1A METHOD FOR DEVELOPING CLIMATOLOGICAL RAINFALL

INFORMATION: A PRELIMINARY APPLICATION TO LUZON

M. A. Estoque and R. T. F. BalmoriClimate Studies Division

Manila Observatory, Quezon City, Philippinesemail: mae@observatory.ph, rochelle@observatory.ph

ABSTRACT

A method for developing climatological information of monthly rainfall for the Philippines is described. The

information consists of monthly rainfall at a uniform array of grid points. This array can be used to construct climatological

rainfall maps and other types of graphical representations with the aid of a geographical information system. The method,

which has been developed, is based on combining three types of rainfall information. These consist of grid point rainfall

values from a fluid dynamical model of the atmosphere, rainfall observations from rain gauge stations and satellite estimates

of rainfall. These three types of information are combined by an objective analysis technique in order to obtain rainfall at a

uniform network of grid points.

The present paper describes an initial application of the method to the island of Luzon for the month of June. In this

application, only model-generated rainfall and rainfall from rain gauge stations are used. Satellite data are not included in

the application because these are still not available. The atmospheric model, which is used in this initial application, is a

simple model--the mixed layer model. The climatological rainfall information, which is produced by the method, is used to

construct a climatological map of rainfall for the month of June. The map is compared with a corresponding map prepared

by the Climate Branch, Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA). The

map, which is produced by the present method, exhibits higher horizontal resolution and greater accuracy.

____________________________________________

1. INTRODUCTION

The first significant study of the climate

of the Philippines has been made by Father

Jose Coronas (1916). An important result of

the study is a regional classification of

rainfall into four climatic types: the so-

called Coronas rainfall types. These types

give a general description of the large-scale

regional variations of the total monthly

rainfall. Subsequent attempts to revise the

classification have been made. However,

the revised version is not significantly

different from the original Coronas

classification. A map of this version is

shown in Fig. 1. An examination of the

geographical distribution of the different

rainfall types indicates that these are

primarily determined by the directions of the

seasonal winds with respect to the

2mountains. In particular, the rainfall patterns

indicate the occurrence of rain over the

windward slopes of mountains and dry

conditions over the leeside. Consequently,

there is little rainfall between mountains.

As indicated in Fig. 1, the Coronas

classification and its subsequent revisions

provide only general information on the time

of occurrence of the rainy and the dry

seasons of the year. It does not provide high

resolution, quantitative information

concerning the geographical distribution of

rainfall.

Information consisting of data sets of

rainfall on a regular grid is needed in

agriculture, hydrology, architectural

planning and other practical applications.

The data are important as input to

geographical information systems which

could produce rainfall maps and other types

of graphical output for many kinds of users.

For example, farmers could utilize such

maps as useful guides in planning cropping

patterns and in designing agricultural

structures. In this report, the authors

describe a method for constructing a set of

gridded rainfall data as well as a high

resolution, accurate rainfall atlas for the

Philippines. The method is based on

combining rainfall output data from fluid

dynamical models of the atmosphere,

satellite observations and rain gauge

observations.

The construction of an accurate data set

on rainfall for the Philippines is difficult due

to the sparse distribution of rain gauge

stations and the large rainfall variations over

mountainous areas. It is well known that

mountainous terrain, or orography, generates

large space and time variations of rainfall.

Coastlines also produce similar variations

with generally less amplitudes. The weather

disturbances, which produce these

variations, are small in horizontal scale.

These consist of convective and mesoscale

disturbances, whose dimensions range from

a few kilometers to tens of kilometers. We

note that these dimensions are relatively

small compared to the distances between the

networks of rain gauge stations in the

Philippines. This condition is indicated in

Fig. 2, which shows the present network of

rain gauge stations. Note the large areas

without stations in Northern Luzon and in

Mindanao. This network does not show all

of the stations which are used in this study.

In the application of the method to be

described here, we have included the

observations from some stations which

stopped operating after the Second World

War. The locations of the current stations

with respect to mountains may be inferred

3from Fig. 3, which shows the topography of

Luzon.

Fig. 1. A revised version by PAGASA of the original classification of rainfall types by FatherJose Coronas. Type 1: Two pronounced season, dry from November to April and wet during therest of the year. Type 2: No dry seasons with a very pronounced maximum rainfall fromNovember to January. Type 3: Seasons not very pronounced, relatively dry from November toApril and wet during the rest of the year. Type 4: Rainfall more or less evenly distributedthroughout the year.

