Global distribution of atmospheric water vapor

Global distribution of atmospheric water vapor

S. Mukai and I. Sano

Kinki University, Faculty of Science & Technology 3-4-1 Kowakae, Higashi-Osaka, 577-8502 Japan

+81-6-6721-2332, mukai C im. kindai. ac. jp (Recieved 2 December, 2002)

Abstract

This paper presents a procedure on how to derive the atmospheric water vapor content from ADEOS / POLDER data. A POLDER sensor, mounted on the Earth observation satellite ADEOS in 1996, is a unique sensor which can gather multi-(up to 14) directional polarization measurements of one target . Two channels of POLDER data in the near infrared wavelengths are examined in this study. The first channel is in the water vapor absorption band of 0.910 µm and the second is in the gas absorption-free band of 0 .865 ,c,m. In practice, a ratio of each reflectance for these two channels is used to estimate the total column water vapor content. This algorithm will be referred to as the two-channel ratio method.

The measured water vapor content from the POLDER data is in close agreement with the ground mea- surements undertaken at several NASA/AERONET stations. Global distribution of water vapor content presents the typical characteristics of spatial and temporal changes over the whole period of the ADEOS satellite's deployment. The water vapor content has high values over the tropical zone and decreases with latitude. Furthermore, the Indian monsoon is clearly demonstrated in the water vapor map . It should be noted that the water vapor content retrieved from the POLDER data has been validated with AERONET measurements.

The global map of water vapor is mutually compared with that of aerosol properties, which are derived from the POLDER polarization data. In addition to the comparison of water vapor content with aerosol loading, it has been found that the WA (water vapor-aerosol) index, i.e. the product of the water vapor content, aerosol optical thickness and Angstrom exponent, is a good indicator to classify the regional characteristics of the atmosphere-surface system. For example, high and low values in the WA index represent a bio-active area and a desert-dust area, respectively.

1 Introduction

It is well known that atmospheric water vapor plays an important role in the Earth-surface system with respect to the hydrological cycle, the energy budget, green house gases etc. Therefore water vapor content data is a critical element in investigating a so- lution to the urgent problem of global warming [Rind et al., 1991]. Furthermore, the interaction between water vapor and atmospheric aerosols has a strong influence on direct and/or indirect climate forcing

[Hegg et al., 1997; Russell et al., 1999]. In other word, atmospheric water vapor, as well as aerosols, are important parameters in Earth Global Circulation Model (GCM) simulations.

Retrieval algorithms for determining the total

amount of atmospheric water vapor by observ-

ing the H20 absorption band in the near infrared

wavelengths have been proposed by several authors

[Frouin et al., 1989; Kaufman and Gao, 1992]. The

POLarization and Directionality of the Earth's Re- flectance (POLDER) sensor on the Japanese AD- vanced Earth Observing Satellite (ADEOS) launched on August 17, 1996 provides the first opportunity to measure the atmospheric water vapor content on a global scale. POLDER has eight observing channels from 0.443 to 0.910 µm. Two channels in the near in- frared wavelength measure the inversion of water va- por content The water vapor absorption band of 0.910 um and the absorption-free band of 0.865 pm are the channels or interest in the present study. In practice, a ratio of these two-channels of data is used to es- timate the total amount of atmospheric water vapor.

A further merit of the POLDER sensor is that it se- quentially obtains the frame image. In this manner the light reflected from one target is measured in 14 different directions. The POLDER sensor takes po- larization measurements at three channels for wave- lengths at 0.443, 0.670 and 0.865 pm. This type of measurement is unique to the POLDER sensor. The polarization information is invaluable in tracking at- mospheric aerosols, clouds, land coverage etc.. The results of water vapor content will be compared with those of aerosols derived from the POLDER polar- ization data [Mukai and Sano, 1999; Goloub et al., 1999].

