Indian Journal of Radio & Space Physics Vol. 33, April2004, pp. 115-124

Integrated satellite microwave and infrared measurements of precipitation during a Bay of Bengal cyclone

R M Gairola, A K Varma, Samir Pokhrel & Vijay K Agarwal

Meteorology and Oceanography Group, Space Applications Centre (ISRO), Ahmedabad 380 015 , India

Received 31 March 2003; revised 24 Ju11e 2003; accepted 3 Ja/luary 2004.

In the present paper, an approach for integrated IR and microwave measure ments using various sate llite sensors have been used for the study of a severe cyc lone over Bay o f Bengal and adjoining Eastern India and Bangladesh during 24-29 April 1991. T he rain estimates from measurements of brightness temperatures from Special Sensor Microwave/Imager (SSM/I) and Outgoing Lo ngwave Radiation (OLR) from IR radi ances o f polar orbiting and geostati onary satellites have been used. Since the OLR derived rainfa ll exhibit a larger spati al spread with lower intensities in rain fa ll fields, we have followed the above procedure for optimally combining both the observations for more realistic rainfall o n regional scale to study precipitation fields. Since the OLR rains are well represented spatially and tempo rally by more frequent observati ons from both geostationary and po lar orbiting satellites, the algorithm sharpens the IR deri ved rains and aug ments it to the microwave derived rain. The combined IR and microwave rain algorithm brings out the finer details of the cyclone structure and precipitati on fi elds, and thu s seems to be highly promising for operatio nal applications. Some aspects of the associated wind fi elds are also examined for their relatio nship with tropical rain intensity in the present case. A comparison of rainfall is performed with rare i11-si1u observat ions over the coastal regions during the severe cyclo ne.

Keywords: Infrared; Microwave; Prec ipitati on; Cyclone; SS1v111 ; Outgoing Longwave Rad iatio n PACS No.: 92.40.Ea; 92.60.Qx IPC Code: GOIWI/00; GO IWI /02

1 Introduction Visible and lR measurements from geostationary

satellites provide very va luable information in monitoring the cyclonic storms. The highly repetitive of geostationary measurements from IR sensors make them more useful. The precipitation from IR measurements is based on indirect method of relating radiances at the cloud top and rainfall on the ocean or land surfaces. Microwave rad iances have a good physical connection with rain rate. The microwave brightness temperatures are thus directly related to the precipitation than those from vi sible and IR channels. The upwelling radiation at these microwave frequencies can therefore be used to assess the latent heat release in the tropical cyclone's precipitation regtons.

The utilization of mtcrowave brightness temperature was initiated with the launch of Electrically Scanning Microwave Radiometer (ESMR) instruments on Nimbus-5 ,6, with a center frequency of 19.35 GHz. Allison et a/. 1 used ESMR to map rainfall areas in the Hurricane and tropical disturbances. Later, Olson2 used Nimbus SMMR (Scanning Multichanel Microwave Radiometer) for physical retrieval of rainfall rate during the tropical

cyclones. Earlier Katsaros et a/. 3 and Gairola4·5 have

used Special Sensor Microwave/Imager (SSM/I), Nimbus and Seasat SMMR data for locating the deep depress ion, cyclonic storms and atmospheric fronts using satellite derived parameters like precipitable water, liquid water, rainfall rate, sea surface temperature and surface wind speed. There are numerous studies and investigations reported using SMMR measurements6

·7

. Further studies by Rao and MacArthur8

; Rao and McCo/; Rodgers and Pierce 10

have related information from microwave data to tropical cyclone intensity, and intensity change with varying degrees of success. So far microwave sensor's capabilities are limited to their observations only in low orbiting satellites and non-portability to the geosynchronous orbits.

