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Journal of the Meteorological Society of Japan, Vol. 88, No. 3, pp. 521--545, 2010.

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DOI:10.2151/jmsj.2010-315

Numerical Simulations of Myanmar Cyclone Nargis and the Associated Storm Surge Part I: Forecast Experiment with a Nonhydrostatic Model and Simulation of Storm Surge

Tohru KURODA, Kazuo SAITO, Masaru KUNII Meteorological Research Institute, Tsukuba, Japan

and Nadao KOHNO Japan Meteorological Agency, Tokyo, Japan (Manuscript received 27 May 2009, in final form 25 February 2010)

Abstract Numerical simulations of the 2008 Myanmar cyclone Nargis and the associated storm surge were conducted using the Japan Meteorological Agency (JMA) Nonhydrostatic Model (NHM) and the Princeton Ocean Model (POM). Although the JMA operational global analysis (GA) and the global spectral model (GSM) forecast underestimated Nargis’ intensity, downscale experiments by NHM with a horizontal resolution of 10 km using GA and GSM forecast data reproduced the development of Nargis more properly. Sensitivity experiments to study the e¤ects of ice phase, sea surface temperature (SST), and horizontal resolutions to Nargis’ rapid development were conducted. In a warm rain experiment, Nargis developed earlier and the eye radius became larger. It was shown that a high SST anomaly preexistent in the Bay of Bengal led to the rapid intensification of the cyclone, and that SST at least warmer than 29 C was necessary for the development seen in the experiment. In a simulation with a horizontal resolution of 5 km, the cyclone exhibited more distinct development and attained a center pressure of 968 hPa. Numerical experiments on the storm surge were performed with POM whose horizontal resolution is 3.5 km. An experiment with POM using GSM forecast data could not reproduce the storm surge, while a simulation using NHM forecast data predicted a rise in the sea surface level by over 3 m. A southerly sub-surface current driven by strong surface winds of the cyclone caused a storm surge in the river mouths in southern Myanmar facing the Andaman Sea. Our results demonstrate that the storm surge produced by Nargis was predictable two days before landfall by a downscale forecast with a mesoscale model using accessible operational numerical weather prediction (NWP) data and application of an ocean model.

1. Introduction Severe meteorological phenomena such as tropical cyclones (TCs) sometimes cause catastrophic damage to human society; therefore, their predicCorresponding author: Tohru Kuroda, Meteorological Research Institute, 1-1, Nagamine Tsukuba, Ibaraki 305-0052, Japan. E-mail: [email protected] 6 2010, Meteorological Society of Japan

tion is important for preventing and mitigating meteorological disasters. In the areas around the Bay of Bengal, historically, there have been several cases in which storm surges induced by TCs gave rise to severe floods (Obashi 1994). In cases such as the 1970 Bohla cyclone (Frank and Husain 1971) and the 1991 Bangladesh cyclone (Katsura et al. 1992; Bern et al. 1993), cyclones generated in the central area of the bay moved northward and made landfall in Bangladesh, and the associated

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storm surges destroyed the lowlands of that country. In 2007, cyclone Sidr struck the same area and caused considerable destruction (MFDM Bangladesh 2008; Hasegawa et al. 2008). In contrast with the above cases, cyclone Nargis that was generated at the end of April 2008, moved eastward. On May 2, it made landfall in southern Myanmar during its strongest period and caused a destructive storm surge over the Irrawaddy Delta and other low-lying areas that claimed more than one hundred thousand lives (Webster 2008). For disaster prediction in the areas mentioned above, forecasts of TCs and the associated storm surges based on numerical weather prediction (NWP) are particularly important. Since 2007, a research project called ‘‘International Research for Prevention and Mitigation of Meteorological Disasters in Southeast Asia’’ has been conducted by the Kyoto University, the Meteorological Research Institute (MRI), and other institutes in Southeast Asian countries (Yoden et al. 2008; Koh and Teo 2009). The goals of this project are to demonstrate the applicability of downscale NWP in Southeast Asia and to propose a decision support tool for preventing and mitigating meteorological disasters. From this point of view, we selected the devastating disasters caused by Nargis as one of the most important targets that we should study in this project. We assumed a minimum lead time of two days before the landfall in order to effectively mitigate Nargis’ storm surge damage and set the initial time of our simulation as 12 UTC on April 30, 2008, the time when Nargis started its eastward movement and one day before its rapid development. Considering the project’s purpose and the real-time accessibility to data required for downscale NWP, the Japan Meteorological Agency (JMA) nonhydrostatic model (NHM) and the JMA’s operational global data are used as the forecast model and for obtaining the initial and/or boundary conditions, respectively; the model and the data are available and accessible to registered users in Southeast Asia. In this paper, we conduct numerical simulation of Nargis and the associated storm surge for the following purposes and scientific interests: 1) To examine the predictability of Nargis two days before its landfall by downscale NWP using NHM and data available to Southeast Asian researchers: Considering the practical availability of the experiment in the case of real-time operation, forecast

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experiments, in which the JMA global spectral model (GSM) forecast data are used as the lateral boundary conditions are conducted. Also, the results are compared with a reproduction experiment, in which global analyses (GA) are used as the lateral boundary conditions instead of the GSM forecast in order to observe the impact of the accuracy of the lateral boundary value. 2) To investigate the impact of the sea surface temperature (SST) and the physical process on the rapid development of Nargis: SST is an important factor controlling the development of tropical cyclones. McPhaden et al. (2009) pointed out that there was a preexisting warm anomaly of SST in the Bay of Bengal in late April 2008 and inferred that this had contributed to the rapid intensification of Nargis. Lin et al. (2009) determined that there were warm anomalies not only in the SST but also in the temperatures of the subsurface layer, and showed that this situation reduced the cyclone-induced ocean cooling by using numerical experiments with a one-dimensional ocean mixed layer model. However, neither McPhaden et al. (2009) nor Lin et al. (2009) have conducted numerical simulation of Nargis using a fullscale atmospheric model. In this paper, we examine the impact of SST on Nargis’ development through sensitivity experiments using di¤erent SST datasets. The impact of ice phase on Nargis’ development is also examined, and the results are compared with the study by Sawada and Iwasaki (2007), in which simplified conditions including a horizontally uniform background were used. The impact of horizontal resolution is also examined. These sensitivity experiments are not necessarily comprehensive, but they give us information that help us to understand the magnitude of the influence of the model uncertainty with respect to the influence of the initial and boundary conditions on Nargis’ rapid development. 3) To examine the predictability of storm surges using downscale NWP and an ocean model: As for storm surges on the Bay of Bengal, Flather (1994) studied storm surges associated with the 1970 Bohla cyclone (Frank and Husain 1971) and the 1991 Bangladesh cyclone using a numerical ocean model. However, this was a two-dimensional open sea model, with surface winds and pressures derived from a semi-analytical cyclone model using the best track data supplied by the US Navy Joint Typhoon Warning Center (JTWC). Recently, Kim et al. (2006) conducted numerical

