A detailed assessment of design wind speeds for Mumbai

Leighton Aurelius a, Stefano Cammelli

b

aBMT Fluid Mechanics Limited, Teddington, Middlesex, UK, laurelius@bmtfm.com

bBMT Fluid Mechanics Limited, Teddington, Middlesex, UK, scammelli@bmtfm.com

1 INTRODUCTION

It has recently been estimated that by 2030 approximately 50% of India’s population is expected to live within urban areas: this rapid growth has led to the need for tall buildings and high-rise structures in the region. With real estates exploring the 'vertical dimension', India – and cities like Mumbai in particular – is set to be the next big home for the tallest skyscrapers in the world.

The often limited size (and awkward shape) of available land within Mumbai, combined with the rather obvious desire for maximising floor space index and therefore revenue, is current-ly leading to schemes – often residential – of high (say greater than 1:6), if not very high (say greater than 1:10), aspect ratio. These types of structure are indeed very wind sensitive and the design of their lateral-stability system potentially entirely driven by vortex-shedding phenome-non: a thorough assessment of the local wind regime is therefore paramount.

This paper presents the results of a wind climate analysis performed for the Mumbai area with the aim to derive ‘best estimate’ design wind speeds which are believed to provide supple-mentary information to the IS : 875 (Part 3) – 1987, the current Indian Standard for wind loads on buildings and structures.

2 REVIEW OF INDIAN WIND CLIMATE

The types of extreme wind events characterising the Indian wind climate include tropical cy-clones (mainly coastal areas), tropical gales, tropical depressions, thunderstorms and monsoons.

The strength of the tropical cyclones developing within the Bay of Bengal is far stronger than the ones that could develop over the Arabian Sea: this clearly makes the East coast far more vulnerable than the West coast. The only region of the West coast notable for being hit by tropi-cal cyclones is the state of Gujarat (Northeast of the country).

The most severe tropical cyclone making landfall on Indian territory in the last 40 years was the '1999 Orissa Cyclone' (a 'Super cyclonic storm' according to the India Meteorological Department tropical cyclone strength classification, or a 'Category 5 tropical cyclone' according to the Saffir–Simpson hurricane scale) which, on 28

th October 1999, reached an intensity of

~160mph (3-min sustained) at ~912mbar (see Fig. 1) subsequently making landfall near the city of Bhubaneswar in the state of Orissa. The storm, one of the deadliest in India history, claimed the lives of thousands with damages of the order of billions of U.S. Dollars.

Historically, tropical cyclones developing over the Arabian Sea and heading towards the Mumbai region downgrade to either tropical storms or tropical depressions before making land-fall.

Between May and June of each year the dry Northeasterly winds flowing over India change direction and warm humid air from the Indian Ocean starts flowing from Southwest, gradually overspreading the Indian subcontinent. Heavy rains, and often severe thunderstorms, accompany the onset of the Monsoon season that typically runs from June to September (the onset date for Mumbai fluctuates around the 7

th of June).

With regard to Monsoon events, a review of the current literature on the nature of the at-mospheric boundary-layer during the Monsoon season has revealed the presence of inversions of the velocity profile in the form of ‘low-level jets’: the height at which these inversions typically occur is in the region of ~1-1.5km above mean sea level (Kalapureddy et al, 2007).

Figure 1. Satellite image of '1999 Orissa Cyclone' on 28th

October 1999, 09:00 UTC (Source: U.S. National Oceanic and Atmospheric Administration).

3 ANALYSIS OF WIND CLIMATE DATA FOR MUMBAI

3.1 Surface level wind data

3.1.1 Parent data The derivation of “best-estimate” wind climate data typically relies on sourcing wind records from reliable meteorological organizations, such as the met office for the region in question (in this case the India Meteorological Department, IMD) or from broader meteorological authorities, such as the National Oceanic and Atmospheric Administration or the United Kingdom Met Of-fice.

