was consistent with the passage of the radar-derived misocyclone. The observed pressure drop was also consistent with that computed from the cyclostrophic equation for the modified Rankine vortex. The observed behavior of two adjacent misocyclones was primarily consistent with the rotational flow associated with the other misocyclone. The generation and development processes of the tornado and misocyclones are discussed.
In this study, ensemble tropical cyclone (TC) track predictions were examined using the THORPEX Interactive Grand Global Ensemble (TIGGE) dataset. The main goal was to investigate the relative benefits of a multi-centre grand ensemble (MCGE) over a single-model ensemble (SME) from both deterministic and probabilistic perspectives. Here, the SME was composed of all ensemble members of the ensemble prediction system (EPS) at a certain numerical weather prediction (NWP) centre, while the MCGE was composed of all ensemble members of all or selected SMEs. Nine NWP centres participating in the TIGGE project were considered, and 58 TCs in the western North Pacific from 2008 to 2010 were verified.In the verification of TC strike probability, the Brier skill score of the MCGE was larger than that of the best SME, which was the ECMWF EPS, on a medium-range time-scale, although this was not true on a short-to medium-range scale. In addition, the reliability was improved by the MCGE, especially in the high-probability range. Moreover, the MCGE reduced the number of missed events by about one tenth compared with the best SME. In the verification of confidence information, the MCGE was successful in extracting confidence information across all prediction times from 1 to 5 days. The relative benefits of the MCGE over the SME were seen in cases where the ensemble spread was extremely small. In such cases, the position errors of the MCGE were generally smaller than those of the best SME, indicating that, when multiple SMEs simultaneously predict a low uncertainty, the confidence level increases and the probability of a large position error decreases. In the verification of deterministic track predictions, the ensemble mean TC track predictions by the combination of the ECMWF, JMA, and UKMO EPSs were found on average to be slightly more accurate for 5-day predictions than those of the best SME, although the differences in the errors were not statistically significant.
Abstract. A total of 87 dual flights of Meisei RS-11G radiosondes and Vaisala RS92-SGP radiosondes were carried out at the Aerological Observatory of the Japan Meteorological Agency (36.06∘ N, 140.13∘ E, 25.2 m) from April 2015 to June 2017. Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) data products from both sets of radiosonde data for 52 flights were subsequently created using a documented processing program along with the provision of optimal estimates for measurement uncertainty. Differences in the performance of the radiosondes were then quantified using the GRUAN data products. The temperature measurements of RS-11G were, on average, 0.4 K lower than those of RS92-SGP in the stratosphere for daytime observations. The relative humidity measurements of RS-11G were, on average, 2 % RH (relative humidity) lower than those of RS92-SGP under 90 % RH–100 % RH conditions, while RS-11G gave on average 5 % RH higher values than RS92-SGP under ≤50 % RH conditions. The results from a dual flight of RS-11G and a cryogenic frost point hygrometer (CFH) also showed that RS-11G gave 1 % RH–10 % RH higher values than the CFH in the troposphere. Differences between the RS-11G and RS92-SGP temperature and relative humidity measurements, based on combined uncertainties, were also investigated to clarify major influences behind the differences. It was found that temperature differences in the stratosphere during daytime observation were within the range of uncertainty (k=2), and that sensor orientation is the major source of uncertainty in the RS92-SGP temperature measurement, while sensor albedo is the major source of uncertainty for RS-11G. The relative humidity difference in the troposphere was larger than the uncertainty (k=2) after the radiosondes had passed through the cloud layer, and the temperature–humidity dependence correction was the major source of uncertainty in RS-11G relative humidity measurement. Uncertainties for all soundings were also statistically investigated. Most nighttime temperature measurements for pressures of >10 hPa were in agreement, while relative humidity in the middle troposphere exhibited significant differences. Around half of all daytime temperature measurements at pressures of ≤150 hPa and relative humidity measurements around the 500 hPa level were not in agreement.
A new method for the estimation of tropical cyclone (TC) intensity utilizing 10, 19, 21, 37 and 85 GHz channel TRMM Microwave Imager (TMI) data from 1999 to 2003 is developed. As a first step, we investigated the relationship between the TRMM/TMI brightness temperature (TB) parameters, which are computed in concentric circles, or annuli of different radius in different TMI frequencies, and the TC maximum wind speed from the TC best track data, and/or observed by microwave scatterometers (QuikSCAT and SeaWinds).In contrast to the previous studies, we found that the parameters with lower frequency channels of 10 or 19 GHz give higher correlation. This would be because that TBs of lower frequencies, that have less sensitivity to rain than those of higher frequencies, reflect the speed of sea surface wind more directly in the TC case. The highest correlation coefficient obtained is 0.7, and the root mean square error (RMSE) of the regression between a parameter of highest correlation case is found to be 6 ms À1 . We developed a TC intensity estimation method, based on the multiple regression equations using a few parameters. After choosing 3 parameters out of all possible combinations, we computed the regression coefficients and chose 10 regression equations, sorted by the lower RMSEs.Finally, we evaluated our estimation method using independent verification data during 2004. The RMSEs are found to be about 8 ms À1 in the entire basin for the best track data, and about 6 ms À1 for the best track data in the northwestern Pacific. Whereas, for the microwave scatterometer data in all basins RMSE is found to be about 7 ms À1 . We also found that the temporal TC intensity change in our method shows good agreement with the TC best track data.
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