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simulation of the storm surge of Hurricane Katrina, which damaged the city of New Orleans in the United States of America in 2005. The simulation results obtained using a sophisticated atmosphere-wave-ocean coupled model were in agreement with the actual observations. However, for achieving a practical disaster prevention, simpler surge predictions using a downscale mesoscale NWP and a one-way nested ocean model may be more desirable, with application to the Bay of Bengal as an urgent subject (Dube et al. 2009). In this study, we conduct a numerical simulation of the storm surge of Nargis, applying the Princeton Ocean Model (POM) to the Bay of Bengal. The advantages of the NHM-simulated surface winds and pressures over the GSM forecast will be shown. This paper is organized as follows: Section 2 reviews the characteristic features of Nargis and its associated storm surge. Section 3 presents JMA’s global analysis and the performance of the JMA GSM forecast. These data are used for obtaining the initial and boundary conditions of NHM. In Section 4, we describe the numerical simulations of Nargis that were conducted using NHM. The sensitivity of Nargis’ development to the SST, the ice phase, and the horizontal resolution are examined. In Section 5, we describe the numerical simulations of the storm surge that were carried out using forecasts from GSM and NHM. The advantages of the downscale high resolution simulation over the global model forecast are demonstrated. The summary and concluding remarks are given in Section 6. 2. Cyclone Nargis and storm surge 2.1 Characteristic features of cyclone Nargis Cyclone Nargis, known as the ‘‘Myanmar Cyclone’’ was first generated as a tropical depression in the center of the Bay of Bengal and was detected as a tropical storm on April 27, 2008. After April 29, it moved eastward as it developed and made landfall in southwestern Myanmar at around 09– 12 UTC on May 2 (Fig. 1a). Figure 1b presents the time sequence of the center pressure of Nargis as estimated by the Regional Specialized Meteorological Center (RSMC), New Delhi and JTWC. The cyclone was analyzed as a tropical storm of around 970–980 hPa until 06 UTC on May 1, and it developed rapidly after that. Nargis reached its maximum intensity of category 4 around 06–12 UTC on May 2, as it ap-

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proached southern Myanmar. The minimum center pressure estimated by JTWC was 937 hPa, while RSMC estimated its intensity as 962 hPa. The rainfall rate observed by the Tropical Rainfall Measuring Mission’s Microwave Imager (TRMM/TMI) at 0137 UTC on May 2 is indicated in Fig. 2, which depicts the typical structure of a developed cyclone with a compact central dense overcast (CDO) and distinct spiral rainbands. After landfall at around 09–12 UTC on May 2, the cyclone moved inland to the northeast, passing over southern Myanmar and rapidly decayed. 2.2 Storm surge of Nargis The destructive damage in southern Myanmar during the passage of Nargis was primarily caused by the storm surge, though the estimated maximum wind speed exceeded 40 m/s. Since the river deltas in southern Myanmar are low-lying, the storm surge reached inland several tens of kilometers from the coastal areas facing the Andaman Sea causing extensive floods. The shaded areas in Fig. 3 indicate the resultant water/wet regions or the vegetation loss. A field survey around the Yangon River was conducted by Shibayama et al. (2008). The Yangon River has a wide mouth of about 8 km, with the river becoming narrower on the upstream side. Water level deviation due to the storm surge was estimated to be more than 3 m at a point about 30 km upriver from the river mouth. Even around Yangon city, 40 km upstream from the river mouth, a water level deviation of more than 1 m was reported. A numerical simulation for a particular point in the river (the Yangon point, indicated by ‘‘Y’’ in Fig. 3) is presented later in this study. In the Ayeyarwaddy district, including the region around the Irrawaddy River mouth (the Irrawaddy point, indicated by ‘‘I’’ in Fig. 3), a higher storm surge might have occurred, but no detailed reports have been made yet. 3. Global analysis and operational forecast of JMA In Section 4, we will describe the downscale experiments conducted using NHM in which JMA’s operational global analysis and forecast data were used as the initial and boundary conditions, respectively. Prior to discussing the experiments, we describe the above data and observe how Nargis was expressed in the NWP operation of JMA. 3.1 JMA global analysis The JMA global analysis is a 6-hourly analysis

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Fig. 1. a) Best track of cyclone Nargis. b) Time sequence of sea-level center pressure estimated by RSMC, New Delhi and JTWC.

produced by the 8 th grade Numerical Analysis and Prediction System (NAPS) of JMA. An incremental four-dimensional variational analysis (4DVAR;

Kadowaki 2005) with 6 h assimilation windows which assimilate conventional observation data (radio sonde, surface, ship, buoy, and aircraft) and

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Fig. 2. Rainfall rate (mm/h) observed by TRMM/TMI at 0137 UTC on May 2, 2008. After JAXA/EORC Tropical Cyclone Database (http://sharaku.eorc.jaxa.jp/TYP_DB/index_e.shtml).

satellite data [NASA/NOAA TIROS Operational Vertical Sounder (TOVS), QuikSCAT, the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-functional Transport Satellite (MTSAT) Cloud Motion Vector, etc.] is employed. The resolutions are T159L60 (about 80 km) in the inner model of the 4DVAR and TL959L60 (about 20 km) in the outer model. For a typhoon in the northwestern Pacific, typhoon bogus data are assimilated, while no bogus data are used for others including those in the Bay of Bengal. Further details of the JMA global analysis are given by Narui (2007). The global analysis data with two di¤erent resolutions are archived at MRI. The first is a highresolution analysis as the initial condition of the operational global NWP at JMA with an original resolution of 20 km (0.1875 Gaussian grids) and 60 model (h-) planes. The second, a coarse mesh analysis with 1.25 (latitude-longitude grids) and 11 pressure planes, has been more widely used by researchers due to its relative ease of handling. The

analysis procedures are the same but the resolutions are di¤erent. In this study, we refer to the high resolution global analysis as ‘‘GA’’, and the coarse mesh pressure plane data as ‘‘GA-p’’. Figure 4a shows the tracking of Nargis on the basis of the JMA global analyses from 12 UTC on April 30, to 06 UTC on May 2, 2008. In this figure, the track of GA (thin solid line) is depicted after interpolation on Mercator grids with a resolution of 10 km. The dotted line is the track of GA-p with the original grids (1.25 ), while the broken line shows a track that was interpolated on the grids with a horizontal resolution of 0.25 using the Bessel interpolation method. It is seen that the starting points in both GA and the interpolated GA-p at 12 UTC on April 30 deviate by about 100 km eastwardly from the best track (thick solid line). At 06 UTC on May 2 (east end points), the eastward positional lags are smaller than those at 12 UTC on April 30, but northward positional lags are seen in both GA and GA-p at most analysis times after 12 UTC on May 1.

