Hurricane Boundary Layer Height Relative to Storm Motion from GPS Dropsonde Composites
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Ensemble Forecast Experiment for Typhoon
Ensemble Forecast Experiment for Typhoon Quantitatively Precipitation in Taiwan Ling-Feng Hsiao1, Delia Yen-Chu Chen1, Ming-Jen Yang1, 2, Chin-Cheng Tsai1, Chieh-Ju Wang1, Lung-Yao Chang1, 3, Hung-Chi Kuo1, 3, Lei Feng1, Cheng-Shang Lee1, 3 1Taiwan Typhoon and Flood Research Institute, NARL, Taipei 2Dept. of Atmospheric Sciences, National Central University, Chung-Li 3Dept. of Atmospheric Sciences, National Taiwan University, Taipei ABSTRACT The continuous torrential rain associated with a typhoon often caused flood, landslide or debris flow, leading to serious damages to Taiwan. Therefore the quantitative precipitation forecast (QPF) during typhoon period is highly needed for disaster preparedness and emergency evacuation operation in Taiwan. Therefore, Taiwan Typhoon and Flood Research Institute (TTFRI) started the typhoon quantitative precipitation forecast ensemble forecast experiment in 2010. The ensemble QPF experiment included 20 members. The ensemble members include various models (ARW-WRF, MM5 and CreSS models) and consider different setups in the model initial perturbations, data assimilation processes and model physics. Results show that the ensemble mean provides valuable information on typhoon track forecast and quantitative precipitation forecasts around Taiwan. For example, the ensemble mean track captured the sharp northward turning when Typhoon Megi (2010) moved westward to the South China Sea. The model rainfall also continued showing that the total rainfall at the northeastern Taiwan would exceed 1,000 mm, before the heavy rainfall occurred. Track forecasts for 21 typhoons in 2011 showed that the ensemble forecast has a comparable skill to those of operational centers and has better performance than a deterministic prediction. With an accurate track forecast for Typhoon Nanmadol, the ability for the model to predict rainfall distribution is significantly improved. -
NWS Unified Surface Analysis Manual
Unified Surface Analysis Manual Weather Prediction Center Ocean Prediction Center National Hurricane Center Honolulu Forecast Office November 21, 2013 Table of Contents Chapter 1: Surface Analysis – Its History at the Analysis Centers…………….3 Chapter 2: Datasets available for creation of the Unified Analysis………...…..5 Chapter 3: The Unified Surface Analysis and related features.……….……….19 Chapter 4: Creation/Merging of the Unified Surface Analysis………….……..24 Chapter 5: Bibliography………………………………………………….…….30 Appendix A: Unified Graphics Legend showing Ocean Center symbols.….…33 2 Chapter 1: Surface Analysis – Its History at the Analysis Centers 1. INTRODUCTION Since 1942, surface analyses produced by several different offices within the U.S. Weather Bureau (USWB) and the National Oceanic and Atmospheric Administration’s (NOAA’s) National Weather Service (NWS) were generally based on the Norwegian Cyclone Model (Bjerknes 1919) over land, and in recent decades, the Shapiro-Keyser Model over the mid-latitudes of the ocean. The graphic below shows a typical evolution according to both models of cyclone development. Conceptual models of cyclone evolution showing lower-tropospheric (e.g., 850-hPa) geopotential height and fronts (top), and lower-tropospheric potential temperature (bottom). (a) Norwegian cyclone model: (I) incipient frontal cyclone, (II) and (III) narrowing warm sector, (IV) occlusion; (b) Shapiro–Keyser cyclone model: (I) incipient frontal cyclone, (II) frontal fracture, (III) frontal T-bone and bent-back front, (IV) frontal T-bone and warm seclusion. Panel (b) is adapted from Shapiro and Keyser (1990) , their FIG. 10.27 ) to enhance the zonal elongation of the cyclone and fronts and to reflect the continued existence of the frontal T-bone in stage IV. -
Probabilistic Evaluation of the Dynamics and Prediction of Supertyphoon Megi (2010)