2 Method

An inversion method for determining the at- mospheric water vapor content using data gathered by the POLDER instrument was proposed as the CNES/POLDER operational algorithm [Bouffies et al., 1997]. The ADEOS/POLDER provides the op- portunity to determine the amount of atmospheric water vapor on a global scale for the first time. This method is based on the POLDER characteristic data as: i) spectral information in the near infrared wave- lengths, i.e. a water vapor absorption band of 0.910 ,am and a gas absorption-free band of 0.865 pm, ii) multi-(up to 14) directional viewing of one target, in which the light reflected from the sun-glint is ob- served (see Fig. 1). Figure 1 presents a brief sketch of the POLDER multi-observational scheme. Since POLDER sequentially obtains the frame image, the measurements are taken in 14 directions reflected from one target. The sun glitter direction component is included in the measurement. Reflectance at the top of the atmosphere with nadir direction, and with the specular direction against the sun (i.e. sun-glint direction), is available over the land and the ocean, respectively. It is reasonable to assume that the nadir direction will provide a good estimation of the total column content of water vapor because it is the short- est path from the target. The necessity and use of sun-glitter data in the determination of water vapor levels over the ocean will be discussed later. Figure 2 shows the atmospheric transmission of water va- por, denoted by the thin solid curves at 0-, 3- and 6-km altitude above sea-level [Kneizys et al., 1988],

and the response functions of the POLDER 0.865 pm and 0.910 ,am bands [Breon, 1997] represented by the thick solid curves. It is clearly shown in Fig. 2 that water vapor is strongly absorbed around 0.94 ,u,m and there is a moderate but significant absorption around the POLDER 0.910 am band. It is reasonable to as-

sume that the reflectance at the POLDER 0.910 ,um band from the Earth atmosphere-surface system is a result of water vapor absorption, the magnitude of which corresponds to the amount of water vapor. On the other hand, the data at the POLDER 0.865 ,am- band is free from absorption by water vapor. There- fore the ratio (X) of the reflectance at 0.910 ,um to that at 0.865 um can be used to estimate the total column water vapor content (Uapp) where

ao + a1 log(X)+a2[log(X)]2 U aPP P

surf {COS (Os )(1)+ COS'

X = R(0.910)/R(0.865), (2)

R represents the reflectance at the top of the atmo-

sphere, Psurf , Os , and 0, denote the surface pressure,

solar and viewing zenith angles, respectively, and the

coefficients ao, a1, and a2 are empirically determined

[Vesperini et al., 1999]. The surface pressure Psurf

is given as a function of altitude above sea level and

position in a longitude-latitude plane according to the

atmospheric model of LOWTRAN 7 [Kneizys et al.,

1988]. This algorithm, referred to as the two-channel

ratio method, is restricted to POLDER data gathered

from clear-sky pixels over the land and sun-glitter

pixels over the ocean. This restriction relates to the background which must be sufficiently bright to al- low detection of atmospheric water vapor absorption.

Further assumptions applied to this method [Bouffies et al., 1997; Vesperini et al., 1999] are as follows;

surface reflectivity is constant over the two channels, scattering by atmospheric aerosols is not significant

at these channels, and the total column water vapor content is riot significantly affected by the vertical profile of water vapor. The restrictions on this two- channel ratio method are considered in the following section with respect to the evaluation and validation of the obtained global map of water vapor content.

3 Global map of water vapor

The two-channel ratio method is applied to each feasible pixel of the POLDER image. By averaging all of the retrieved results through a one-month pe- riod over the whole world including both land and ocean, global distributions of atmospheric water va- por content can be obtained. Fig. 3 shows the global map of water vapor content over the entire function- ing period of the ADEOS from November, 1996 to June, 1997. The color scale, at the base, indicates in- creasing content from left to right. The black color represents the area without feasible POLDER data.

The ground measurements with NASA/ AERONET are represented by small squares in Fig. 3. It can easily be seen that both results correspond closely with each other. High values appear over the trop- ical zone throughout the entire period. This area corresponds to the Inter-Tropical Convergence Zone (ITCZ), which is a zone of heavy rainfall in the trop- ics. Furthermore it was found that the location of the high value zone over the tropics varies slightly, moving to and fro according to the location of the ITCZ. The water vapor content decreases with lati- tude away from the ITCZ. The values over the tropics were a little higher than expected. Overestimation of the water vapor content over the tropical zone is con- firmed by the AERONET data, which is of a lesser value to the results obtained from the POLDER. It has been noted that the two-channel ratio water vapor algorithm tends to overestimate in the case of large water vapor content [Vesperini et al., 1999]. Figure 4 is a scattergram plotting the estimated values of water vapor content from the POLDER data against the observed values from the AERONET. The scat- tergram exhibits a closer correlation, the correlation coefficient being 0.96, between POLDER estimates and AERONET measurements. Linear regression has a 1.09 slope and a —2.07 kg/m2 intercept. It is of in- terest to note that the water vapor content retrieved

from the POLDER data has been validated by the AERONET measurements. Hence seasonal and/or spatial variation of the water vapor is considerable based on the maps obtained.