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Fig. 3. Storm surge-a¤ected areas in southern Myanmar observed by Terra SAR-X micro wave radiometer. The shaded areas indicate water/wet regions or vegetation losses and the two rectangles show footprints of Terra SAR-X on May 8, 2008. The Irrawaddy and Yangon points are indicated by the circled ‘‘I’’ and ‘‘Y’’, respectively. Source: the Information Technology for Humanitarian Assistance, Cooperation and Action (ITHACA; www.ithacaweb.org) in cooperation with the United Nations World Food Programme (WFP) and the German Aerospace Center (DLR).

The sea level pressure of the cyclone center represented in GA and GA-p is seen in Fig. 4b. Center pressures in GA are lower than those in GA-p at almost all analysis times. This shows that GA can represent the cyclone more properly than GA-p. Although both analyses capture the evolution of Nargis to some extent, the represented center pressure is quantitatively insu‰cient compared with the central pressure obtained from the best track (Fig. 1b). The insu‰ciency of Nargis’ expression in the JMA global analysis mentioned above is probably due to a lack of the TC bogus data and the shortage of assimilated observation data in the Bay of Bengal.

3.2 Operational global forecast of JMA Forecasts made by the operational NWP at JMA using a global model (GSM) on Nargis will now be reviewed. GSM is a global spectral model of JMA with the world’s highest resolutions of TL959L60 as an operational global model. Details of the model are given in Kitagawa et al. (2007). We used GRIB2 formatted GSM data distributed by the Japan Meteorological Business Support Center (JMBSC). The data comprise Grid Point Values (GPVs) obtained every 6 h in the GRIB2 format with a horizontal resolution of 0.5 and 17 levels of pressure planes. The real-time data are disseminated mainly to commercial users but archived

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Fig. 4. a) Tracks of Nargis by the JMA global analyses from 12 UTC on April 30 to 06 UTC on May 2, 2008. The thin solid and dotted lines indicate the tracks by GA and GA-p, respectively, and the thick solid line indicates the best track. The broken line was depicted by the interpolation of GA-p. The triangles indicate the positions at 00 UTC on 1 and May 2 eastwardly on the track. b) Time sequence of center pressure of the cyclone. The solid and broken lines represent GA and GA-p, respectively.

data are available to the research community. Depressions corresponding to Nargis in GSM forecasts with four (12-hourly) initial times (00 UTC and 12 UTC on April 30 and May 1) are depicted in Fig. 5. Forecasts with later initial times tended to predict lower center pressures. This tendency was probably due to the representation of the cyclone at the initial conditions; the later the initial times, the lower the center pressures that appear in the global analysis. The maximum decrease of center pressures in the GSM forecasts was about 10 hPa at best. Tracks of Nargis by GSM forecasts with initial

times from 00 UTC on April 30 to 00 UTC on May 1 are plotted in Fig. 6a, and the eastward motion of the tracks is depicted in Fig. 6b. These tracks were obtained from interpolated data with a horizontal resolution of 0.1 using Bessel interpolation. The forecasted center positions at 06 UTC on May 2 deviate northwardly from the best track, and the landfall times were earlier than those of the best track, except in the forecast with an initial time of 00 UTC on April 30. As mentioned previously, GSM, one of the world highest resolution operational global models, is believed to have the ability to simulate some meso-b

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Fig. 5. Time evolution of sea-level cyclone center pressures by GSM forecasts. Broken line, thick solid line, thin solid line, and dotted line indicate the forecasts with initial times of 00 UTC on April 30, 12 UTC on April 30, 00 UTC on May 1 and 12 UTC on May 1, respectively.

scale phenomena, but the forecasted minimum center pressure of Nargis at an initial time of 12 UTC on April 30 (two days before landfall) was around 992 hPa. It was di‰cult to foresee the cyclone’s catastrophic disaster from this forecast. 4. Numerical simulation with NHM 4.1 Numerical model and design of experiments The JMA nonhydrostatic model (NHM) was used for performing the numerical simulations in this study. The model was originally developed as a community mesoscale model for research and weather forecasting by a collaboration between MRI and the Numerical Prediction Division of JMA (Saito et al. 2001) and has been used for operation at JMA since September 2004 (Saito et al. 2006). The horizontal resolution of operational mesoscale forecasts has been enhanced from 10 km to 5 km since March 2006 (Saito et al. 2007). A three-ice bulk cloud microphysics scheme based on Murakami (1990) that predicts cloud water, rain, cloud ice, snow, and graupel, and a Kain-Fritsch convective parameterization scheme (Kain and Fritsch 1993) were included as the moist processes, whereas several points were modified for an operational NWP with horizontal resolutions of 5 to

10 km as described by Saito et al. (2006; 2007). The Mellor-Yamada-Nakanishi-Niino’s level 3 (MYNN-3) turbulent closure model developed by Nakanishi and Niino (2004) was implemented as the first operational NWP model in the world (Hara 2008). Surface momentum, heat, and moisture fluxes over the sea were computed using Beljaars and Holtslag’s (1991) scheme. Here, we considered the wind to be at 10-m height as the surface wind, which is diagnosed from the lowest level wind by the similarity law using a bulk momentum coe‰cient (see Eq. (4.5.89) in Japan Meteorological Agency 2007). In this study, we selected 12 UTC on April 30, 2008 as the initial time of our numerical simulations with NHM. This initial time was about 48 h before the landfall of Nargis and was chosen considering the two day lead time required to issue cyclone or storm surge warnings. We paid attention to the valid time of 06 UTC on May 2 (FT ¼ 42), just before landfall. In the latter half of this 42-h period, Nargis rapidly developed from Category-1 to Category-4 as seen in Fig. 1b. NHM with a horizontal resolution of 10 km was used in most experiments. Its domain was a square of 3400 km size (1 S–30 N, 73 E–107 E) that cov-