Over the last few years, a number of groups have embarked on development of so-called hybrid techniques. Here the advantages of geosynchronous , viz. vast coverage and high space-time sampling, and polar passive microwave radiometers, viz. more physically based retrievals , are synergistically combined to generate global maps of tropical rainfall on climate scales. Generally the IR based estimates are calibrated agai nst the microwave estimates over

116 INDIAN J RADIO & SPACE PHYS, APRIL 2004

the common areas of overlap. The required adjustments are then appl ied to lR estimates to fi ll the

. h f . 11 -13 gaps 1n t e coverage o m1crowave measurements . Most of these methods are initially developed for the larger temporal and spatial domains. Recently Varma et a/. 14 and Gairola and Yarma 15 have presented a combined method for active and passive microwave measurements of rain over global tropics using Topex microwave radiometer and dual frequency radar altimeter. The regional to global scale rainfall maps produced in this manner represent the known features more closely. Gairola and Kri shnamurti 4 (hereafter GK, for brevity) have also developed an algorithm to integrate lR and microwave observations of precipitation from different satellites (geostationary­lR and polar orbiting-microwave, respectively). The algorithm has been well tested for various case studies in the global tropics 16

·17 by physical initialization in

Numerical Weather Prediction models wi th a large impact on medium range forecasts.

ln this paper we follow GK algorithm that integrates the in-situ and different satell ite sources of lR and mi crowave measurements for obtaining the precipitation on regional scale during a very severe cyclon ic storm over the Bay of Bengal during 24-29 April I 991. Knowledge of extreme I -day rainfall at specific location is useful in the planning and design of water resources projects for conservation of storm runoff and in designing waterways under bridg( ~s and culverts. Also, such rain occurs due to highly devastating cyclonic storms. For example, this cyclone caused very heavy damage and huge loss of life (around 0.12 million), which could be named as super cyclon ic storm. The track of this cyclone is given in Fig. 1 (taken from De et a/.) 18

• This was the only severe cyclone system during the year I 991. Here the main objective is to demonstrate the capabi lity of GK algorithm to properly est imate rain fie lds in the regional scale phenomena like cyclone, compared to the global scale for which the algorithm was initi ally developed . It was demonstrated to be highly useful for physical initialization in numerical weather prediction and still being used continuousl/ 9

for the same application.

2 Data sources

2.1 m database The Outgoing Longwave Radiation (OLR) data are

available from twice-daily (12 h interval) measure­ments from NOAA polar orbiting satellites and on almost continuous basis ( ~ 3 h) from the

Geostationary satelli tes. Operational es timation of the broadband OLR from NOAA satelli tes commenced20

in June 1974 and is sti ll continuing. The NOAA global data sets, in spite of some differences in satellite radiometer characteristics and eq uatorial crossing time, have been continuous enough to be brought out in the grid form21

• The IR radiances were procured from the Florida State University, which they obtained from National Centre for Atmospheric Research (NCAR), Colorado, Boulder. These es timates of OLR are made by means of a regress ion equation relating OLR and the actua l narrow band radi ance measurements of a panicular satel lite radiometer. The form of the regression is in terms of the black body flux temperature, the narrow channel effective black body temperatu re and associated constants. In the present study the details of the methodology for es timation of rainfall from daily OLR (IR) are followed from Kri sh namurti et al22

.

The lR statistical estimates of precipitation are generally based on the relationship between the accumulated precipitations and the spatia-temporally averaged extent of clouds radiati ve ly colder than a chosen threshold . It is assumed that al l cold clouds having a cloud top temperature below a determined temperature threshold are producing rain, and the same amount of rainfall is assigned to all such pixels. This may not always be true and may be acceptable on ly when the large spatia-temporal averages are estimated. Arkin and Meisner23 and Arkin et at?~. have applied thi s methodology known as, Global Precipitation Index (GPI) to produce monthly estimates of rainfall at large scales (2 .5°X2.5°) over Indian region using lNS AT-IB data. Similar study was conducted by Jobard and Des bois 11

, using a simple thresholding technique with Meteosat IR data.

2.2 Microwave database Satellite passive microwave and more recentl y,

active microwave rain radar are able to prov ide accurate estimation of rain rates, but the poor temporal sampling can only be used more accurately over longer periods of a month or so. Again the est imates fro m polar orbiting satelli tes are subject to the bias in regions where diurnal cycle of rainfall is pronounced. Still the instantaneous measurements of rainfall from microwave sensors are more accurate and thus arc useful in calibrating other sensors li ke vis ible and IR. At present, the algorithms for the quantitative estimation of rainfall from microwave observations are quite successfu l but suffer from a

GAIROLA eta/.: MICROWAVE & INFRARED MEASUREMENTS OF PRECIPITATION 11 7

C'l Q)

"0

w c ::J ......