4Fig. 2. Map showing the current network of rain gauge stations in the Philippines.

5Fig. 2 shows the average distance

between stations is of the order of 100 km.

This is about ten times bigger than the

horizontal scale of weather disturbances,

which are generated by mountainous and

coastal regions. Such disturbances produce a

considerable amount of rainfall in the

Philippines and other tropical regions. The

large space variability of rainfall associated

with mountainous terrain is indicated in Fig.

4. The figure shows a comparison of the

July climatological rainfall for three pairs of

stations. The three pairs consist of Dagupan

and Baguio, Iba and Tarlac (about 50 km to

Fig. 3. Map of the terrain heights (m) for Luzon and vicinity.

6the east), and Manila and Antipolo (about 20

km east of downtown Manila). Note that

Baguio has 500 mm of rainfall more than

Dagupan, a station only about 50 km away

from Baguio. This big difference is due to

the fact that Baguio is on the windward side

of the Cordillera Mountains while Dagupan

is located in the lowlands along the

shoreline of Lingayen Gulf. The equally

large rainfall difference of 500 mm between

Tarlac and Iba is due to fact that Iba is near

the windward side of the Zambales

Mountains. In contrast, Tarlac is located on

the leeside to the east. The difference

between Antipolo and Manila rainfall is

smaller than those of the preceding pair of

stations. Nevertheless, it is still substantial

if one considers that the distance between

the stations is only 20 km. The larger

amount of rainfall in Antipolo is presumably

due to the effect of the Sierra Madre

Mountains. The large space variability of

rainfall, which is indicated in Fig. 4, cannot

be resolved by the current network of rain

gauge stations. This lack of resolution is a

formidable obstacle in the construction of an

accurate rainfall atlas.

The rainfall comparison described

above indicates the important role of

mountains in determining the variations in

rainfall. The enhancement of rainfall by

mountains is primarily the result of two

physical processes: mechanically forced and

thermally forced upward motions.

Mechanical forcing occurs when air is

forced upward as it blows along the

windward slopes of a mountain. Fig. 5

illustrates this effect of mountains in

generating rainfall over the windward

slopes. In contrast, the leeside is generally

characterized by dry conditions. This is due

to the fact that, as the air descends on this

side, it becomes warmer and less humid.

Thermally forced upward motions occur

during the daytime on sunny days because

the mountain acts as an elevated heat source

under these conditions. The heat source

generates upslope motions during the

daytime. The upward motions which are

generated by mechanical and thermal

forcing cool the air by adiabatic expansion.

The cooling, in turn, produces clouds and

rainfall if the air is sufficiently moist. In

summary, the large variability of rainfall

produced by mountains, together with the

usual lack of rain gauge observations over

mountainous regions, are the biggest

problems in the construction of rainfall maps

over these regions. In this connection, one

finds that this is a major problem in

constructing rainfall maps for Northern

7Luzon, which is characterized by

mountainous terrain.

0

200

400

600

800

1000

Rai

nfal

l (m

m)

1 2 3

Stations

Baguio

Dagupan

Tarlac

Iba

Manila

Antipolo

Fig. 4. Variability of rainfall associated with orographic effects. Baguio, Iba andAntipolo are located over the windward side of a mountain.

Fig. 5. Schematic diagram showing the generation of rainfall by a mountain.Adapted from Barros and Lettenmaier (1994).

8Methods for estimating rainfall due to

orographic effects have been developed by

several investigators on the basis of fluid

dynamical and statistical models. Examples

of methods which use fluid dynamical

models of the atmosphere for estimating

rainfall due to mechanically-forced lifting

have been described by Barros and

Lettenmaier (1994). These methods have

been applied over the mountainous regions

of the Western United states. They are

primarily suitable for describing rainfall

variations whose horizontal scales range

from tens to hundreds of kilometers and for

time scales corresponding to climatological

time scales. Fluid dynamical models for

simulating rainfall due to thermally forced

lifting by mountains have been formulated

by Orville (1966). A model for simulating

rainfall generated by sea breezes has been

described by Estoque et al. (1994). In

connection with the use of the statistical

method for estimating rainfall over

mountainous regions, Daly et al. (1994)

have described an ingenious method. It

estimates rainfall at points of a regular grid

from irregularly distributed rain gauge

observations.