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Fig. 6. a) Cyclone tracks until 06 UTC on May 2 by GSM forecasts with various initial times. The points specify the locations of the cyclone center every 6 h and the triangles depict the 00 UTC positions during the forecast period. The thin solid line indicates the forecasted track whose initial time is 12 UTC on April 30. The initial times represented by the two dotted lines are 00 UTC and 06 UTC on April 30 (earlier than 12 UTC on April 30), and broken lines are 18 UTC on April 30 and 00 UTC on May 1 (later than 12 UTC on April 30). The thick solid line is the best track from 00 UTC on April 30. b) Eastward motion of cyclone tracks appeared in a).

ers the Bay of Bengal and the surrounding area, including Myanmar (Fig. 7). Forty-level terrainfollowing hybrid coordinates were employed vertically, with vertical grid distances stretching from 40 m near the surface to 1180 m at the model top and with the lowest level located 20 m above ground level. The topography was obtained from the Global 30 Arc Second Elevation Data Set (GTOPO30). Also, the land use data was based on the United

States Geographical Survey (USGS) Global Land Cover Characterization (GLCC) with 1-km horizontal resolution. These high resolution data were averaged and smoothed in the NHM grids. JMA global analysis and forecast data were used as initial and/or boundary conditions. No bogus data were used. A JMA global SST analysis with 0.25 resolution and a JMA global land analysis with 0.1875 resolution were used with interpolation to the grid of the NHM experiment. SST and land

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Fig. 7. Domain used for NHM forecasts with 10-km horizontal resolution. The dotted square is the domain of nested experiments (NEST1 and NEST2 in Table 1), and broken rectangle indicates the domain of POM for storm surge simulations.

temperature at the initial time were used and were assumed to be constant throughout the forecast period until FT ¼ 72. The NHM experiments conducted in this study are listed in Table 1. 4.2

Numerical simulation using global analysis data (reproduction experiment) Prior to the forecast experiment with NHM using the JMA global analysis as the initial condition and the JMA global forecast as the lateral boundary condition, we conducted numerical experiments using the global analysis data (GA and GA-p) as the initial and lateral boundary conditions to observe their quality and ability to reproduce Nargis’ track and intensity. The coarse mesh pressure plane global analysis data (GA-p) are easy to handle and have been widely used in the research community in Japan, while we developed a new preprocessing tool to use high resolution Gaussian grid model plane data (GA) for numerical experiments with NHM over Southeast Asia. As listed in Table 1, the reproduction experiment using GA as the initial

and boundary conditions is named ‘‘GAGA’’, and the experiment using GA-p is ‘‘GAPGAP’’. The cyclone tracks up until FT ¼ 42 (valid time 06 UTC on May 2) predicted by the two experiments appear in Fig. 8a. The positional deviation of GAPGAP (broken line) was remarkable in the northeast direction. The GAGA track (thin solid line) was close to the best track (thick solid line), but the positions at each forecast time deviated east-northeastward from the best track. As mentioned in previous sections, global analyses also have east-northeastward positional lags (Fig. 4a), and the GSM forecast for the same initial time had an even larger positional lag (Fig. 6). The position at FT ¼ 42 was (16.4N , 95.0E ) for GAGA and (16.0N , 95.5E ) for GSM, and the positional lags of GAGA from the best track was 150 km while that of GSM was 193 km. GAGA thus represents a better track than GSM. In GAPGAP, the cyclone exhibited unnatural northward movement after the initial start-up and the positional deviation at FT ¼ 42 was very large in the northeast direction.

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List of numerical experiments. Kain–Fritsch convection parameterization is used except for NEST2. Horizontal resolution

Initial time

Initial condition

Boundary condition

GAGA

10 km

12 UTC 30 Apr

GA

GA

JMA

976

GAPGAP

10 km

12 UTC 30 Apr

GA-p

GA-p

JMA

976

GAGSM (CNTL)

10 km

12 UTC 30 Apr

GA

GSM

JMA

974

WR

10 km

2 UTC 30 Apr

GA

GSM

JMA

971

GAGSM_SST30

10 km

12 UTC 30 Apr

GA

GSM

JMA (Max. 30 C)

974

GAGSM_SST29

10 km

12 UTC 30 Apr

GA

GSM

JMA (Max. 29 C)

985

GAGSM_SSTN

10 km

12 UTC 30 Apr

GA

GSM

NCEP

971

NEST1

5 km

00 UTC 01 May

GAGSM

GAGSM

JMA

968

NEST2

3 km

00 UTC 01 May

GAGSM

GAGSM

JMA

982

Name

Its center position at that time was (17.6N , 95.3E ), and the positional lag from the best track was 252 km. The forecasted center pressures are given in Fig. 8b. The minimum values for GAGA and GAPGAP were almost the same (976 hPa), but the intensification of the cyclone started earlier in GAGA than in GAPGAP. Both forecasts with NHM reproduced the development of Nargis more accurately than GA and GA-p themselves. Next, we will compare the GAGA forecast with satellite observations. The TRMM/TMI-observed 1-h rainfall rate at 0137 UTC on May 2, 2008 is presented in Fig. 2. At this time, the cyclone center was located o¤ the coast of southwestern Myanmar (15.8 N, 93.2 E). The rainfall intensity was 10 to 20 mm/h in the major part of the spiral bands with maximum values of 20 to 25 mm/h. The diameter of the CDO was less than 200 km, and a wide spiral rainband adjoined the western part of the CDO. Another distinct spiral band was seen south of the CDO. Figure 9a gives the rainfall rate predicted by GAGA at FT ¼ 31, when the position of the simulated cyclone center (16.0 N, 93.1 E) was close to the cyclone center seen in the TRMM image. The predicted CDO was also compact, with a size comparable to Fig. 2. There were two major spiral bands, one to the west and the other to the south of the cyclone center, while weak orographicallyinduced rains had already started in the coastal areas of southern Myanmar. Although the simu-

SST

Minimum center pressure

lated rainfall rates were 10 to 20 mm/h and were thus slightly lesser than those in the TRMM image, the characteristics of Nargis are well reproduced in the simulation as a whole. 4.3 Forecast experiment In this section, we used the GSM forecast data from JMBSC, described in Section 3.2, as the lateral boundary condition. Considering the result in the previous section, we used a high resolution global analysis (GA) at 12 UTC on April 30 as the initial condition. The domain, resolution, and other experiment conditions were the same as those in the reproduction experiment. We refer to this forecast experiment as GAGSM. The cyclone tracks predicted by NHM (GAGSM; thin solid line) and GSM (broken line) are plotted in Fig. 10a. The predicted position (16.6 N, 94.6 E) at FT ¼ 42 in GAGSM again deviates northwardly from the best track (thick solid line), but its positional lag of 124 km was lower than that in GSM (193 km). Positional lags in the initial and boundary conditions mentioned in Section 3 probably caused the positional lag in the NHM forecast. The change in the center pressures over time predicted by NHM (GAGSM) and GSM is illustrated in Fig. 10b. The minimum sea-level pressure of GSM was 993 hPa and that of GAGSM was 974 hPa, which implied that NHM could predict Nargis’ rapid development more accurately than

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Fig. 8. a) Tracks of Nargis by GAGA (thin solid line) and GAPGAP (broken line) and the best track (thick solid line). The points represent locations of the cyclone center every 6 h. Every track starts from 12 UTC on April 30 and is depicted until 06 UTC on May 2. The triangles indicate the positions at 00 UTC on May 1 and May 2 eastwardly on the track. b) Time sequence of cyclone center pressures obtained from GAGA (solid line) and GAPGAP (broken line). Initial time is 12 UTC on April 30, 2008.

