Abstract

Typhoon Morakot struck Taiwan during 7–9 August 2009 and became the deadliest tropical cyclone (TC) in five decades by producing up to 2635 mm of rain in 48 h, breaking the world record. The extreme rainfall of Morakot resulted from the strong interaction among several favorable factors that occurred simultaneously. These factors from large scale to small scale include the following: 1) weak environmental steering flow linked to the evolution of the monsoon gyre and consequently slow TC motion; 2) a strong moisture surge due to low-level southwesterly flow; 3) asymmetric rainfall and latent heating near southern Taiwan to further reduce the TC’s forward motion as its center began moving away from Taiwan; 4) enhanced rainfall due to steep topography; 5) atypical structure with a weak inner core, enhancing its susceptibility to the latent heating effect; and 6) cell merger and back building inside the rainbands associated with the interaction between the low-level jet and convective updrafts. From a forecasting standpoint, the present-day convective-permitting or cloud-resolving regional models are capable of short-range predictions of the Morakot event starting from 6 August. At longer ranges beyond 3 days, larger uncertainty exists in the track forecast and an ensemble approach is necessary. Due to the large computational demand at the required high resolution, the time-lagged strategy is shown to be a feasible option to produce useful information on rainfall probabilities of the event.

Abstract

Following an earlier diagnostic study, the present paper performs numerical simulations of the rare wintertime supercell storms during 19–20 December 2002 in a subtropical environment near Taiwan. Using Japan Meteorology Agency (JMA) 20-km analyses and horizontal grid spacing of 1.5 and 0.5 km, the Cloud-Resolving Storm Simulator (CReSS) of Nagoya University successfully reproduced the three major storms at the correct time and location, but the southern storm decayed too early over the Taiwan Strait. The two experiments produce similar overall results, suggesting that the 1.5-km grid spacing is sufficient even for storm dynamics. Model results are further used to examine the storm structure, kinematics, splitting process, and the variation in the mesoscale environment. Over the Taiwan Strait, the strong surface northeasterly flow enhanced low-level vertical shear and helped the storms evolve into isolated supercells. Consistent with previous studies, the vorticity budget analysis indicates that midlevel updraft rotation arose mainly from the tilting effect, and was reinforced by vertical stretching at the supercell stage. As the ultimate source of vorticity generation, the horizontal vorticity (vertical shear) was altered by the baroclinic (solenoidal) effect around the warm-core updraft, as well as the tilting of vertical vorticity onto, and rotation of vortex tubes in the x–y plane, forming a counterclockwise pattern that pointed generally northward (westward) at the right (left) flanks of the updraft. In both runs, model storms travel about 15°–20° to the left of the actual storms, and they are found to be quite sensitive to the detailed low-level thermodynamic structure of the postfrontal atmosphere and the intensity of the storms themselves, in particular whether or not the existing instability can be released by forced uplift at the gust front. In this regard, the finer 0.5-km grid did produce stronger storms that maintained longer across the strait. The disagreement in propagation direction between the model and real storms is partially attributed to the differences in environment, while the remaining part is most likely due to differences not reflected in gridded analyses. Since the conditions (in both the model and real atmosphere) over the Taiwan Strait are not uniform and depend on many detailed factors, it is anticipated that a successful simulation that agrees with the observation in all aspects over data-sparse regions like this one will remain a challenging task in the foreseeable future.

Abstract

In this study, the performance of a new ensemble quantitative precipitation forecast (QPF) system for Taiwan, with a cloud-resolving grid spacing of 2.5 km, a large domain of 1860 km × 1360 km, and an extended range of 8 days, is evaluated for six typhoons during 2012–13. Obtaining the probability (ensemble) information through a time-lagged approach, this system combines the strengths of high resolution (for QPF) and longer lead time (for hazard preparation) in an innovative way. For the six typhoons, in addition to short ranges (≤3 days), the system produced a decent QPF at a longest range up to days 8, 4, 6, 3, 6, and 7, providing greatly extended lead times, especially for slow-moving storms that pose higher threats. Moreover, since forecast uncertainty (reflected in the spread) is reduced with lead time, this system can provide a wide range of rainfall scenarios across Taiwan with longer lead times, each highly realistic for the associated track, allowing for advanced preparation for worst-case scenarios. Then, as the typhoon approaches and the predicted tracks converge, the government agencies can make adjustments toward the scenario of increasing likelihood. This strategy fits well with the conventional wisdom of “hoping for the best, but preparing for the worst” when facing natural hazards. Overall, the system presented herein compares favorably in usefulness to a typical 24-member ensemble (5-km grid size, 750 km × 900 km, 3-day forecasts) currently in operation using similar computational resources. Requiring about 1500 cores to execute four 8-day runs per day, it is not only powerful but also affordable and feasible.