The use of fluid dynamical models for

estimating rainfall, in combination with

satellite observations, has been done by

several investigators. For example,

Grassotti and Garand (1994) have developed

a method for estimating rainfall at grid

points with the aid of numerical model

forecasts and geostationary satellite

observations (infrared and visible). On the

other hand, Xie and Arkin (1996) have

developed an algorithm for constructing

gridded fields of monthly rainfall by using

estimates from four sources of satellite

observations: infrared, visual, microwave

scattering and microwave emission. These

observations are merged with rainfall

generated by a numerical prediction model

to estimate rainfall. A similar method has

been described by Huffman et. al. (1995). It

must be mentioned that the methods which

have been developed by these investigators

are suitable only for the determination of

rainfall distributions due to large-scale

(synoptic) weather disturbances. The

horizontal resolution of such distributions is

of the order of one hundred kilometers.

3. METHODOLOGY

In this section, the authors describe the

method for the construction of a gridded

distribution of rainfall data for the

Philippines. The method is basically a

9modification of the methods mentioned in

the preceding section. Modifications are

also introduced in order to incorporate the

effects of small-scale rainfall variations

which occur in the Philippines, especially

those associated with orographic effects.

The modifications involve the use of a high-

resolution fluid dynamical model (horizontal

grid distance of about 5 km) and satellite

observations with about the same resolution.

The gridded information from the fluid

dynamical model and from the satellite data

are subsequently combined with rainfall

observations from rain gauge stations. By

combining the high-resolution model rainfall

and satellite rainfall estimates with the low-

resolution rain gauge data, our method is

able to produce a relatively accurate, high-

resolution set of gridded rainfall data.

The method for combining grid point

rainfall values from a fluid dynamical model

and satellite data with rain gauge

observations is based on the Cressman

objective analysis technique. This technique

requires the use of a preliminary or first

guess of the grid point values of the rainfall.

We plan to construct the first guess as a

weighted average of the grid point values

from the model and from the satellite data.

In accordance with the technique, the first

guess grid point values are subsequently

modified by a scanning procedure which

incorporates the contribution of rain gauge

observations in the vicinity of the grid

points. Several scans with varying radial

distances from the grid points are used.

4. INITIAL APPLICATION OF THE

METHOD

We have made an initial application of

the method described above in order to

determine its feasibility. This application is

limited in terms of the following aspects:

1. The area is limited to the island of

Luzon and only for the month of

June. We have confined our

application to the area to Luzon

because its northern sections are

mountainous. Hence, it could

provide a good test for the ability of

the method to incorporate orographic

effects.

2. Only rainfall observations from rain

gauge stations are used in this initial

application. Satellite observations

are not used because these are not

available at the present time.

10

3. The model rainfall is generated by

the mixed layer model, a simple

model of the atmosphere. More

sophisticated three-dimensional fluid

dynamical models will be considered

in future applications of the method.

In brief, the initial application will

construct a rainfall atlas by combining

model-generated rainfall data with

observations from rain gauge stations. The

fluid dynamical model which is used to

generate the rainfall is the so-called mixed

layer model. It has been used in previous

studies to simulate rainfall for different

types of weather conditions. It was used by

Lavoie (1972) to study rainfall which is

associated with lake-effect storms over Lake

Ontario. It was also used by Estoque and

Ninomiya (1976) for studying precipitation

over Japan. The precipitation is generated by

the warm waters of the Japan Sea during

outbreaks of cold air from Siberia in the

winter monsoon season. The model

equations are derived by assuming that the

atmosphere has the simple vertical structure

shown in Fig. 6.

The diagram shows that the complex

structure of the real atmosphere is replaced

by three layers: (1) a surface layer adjacent

to the ground; (2) an overlying layer called

the mixed layer; (3) a stable layer which

represents the upper layers of the

atmosphere. The surface layer serves as a

convenience for estimating the turbulent

fluxes of heat, momentum and other

atmospheric properties between the ground

and the mixed layer. The mixed layer is the

active layer, which describes the variations

in time and space of the important

atmospheric variables, such as wind,

temperature, clouds, rain and others. Here,

the variables are assumed to be constant

along the vertical; however, they vary both

temporally and horizontally. The figure

indicates the important parameters which

describe the inversion. These are the height

of the inversion (ZT) and the strength of the

inversion (DTN). These two quantities

describe the thermal stability of the

atmosphere with respect to buoyancy-

generated motions. These motions are

important in the generation of clouds and

rain.