GSM. The higher resolution of NHM contributed to the better representation of the cyclone center. Another possible cause is the di¤erence in the physical processes, especially the convective parameterization schemes. The Kain–Fristch (K–F) scheme employed in NHM tends to heat the atmosphere at lower levels than does the Arakawa–Schubert (A– S) scheme in GSM. Recently, E. Shindo (private communication) has reported that NHM tends to develop a typhoon more rapidly than GSM in its early stage with the same horizontal resolution and that GSM with the K–F scheme develops a ty-

phoon stronger than GSM with the A–S scheme. The maximum surface wind speed predicted by GSM was less than 20 m/s, while that predicted by GAGSM was more than 30 m/s (Fig. 10c). The rainfall intensity at FT ¼ 33 is indicated with the cyclone center (16.4 N, 93.2 E) in Fig. 9b. The features are similar to those in GAGA (Fig. 9a), where Nargis’ observed characteristics (compact CDO with spiral rainbands to the west and south) were simulated. Precipitation areas with rates of 5 to 20 mm/h in spiral bands were larger than in GAGA.

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Fig. 9. a) Rainfall rate (mm/h) simulated by GAGA at FT ¼ 31. b) Same as in a) but GAGSM (CNTL) at FT ¼ 33. c) NEST1 at FT ¼ 21.

4.4 Sensitivity of Nargis’ development to ice phase Experiment GAGSM in the previous subsection employed a three-ice cloud microphysics scheme with K–F convective parameterization. Sawada and Iwasaki (2007) studied the impact of the ice phase on the typhoon development. They conducted numerical experiments using NHM with and without ice phase in cloud microphysics and examined the results. In their study, the ice-phase process delays the intensification of the cyclone, though the maximum intensity is not very di¤erent. The radius of the eye becomes smaller with an ice phase due to the weaker tangential wind. However, their experiments were done with simplified condi-

tions including a horizontally uniform background. Since their simulation was performed with 2-km resolution, convective parameterization was not employed. In this subsection, we describe a warm rain experiment (WR) that we conducted and examine the sensitivity of the ice phase to Nargis’ development, and thus, confirm the results of the study conducted by Sawada and Iwasaki (2007) in a practical situation. Figure 11a plots the maximum azimuthally averaged tangential surface wind velocity around the cyclone center (averaged V T ). In the WR (broken line), the averaged V T was larger than that in the CNTL (¼ GAGSM) experiment with the ice phase

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Fig. 10. a) Predicted tracks of Nargis until FT ¼ 42 (06 UTC on May 2) by NHM (GAGSM, thin solid line) and GSM (broken line), and the best track (thick solid line). The triangles indicate the positions at 00 UTC on May 1 or May 2. b) Time evolution of sea-level center pressure of Nargis by NHM (GAGSM) and GSM forecasts. Initial time is 12 UTC on April 30, 2008. c) Maximum predicted surface wind speed by NHM (GAGSM) and GSM.

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Fig. 11. a) Time sequence of the maximum averaged V T with the control run (CNTL) and the warm rain experiment (WR). b) Same as in a) but eye radius. Initial time is 12 UTC on Apr. 30, 2008.

during most of the simulation period. This means that Nargis develops earlier without the ice phase in the development stage. The peak value of the maximum averaged V T in the warm-rain experiment was comparable to that in CNTL. These results are consistent with those reported by Sawada and Iwasaki (2007). Figure 11b indicates the radius that gives the maximum averaged V T . We refer to this radius as the ‘‘eye radius’’ for simplicity. The eye radius

in WR tended to be larger than that in CNTL during most of the period after the mature stage (FT ¼ 36). Again this tendency is consistent with that reported by Sawada and Iwasaki (2007). However, in the developing stage, up until FT ¼ 36, an opposite tendency is observed, i.e., the eye radius in WR was smaller than that in CNTL. The earlier organization of WR represented as a rapid intensification in Fig. 11a may bring about this tendency.

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Fig. 12. a) Surface temperature by the JMA land and SST analyses at 12 UTC on April 30, 2008. b) Same as in a) but NCEP SST analysis. c) Di¤erence between the JMA SST and NCEP SST. d) Same as in a) but SST in GAGSM_SST29.

4.5 Impact of SST In this subsection, we examine the impact of SST on Nargis’ development through sensitivity experiments that were conducted over a wide range of SSTs. Figure 12a indicates the surface temperature at 12 UTC on April 30, 2008 as employed in the control forecast experiment (GAGSM), in which a JMA SST analysis with a horizontal resolution of 0.1875 is used. High SST areas warmer than 30 C are seen in the central and western parts of the Bay of Bengal. McPhaden et al. (2009) reported this pre-existing warm anomaly in the SST in the Bay of Bengal and suggested that it might have contributed to the rapid intensification of Nargis.

In order to observe the impact of SST, we conducted another forecast experiment (GAGSM _SSTN) by replacing the JMA SST with the National Centers for Environmental Prediction (NCEP) SST (Fig. 12b; the horizontal resolution is 1 ). The di¤erence between the JMA and NCEP SST is evident in Fig. 12c, where higher SST areas are seen in the eastern and southern part of the Bay of Bengal in the NCEP SST. Thick (thin) solid lines in Fig. 13 indicate the time evolution of cyclone center pressure predicted by GAGSM (GAGSM_SSTN). The center pressure of Nargis predicted by GAGSM_SSTN reached 970 hPa. The warmer SST in the NCEP

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Fig. 13. Time evolution of cyclone center pressures. The thick solid line shows the GAGSM (CNTL) case, and the thin solid line represents the case when NCEP SST is used (GAGSM_SSTN). The broken and dotted lines indicate the results of SST suppressed to 30 C and 29 C, respectively (GAGSM_SST30 and GAGSM_SST29). Initial time is 12 UTC on April 30, 2008.