~ ..J

NOTE

AloiN 0

o A.C.W.C. WITH C.D.R. STATION e C.W.C. STATION • C.D.R. STATION o C.W.C. WITH C.D.R. STATION

70"

.. ' 0',

BHP ..... ,

0 HYD

0 NGP

"'f,

TRACKS OF STORMS I DEPRESSIONS (FOR THE YEAR 1991)

--- -DEPRESSION -- STORM -- SEVERE STORM

0 POSITION AT 0300 UTC

e POSITION AT 03 & 12 UTC

-- it-·---

' ll "'· -q_ '

rr

e POSITION AT 1200 UTC

-+ POINT OF DISSIPATION

' \ \

..... lit .

'6 Jl ·"'·

! ~ g

PBL (}

so· as· 90. E

LONGITUDE, deg

Fi g. I - The tracks of storm and depressions for the year 1991 [The dark line shows cyclone track during the Bay of Bengal cyclone from 24 to 29 Apr. 1991 (taken from De et a/. 18

)]

scarcity of sufficient veri fication data over the oceans. Theoretical and empirical studies addressing the problem of rain retrieval and their critical assessment continues to appear. A definitive algo ritf1111 is in a stage of continuous development, because of the complex and variable microphysical and mesoscale structure of precipitation, vis-a-vis coarser resolutiOI~

of microwave sensors. As a source of microwave measurements, we use the rainfall data from the SSM/I Defense Meteorological Satellite Program (DMSP) F-10. Several algorithms have been developed for various SSM/I sensors of the DMSP series. The SSM/I rainfall used here is based on Ferraro and Marks25

. The SSM/I had a conical scan with a swath width of about 1400 km. Integrating microwave measurements with those of VI/IR is most pl ausible choice to mitigate the problem of resolution

in microwaves and augmentation of rain estimates from IR measurements.

2.3 In-situ data

The rain gauges are one of the most important ground truth/in-situ data sources for rainfall measurements. However during a severe cyclone like the one in present study, there are a few data values that are available from the coastal rain gauge stations for both quantitative comparison and inclusion in the above procedure. The data sources that are used here for the comparison are taken from De et a/ 18

• Here we explain the different stages of this cyclone through the precipitation from IR and microwave data and a mix of both in assoc1at10n with some data, like conventional wind field data of European Center for Medium Range Weather Forecasting (ECMWF).

118 INDIAN J RADIO & SPACE PI-IYS, APRJL 2004

Some comparisons with the fi nal rainfall measurements with the coastal rain gauges are sti ll addressed as and where they are available during the cyclone event under study .

3 Scheme for integrating IRIMW observations As stated in earlier sections, blended passive

microwave and IR algorithms have been developed in the past, many of which represent the GPI algorithm. Adler et a/. 26 developed the adjusted GPI (AGPI), in which the correction factor is derived from comparison of microwave with GPI est imates for coincident time slots of around a month. They applied this correction to the entire hourly GPI estimate during that period. AGPI is routinely used, by

27 . Huffman et a/. in the GPCP global monthly merged rainfall product. Kummerow and Giglio28 and Xu et a/. 2~ have also developed Universally Adjusted GPI (UAGPI) approach, wherei n both the monthly IR threshold and GPI are optimized using coincident microwave and IR data over an extended period. Anagnostou et a/. 30 developed an approach for the study of diurnal variability of rainfall based on microwave calibrated IR rainfall. Most recently Todd et a/. 31 presented a new combined microwave and IR satellite algorithm for the estimation of rainfall at the smallest possible time-space scales. It is based on the assumption that the microwave algorithm can provide accurate estimates of rain rates and this information can be used to calibrate IR parameters, to improve

OLR Data Base

SSM/I Data Base

rainfall estimates from IR data at high temporal frequency. In this approach, the frequency distribution of microwave estimated rain rates and IR brightness temperature values are derived from coincident satellite imagery, accumulated over some space and/or time domain large enough to ensure sufficient observations from both the measurements. This microwave and IR data set is applied with the Probability Matching Method (PMM) of Atlas and Bell32 to derive IR-TB and rain ratv relationship. The combined IR and microwave-ble ding scheme followed in the present case study is uniquely different and is outlined briefly as below.