A detailed description of the dynamical

equations of the mixed layer model is given

in the abovementioned studies and in other

subsequent articles. We will, therefore,

limit the discussion of the model to the

equations which predict rainfall. The

original formulation for the rainfall

prediction in previous studies of Lavoie and

11

Estoque & Ninomiya is somewhat semi-

empirical. It estimates the precipitation rate

in terms of the height of the cloud base, the

inversion height and the vertical velocity.

We have replaced this by a more realistic

treatment which is based on the cloud

microphysics formulation of Kessler (1969).

Kesslers formulation involves equations

which predict the amount of water in cloud

droplets and in the bigger raindrops. The

formulation assumes that cloud droplets are

formed after the air becomes supersaturated.

The cloud droplets subsequently grow into

bigger drops (raindrops) as a result of two

physical processes. The first, auto-

conversion (AUCON), describes the

diffusion of small droplets toward bigger

droplets. The second (ACR) describes the

accretion or capture of cloud droplets by the

bigger raindrops as these accelerate

downward due to gravity. The formulation

also incorporates the depletion of raindrops

by evaporation (EVAP).

O

Potential Temperature ()

STABLE LAYER

SURFACE LAYER10 mZo

ZS

ZT

ZH

M

H

Hei

ght

Inversion Strength, DTN

MIXED LAYER

Fig. 6. Vertical structure of the atmosphere according to the mixed layer model.

12

The two processes, which involve the

formation of rain and its evaporation, are

described by the equations,

Eq. (1)

Eq. (2)

where,

[ ]2SS1 K)QQ(KAUCON = ( )( ) 875.06rsss3e 10xQQQKCACR =

[ ]( ) 65.06rsss4 10QQQKEVAP =

The important variables are defined as

follows,

Q, amount of water in vapor and cloud

droplets

Qr, amount of water in raindrops

u, wind component along the x-axis

v, wind component along the y-axis

w, wind component along the z (vertical

axis)

Vr, terminal velocity of raindrops

KH, horizontal diffusion coefficient

KV, vertical diffusion coefficient

s, standard air density of the mixed layer

The most important output of the model

is the rainfall rate; this quantity is given by,

Rainfall Rate = -s(VrQr)

We have adapted these equations for

our particular model so that they apply to

entire air columns of the mixed layer. This

is done by integrating the equations with

respect to height from the surface layer to

the top of the mixed layer. The integration

simplifies the model by eliminating the

dependence of the equations along the

vertical coordinate.

In spite of all the simplifications

introduced above, the model is able to

simulate the rainfall contributions due to

mechanically forced ascending motions over

the windward slopes of mountains and due

to weather disturbances, which are generated

by the daytime heating of the ground. This

heating is incorporated by assuming a

diurnal variation of the surface temperature.

The equation specifies a sinusoidal variation

of the ground temperature with a maximum

at noon and a minimum at midnight.

( )( )[ ])QQ(EVAP)QQ(ACRQQAUCON1

QVzz

Qwy

Qvx

Qut

Q

rrrs

rrsrrrr

+

+

=

( )[ ])QQ(EVAP)QQ(ACRQQAUCON1zQK

yQ

xQK

zQw

yQv

xQu

tQ

rrrs

2

2

V2

2

2

2

H

++

+

+

+

=

13

Eqs. (1) and (2), together with the

dynamical equations for the mixed layer,

have been integrated numerically in order to

generate gridded rainfall data. A rectangular

coordinate system is used. The grid distance

is equal to 8 km along both the x (east-west)

and the y (northsouth) directions. Initial

values of the potential temperature, wind,

height of the mixed layer and the different

water variables are specified. In particular,

the corresponding amounts of cloud and rain

material are initially set to zero. The initial

wind is assumed to be from the southwest

with east-west and north-south components

equal to 5 m/s. The corresponding values of

the temperature and water vapor are

determined from a typical sounding in June.

The initialization is quite subjective due to

the use of the mixed layer model as a

representation of the actual atmosphere. The

subjectivity is introduced in the specification

of the parameters which define the wind,

moisture content and the temperature-related

variables, such as the height of the mixed

layer and the strength of the inversion

(DTN). Fortunately, numerical experiments

with the model indicate that the rainfall

pattern is relatively insensitive to most of

the initial conditions. However, the

magnitude of the rainfall is quite sensitive to

the mixing ratio for water vapor and the

height of the inversion.

