COVER ARTICLE

Photonirvachak Journal of the Indian Society of Remote Sensing, Vol. 33, No. 3, 2005

EXTREME RAINFALL EVENT OF JULY 25-27, 2005 OVER MUMBAI, WEST COAST, INDIA ANUP K. PRASAD AND RAMESH R SINGH~ Department of Civil Engineering, Indian Institute of Technology, Kanpur-208 016, India ~'Corresponding author : [email protected]

Introduction Extreme and sporadic rainfall events are common during summer monsoon season along western and eastern parts of India. Western coast is severely affected by these extreme events every year that lead to tens o f m m of rain (>50 mm) in a day resulting into sudden flooding of the region (Francis, 2002; Francis and Gadgil, 2002; Gadgil et al., 2004; May, 2004). Such sporadic extreme rain also has large influence on seasonal mean rainfall (Stephenson et al., 1999) and consequently on climatic models. Changes in extreme rainfall events are a cause of concern as it may lead to sudden flooding and drought conditions and also influence weather and climatic conditions in rest of the year (Mason et al., 1999). Mumbai located in the west coast of India is the largest populated metro in India and third largest populated city in the world (18 million, http://worldatlas.com). Intense rainfall started on July 26, 2005 and the city received a record 37.1 (942.34 mm) inches of rain in a day, causing sudden flooding in large parts of the city. Life of million people was affected. People were trapped in their houses and offices for few days.

Received 10 August, 2005; in final form 17 August, 2005

Earlier known single day rainfall record in India was 33 inches (838.2 mm) in 1912 (http:// earthobservatory.nasa.gov/). Measurement o f precipitation using satellite data such as TRMM (Tropical Rainfall Measuring Mission) recorded rain rate as high as 50 mm/hr localised over Mumbai on July 26, 2005 (3:39 pm, local time) (http:// earthobservatory.nasa.gov/Newsroom/Newlmages/ images.php3?img_id=16985). We have analyzed hourly variation using IR (Infra red) brightness temperature data (unit: Kelvin) (equivalent blackbody temperature), merged from available geostationary satellites (Geostationary Operational Environmental Satellite (GOES-8/10), METEOSAT-7/ 5 and Geostationary Meteorological Satellite (GMS)) (Janowiak et al., 2001) of the extreme rainfall event (July 25-27, 2005) over western India (including Mumbai). Hourly gridded (4 km resolution) IR brightness temperature data conspicuously delineate this rainfall event that was concentrated over Mumbai (Figs. 1 and 2). Intense rainfall over Mumbai is found to be associated with rapid decrease in cloud top temperature (IR brightness temperature) on July 26 and 27, 2005 that may have lead to favourable conditions/'or high amount of rainfall.

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Fig. 1. Spatial distribution of IR Brightness Temperature (B. Temp., unit: Kelvin) over western India (July 25-27, 2005). An i n t e g r a t e d a p p r o a c h is r e q u i r e d to understand underlying processes related to such intense rainfall events that cause huge loss of life and property annually. As of August 3, 2005, about

$2.2 billion loss of properties is estimated due to this extreme rain m the Mumbai-Thane region. Death toll due to flood is in excess of 850 in Mumbai alone (http://timesofindia.indiatimes.com/).

Extreme Rainfall Event of July 25-27, 2005 over... With the availability of multi sensors satellites, it is now possible to monitor various meteorological, atmospheric, ocean and land parameters on hourly basis. Such data together with ground observations can be used in early warning of such extreme events. A 72 hour record (hourly) of IR brightness temperature data is shown in Figure 1 that can be assimilated in multi-sensor and ground observatories data for accurate information about such events. Detailed spatial and temporal characteristics of these extreme hydrometeorological anomalies can be studied in near real-time using multi sensor satellites (TRMM, GOES, METEOSAT, Geostationary Meteorological Satellites (GMS), Indian National Satellite (INSAT, htlp://www.imd.gov.in/) etc, ground data (rain gauge, meteorological data) and hybrid data (merged satellite and ground estimates) available from various international and national agencies. Global climatology and precipitation data can be obtained from various sources such as Climate Prediction Center (CPC), Global Precipitation Climatology Center (GPCC) etc. IR Brightness Temperature data

Near real-time Globally-merged (60~ - 60 ~ S) full pixel-resolution (-4 km) IR brightness temperature data is produced using -11 micron IR channels onboard the GMS-5, GOES-8, GOES-10, Meteosat-7 and Meteosat-5. Global gridded data can be obtained from National Centres for Environmental Prediction NCEP-CPC tip site (tip:// ftpprd.ncep.noaa.gov/pub/precip/global_ full res IR). Each record is a 9896 x 3298 Fortran array of IR brightness temperature. Data have been scaled to Kelvin by adding an offset of 75 to each pixel. Data can be directly read in Grid Analysis and Display System (GRADS, http://grads.iges.org/ grads/). The data have been corrected for zenith angle dependence (Joyce and Arkin, 1997). No intercalibration among the sensors has been performed although this effect is considerably smaller compared to the zenith angle effects.