Figure 2 shows the flow chart that illustrates the procedure we have currently followed to obtain rain rates following the GK algorithm mentioned above. This flow chart contains two halves. The first half is the regression procedure for the sharpening of the OLR rains by SSM/I rain. The two data sets are merged using the conditional relaxation method. The OLR rain data was inserted into the SSM/I rain field using Poisson's equation

where C is the final gridded field of the rainfall and 'F a forcing term, which is defined as the Laplacian of the gridded satel lite rainfall data from both sources. The sharpening of the OLR rain is described by a simple linear regression of the '12 of the SSM/I

OLR Data (latilong grid)

V2 RoLE

Regression V2 RssMA = a V2 RoLR + b

SSM/I Data Ground­Truth Data

Final Rain Rate

Fig. 2 -Flow chart for rainfall estimation from a combi nation of OLR, SSM/I and rain gauge data sets

GAIROLA el a/.: MICROWAVE & INFRARE D MEASUREMENTS OF PRECIPITATION 119

derived rainfall against \12 of the OLR derived rainfall. For this purpose, a 10-day data base of the rain from OLR and SSM/I was first prepared. This was based on the analysis of two products within the tropical latitudes using ( l0 Xl 0 ) !at/long resolution grid . A finite difference based Laplacian of the two fields were calculated for this period using the data base that consisted of 75,600 pairs of collocated values of the respective \12 of both precipitation parameters. These were next subjected to the linear regression illustrated on Fig. 2. The second half of the now chart in Fig. 2 illustrates an objective analysis procedure for obtaining the rainfall totals. This was carried out over the same tropical latitudes. I I ere the OLR rain sharpened by the \12 procedures is used as a first guess field and the SSM/I rain along the satellite swaths are used to obtain the final analysis. The in sit11 data serves as internal boundary conditions and could be inserted directly into the solution fie ld C

where subscript ' ibc ' indicates internal boundary condition and I is the in sit11 value of the rainfall. Since the rainfall itself is a highly varying parameter,

A

25 .0

15.0

5.0

both spati ally and temporally, blendi ng of any highly varying local in sit11 data (point measurement) with the satellite data of I 0 X I 0 grids or vice-versa could extend their innuence across the physical and geographical domai ns, producing unreali stic representation in the blended data set. It needs the procedure that would allow the assimilation of the available ground truth data (like raingauge data) based on a proper objective analysis scheme. Here the objective analysis is based on the uni variate opti mum interpolation , which in principle takes into account the asynoptic nature of the data distribution.

4 Results and discussion There are three types of cyclonic vortex

encountered in the bay. These are the monsoon depress ion, the mid-tropospheric cyclone and the tropical cyclone. The last of these three, the tropical cyclone, is the most dangerous weather phenomenon occurring in the bay, although violent squalls are common at certain times of the year, which can also cause severe damages . One such cyclone has been reported here with the potential of satellite data in a synergts ttc manner from various sources. For example, most of the daily OLR comes from the

B

80.0 100.0 120.

D

25.0

15.0

5.0

-5.0 ~~;;.;.;J..-.:.JO......:..~~~~~::L::::::U.J 60.0 80.0 100.0 120.

LONGITUDE, deg

Fig. 3(A-D)- Streamline flow at 200,500, 850 and 1000mb levels on 27th Apr. 1991

120 INDIAN J RADIO & SPACE PHYS, APRIL 2004

measurements by the IR sensor VHRR onboard Geostationary satel lites like GOES, INSAT, etc. and scanning A VHRR radiometers onboard NOAA polar orbiting satellites. The OLR is w idely used as a representative of the convective activity. The low OLR values correspond to the regions of high cloudiness associated with deep convection. Visible and infrared (IR) imagery from geostationary sate llites provide the most continuous source for the monitoring of cyclones. Mostly , the deep low­pressure centers are marked as areas of strong convergence and higher ra in rates . A tropical cyclone intensification process can also be characterized by the dynamic conditions at different pressure levels starting from the surface . Intensification of environmental wind fie ld is one such very important dynamic parameter. For brevity, the streamline motion of the system is shown o nly for a day at 200 mb, 500 mb, 850 mb and 1000 mb levels in Fig. 3(A-D) on 27th April. The wind fi e lds in these figures also show the transverse ci rculati on of well­organ ized cyclone features. The wind field at different level s shows that the system first appeared as a low-