Starting with an initial time of 6 A.M.,

we integrated the model for a period of 24

hours. At the end of the period, the model

calculates the total or the accumulated

rainfall values at grid points. The pattern of

the accumulated rainfall is shown in Fig. 7.

Looking at the rainfall distribution, we note

a striking similarity between this distribution

and the pattern of terrain elevation in Fig. 3.

Note that high (low) values of rainfall are

associated with correspondingly high (low)

values of terrain elevations. The dominant

features of the rainfall distribution are the

three major north-south bands of rainfall

maxima. These are associated with the

Zambales Mountains, Sierra Madre

Mountains and the Cordilleras over

Northern Luzon. The rainfall bands are

associated with orographic effects due to

mechanical forcing of the southwest

monsoon flow and, to a lesser extent, due to

the thermal forcing during the daytime.

Over Northern Luzon, one can see a rainfall

minimum along the Cagayan Valley

between a maximum along the Cordilleras

toward the west and the Sierra Madre

Mountains toward the east. A similar

rainfall minimum is indicated over Central

14

Luzon between the Zambales Mountains and

the Sierra Madre Mountains to the east.

The grid point values corresponding to

the rainfall distribution in Fig. 7 are

subsequently multiplied by a suitable factor

in order to convert them into preliminary

climatological values for June; the factor is

subjectively specified. Next, the

preliminary grid point values are added to a

geographical rainfall average for the

domain; the average is computed by using

the climatological rain gauge observations

for June. The addition produces a final set of

grid point values of model rainfall. In

essence, this additive process of arriving at

the model rainfall assumes that the rainfall

consists of the contributions of mesoscale

and large synoptic scale weather

disturbances. The synoptic scale

contribution corresponds to the average of

the observed rainfall from rain gauges.

Finally, the set of grid point values is used

as the input (first guess) for an objective

analysis technique. This technique is based

on the Cressman successive correction

method.

Fig. 7. Rainfall pattern for June produced by the dynamical model.Units: rainfall depth in cm.

15

5. RESULTS AND DISCUSSION

As indicated in the previous section, our

first application of the method involves the

construction of a rainfall map of Luzon for

the month of June. The result of the

application is shown in Fig. 8. Looking at

the map, one notes that high elevations are

generally associated with high rainfall.

Maximum values of 900 mm occur over the

Cordilleras and the Zambales Mountains.

These values appear to agree with the large

rainfall values at Iba (Zambales) and Baguio

in Table 1. On the other hand, the map

shows low rainfall along the Cagayan Valley

with a minimum value of about 100 mm.

This is confirmed by the low rainfall

observed at Aparri and Tuguegarao in Table

1. The map also shows relatively low values

of about 200 mm over the Central Plain of

Luzon. The low rainfall along the Cagayan

Valley and over the Central Pain is

consistent with the picture shown in Fig. 5.

These two regions are located at the leeside

of mountain ranges. At this juncture, it is

interesting to speculate whether the large

values of rainfall shown in Fig. 8 over the

Northern Cordilleras east of Vigan are

realistic. These large values are also seen

over the mountains of Mindoro. In these

two regions, there are no observations (see

Fig. 2).

Next, we discuss a more quantitative

evaluation of the performance of the

method. This is done by calculating the

rainfall obtained by the method at the

location of the rain gauge stations. The

calculation is an interpolating technique

which uses the four grid points surrounding

a particular station. The technique computes

a weighted average of the four grid point

values surrounding the rain gauge station.

The weight is inversely proportional to the

distance between the grid point and the rain

gauge station. The interpolated values are

then compared with the rain gauge

observations; these interpolated values and

the observations are shown in Table 2. The

table shows a close agreement between the

observed rainfall and the rainfall obtained by

the method. The largest error is found in

Baguio with a value of 74 mm.

16

Fig. 8. Climatological rainfall (mm) for the month of June obtained by using the method.

17

Table 1. June climatological rainfall at present rain gauge stations of PAGASA.

STATION LONGITUDE (O) LATITUDE (O) TOTAL RAINFALL (MM)Iba 15.3 120 601

Baguio 16.4 120.6 501Dagupan 16.1 120.3 374

NAIA 14.5 121 245Laoag 18.2 120.5 350Vigan 17.9 120.4 340Aparri 18.4 121.6 154

Tuguegarao 17.6 121.7 158Calapan 13.4 121.2 191Infanta 14.8 121.6 248Daet 14.1 123 182

Legaspi 13.1 123.7 252Casiguran 16.3 122.1 222Ambulong 14.1 121.1 256

Table 2. Comparison between calculated rainfall and rainfall from rain gauge observations.