For the purpose of the analysis presented within this paper, wind records were acquired for three meteorological stations within the Mumbai region, namely Mumbai (Station ID: 43003, from 1944-2007), Ratnagiri (Station ID: 43110, from 1944-2008) and Surat (Station ID: 42840, from 1944-2008). A regional view of the area indicating the location of the weather stations ex-amined is shown in Figure 2. It has been confirmed by the Indian Meteorological Office that each sample is a 10-minute mean recorded at the start of the observation period.

In addition to the aforementioned stations, there are additional stations within the region (see Fig. 2): Mahuva, Nasik, Nasik Ozar, Veraval and Poona. However, these have been consid-ered unreliable due to either lack of significant records, or being too biased by local topographic effects (particularly the case for Nasik, Nasik Ozar and Poona anemometers where the wind cli-mate is highly susceptible to the local topographic effects / surrounding mountains and valleys).

Figure 2. Aerial view of the Mumbai region indicating locations of weather stations (Source: Google Earth).

3.1.2 Data transposition to open country terrain Before the extraction of the extreme events, and in order to accurately take into account the dif-ferent terrain roughness / morphology changes experienced by the wind approaching the meteor-ological stations in question (Mumbai, Ratnagiri and Surat), the three entire data sets have been transposed to a reference terrain category (open country, z0=0.03m, in this specific case) follow-ing the guidelines provided by ESDU (Data Item 84011 (1993) and Data Item 01008 (2005)).

3.1.3 Data quality assurance and storm separation The now transposed wind speed records were then subjected to detailed quality checks by BMT Fluid Mechanics Ltd. (BMT) to ensure that the data sets are self-consistent. Data points consid-ered as unreliable are removed from the record. Based on BMT experience it is highly likely that these events correlate to measurement anomalies or often misreported gust events.

To examine the effect of thunderstorm events in the region (which are flagged within the data records), these have been removed from the data sets and examined separately. A separate analysis of these events has indicated that these are non-dominant events for the Mumbai region. Furthermore, it should be noted that the data sets do not specifically flag wind records related to Monsoon events, and as such these events are inherent in the filtered data.

The transposed and filtered wind speed records have then been sorted such that the strong-est wind speed from each year are extracted from the data set for subsequent extreme value anal-ysis to take place. It should be noted that as these are not continuous samples (i.e. 10-minutes at the start of the observation period), then it is appropriate that these are treated as mean-hourly records.

3.1.4 Data analysis

The design wind speeds (10m height in open country terrain) for the three stations examined have been obtained by fitting the annual wind speed maxima employing the Lieblein method (Lieblein, 1974).

A comparison of the design wind speeds derived for Mumbai, Ratnagiri and Surat is pre-sented in Figure 3.

North~250km

Data acquired and analysed

Data examined

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Figure 3. Comparison of design wind speeds derived for Mumbai, Ratnagiri and Surat.

3.2 Upper level wind data

Upper level wind data were provided by the IMD for the Mumbai weather station. This data set consists of wind speed and direction readings taken at altitudes of 10m (V10m), 150m (V150m), 300m (V300m) and 600m (V600m) via rawinsonde balloon measurements. The data consist of two daily readings, at 0600 hours and 1800 hours, throughout the period of 1999 to 2008.

The design wind speeds derived from the data do not correlate well with those taken from the surface level records. This is understandable as the balloon data is not as thorough, nor repre-sentative, as the surface level data, primarily due to the differences in sampling methodology be-tween the two (with the balloon data being a near instantaneous, or snap-shot, of the wind cli-mate as the balloon moves through the atmosphere, rather than a prolonged average).

However, the balloon data does provide the opportunity to compare the ratio of wind speed at 10m to that at the various height levels up to 600m, and compare these with the Deaves and Harris boundary-layer model. A comparison of V600m/V10m, V300m/V10m and V150m/V10m between the balloon data and the Deaves and Harris boundary-layer model (Deaves and Harris, 1980) is shown in Table 1. This comparison indicates that the ratios between upper and surface level wind speeds in both the balloon data and that predicted by the Deaves and Harris model are in well agreement. Table 1: Comparison of balloon data to Deaves and Harris boundary-layer model.