25.0

15.0

5.0

g> -5.0 "C

~ 60.0

;:) ..... ~ 25.0 ..J

15.0

5.0

-5.0

A

80.0 100.0 120.0

c

pressure area over SE Bay and to a depress ion on April 24 (not shown for brevity) in NW direction. Within four clays it intensified into severe cyclonic storm on 27th April. T he well defined vortex is located at almost the same position as the c loud clusters in INSAT pictures (not shown here). A whirl with a very high wind circulating around it in an ti-clockwise direction is clearly ev ident. Then recurring to the NE it crossed Bang ladesh coast near Sondwip Is land around the midnight and diss ipated. The IMD reported classified maximum intens ity of the system was T6.5 on D 'vorak 's scale from 28/ 1200 UTC to 29/0300 UTC 18

•

The surface leve l convergence can produce a vertical velocity, aclvecting moisture upward , leading to dense cloudiness. OLR is an indicator of the cloudiness. Figure 4(A-D) shows the OLR over the area of study and the corresponding OLR derived rainfall in Fig. 5(A-D) . ln Fig. 4(C) the OLR field shows the lowest va lue of 85 W/m2 on April 26, at the Centre of the cyclone, whi le it was sti ll in the stage of development. During the landfall day on 29th April th e lowest value was 109.3 W/m2 near the center. The

B

25.0

15.0

5.0

-5.0

60.0 80.0 100.0 12C D

60.0 80.0 100.0 120.0 60.0 80.0 100.0 120.

LONGITUDE, deg

Fig. 4(A-D)- OLR field during the Bangladesh Cyclone from 26-29 Apr. 1991

GAIROLA eta/.: MICROWAVE & INFRARED MEASUREMENTS OF PRECIPITATIO 121

corresponding precipitation shows the maximum from 25.6 mm to 22.2 mm. These values are very small as far as the cyclonic rainfall is concerned. At the same time the lateral spread is very high and shows the unrealistic rain patterns over both the ocean and the landmasses. This could show a lot of spi llover as cirrus clouds, from the convection activity during the cyclone formation. The lR observations are affected by the presence of cirrus clouds and in variably translate them in rainfall amounts clue to their low temperatures, which seem to be the reason for large horizontal scale of rainfall in the present study. Similarly, the OLR derived rains over the Himalayan region are always persistent irrespective of the presence of clouds. This is also clue to the cool radiation background of the snow and ice as can be seen at lR wavelengths. However, data at microwave frequencies from polar-orbiting satellites :-~re more directly related to the precipitati on than those from visible and IR. The SSM/I rain estimates are generall y reported to be quite realistic over the oceans. Hence, it is inevitable to augment theIR rainfall stati stica lly, in conjunction with SSM/I rain fields in .terms of both its qualitative and quan titative magnitude. Generally, a single pass is capable of sweeping typically the whole

A

25.0

15.0

5.0

en (I) -5.0 "C ,.....-:::::-----, w 60.0 80.0 100.0 0 120.0 :::::> c t:

25.oF_........ ~ ...J ~

15.0

area of the Bay of Bengal. The data gap areas can very easi ly be interpolated in the objective analysis scheme, since the data coverage is much more than the data gaps.

The characteristics of atmospheric systems, like cyclones, in microwave wavelengths are dependent on two major properties of emiss ion and scattering clue to clouds and rain . In microwave radiometry the lower frequency ch::mnel s (6 and I 0 GHz) depend weakly on the absorption due to water vapour and liquid water. These frequency channels are not present in SSM/I , whereas, 18, 21, 37 and 85 GHz channels that are affected by atmospheric constituents are called atmospheric channels. The brightness temperatures of these channels during clouds and rainfall associated with tropical cyclone updrafts have a physical link, but in different magnitudes and manners. The brightness temperature at 85 GHz (in both polarizati on's) is linked with the deep convection in inverse manner due to the scattering from ice aloft, i.e. the decrease in brightness temperature clue to rainfal l. At 18 and 21 GHz the emission dominates, and the brightness temperature increases with the clouds and rains in a non linear fashio n. The 37 GHz fa ll s in intermediate category, i.e. it carries both

25.0

15.0

5.0

-5.0

60.0 80.0 100.0 120.

D

25.0

15.0

5.0

60.0 80.0 100.0 120.0 60.0 80.0 100.0 120.

LONGITUDE, deg

Fig. S(A-D)- OLR derived rainfall for the cyclone from 26 to 2Q Apr. 1991

122 INDIAN 1 RADIO & SPACE PHYS, APRIL 2004

emiSSion and scattering signatures. Algorithm used here is for rain estimation from all these channels of SSM/I as described earlier. Finally the SSM/I rainfall is combined with IR based rainfal l using GK algorithm as described earlier.