STATIONS OBSERVED RAINFALL (MM) CALCULATED RAINFALL (MM)Iba 601 601

Baguio 501 427Dagupan 374 374

NAIA 245 268Laoag 350 349Vigan 340 340Aparri 154 154

Tuguegarao 158 158Calapan 191 191Infanta 248 248Daet 182 182

Legaspi 252 252Casiguran 222 222Ambulong 256 245

The comparison is summarized by the

scatter diagram in Fig. 9. The diagram

shows a very high correlation between the

two quantities. The largest difference

between the two quantities is found for

Baguio; here, the method underestimates the

rainfall. Some statistical measures of the

accuracy are: Square of the correlation

coefficient: 0.93; root mean square error:

1.251; average absolute error: 3.875. The

18

regression equation in Fig. 9 may be used to

estimate the actual rainfall from grid point

rainfall in regions without rain gauge

stations.

A final evaluation of the method is done

by comparing the rainfall map, which is

produced by the method, with a

corresponding map prepared by the Climate

Branch of PAGASA (See Fig. 10). Looking

at both maps, one finds that there are some

similarities in the general patterns of

rainfall. The most important similarity

involves the existence of minimum rainfall

along the Cagayan Valley in both maps.

However, the enhancement of rainfall by

mountains is not accurately portrayed by the

PAGASA map. For example, this map

shows a rainfall maximum which occurs

over the coastal region in the province of La

Union to the northeast of Lingayen Gulf.

This maximum is not consistent with the

relatively low rainfall, which is observed

along the entire coast of the Ilocos Region.

There are other differences between the two

maps. For example, the PAGASA map

shows a narrow strip of maximum rainfall

along the western coast of Zambales

Province. On the other hand, our present

map locates these regions of maxima farther

to the east over the western slopes of

mountains. Still another difference involves

rainfall patterns in the vicinity of the Sierra

Madre Mountains. The PAGASA map

shows no evidence of orographic effects. In

contrast, these effects are well-defined in

our map. The lack of orographic effects of

the Sierra Madre Mountains in the PAGASA

map results in the absence of a well-defined

minimum rainfall over Central Luzon. The

present map indicates a well-defined

minimum. In general, the evaluation shows

that the present map has more details and

greater accuracy in the rainfall patterns than

the PAGASA map.

y = 0.9079x + 25.004R2 = 0.9332

0

100

200

300

400

500

600

700

0 100 200 300 400 500 600 700

Observed

Cal

cula

ted

Fig. 9. Scatter diagram showing the relationship between observed rainfall at rain gaugestations and corresponding calculated rainfall.

19Fig. 10. Climatological rainfall atlas for the month of June prepared by PAGASA.Adapted from a chart available from the Climate Branch of PAGASA.

20

6. SUMMARY AND FUTURE WORK

This paper describes a method for

constructing a grid point array of

climatological rainfall values; this can be

portrayed graphically as a climatological

rainfall map. The grid point values are

obtained by combining rainfall produced by

a fluid dynamical model of the atmospheric

model with rainfall observations. The

observations, in turn, may consist of rainfall

observations from ground-based stations as

well as satellite-derived rainfall. The three

data sets (model rainfall, rain gauge

observations and satellite-derived rainfall)

can be combined in order to generate a

uniformly-spaced grid point array of rainfall.

This is done with the aid of an objective

analysis technique. The result of an initial

application of the method is presented. In

this limited application, we use a mixed

layer model to obtain the model-derived

rainfall. The observations are limited only to

rain gauge observations; satellite

observations are not used because they are

currently not available. The method is

applied in the construction of a

climatological rainfall map for Luzon for the

month of June. The map is compared with a

corresponding map which has been prepared

previously by PAGASA. The comparison

indicates that present map incorporates the

effects of mountains more realistically than

that of the PAGASA map. Furthermore, the

map appears to have a higher horizontal

resolution and greater accuracy. Future

applications of the method will use a more

realistic three-dimensional model of the

atmosphere instead of the simple mixed

layer model. In addition, satellite-derived

rainfall will be included as an input to the

method. Ultimately, we expect to construct a

complete set of climatological rainfall data,

together with rainfall atlases for all months

of the year and for the entire Philippines.