Source Height ratios [-] V600m/V10m V300m/V10m V150m/V10m

Deaves and Harris 2.24 2.00 1.79 Balloon data 2.43 2.15 1.89

An examination of the balloon records also provides some insight into the change in on-coming wind direction (θ) with height over the range of heights examined. Shifts of wind direc-tion with height in boundary-layer flow are caused the relaxation of shear stress with height in the atmosphere. At any given height there is a vector balance between the Coriolis force, the shear stress (or friction) between atmospheric layers, and the ambient pressure gradient.

The friction term varies with height, starting at a maximum near the surface to a very low value above the boundary-layer (the disappearance of this friction term is in fact one definition of the top of the boundary-layer). As the friction term decreases, the vector balance changes, causing a change in wind direction. This phenomenon is called the Ekman spiral (Ekman, 1905). Based on general meteorological theory the difference between the surface level and the upper

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4 INDIAN STANDARD WIND SPEEDS: A COMPARISON

A comparison between the design wind speeds derived for Mumbai, Ratnagiri and Surat and those specified in the IS : 875 (Part 3) – 1987 is shown in Figure 4.

Figure 4. Comparison between design wind speeds derived for Mumbai, Ratnagiri and Surat and those specified in IS : 875 (Part 3) – 1987 (all data are 10m height mean-hourly referenced to open country terrain).

5 DISCUSSION

There is of course a level of uncertainty associated to some of the assumptions made during the data analysis (history of anemometer(s) location, terrain morphology, topographic effects, etc): this can lead to potential variations of the magnitude of the design wind speeds as well as to shifts of the associated directionality. In order to provide the best estimate design wind speeds (including directionality) for Mumbai, a thorough review and investigation of these potential is-sues was conducted and reported in the following sections of this paper.

5.1 Design wind speeds

An examination of the local wind climate at the three stations examined has yielded some simi-larities between the stations that give confidence to the estimated design wind speeds for Mum-bai.

One key similarity is the predicted dispersion (1/a) associated with Mumbai and Ratnagiri. At a mesoscale level one would in fact expect this term to be similar over the geographical area of interest. Confidence in this parameter is key in the estimation of the larger return period de-sign wind speeds.

Another key similarity is the mode (U) for each of the three stations, all of which are com-parable.

However, there are some key differences / uncertainties that must be recognised:

The dispersion associated to Surat is considerably different than those at Mumbai and Ratnagiri; this is likely an effect of Surat’s geographical proximity to the Gulf of Khambhat

The mode for Mumbai was found to be very sensitive to the range of years selected for analy-

sis, to the order of ~10%

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After having conducted a series of sensitivity analysis it was found that the assumptions made on the terrain morphology and topography at the anemometer site can lead to a variation in the design wind speeds for Mumbai of up to ~5%

Considering the above, the following expression defines the best estimate design wind speeds (mean-hourly wind speed referenced to 10m height, open country terrain) for Mumbai as a func-tion of the selected return period:

���� � 43 � 28 ∙ �� .� (1)

where R = return period in years. It should be noted that the above expression compares very well with that documented within “Design Wind Speeds for the Asia-Pacific Region” (HB 212-2002).

5.2 Wind directionality

As per the design wind speeds, there were some key differences / uncertainties between the three stations that must be recognised and accounted for in the estimation of wind directionality. Key issues are: The data from Surat and Mumbai anemometer stations show a strong prevalence of North-

easterly extreme events (this effect was also noticed at the Veraval anemometer station); this effect is not present for Ratnagiri: this is likely the result of localised topographic effects, such as the alignment of the mountain ranges near Mumbai, and the steep slope of the Western coast approaching the Ratnagiri station

Furthermore, the data from the Ratnagiri station showed a prevalence of extreme events from

the Northwest; this is likely due to deflected wind flow patterns originating from around the Gujarat Peninsula to the Northwest

Especially in the design of super-tall structures (height greater than ~300m), the variation in

wind directionality with height should be accounted for in some form; as such, appropriate smoothing is applied to allow for this phenomenon to be captured

Considering the above, the directional distribution of best estimate design wind speeds for the Mumbai region (10m height mean-hourly referenced to open country terrain) are presented in Figure 5.