1562 WEATHER AND FORECASTING VOLUME 28 Probabilistic Evaluation of the Dynamics and Prediction of Supertyphoon Megi (2010) CHUANHAI QIAN Nanjing University of Information Science and Technology, Nanjing, and National Meteorological Center, Beijing, China, and Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania FUQING ZHANG AND BENJAMIN W. GREEN Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania JIN ZHANG National Meteorological Center, Beijing, China XIAQIONG ZHOU NOAA/NCEP, College Park, Maryland (Manuscript received 21 November 2012, in final form 21 July 2013) ABSTRACT Supertyphoon Megi was the most intense tropical cyclone (TC) of 2010. Megi tracked westward through the western North Pacific and crossed the Philippines on 18 October. Two days later, Megi made a sharp turn to the north, an unusual track change that was not forecast by any of the leading operational centers. This failed forecast was a consequence of exceptionally large uncertainty in the numerical guidance—including the operational ensemble of the European Centre for Medium-Range Weather Forecasts (ECMWF)—at various lead times before the northward turn. This study uses The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble dataset to examine the uncertainties in the track forecast of the ECMWF operational ensemble. The results show that Megi’s sharp turn is sensitive to its own movement in the early period, the size and structure of the storm, the strength and extent of the western Pacific subtropical high, and an approaching eastward-moving midlatitude trough. In particular, a larger TC (in addition to having a stronger beta effect) may lead to a stronger erosion of the southwestern extent of the subtropical high, which will subsequently lead to an earlier and sharper northward turn. -
7 the Analysis of Storm Surge in Manila Bay, the Philippines
INTERNATIONAL HYDROGRAPHIC REVIEW MAY 2019 THE ANALYSIS OF STORM SURGE IN MANILA BAY, THE PHILIPPINES By Commander C. S. Luma-ang Hydrography Branch, National Mapping and Resource Information Authority, (Philippines) Abstract In 2013, Typhoon Haiyan produced a storm surge over seven metres in San Pedro Bay in the Philippines that killed approximately 6,300 people. The event created significant public awareness on storm surges and exposed the lack of records and historical research in the Philippines. This study investigated the tidal height records during intense cyclone activities in 2016 and 2017 to provide accurate information about storm surge development in the largest and most populated coastal area in the country – Manila Bay. The results of this investigation indicated that there are consistencies in the characteristics of tropical cyclones that produce larger storm surges. The results also show that actual storm surge heights are generally smaller than predicted height values. Résumé En 2013, le typhon Haiyan a provoqué une onde de tempête de plus de sept mètres dans la Baie de San Pedro aux Philippines, faisant près de 6 300 victimes. Cet événement a provoqué une importante sensibilisation du public envers les ondes de tempête et a mis en évidence le manque d’archives et de recherches historiques aux Philippines. La présente étude a examiné les enregistrements des hauteurs des marées au cours d’activités cycloniques intenses en 2016 et 2017 afin de fournir des informations précises sur le développement d’ondes de tempête dans la zone côtière la plus étendue et la plus peuplée du pays, la Baie de Manille. -
Learning from Hurricane Hugo: Implications for Public Policy
LEARNING FROM HURRICANE HUGO: IMPLICATIONS FOR PUBLIC POLICY prepared for the FEDERAL INSURANCE ADMINISTRATION FEDERAL EMERGENCY MANAGEMENT AGENCY 500 C Street, S.W. Washington, D.C. 20472 under contract no. EMW-90-G-3304,A001 June 1992 CONTENTS INTRODUCTION ............................... 1.............I PHYSICAL CHARACTERISTICS OF THE STORM . 3 Wind Speeds .3 IMPACTS ON NATURAL SYSTEMS 5 ................................ Biological Systems ....... .................................5 Dunes and Beaches ....... .5............................... Beach Nourishment . .................................7 IMPACTS ON HUMANS AND HUMAN SYSTEMS ............................ 9 Deaths and Injuries ............................ 9 Housing ............................ 9 Utilities ................... 10 Transportation Systems .1................... 10 The Economy ................... 11 Psychological Effects ................... 11 INSURANCE .......................... 13 COASTAL DEVELOPMENT .......................... 14 Setbacks ........................... 15 Coastal Protection Structures .......................... 16 PERFORMANCE OF STRUCTURES ..... .... 18 Effects of Wind and/or Water ...... .... 18 Effects of Water, Waves, or Erosion . .. .18 Effects of Wind .............. .... 19 Foundations .................. .... 21 Slabs ................ .... 22 Piers and Columns ....... .... 22 Pilings............... .... 22 Elevation .................. .... 23 Lower Area Enclosures .... .... 23 Connections ................. ....24 Manufactured Housing .......... .... 24 -