The results over eight months shown in Fig. 3 pro- vide information concerning the seasonal change in atmospheric water vapor. In the case of the Indian monsoon, the monthly changes in water vapor con- tent are clearly shown. Very low values of water va- por content are apparent from November to Febru- ary, which corresponds to the well-known dry sea- son in India. Water vapor starts to increase in March.

March is the transit period from the dry season to the wet season in India. April marks the beginning of the rainy season. The water vapor pressure reaches its maximum in June. The wet, warm air mass swept in from the ocean encounters the Himalayas and results in heavy rainfall (the Indian monsoon) . The mon- soon phenomena is found not only in India but also all over south-east Asia. Fig. 3 presents data on the area of Asian monsoon activity, which increases with the onset of the season. For instance, the monsoon ef- fect, redirected by the Himalayas heads toward Japan and appears as the Japanese rainy season in June. Sig- nificant water vapor content is shown in June in the southern part of Japan.

Note that the Himalayas is always rendered in vi- olet. In other word, the water vapor content is small or almost zero during all seasons in the Himalayas.

This result is made clear in Fig. 2, where atmospheric transmission is presented at various altitudes above sea level. It: can be seen that absorption by atmo- spheric water vapor decreases with altitude, and fur- thermore, water vapor absorption is not detectable in any region higher than a 6km altitude. Almost all of the Himalayas is located at altitudes higher than this level.

—3—

4 Atmospheric aerosols and water Water vapor is one of the basic constituents of the

atmosphere. Therefore the quantity of water vapor has a strong influence upon the behavior of such other atmospheric constituents as aerosols and/or cloud particles. As a result, radiative forcing is modified according to the content [Russell et al., 1999]. In this section, the relationship between water vapor content and atmospheric aerosols will be considered. , This is especially necessary because aerosols are an un- certain key factor in the radiation budget in the Earth atmosphere-surface system. First an algorithm for describing aerosol properties using POLDER's radi- ance and polarization, as well as the multi-viewing information, will be briefly discussed. The investi- gation of aerosol content relies on two near infrared wavelength channels, i.e., 0.670 and 0.865 pm, where the POLDER sensor measures polarization as well as radiance. The basic idea for aerosol estimation is based on comparing the space-based reflectance with the simulated reflectance from multiple scat- tering calculations in an atmosphere-surface model [Mukai and Sano, 1999]. In practice, the radiance and the polarization degree are used in the case of aerosol estimation over the ocean, and polarized ra- diance is applied to aerosols over the land [Deuze et al., 2001, Sano et al., 2001].

The Earth's surface is classified into three cat- egories with respect to the current simulations (sea surface, soil surface and fields covered with vegeta- tion) (see Fig. 5). The sea surface is simulated with multiple facets using slopes that vary according to the isotropic Gaussian distribution with respect to wind speed [Cox and Munk, 1954]. The ocean is assumed to be completely absorbent in the near infrared wave- lengths. The land surface covered with soil is ex- pressed by empirical BPDF [Nadal and Breon, 1999]

and the vegetated field is interpreted as a Lambert surface. The height of the land surface above sea level is considered within 1 km accuracy . Finally aerosol characteristics are extracted by comparing the POLDER data with the simulated values stored in the look up table (LUT).