Figure 6(A-D) shows the results of combined IR and microwave algorithm, that gives robust values of rainfal l in the vicinity of the cyclone. The maximum rainfal l by OLR rains is only 45 mm while the combined algorithm gives a va lue of 110 mm. We view these maximum values as the average values on grid squares of the order of 100 km. The radial bands of the rainfall patterns in the present cyclone system over the Bay of Bengal shows the maximum rainfall as I 02 mm and further I 07 mm on the day of landfall on 29' 11 April. The rain bands clearly show the direct ion of turning of the system towards Bangladesh . The OLR rain patterns alone show dissipation of the system more clearly when the rain bands are towards further eastern side of Bangladesh and north-west China. The OLR and SSM/I mix rainfall data indicates that cyclonic precipitation is rather concentrated in the localized heavy convective region than otherwise shown by OLR rain bands alone. As expected the inner core area shows a much

25.0 ~H

C'l (l)

15.0

5.0

"0 -5.0 w c ::::> I-t= <t: ..J

60.0

25.0

15.0

5.0

-5.0

60.0

- 0

80.0

80.0

A

100.0 120.0

c

100.0 120.0

larger incidence of rainy episodes than found in the cloud clusters as indicated by OLR values. On dai ly basis , the extent of moderate and heavy rainfall agrees relatively well with the convective cloud covers, which is approximately 250-300 km as evidenced by 850 mb circulation fields. The fina l rainfall field seems to have clearly delineated the cirrus impact from the OLR derived rainfall. Even these rainfall amounts coming from the blend of high resolution lR with MW measurements are somewhat resolution dependent. However the very essence of the combined IR and microwave rainfall is that it can also be applied for a finer resolution of data in future, as the spatial resolution in IRis gener·ally very good (5 -8 km) with high frequency (half to three hours) of observation. On the other hand, the resolutions in microwave have also improved due to the low orbiting miss ions like Tropical Rain Measuring Miss ion (TRMM). These missions provide still better opportumties for integrated IR and MW measurements for both regional and g lobal scales.

In order to compare the final rainfall from IR and microwave satellite sensors with the ground truth , we have used the analysis from De et a/ 18

. The magnitude of rainfall in the coastal regions of Bangladesh are:

B

25.0

15.0

5.0

-5.0 ('e>

60.0 80.0 100.0 120

D

25.0

15.0 (7'

() 5.0

~ 37.4

-5.0

60.0 80.0 100.0 120.

LONGITUDE, deg

Fig. 6(A-D)- Final rain patterns using OLR and SSM/I data from 26 to 29 Apr. 1991

GAIROLA e1 a/.: M ICROWAVE & INFRARED MEASUREMENTS OF PRECIPITATIO 123

(2 1 0 mm) Teknaf, 160 mm (Situkundi), (L 10 mm) Bhola and (100 mm) Kutubdia. Similarly distant areas li ke Assam, Meghalaya, Nagaland, Manipur, Mizoram and Tri pura in India received the rain fa ll of magnitude in the range 40-90 mm . Though it is not possible to have exact quantitati ve comparison of the point measurements wi th the satelli te grid sizes of arou nd IOOx lOO km, it docs show a very close prox imity with the combined lR and microwave rain fa ll than the OLR-rain . The maximum ra in fa ll fro m JR have been fo und of the order of 45 mm while combined rainfall are of the order of II 0 mm. Particularly the IR rain fa ll amounting to not more than 45 mm is quite unreali sti c fo r most of the application purposes, part icularly du ri ng a cyc lone event of thi s kind. Microwave observations augment the IR rains to more reali sti c levels of the ground truth . However the IR measurements with hi gher resolution have immense potenti al fo r integrating mi crowave measurements, like from prec ipitation radar of the TRMM with a resolution of below 5x5 km. The present algorithm used here for integrating the IR and microwave m ·~as urement s for ra in is being applied fo r the combination of JNSAT and TRMM­PR, which will prov ide more acc urate rai nfa ll for various applications .