analysis resulted in greater intensification in GAGSM_SSTN. To confirm the influence of the warm anomaly in SST in the Bay of Bengal, we prepared two artificial SSTs in which the JMA SST was suppressed to 30 C or 29 C, and conducted additional sensitivity experiments (GAGSM_SST30 and GAGSM _SST29). The sea level pressure of the cyclone center in these experiments is indicated in Fig. 13 by the broken and dotted lines. GAGSM_SST30 exhibits slower development and earlier decay, but the minimum cyclone center pressure (975 hPa) was similar to that of GAGSM. In GAGSM _SST29, the development of Nargis was drastically suppressed, and the minimum center pressure was only 985 hPa. The above results indicate that the high SST anomaly preexistent in the Bay of Bengal led to the rapid intensification of the cyclone and that an SST over 29 C at least was indispensable in this forecast experiment. 4.6 Experiments with higher resolutions In previous subsections, we performed experiments with a horizontal resolution of 10 km. Since the current operational mesoscale NWP at JMA is

conducted with a horizontal resolution of 5 km, experiments with higher resolutions are worth conducting to observe the performance of NHM. Forecast experiments with horizontal resolutions of 5 km (NEST1) and 3 km (NEST2) were conducted using the GAGSM (CNTL) forecast at 00 UTC on May 1 as the initial condition and using 3-hourly GAGSM forecasts as the boundary conditions. The domain is a square of about 2000 km size indicated by a dotted line in Fig. 7 (5.4 N–24.2 N, 80.0 E–100.0 E). The physical processes in NEST1 were the same as those in CNTL. NEST2 does not use convective parameterization; only cloud microphysics was used for moist processes. The track plotted by NEST1 was similar to that of GAGSM, as can be seen in Fig. 14a. The rainfall rate (mm/h) by NEST1 at FT ¼ 21 (21 UTC on May 1) is illustrated with the cyclone center (16.4 N, 93.2 E) in Fig. 9c. Areas with rates greater than 20 mm/h were seen not only in CDO but also in spiral bands. The track plotted by NEST2 was similar to that of NEST1 until FT ¼ 18, but the eastward deviation from the best track became larger after that, and the landfall was too early. Time evolutions of the cyclone center pressures in these experiments are presented in Fig. 14b. In

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Fig. 14. a) Tracks of the cyclone until 06 UTC on May 2 forecasted by GAGSM (CNTL, thin solid line), the 5-km nesting run (NEST1, broken line) and the 3-km run (NEST2, dotted line). The GAGSM track and the best track (thick solid line) start from 12 UTC on April 30, while tracks of NEST1 and NEST2 start from 00 UTC on May 1. The triangles indicate the positions at 00 UTC on May 1 or May 2. b) Time evolution of cyclone center pressures by GAGSM (solid line), NEST1, (broken line) and NEST2, (dotted line). Initial time of GAGSM is 12 UTC on Apr. 30, 2008, while initial time for NEST1 and NEST2 is 00 UTC on May 1.

NEST1, the simulated cyclone exhibited more distinct development than that in GAGSM, attaining 968 hPa around FT ¼ 36 even with JMA’s SST analysis. This intensity was still weaker than that obtained in the JTWC analysis in Fig. 1a but was comparable to the estimate made by the RSMC (972 hPa at 00 and 03 UTC and 962 hPa at 06 UTC). The reason for the positional lag and insufficient development in NEST2 has not been investigated fully. It seems that the fast motion of Nargis in NEST2 impeded the cyclone’s full organization by its early landfall. We should note that the 3-km horizontal resolution is too coarse to remove the

convective parameterization as mentioned by Noda and Niino (2003), and Lean and Clark (2003). 5. Numerical simulation of storm surge 5.1 Princeton Ocean Model and design of experiment Storm surge simulations were performed using the Princeton Ocean Model (POM; Blumberg and Mellor 1987). POM is a free surface, threedimensional, community, general circulation ocean model developed at Princeton University. Oceanic currents and water levels were calculated with sigma (terrain-following) coordinates using the sur-

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face pressure and winds as the input data as described later. The bathymetry and topography data were obtained from the National Geophysical Data Center (NGDC) ETOPO2 databases of seafloor and land elevations on a 2-min latitude-longitude grid. The coastal boundary was assumed to be a rigid wall where the land height in ETOTO2 is positive and inundation was not considered. The minimum depth of the ocean was assumed to be 3 m in order to prevent dry up; this assumption may suppress the surge overestimation near the wall that can be caused by the wind setup e¤ect since this effect was inversely proportional to the water-depth. The maximum ocean depth was assumed to be 1000 m, since the water depth a¤ects the wind setup only in shallow-water regions and never alters the inverse barometer e¤ect in storm surge phenomena. Faster horizontal oceanic motion was e‰ciently simulated using vertically averaged current every time step (2-dimensional; external mode), with slower vertical motion calculated every 30 time steps (3-dimensional; internal mode). These assumptions enabled us to represent a storm surge including these major e¤ects with reasonable calculation cost. The open-sea boundary was assumed to follow a static balance with the atmospheric surface pressure, and deviations from the statically balanced level caused inflow or outflow current and gravitational waves. Note that JMA has been operating an original two-dimensional storm surge prediction model (Higaki et al. 2009) that di¤ers from POM. In this study, 12 UTC on April 30, 2008 was taken as the initial time, and the ocean model was initiated from a static state. The astronomical tide was not taken into account, and thus, only the deviation of water level was computed with respect to the ocean’s vertical motion. Although the e¤ect of waves was not considered in order to save calculation cost, the assumptions mentioned above can su‰ciently represent major processes of a storm surge. The momentum flux balance across the airsea interface based on the law of the wall (see Eq. B2 of Appendix B in Mellor 2004) is achieved by setting the wind stress coe‰cient to 2:6  103 N/ m 2 . Input winds and pressures given by NWP in NHM forecasts were then used as external driving forces. Sea level pressures were determined for the input data every 10 min from GSM or NHM forecasts through temporally and spatially linear interpolation to the grids of POM. For a 10-m horizontal wind, the wind speed and direction are linearly