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Satellite Observation and Discussion

Geostationary satellites such as GOES provide information about cloud characteristics related to rainfall process using combination of 3.9, 6.7, 10.7 and 12 pm. Lovejoy and Austin (1979) have discussed relationship of visible channel to cloud thickness and rainfall. Information on the state of cloud water can be obtained from 3.9 pm band (Scorer, 1989). The 6.7 pm band is strongly related to upper level moisture. Information on cloud top temperature is provided by IR band (10.7 jam) that can be used in conjunction with 3.9 ~tm band to distinguish non-raining cloud (Vicente, 1996). Texture and rate of change of cloud top temperatures can provide valuable information on rainfall intensities (Griffith et al., 1978; Wu et al., 1985; Adler and Negri, 1988; Alder et al., 1993). The 10.7 lam IR band plays a key role in rainfall estimation. During developing stage, a convective cloud produces greater rainfall compared to later stages (mature and decaying stage). A rapid fall in cloud top temperature, which can be obtained from 10.7 pm IR band, is indicative of rapid vertical uplitt and associated intense rainfall (Griffith et al., 1978; Wu et al., 1985; Adler and Negri, 1988; Alder et al., 1993). Additional information like texture of cloud and state of cloud water using other available bands can distinguish clouds that produce high rain rate compared to others. Mumbai received a very high rainfall rate o f - 5 0 mm/hr (TRMM estimate, http:// earthobservatory.nasa.gov/) on July 26, 2005. Study of IR brightness temperature data over western India clearly identifies rapid decline of IR brightness temperature and appearance o f these low temperature clouds on July 26, 2005 over Mumbai causing heavy rainfall on July 26 and 27, 2005 (Figs. 1 and 2). A high rate of change of IR brightness temperature is also observed over Ahmedabad, Nagpur and Hyderabad cities. The IR brightness temperature range is found to be different compared

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to that over Mumbai (Fig. 2). Merging of datasets from various satellites, NCEP wind and ground meteorological data can help in identification of type of this extreme event and spatial-temporal characteristics of such clouds capable of producing very high rain. An increase in the frequency of heavy rainfall events in South and Southeast Asia (IPCC, 1998) is expected as a result of rise in temperature due to greenhouse gases (Lonergan, 1998). Lonergan

(1998) has estimated that Indian climate may become warmer by 2.33-4.78~ with doubling of CO 2 concentrations. Land-Ocean thermal contrast that drives the monsoon mechanism is likely to change significantly by the year 2040 compared to the year 1980 due to increasing aerosols loading (Lal et al., 1995). However, uncertainties in these projections are high considering impacts of aerosols, soot, and anthropogenic activities on the monsoon. The role of aerosols in associated climate change over Indian continent is very complex and is not well

Fig. 2. Three day (July 25-27, 2005) hourly IR Brightness Temperature (unit: Kelvin) over cities (mean of 40 km2 and 400 km2).

Extreme RainfallEventof July 25-27, 2005 over...

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Fig. 3. Daily rainfall over Mumbai since June 2005. Colaba (CLB) and Santacruz (SCZ) (Source: http://www.imdmumbai.gov.in/drf_mbi.htm). understood due to non availability of ground observations. The recent launch of multi sensor satellite by various space agencies will be helpful in bridging the gap and better understanding of the atmospheric processes that are responsible for controlling the climatic changes.

Conclusion Extreme localised rainfall events are part of monsoon system occurring every year. Satellite data (TRMM and other geostationary satellites) together with ground observations will be useful in providing spatial and temporal variability of atmospheric changes. The short and long term variability is required for better understanding of the local and regional climatic conditions through detailed modelling. Such information will play a key role in real time data analysis and dissemination system to the disaster management groups in the

country to minimize losses due to these extreme rainfall events.

Acknowledgements The remote sensing data used in the present study are taken from Climate Prediction Center (CPC) website (http://www.cpc.ncep.noaa.gov/). The meteorological data is taken from Indian Meteorological Department (http:// www.imdmumbai.gov.in). The present study is supported through research grant sponsored by the Indian Space Research Organisation under Geosphere Biosphere Program to Ramesh P. Singh.

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