Abstract

Intense tropical cyclones (TCs) sometimes cause huge disasters, so it is imperative to explore the impacts of climate change on such TCs. Therefore, the authors conducted numerical simulations of the most destructive historical TC in Japanese history, Typhoon Vera (1959), in the current climate and a global warming climate. The authors used four nonhydrostatic models with a horizontal resolution of 5 km: the cloud-resolving storm simulator, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, the Japan Meteorological Agency (JMA) operational nonhydrostatic mesoscale model, and the Weather Research and Forecasting Model. Initial and boundary conditions for the control simulation were provided by the Japanese 55-year Reanalysis dataset. Changes between the periods of 1979–2003 and 2075–99 were estimated from climate runs of a 20-km-mesh atmospheric general circulation model, and these changes were added to the initial and boundary conditions of the control simulation to produce the future climate conditions.

Although the representation of inner-core structures varies largely between the models, all models project an increase in the maximum intensity of future typhoons. It is found that structural changes only appeared around the storm center with sudden changes in precipitation and near-surface wind speeds as the radius of maximum wind speed (RMW) contracted. In the future climate, the water vapor mixing ratio in the lower troposphere increased by 3–4 g kg⁻¹. The increased water vapor allowed the eyewall updrafts to form continuously inside the RMW and contributed to rapid condensation in the taller and more intense updrafts.

Abstract

A best-fit power-law relationship (Z = 427 R ^1.09) between 1-minute integrated averages of snowfall rate (R) and radar reflectivity factor (Z) was determined on the basis of observations made by using high sensitivity snow gauges (accuracy 0.03 mm h⁻¹) and a radar (wavelength 3.2 cm, beamwidth 1.1°) of three 1987 Sapporo snowstorms. The relationship Z = 554R ^0.88, using 30-minute integrated averages of Z and R, produced the best radar estimate of total snowfall. The ratio of the estimated to the observed amount of snowfall decreased with increasing density of new fallen snow ρ, the ratio roughly equaling 1, when ρ ≈ 0.05 g cm⁻³.

Abstract

During the morning hours on 23 May 2002, a convective line associated with a mei-yu front brought heavy rainfall along the coast of central Taiwan under favorable synoptic conditions of warm air advection and large convective available potential energy (CAPE) of over 3000 m² s⁻². Doppler radar observations indicated that deep convection was organized into a linear shape with a northeast–southwest orientation along the front about 70 km offshore from Taiwan over the northern Taiwan Strait. The system then moved toward Taiwan at a slow speed of about 4 m s⁻¹. In the present study, the effects of Taiwan topography on this convective line and subsequent rainfall distribution were investigated through numerical modeling using the Nagoya University Cloud-Resolving Storm Simulator (CReSS) at a 2-km horizontal grid size. Experiments with different terrain heights of Taiwan, including full terrain (FTRN), half terrain (HTRN), and no terrain (NTRN), were performed. The control run using full-terrain and cold rain explicit microphysics realistically reproduced the evolution of the convective line and the associated weather with many fine details.

Two low-level convergence zones were found to be crucial in the development of this convective line and the subsequent rainfall distribution over Taiwan. The first was along the mei-yu front and forced mainly by the front, but was terrain enhanced off the northwestern coast of Taiwan due to the blocking of air on the windward side of the Central Mountain Range (CMR). After formation, convective cells along this zone propagated southeastward and produced rainfall over the northwestern coast. As the front moved closer to Taiwan, a second arc-shaped convergence zone with a nearly north–south orientation along about 120°E formed ahead of the front between the prevailing flow and near-surface offshore flow induced by the blocking. This second zone was terrain induced, and convection initiated near its northern end was found to be responsible for the rainfall maximum observed near the coast of central Taiwan. Its intensity and position were highly sensitive to terrain height. In the HTRN run where the terrain was reduced by half, a weaker zone closer to the CMR (by about 50 km) was produced, and the rain fell mostly over the windward slope of the terrain instead of over the coastal plain. When the terrain was removed in the NTRN run, no such zone with the correct orientation formed. It was also found that the frontal movement near northern Taiwan was slightly delayed with the presence of terrain, and this affected the timing and distribution of local rainfall during the later stages of this event.