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interpolated independently. The computation domain of POM covered the Bay of Bengal, indicated by a broken rectangle in Fig. 7 (10 N–23 N, 84 E– 99 E). The horizontal resolution was 3.5 km, and 12 layers were used vertically. The layers were defined by depth normalized by the seabed depth (as 0, 0.016, 0.031, 0.062, 0.125, 0.250, 0.375, 0.500, 0.625, 0.750, 0.875, 1.000). Though the thickness varies in the Bay of Bengal, the di¤erences in POM level thickness had little effect on the result of the simulation because strong current is usually generated in shallow-water regions rather than in the deep sea during storm surge phenomena. 5.2 Storm surge simulation with the GSM forecast First, we prepared a storm surge simulation using the GSM forecast. Here, we pick up two numerical ocean grid points of POM, the Irrawaddy point (16.10 N, 95.07 E) and the Yangon point (16.57 N, 96.27 E), indicated in Fig. 3. The Yangon point corresponds to the location where the Yokohama National University conducted a field survey along the Yangon River (Shibayama et al. 2008). The Irrawaddy point is the location where the maximum water-level deviation occurs in the NHM forecast, as discussed below. Figures 15a–c plot the GSM-predicted surface wind and POM-predicted water level at the Irrawaddy point during the 72-h simulation. As seen in Fig. 15a, the surface wind predicted by GSM was weak (about 6 m/s). The wind direction (Fig. 15b) changed from south to west before the wind speed became maximum at around 06 UTC on May 2. This clockwise change in the wind direction indicated that the simulated cyclone passed north of the Irrawaddy point. The simulated maximum water level was about 0.7 m (Fig. 15c). At the Yangon point, the wind direction (Fig. 15e) changed between 06 UTC and 12 UTC with a maximum wind speed of about 6 m/s and water level of 0.5 m. These simulated water levels were quantitatively too small to foresee the storm surge disaster caused by Nargis. 5.3 Storm surge simulation with the NHM forecast Next, we used the NHM forecast (GAGSM) as input data to the storm surge simulation by POM. The results for the two points specified in the previous section are depicted in Fig. 16. As seen in Figs. 16a and 16d, the surface wind speeds obtained were much higher than those obtained in the GSM fore-

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Fig. 15. a) Time sequence of the surface wind speed by the GSM forecast at the Irrawaddy point (16.10 N, 95.07 E). b) Same as in a) but for wind directions. c) Same as in a) but water levels simulated by POM. d)–f) Same as in a)–c) but at the Yangon point (16.57 N, 96.27 E).

cast, reaching 25 m/s at the Irrawaddy point and 20 m/s at the Yangon point. The trend of the wind direction (Figs. 16b and 16e) was similar to that in the GSM case. Simulated water levels are depicted

in Figs. 16c and 16f. At the Irrawaddy point, the water level became highest when the southerly wind was strongest. At 07 UTC on May 2 (FT ¼ 43), the displacement of the sea surface level

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Fig. 16. Same as in Fig. 15 but with the NHM forecast (GAGSM).

reached 3.2 m (Fig. 16c), which was the largest value both spatially and temporally in this simulation and was roughly of the same magnitude as the displacement due to the storm surge at the Yangon River reported by Shibayama et al. (2008). The water level was quite high in the upriver area, reaching

a height of several meters as mentioned above, while the water level at the river mouth, closer to the sea, was about 1 m. At the Yangon point, a similar tendency was seen in the sense that the maximum water level was observed at 10 UTC (Fig. 16f) just before the

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wind speed reached the maximum value at 13 UTC (Fig. 16d) but the water level value (1.5 m) was somewhat lower than that at the Irrawaddy point (Fig. 16f), and the water level in the estuary was 1 m at that time. Figure 17 illustrates the displacement of the sea surface level simulated by POM at 00 UTC on May 2, 2008 (FT ¼ 36). At this time, the center of the simulated Nargis was located o¤ the west coast of southern Myanmar. The rise in sea level due to low pressure near the cyclone center (the inverse barometer e¤ect) is seen as a circular contour (Fig. 17a). In the enlarged view (Fig. 17b), we can see that a southerly ocean current generated by strong surface winds caused by the cyclone flows into river mouths. The accumulated water brought about a rise in water level (the wind setup e¤ect) in the coastal region of southern Myanmar facing the Andaman Sea. Since the sea-level rise due to pressure depression was less than 0.5 m, the major part of the storm surge was caused by the ocean current generated by strong wind. The ocean current speed reaches the maximum value at the shallowest level while the speed decreases with depth. Our simulation suggests that the storm surge generated by Nargis was predictable two days before landfall using a downscale forecast with a high resolution regional model and the appropriate application of an ocean model. Though some errors still exist, the prediction was shown to be possible overall using the resources introduced in this study. 6. Summary and concluding remarks The 2008 Myanmar cyclone Nargis was numerically simulated with NHM using JMA global data. First, the quality of the JMA analysis data and the operational global forecast from GSM were examined. The JMA analyses captured the evolution of Nargis to some extent, but the intensities were quantitatively insu‰cient and northeastward positional lags were seen in most analysis times before landfall. The operational forecast of JMA using GSM exhibits the same tendencies—weak intensity and northeastward positional lags. The GSMforecasted center pressure of Nargis with an initial time of two days before landfall was only 992 hPa and thus too weak to foresee the cyclone’s disastrous impact. Downscale experiments by NHM with a horizontal resolution of 10 km were performed using JMA global analyses to establish the initial and lateral boundary conditions. When the low-resolution

Fig. 17. a) Displacement of the sea surface level simulated by POM at 00 UTC on May 2, 2008 (FT ¼ 36). b) Enlarged view (FT ¼ 36) depicts the beginning of the sea level rise (gray scale) with showing the sea-level pressure (thick contour indicates 1000 hPa and contour interval is 1 hPa) and vertically averaged current (arrows, m/s) which flows into the river mouths in southern Myanmar. The triangle and circle indicate the Irrawaddy and Yangon points, respectively.

pressure-plane global-analysis data (GA-p) were used, the forecasted track had a large positional lag. With high-resolution global-analysis data