Figure 5. Directional distribution of best estimate design wind speeds (10m height mean-hourly referenced to open country terrain) derived from the analysis of local wind climate data.

5.3 Indian Standard

Upon examination of the strength and directionality of the best estimate and the IS : 875 (Part 3) – 1987 design wind speeds (the latter does not provide any guidance on the directionality of ex-treme wind events), it is clear that the use of the best estimate figures could lead to reductions in wind loading in the range of ~30%-50%.

One key reason for this is the rate of increase in design wind speed with return period provid-ed by the Indian Standard. It would appear that the wind climate provided within the Indian Standard is actually one that reflects a mixed wind climate that considers the mixing between a wind climate with moderate intensity storms and a wind climate with weakening Typhoons (see Figure 6).

Figure 6. Comparison between design wind speeds for: i) Moderate wind climate; ii) Severe wind climate with Ty-phoons; iii) mixed wind climate considering climates (i) and (ii); iv) design wind speeds as stipulated by IS : 875 (Part 3) – 1987 (all data are 10m height mean-hourly referenced to open country terrain).

As shown in Figure 6, for larger return periods (those in excess of 10-years), the design wind speeds stipulated within the Indian Standard would appear to be best represented by a wind cli-

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mate dominated by severe thunderstorms and weakening Typhoon events. Whilst these events are not uncommon within the more northern parts of the western coast (for example within the state of Gujarat), the inclusion of these events within the analysis realm for design wind speeds in the Mumbai region is considered entirely unnecessary.

5.4 Ultimate limit state design

In most of wind codes (and certainly this is the case for the IS : 875 (Part 3) – 1987) in order to obtain ultimate limit state (ULS) design wind loads an appropriate loading factor (typically rang-ing between 1.4 and 1.6) has to be applied to the design wind pressures evaluated based on the 50-year return period design wind speed.

However, because the relationship between mean wind speed and resulting loading on the structure may in some cases not be following a square relationship (as is the case for excitations driven by cross-wind / vortex-shedding), for the ULS design of tall and super-tall buildings it is highly recommended to evaluate the wind loading directly at the “ultimate” design wind speed. The return period (typically ranging between ~700 and ~1700 years) associated to the ultimate design wind speed should be evaluated taking into account both the expected life of the structure and the associated risk of exceedence of wind speed.

6 CONCLUSIONS AND RECOMMENDATIONS

A thorough review of the local wind climate within and around the Mumbai region has been per-formed such that best estimates of design wind speeds, and wind directionality, were derived for use in design.

The analysis has revealed that the design wind speeds derived for the local wind climate are considerably less than those stipulated within the Indian Standard: this is enhanced when wind directionality is taken into account.

It is also recommended that for the ULS design of tall and super-tall buildings the wind load-ing should be evaluated directly at the “ultimate” design wind speed rather than being obtained through the use of loading factors.

7 REFERENCES

Deaves, D. M and Harris, R. I., 1980. The structure of strong winds. Paper 4 of CIRIA Conf. on “Wind engineering in the eighties”, Construction Ind. Res. and Inf. Assoc., London

Ekman, V.W. 1905. On the influence of the Earth’s rotation on ocean-currents. Arkiv for Matematik, Astronomi och Fysik 2 (11): 1–52.

ESDU, Wind speed profiles over terrain with roughness changes, Data Item 84011, ESDU (Engineering Science Da-ta Unit) International, London, 1993

ESDU, Computer program for wind speeds and turbulence properties: flat or hilly sites in terrain with roughness changes, Data Item 01008, ESDU (Engineering Science Data Unit) International, London, 2005

Indian Standard – Code of Practice for Design Loads (other than earthquake) for buildings and structures, IS: 875 (Part 3) – 1987

Kalapureddy, M. C. R. et al. 2007. Wind profiler observations of a monsoon low-level jet over a tropical Indian sta-tion. Annales Geophysicae, Vol. 25, 2125-2137

Lieblein, J. 1974. Efficient methods of extreme-value methodology. Report NBSIR 74-602. Washington, National Bureau of Standards

Standards Australia, Design Wind Speeds for the Asia-Pacific Region, HB 212-2002