Cloud and precipitati on fo rmation is one of the key issues in the tropical prec ipitati on study. Optimal combinations of di fferent measurements of rainfall , mainly from IR data and microwave data have been demonstrated during a tropical cyclone over the Bay of Bengal. The results prov ide very good insight and indicates still better possibiliti es of properly integrating rain fa ll from the TRMM, which is currently operati onal and prov iding very hi gh quality data from both the IR and mi crowave sensors. With high temporal sampling by geostationary IR measurements, a mi x of both IR and microwave precipitation is hi ghly desirable. The delineation of regional scale rainfall features and their qu antitati ve assessment have been successfully brought out by the present algorithm with the study of the Bangladesh Cyclone, based on the algorithm which was originally developed fo r the global scale precipitation estimates.

In this regard, possibilities with the pair of IR and microwave satellite measurements could be envi saged for precipitation and prec ipitable water estimates in a

. . 3334 Th . I synergtsttc manner · · . e stmu t::meous measure-ment of the precipitable water by passive microwave radiometry is an important parameter as it is intimately coupled to the distribution of clouds and

rain fa ll. Because of the unusually large latent heat associated with the changing phases of water in the atmosphere, the di stribution of water vapour also plays a critical role in the vertical stability of the atmosphere and the structure and evo lution of the atmospheri c storm systems. The total precipitable water can be used to generate vertical humidity profi les35 fo r its proper association with the evolu tion processes of the atmospheric systems. These two parameters wi th the rotati onal part of the wind field are very valuable and unique to study the large-scale ci rculation patterns associated with tropical cyclone development.

5 Conclusions In the present paper, an approach fo r integrated IR

and microwave measurements usi ng various satell ite sensors have been used for the study of a severe cyc lone over 13 ay of Bengal and adjoi ni ng Eastern Ind ia and Bangladesh during 24-29 April 199 1. The rai n estimates from polar orbiting SSM/I and OLR from geostationary satellite IR rad iances have been used. The fin al rain fa ll fields are able to charac terize the structure of severe weather phenomenon like depressions and cyclones in much more realist ic manner. The fin al rain fa ll data matches well with the data from near coastal rain gauge stat ions. The motivation fo r thi s study, stemmed from the demonstration of the rainfall fields from global tropical SSM/I and OLR data base, which is used to describe the initi al state of tile atmosphere fo r physical initialization process and whi ch provided a large impact in numerical weather prediction on medium range 16 and in generating mesoscale rain structures 17

• The regional area rain fa ll thus al so makes a much better input to the mesoscale numerical models as an initial state and for physical initi ali zation.

The SSM/I onboard various DMSP series of satellites are now providing requ ired spatial and temporal measurements of total precipitable water and rainfall. The TRMM having various packages of YIS/IR and microwave sensors36

'37

. Thus the rainfa ll es timation through the integrated IR and microwave technique and the analysis of the combined rainfall offers tremendous potential for cyclone analys is in the data sparse regions of oceans.

The TRMM data include hydrometeors distribution, latent heat profiles derived from the Goddard Cloud Ensemble Model, separated convective and stratiform rainfall data, cloud

124 INDIAN J RADIO & SPACE PHYS, APRIL 2004

electrification, and so on. Some more such missions are being planned, such as Indo-French (ISRO-CNES) Megha-Tropique and the constellation of satellites under Global Precipitation Mission (GPM). We are investigating the applications of TRMM-TMI and more importantly high reso lution data of microwave Precipitation Radar in synergism with Geostationary IR data from INSA T and Meteosat for various studies that would be reported in the near future.

Acknowledgements The authors wish to thank the Director, Space

Applications Centre and Group Director, Meteorology and Oceanography Group for their encouragement. The SSM/I data procured from PO-DAAC-NASA is thankfully acknowledged.

References Allison L J, Rodgers E B, Wilheit T T & Feu R W, Bull Am Meteoro/ Soc (USA), 55 (1974) 1074.

2 Olson W S, J Geophys Res (USA), 74 ( 1989) 2269. 3 Katsaros K B, Bhatti I, Me Mudri L A & Petty G, Weather

aud Forcastiug, 4 ( 1989) 449. 4 Gairola R M & Krishnamurti T N, Meteorol Atmos Phys

(Austria) , 50 ( 1992) 165. 5 Gairola R M, Gohil B S & Pandey PC, National symposium,

Indian Geophysical Union, 20-22 May (1987), Hyderabad, India.