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(GA), however, the track forecast was considerably ameliorated. NHM reproduced the development of Nargis more properly than GSM and more accurately than even the global analyses themselves. A forecast experiment for Nargis with NHM was conducted using the GA and GSM forecast as the initial and boundary conditions. Despite the small northward bias in the track forecast in NHM, quantitatively better forecasts than GSM were simulated, and a maximum surface wind speed of more than 30 m/s was obtained. The NHM-predicted cyclone exhibited characteristics similar to those of the TRMM/TMI satellite observation. Experiments were conducted to study the sensitivity of Nargis’ rapid development to ice phase, SST, and horizontal resolution. In a warm rain experiment, Nargis developed earlier and the eye radius became larger; these results were consistent with those of Sawada and Iwasaki’s (2007) ideal experiments. It was demonstrated that a high SST anomaly preexistent in the Bay of Bengal led to the rapid intensification of the cyclone, and that an SST over 29 C at least was required in Nargis’ case. In a simulation with a horizontal resolution of 5 km, the cyclone showed more distinct development and attained a center pressure of 968 hPa, but a 3-km simulation without convective parameterization resulted in a larger positional lag. Numerical experiments on the storm surge were performed with POM. Though the experiment using the GSM forecast could not represent the storm surge, the simulation using the NHM forecast predicted a storm surge of more than 3 m. A southerly ocean current driven by the strong surface winds of the cyclone caused the disastrous storm surge at the river mouths in southern Myanmar facing the Andaman Sea. Although our results demonstrated the predictability of Nargis’ storm surge given a lead time of two days, there were several quantitative discrepancies between the forecast and the real situations involving the cyclone intensity, track, and timing. For example, the storm surge at the Yangon River was about 4 m (Shibayama et al. 2008b), while the maximum level at the Yangon point in our simulation was 1.5 m. Errors in the initial and boundary conditions and SST, as well as insu‰ciencies of the model resolutions and physics, cause forecast errors. Thus, if the northward bias of the TC track predicted by NHM were reduced, a higher water level might have been simulated at the Yangon point. Risk management should be undertaken con-

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sidering forecast errors and reliability. In Part 2 (Saito et al. 2010), we will perform ensemble predictions of Nargis and the associated storm surge to consider forecast errors due to uncertainties in the initial and boundary conditions. Nargis’ intensity in the JMA global analysis was very weak, which may be due to the lack of the TC bogus data and the shortage of assimilated observation data in the Bay of Bengal. A data assimilation study to improve the accuracy of the initial conditions will be conducted by Kunii et al. (2010). Acknowledgements This work was supported by the Ministry of Education, Culture, Sports, Science and Technology in Japan (MEXT) and its Special Coordination Funds for Promoting Science and Technology ‘‘International Research for Prevention and Mitigation of Meteorological Disasters in Southeast Asia’’, represented by Professor Shigeo Yoden of Kyoto University. The authors are grateful to Mitsuru Ueno, Shunsuke Hoshino, Eiki Shindo, and Yoshinori Shoji of MRI for their valuable comments and information. Thanks are extended to two anonymous reviewers whose comments significantly improved the quality of this paper. References Beljaars, A. C. M., and A. A. M. Hotslag, 1991: Flux parameterization over land surfaces for atmospheric models. J. Appl. Meteor. 30, 327–341. Bern, C., J. Sniezek, G. M. Mathbor, M. S. Siddiqi, C. Ronsmans, A. M. Chowdhury, A. E. Choudhury, K. Islam, M. Bennish, E. Noji, and R. I. Glass, 1993: Risk factors for mortality in the Bangladesh cyclone 1991. Bulletin of WHO, 71, 73–78. Blumberg, A. F., and Mellor, G. L. 1987: A description of a three-dimensional coastal ocean circulation model. Three-Dimensional Coastal Ocean Models, edited by N. Heaps, American Geophysical Union, 208 pp. Dude, S. K., Indu Jain, A. D. Rao, T. S. Murty, 2009: Storm surge modelling for the Bay of Bengal and Arabian Sea. Nat. Hazard, 51, 3–27. Flather, R. A., 1994: A storm surge prediction model for the northern Bay of Bengal with application to the cyclone disaster in April 1991. J. Phys. Oceanogr., 24, 172–190. Frank, Neil L., and S. A. Husain, 1971: The Deadliest Tropical Cyclone in History. Bull. Amer. Meteor. Soc., 52, 438–445. Hasegawa, K., and Investigation Team of Japan Society of Civil Engineering, 2008: Prompt report on the

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Bangladesh Cyclone disaster, JSCE Magazine, 93, No. 3, 46–51, (in Japanese). Hara, T., 2008: Turbulent process. Suuchiyohoka Houkoku Bessatsu, 54, 117–145, (in Japanese). Higaki, M., H. Hayashibara, and F. Nozaki, 2009: Outline of the Storm Surge Prediction Model at the Japan Meteorological Agency. RSMC Tokyo— Typhoon Center Technical Review, No. 11, 25–38. Japan Meteorological Agency, 2007: Meso-Scale Model (JMA-MSM0603), Outline of the operational; Numerical Weather Predction at the Japan Meteorological Agency (available online at http://www .jma.go.jp/jma/jma-eng/jma-center/nwp/outlinenwp/pdf/pdf4/outline4_5.pdf ). Kadowaki, T., 2005: A 4-dimensional variational assimilation system for the JMA Global Spectrum Model. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 34, 1–17. Kain, J., and J. Fritsch, 1993: Convective parameterization for mesoscale models. Meteor. Monogr., 24, 165–170. Katsura, J., and Cyclone Disaster Research Group, 1992: Storm surge and strong wind disaster due to 1991 cyclone in Bangladesh. Annuals of Disas. Prev. Res. Inst., Kyoto Univ., 35A, 119–159, (in Japanese). Kim, K. O., H. S. Lee, M. Haggag, and T. Yamashita, 2006: Storm surge field simulation on Hurricane Katrina using an atmosphere-wave-ocean coupled model. Annual J. Coastal Eng., JSCE, 53, 416– 420, (in Japanese). Kitagawa, H., K. Tamiya, M. Nakagawa, T. Komori, K. Yamada, M. Hirai, K. Iwamura, and T. Sakashita, 2007: Global Spectral Model (JMA-GSM0603). Outline of the operational numerical weather prediction at the Japan Meteorological Agency. 41– 66. (Available online at http://www.jma.go.jp/jma/ jma-eng/jma-center/nwp/outline-nwp/pdf/pdf4/ outline4_2.pdf) Koh, Tieh-Yong, and Chee-Kiat Teo, 2009: Toward a Mesoscale Observation Network in Southeast Asia. BAMS, 90, 481–488. Kunii, M., Y. Shoji, M. Ueno, and K. Saito, 2010: Mesoscale Data Assimilation of Myanmar Cyclone Nargis Part I. J. Meteor. Soc. Japan, 88, 455–474. Lean, H. W., and P. A. Clark, 2003: The e¤ects of changing resolution on mesocale modelling of line convection and slantwise circulations in FASTEX IOP16. Quart. J. Roy. Meteor. Soc., 125, 2255– 2278. Lin, I., C. Chen, I. Pun, W. Liu, and C. Wu, 2009: Warm ocean anomaly, air sea fluxes, and the rapid intensification of tropical cyclone Nargis (2008). Geophys. Res. Lett., 36, L03817. McPhaden, M. J., G. R. Foltz, T. Lee, V. S. N. Murty, M. Ravichandran, G. A. Vecchi, J. Vialard, J. D. Wiggert, and L. Yu, 2009a: Ocean-atmosphere in-

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