Assimilating AMSU-A Radiance Data with the WRF Hybrid En3dvar System for Track Predictions of Typhoon Megi (2010)
ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 32, SEPTEMBER 2015, 1231–1243 Assimilating AMSU-A Radiance Data with the WRF Hybrid En3DVAR System for Track Predictions of Typhoon Megi (2010) SHEN Feifei∗ and MIN Jinzhong Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044 (Received 28 October 2014; revised 12 December 2014; accepted 29 December 2014) ABSTRACT The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) on the track prediction of Typhoon Megi (2010) was studied using the Weather Research and Forecasting (WRF) model and a hybrid ensemble three- dimensional variational (En3DVAR) data assimilation (DA) system. The influences of tuning the length scale and variance scale factors related to the static background error covariance (BEC) on the track forecast of the typhoon were studied. The results show that, in typhoon radiance data assimilation, a moderate length scale factor improves the prediction of the typhoon track. The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts, even when the static BEC was carefully tuned to optimize its performance. When the hybrid DA was employed, the track forecast was significantly improved, especially for the sharp northward turn after crossing the Philippines, with the flow-dependent ensemble covariance. The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically. The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR. -
Massachusetts Tropical Cyclone Profile August 2021
Commonwealth of Massachusetts Tropical Cyclone Profile August 2021 Commonwealth of Massachusetts Tropical Cyclone Profile Description Tropical cyclones, a general term for tropical storms and hurricanes, are low pressure systems that usually form over the tropics. These storms are referred to as “cyclones” due to their rotation. Tropical cyclones are among the most powerful and destructive meteorological systems on earth. Their destructive phenomena include storm surge, high winds, heavy rain, tornadoes, and rip currents. As tropical storms move inland, they can cause severe flooding, downed trees and power lines, and structural damage. Once a tropical cyclone no longer has tropical characteristics, it is then classified as a post-tropical system. The National Hurricane Center (NHC) has classified four stages of tropical cyclones: • Tropical Depression: A tropical cyclone with maximum sustained winds of 38 mph (33 knots) or less. • Tropical Storm: A tropical cyclone with maximum sustained winds of 39 to 73 mph (34 to 63 knots). • Hurricane: A tropical cyclone with maximum sustained winds of 74 mph (64 knots) or higher. • Major Hurricane: A tropical cyclone with maximum sustained winds of 111 mph (96 knots) or higher, corresponding to a Category 3, 4 or 5 on the Saffir-Simpson Hurricane Wind Scale. Primary Hazards Storm Surge and Storm Tide Storm surge is an abnormal rise of water generated by a storm, over and above the predicted astronomical tide. Storm surge and large waves produced by hurricanes pose the greatest threat to life and property along the coast. They also pose a significant risk for drowning. Storm tide is the total water level rise during a storm due to the combination of storm surge and the astronomical tide. -
Dynamic Response of a Philippine Dipterocarp Forest to Typhoon Disturbance