Abstract

The sporadic formation of short-lived convective clouds in the eye of Tropical Cyclone (TC) Trami (2018) is investigated using dropsonde data and simulation results from a coupled atmosphere–ocean model. According to the satellite data, top height of the convective clouds exceeds 9 km above mean sea level, considerably taller than that of typical hub clouds (2–3 km). These clouds are located 10–30 km away from the TC center. Hence, these convective clouds are called deep eye clouds (DECs) in this study. The dropsonde data reveal an increase in relative humidity in the eye region during the formation of DECs. Short-lived convective clouds are simulated up to the middle troposphere in the eye region in the coupled model. Investigation of thermodynamic conditions shows a weakened low-level warm core and associated favorable conditions for convection in the eye region during the formation of DECs. DECs are formed after the weakening and outward displacement of convective heating within the eyewall. To elucidate the influence of the changes in convective heating within the eyewall on the formation of DECs, we calculate secondary circulation and associated adiabatic warming induced by convective heating within the eyewall using the Sawyer–Eliassen equation. In the eye region, weakening of subsidence and associated vertical potential temperature advection is observed as DECs are formed. This suggests that the weakening and outward displacement of convective heating within the eyewall create favorable conditions for the sporadic formation of DECs.

Abstract

Typhoon Morakot struck Taiwan during 6–9 August 2009, and it produced the highest rainfall (approaching 3000 mm) and caused the worst damage in the past 50 yr. Typhoon–monsoon flow interactions with mesoscale convection, the water vapor supply by the monsoon flow, and the slow moving speed of the storm are the main reasons for the record-breaking precipitation. Analysis of the typhoon track reveals that the steering flow, although indeed slow, still exceeded the typhoon moving speed by approximately 5 km h⁻¹ (1 km h⁻¹ = 0.28 m s⁻¹) during the postlandfall period on 8 August, when the rainfall was the heaviest. The Cloud-Resolving Storm Simulator (CReSS) is used to study the dynamics of the slow storm motion toward the north-northwest upon leaving Taiwan. The control simulations with 3-km grid size compare favorably with the observations, including the track, slow speed, asymmetric precipitation pattern, mesoscale convection, and rainfall distribution over Taiwan. Sensitivity tests with reduced moisture content reveal that not only did the model rainfall decrease but also the typhoon translation speed increased. Specifically, the simulations consistently show a discernible impact on storm motion by as much as 50%, as the storms with full moisture move slower (~5 km h⁻¹), while those with limited moisture (≤25%) move faster (~10 km h⁻¹). Thus, in addition to a weak steering flow, the prolonged asymmetric precipitation in Typhoon Morakot also contributed to its very slow motion upon leaving Taiwan, and both lengthened the heavy-rainfall period and increased the total rainfall amount. The implications of a realistic representation of cloud microphysics from the standpoint of tropical cyclone track forecasts are also briefly discussed.

Abstract

A fuzzy-logic-based hydrometeor classification (HC) method for X-band polarimetric radar (X-pol), which is suitable for observation of solid hydrometeors under moist environments producing little or no hail, is constructed and validated. This HC method identifies the most likely hydrometeor at each radar sampling volume from eight categories: 1) drizzle, 2) rain, 3) wet snow aggregates, 4) dry snow aggregates, 5) ice crystals, 6) dry graupel, 7) wet graupel, and 8) rain–hail mixture. Membership functions are defined on the basis of previous studies. The HC method uses radar reflectivity Z _h , differential reflectivity Z _dr, specific differential phase K _dp, and correlation coefficient ρ _hv as its main inputs, and temperature with some consideration of relative humidity as supplemental information. The method is validated against ground and in situ observations of solid hydrometeors (dry graupel, dry snow aggregates, and ice crystals) under a moist environment. Observational data from a ground-based imaging system are used to validate the HC method for dry graupel and dry snow aggregates. For dry snow aggregates and ice crystals, the HC method is validated using simultaneous observations from a balloonborne instrument [hydrometeor videosonde (HYVIS)] and an X-pol range–height indicator directed toward the HYVIS. The HC method distinguishes effectively between dry graupel, dry snow aggregates, and ice crystals, and is therefore valid for HC under moist environments.