REFERENCES:

Barros, A.P. and D.P. Lettenmaier, 1994:

Dynamic modeling of orographically

induced precipitation. Rev. of Geophys, 32,

265-284.

Coronas, S. J., 1920: The climate and the

weather of the Philippines, 1903 to 1918.

Bureau of Printing, Manila, 194 pp.

Daly, C. and Co-authors, 1994: A statistical-

topographic model for mapping

climatological precipitation over

21

mountainous terrain. J. Appl. Meteor., 33,

140-158.

Estoque, M. A. and K. Ninomiya, 1976:

Numerical simulation of Japan Sea Effect.

Tellus, 28, 243-253.

Grassotti, C. and L. Garand, 1994:

Classification-based rainfall estimation

using satellite and numerical forecast

model fields. J. Appl. Meteor., 33, 159-

178.

Huffman, G. J, and Co-authors, 1995:

Global precipitation estimates based on a

technique for combining satellite-based

estimates, rain gauge analysis, and NWP

model precipitation information. J.

Climate, 8, 1284-1295.

Kessler, E., 1969: On the Distribution and

Continuity of Water Substance in

Atmospheric Circulation. Meteor.

Monogr. No. 32, Amer. Meteor. Soc.,

Boston, Mass., 84.

Lavoie, R. L., 1972: A mesoscale numerical

model of lake effect storms. J. Atmos. Sci.,

29, 1025-1040.

Orville, H.D., 1965: A numerical study of

the initiation of cumulus clouds over

mountainous terrain. J. Atmos. Sci., 22,

684-699.

Xie, P. and P. A. Arkin, 1996: Analysis of

global monthly precipitation using rain

gauge observations, satellite estimates, and

numerical model predictions. J. Climate, 9,

840-858.

22

LIST OF SCIENTIFIC REPORTS

Report No. 1 CLIMATOLOGY OF RAINFALL AND WIND FOR THEPHILIPPINE-SOUTH CHINA SEA REGION PART 1: MONTHLYVARIATIONS (M.A. Estoque, M.V. Sta Maria and J.T. Villarin S.J.;Quezon City, Philippines; March 2000)

Report No. 2 CLIMATOLOGY OF RAINFALL AND WIND FOR THEPHILIPPINE-SOUTH CHINA SEA REGION PART 2: VARIATIONS DUETO EL NINO AND LA NINA (M.A. Estoque, M.V. Sta Maria and J.T.Villarin S.J.; Quezon City, Philippines; May 2000)

Report No. 3 CLIMATE CHANGES DUE TO THE URBANIZATION OFMETRO MANILA (M.A. Estoque and M.V. Sta Maria; Quezon City,Philippines; June 2000)

Report No. 4 CLIMATOLOGICAL CHANGES IN RAINFALL DUE TOURBANIZATION (M.A. Estoque and E.R. Castillo; Quezon City,Philippines; March 2001)

Report No. 5 CHANGES IN RAINFALL PATTERNS DUE TO THEGROWTH OF AN URBAN AREA (M.A. Estoque and R.T.F. Balmori;Quezon City, Philippines; March 2002)

Report No. 6 PREDICTABILITY OF DROUGHTS AND FLOODS DUE TO EL NIOAND LA NIA EPISODES (M.A. Estoque and R.T.F. Balmori; QuezonCity, Philippines; June 2002)

Report No. 7 ENVISIONING FUTURE CLIMATE CONDITIONS IN THEPHILIPPINES: AN ANALYSIS OF GENERAL CIRCULATIONMODELS BASED ON THE SPECIAL REPORT ON EMISSIONSSCENARIO (E.R. Castillo and J.T. Villarin S.J.; Quezon City, Philippines;May 2002)

Report No. 8 GEODYNAMICS AND GEOKINEMATICS OF SEA LEVEL RISE:SURVEYING THE KEY FACTORS (E.R. Castillo et. al.; Quezon City,Philippines; June 2002)

Report No. 9 DIURNAL VARIATIONS OF AIR POLLUTION OVER METROMANILA (M.A. Estoque and R.T.F. Balmori; Quezon City, Philippines;December 2002)

Report No. 10 A METHOD FOR DEVELOPING CLIMATOLOGICAL RAINFALLINFORMATION: A PRELIMINARY APPLICATION TO LUZON (M.A.Estoque and R.T.F. Balmori; Quezon City, Philippines; May 2003)

Climate Studies DivisionManila Observatory, Quezon City, PhilippinesLIST OF SCIENTIFIC REPORTS