Journal of Vegetation Science 27 (2016) 133–143 Dynamic response of a Philippine dipterocarp forest to typhoon disturbance Sandra L. Yap, Stuart J. Davies & Richard Condit Keywords Abstract Biomass; Dipterocarp forest; Forest dynamics; Forest resilience; Mortality and recruitment; Questions: Natural hazards can wreak catastrophic damage to forest ecosys- Regeneration; Tree demography; Typhoon tems. Here, the effects of typhoon disturbance on forest structure and demogra- disturbance phy of the 16-ha Palanan Forest Dynamics Plot in the northeast Philippines were examined by comparing census intervals with (1998–2004) and without Nomenclature (2004–2010) a strong typhoon. Category 4 Typhoon Imbudo, with wind gusts Co et al. (2006) exceeding 210 kph, hit Palanan in July 2003. In this study, we ask: (1) was there Received 5 August 2014 an effect of the typhoon on stand structure and biomass; (2) was there an impact Accepted 5 February 2015 on species diversity; (3) did annual mortality, growth and recruitment change Co-ordinating Editor: Kerry Woods significantly between typhoon and non-typhoon periods; and (4) did the typhoon’s impact vary with local topography, from leeward to windward sides Yap, S.L. ([email protected])1, of a ridge? Davies, S.J. (corresponding author, Location: Lowland mixed dipterocarp forest, Palanan, Isabela, Philippines. [email protected])2, Condit, R. ([email protected])3 Methods: Census data from 1998, 2004 and 2010 for all trees ≥1 cm DBH in a 1Institute of Biology, University of the 16-ha permanent plot in Palanan, Isabela, were used to assess tree demography. Philippines, Diliman, Quezon City, PH 1101, Recorded in the census were species identification and measurements of DBH Philippines; and tree locations. -
1 Vertical Structure of Tropical Cyclone Rainbands As Seen by The
Vertical Structure of Tropical Cyclone Rainbands as seen by the TRMM Precipitation Radar Deanna A. Hence and Robert A. Houze, Jr. University of Washington, Seattle, Washington Submitted to Journal of Atmospheric Sciences November 2011 Corresponding author address: Deanna Hence, Department of Atmospheric Sciences, University of Washington, Box 351640, Seattle, WA 98195 E-mail: [email protected] 1 ABSTRACT 1 Ten years of data from the Tropical Rainfall Measurement Mission satellite’s Precipitation 2 Radar (TRMM PR) show the vertical structure of tropical cyclone rainbands. Radar-echo 3 statistics show that rainbands have a two-layered structure, with distinct modes separated by the 4 melting layer. The ice layer is a combination of particles imported from the eyewall and ice left 5 aloft as convective cells collapse. This layering is most pronounced in the inner region of the 6 storm, and the layering is enhanced by storm strength. The inner-region rainbands are vertically 7 confined by outflow from the eyewall but nevertheless are a combination of strong embedded 8 convective cells and robust stratiform precipitation, both of which become more pronounced in 9 stronger cyclones. 10 Changes in rainband coverage, vertical structure, and the amount of active convection 11 indicate a change in the nature of rainbands between the regions inward and outward of a radius 12 of ~200 km. Beyond this radius, rainbands consist of more sparsely distributed precipitation that 13 is more convective in nature than that of the inner-region rainbands, and the outer-region 14 rainband structures are relatively insensitive to changes in storm intensity. -
Influence of the Size of Supertyphoon Megi (2010) on SST Cooling
VOLUME 146 MONTHLY WEATHER REVIEW MARCH 2018 Influence of the Size of Supertyphoon Megi (2010) on SST Cooling IAM-FEI PUN Graduate Institute of Hydrological and Oceanic Sciences, National Central University, Taoyuan, Taiwan I.-I. LIN,CHUN-CHI LIEN, AND CHUN-CHIEH WU Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan (Manuscript received 28 February 2017, in final form 13 December 2017) ABSTRACT Supertyphoon Megi (2010) left behind two very contrasting SST cold-wake cooling patterns between the Philippine Sea (1.58C) and the South China Sea (78C). Based on various radii of radial winds, the authors found that the size of Megi doubles over the South China Sea when it curves northward. On average, the radius of maximum wind (RMW) increased from 18.8 km over the Philippine Sea to 43.1 km over the South 2 China Sea; the radius of 64-kt (33 m s 1) typhoon-force wind (R64) increased from 52.6 to 119.7 km; the radius 2 of 50-kt (25.7 m s 1) damaging-force wind (R50) increased from 91.8 to 210 