6 Prabhakara C, Dalu G & Vollmer B E, IVth Conference 0 11

Satellite Meteorology aud Oceanography, May 16-19, San Diego, Am Met Soc, ( 1989) 79.

7 Spencer R W, Geodman H M & Hood R E, J Atmos & Ocea11 Tee/mol (USA), 6 (1989) 254.

8 Rao G V & Macarthur P D, Mou Weather Rev (USA) , 122 (1994) 1568.

9 Rao G V & McCoy J H, Nat Hazards (Netherlands), 15 ( 1997) I.

10 Rodgers E B & Pierce H F, J Appl Meteorol (USA), 34 ( 1995) 2587.

II Jobard I & Desbois M, Atmos Res (Nether/auds), 34 (1994) 285.

12 Adler R F, Huffman G J & Keehn P R, Remote SellS Rev (USA), 11 ( 1994) 125.

13 Huffman G J, Adler R F, Rudolf B, Schneider U & Keehn P R, J Clim (USA) Part II, (1995) 1284.

14 Varma A K, Gairola R M, Ki shtwal C M, Pandey P C & Singh S K, IEEE Traus Geosci & Remote Seus (USA), 37, 5 ( 1999) 2556.

15 Gairola R M & Varma A K, 2002, Rem01e Seus Rev (USA), (2003) ln Press.

16 Kri shnamurti TN, Rohali G & Bedi H S. Tel/us (SIVeedeu), 64A (1994) 598.

17 Kri shnamurti T N, Bhowmik S K R, Oosterhof D K. Rohali G & Surgi N. Mou Weath er Rev (USA), 123 ( 1995) 2771.

18 De U S, Desai D S & Biswas N C. Mausam (ludia ), 43. 3 (1992) 231.

19 Kri shnamurti TN, Han W & Oosterhof D K, Meteorol At1110s Phys (A ustria), 65 ( 1998) 171 .

20 Gruber A & Krueger A F, Bull Am Meteorol Soc (USA). 65 (1984) 958.

2 1 Janowiak J E, Krueger A F, Arkin P A & Gruber A, No. 6 US dept. of Commerce NOAA/NWS Silver Spring MD. 1985 .

22 Krishnamurti T N, Cocke S, Pasch R & Low-Nam. 1983. Dept. of Meteorology Florida State Un iversity. Tallahassee Florida 32306.

23 Arkin P A & Meisner B N, Mon Weather Rev (USA). 115 (1987) 51.

24 Arkin P A, Krishna Rao A V R & Kelker R R, J Clim (USA), 2 (1989) 619.

25 Ferraro R R & Marks G F, J Atmos Ocem1 Tee/mol (USA). 12 (1995) 755.

26 Adler R F, Negri A J, Keehn P R & Hakkarinen I M. J Appl Meteorol (USA), 32 (1993) 335.

27 Huffman G J & Coauthors, Bull Am Meteorol Soc (USA) , 78 (1997) 5.

28 Kummerow C W & Giglio L, J Atmos Ocecm Techno/ (USA), 12 (1995) 33.

29 Xu L, Gao X, Sorooshian S & Arkin PA. J Appl Meteorol (USA), 38 ( 1999) 569.

30 Anagnostou E N, Negri A J & Adler R F, J Geophvs Res (USA), 104, D24 (1994) 31 ,477,488.

3 1 Todd M C. Kidd C, Kniveton D & Bellerby L J, J At1110s Ocean Techuol (USA), 18 (2001) 742.

32 Atlas D & Bell T L, Mou Weather Rev (USA) , 120 ( 1992) 1997.

33 Gairola R M, Basu S K, Varma A K & Pandey PC. Indo-US Symposium/Workshop on Remote Sensing and Its Applications, 6-9 Oct (1996) liT-Bombay , India.

34 Gairola R M, Basu S K, Gohil B S, Varma A K & Pandey P C 1994, (ICORGE-1994), Edited by IV Murlikri shnan, (Tata McGraw-Hill , New Delhi ), 757-761.

35 Basu S K, Gairola R M, Ki shtwal C M & Pandey P C. J Geophys Res (USA), 100 (1995) 23009.

36 Kummerow C, Barnes W, Kozu T & Simpson J, J Al1110s Ocean Tee/mol (USA), 15 ( 1998) 809.

37 Simpson J, Kummerow C, Tao W K & Adler R F, Meteorol Atmos Phys (Austria), 60 ( 1996) 19.