km; and the radius of 34-kt 2 (17.5 m s 1) gale-force wind (R34) increased from 162.3 to 358.5 km. To investigate the typhoon size effect, the authors conduct a series of numerical experiments on Megi-induced SST cooling by keeping other factors unchanged, that is, typhoon translation speed and ocean subsurface thermal structure. The results show that if it were not for Megi’s size increase over the South China Sea, the during-Megi SST cooling magnitude would have been 52% less (reduced from 48 to 1.98C), the right bias in cooling would have been 60% (or 30 km) less, and the width of the cooling would have been 61% (or 52 km) less, suggesting that typhoon size is as important as other well- known factors on SST cooling. -
Philippines: Typhoon Megi
Information bulletin n° 2 Philippines: GLIDE TC-2010-000205-PHL Typhoon Megi 19 October 2010 This bulletin is being issued for information only, and reflects the current situation and details available at this time. The International Federation of Red Cross and Red Crescent Societies (IFRC) and Philippine Red Cross (PRC) have determined that external assistance is currently not required. Therefore, funding or other assistance from donors is not being sought at this time. Typhoon Megi smashed into the Philippines with powerful force. Winds toppled electrical posts – causing power outages – and uprooted trees. Fallen electrical posts meant a dark night for residents of Kalinga. Photo: Hajime Matsunaga/IFRC Typhoon Megi (local name Juan) battered the Philippines on Monday, 18 October 2010, with winds of more than 220km/h and heavy rains. According to Philippine weather authorities, the super storm made landfall near Divilacan Island on the eastern coast of Isabela Province. Its powerful winds toppled electrical posts – causing power outages – uprooted trees, and blew roofs away. Preliminary data indicates seven confirmed deaths and 12 injuries. In total, there are some 2,300 families affected in 11 provinces, and at least 35 houses damaged in three provinces. More than 1,700 families are currently in over 50 evacuation centres established across the affected provinces. Philippine Red Cross (PRC) emergency response units and specialized volunteers have begun responding to needs, delivering food and non-food items to families in evacuation centres. The teams that were on standby in affected provinces are on the ground conducting assessments. The IFRC country office has also deployed delegates to augment assessment efforts. -
FDA) Super Typhoon Mangkhut (Philippines: Ompong
Center for Disaster Management and Risk Reduction Technology CEDIM Forensic Disaster Analysis Group (FDA) Super Typhoon Mangkhut (Philippines: Ompong) Information as of 26 September 2018 – Report No. 1 Authors: Bernhard Mühr, James Daniell, Johanna Stötzer, Christian Latt, Maren Glattfelder, Fabian Siegmann, Susanna Mohr, Michael Kunz SUMMARY Landfall Official Disaster Name Date Local Duration UTC Super Typhoon 26W Mangkhut (Ompong) – PH 14-09 18 UTC +8 Typhoon 26W Mangkhut – CN 16-09 09 UTC +8 Tropical Depression, Tropical Storm, 07-09 – 17-09 10 days Typhoon Cat 1 – Cat 5, Tropical Depression PREFERRED HAZARD INFORMATION: Min Sea Move Definition (Saffir- Wind Wind Location Level Time ment Simpson Scale) Gusts Sustained Pressure 100 km N of Rongelap 07-09 20 kt NW Tropical Dperession 1006 hPa Atoll, Marshall Is. 00 UTC 37 kph 07-09 35 kt 120 km N of Bikini Atoll W Tropical Storm 998 hPa 18 UTC 65 kph 09-09 65 kt 1100 km E of Guam W Category 1 975 hPa 00 UTC 120 kph 10-09 90 kt 30 km NE of Rota Island WSW Category 2 955 hPa 06 UTC 165 kph 10-09 100 kt 90 km W of Guam WSW Category 3 950 hPa 12 UTC 185 kph 11-09 120 kt 2050 km E of Luzon N Category 4 935 hPa 00 UTC 222 kph Super Typhoon 11-09 140 kt 1780 km E of Luzon WNW 905 hPa Category 5 12 UTC 260 kph LOCATION INFORMATION: Most Economic Pop. Country ISO Provinces/Regions HDI (2014) Urbanity Impact Exposure affected Guam / Northern USA US Rota Island Marianas Cagayan, Ilocos Philippines PH Norte Hongkong, Guangdong, China CN Macao, Guangxi Jiangmen CEDIM – Typhoon Mangkhut – Report No.1 2 1 Overview In mid-September, the world's strongest tropical system of 2018 so far, hit the northern main island of the Philippines, Luzon, and southern China.