The aforementioned procedure is applied to each pixel of the satellite image. Namely viewing angles of the sun and the satellite on each pixel of the im- age correspond to the angles of incident and scat-

vapor

tered light through the scattering process. In other word, each pixel of the image yields the scattering angle. Thus the scattering angle correlates the radi- ance/polarization of a pixel of the POLDER image with a simulated value in the LUT. Therefore in or- der to estimate the aerosol content, the interpolation process with respect to angles is necessary in order to

estimate a properly simulated value corresponding to the observational geometry. As a result an optimized aerosol model is retrieved which minimizes the rela- tive error between the measurements and the simula- tions for each pixel [Sano and Mukai, 2000].

Global distributions of the aerosol parameters are obtained by averaging all of the retrieved results throughout a one-month period over the whole world including both the land and the ocean. Figures 6(a) and 6(b) show the obtained aerosol's optical thick- ness at a wavelength of 0.55 am and Angstrom expo- nent in December 1996 and in June 1997 on a global scale. It is of interest to mention that aerosol charac- teristics over coastal zones have natural features, i.e.

they have a continuous distribution from the land to the ocean. For example, dust aerosols blown over the ocean from the Sahara desert are clearly shown in the northwest coast of Africa and Asian dust particles are evident over the northeast Pacific Ocean.

The global map of water vapor is mutually com- pared with that of aerosol properties. It is known that relative humidity has an influence on the formation and growth of aerosols. Hence atmospheric water vapor content could be related to the aerosol's prop- erties. The similarity and/or diversity between wa- ter vapor content in Fig. 3 and aerosol properties in Figs. 6(a) and 6(b) require discussion. In respect to India in June, both the water vapor content and the aerosol loading are high because of the annual Asian monsoon. The atmosphere over the desert, e.g. Sa- hara desert, mid Asian continent etc, is dry and has a high soil dust content, the amount of which exhibits a seasonal change. On the other hand, high quantities of aerosols due to bio mass burning and condensed water vapor exist over the tropical rain forests. In addition to a visual comparison of the water vapor content with respect to aerosol loading, it is possi- ble to estimate it quantitatively. Figure 6(c) shows the product of water vapor content, aerosol optical

—4—

thickness and the Angstrom exponent, which shall be referred to as the WA (Water vapor-Aerosol) index hereafter. For reference, Fig. 6(d) presents the Nor- malized Differential Vegetation Index (NDVI). The WA index has a low constant value in almost all of the cases with a low NDVI. Low vegetation repre- sents the dessert/soil region, where the hydrological resources are limited and hence the amount of water vapor is very low and aerosols are mainly supplied from large dust particles, which correspond to small values of the Angstrom exponent. On the other hand

high WA indices, which result from a high aerosol loading, large Angstrom exponent and a high water vapor content, are found over the region with high values of NDVI. This result seems to be indicative of the biomass burning phenomena. More detailed discussions are required but preliminary conclusions would indicate that the WA index as an indicator for aerosols should be classified into dry type and wet type. In other words, the WA index represents the bio-activities in the atmosphere-surface system over that region.

5 Conclusion

Global maps of the precipitable water vapor con- tent and aerosol loading on clear days have been pre- sented. Two of the eight channels available from the ADEOS/POLDER, i.e. the H2O absorption band at 0.910 pm and the 0.865 µm channel, gaseous absorp- 2) tion free band, have been used to estimate the total column water vapor content. In addition to the spec- tral information, characteristic POLDER measure- 3) ments of polarized radiance and multi-angle viewing are vital to the current study. The two-channel ra- tio method for the inversion of atmospheric water va- 4) por content has been discussed in detail in previous

works [Bouffies et al., 1997; Vesperini et al., 1999].

Whereas the uncertainties of this method with respect to the vertical structure of the Earth atmosphere, the variability of the Earth surface and the spatial distri- 5) bution of atmospheric aerosols have not been solved yet. The last two terms are under consideration at present. This being the case, when results presented in this paper are compared to the data provided by 6) NASA/AERONET, the following results have been validaited.

1) The global distribution of water vapor content can be derived by using multi-directional ra-

diance data in the of 0.865 4um and ADEOS/POLDER.

near-infrared 0.910 ,km

wavelengths as given by

The spatial and seasonal changes of water vapor content have been detected.

The Asian monsoon is clearly shown on the water vapor maps, especially in India,

The global maps of the aerosol properties cannot only be derived over the ocean but also over the

land using multi-directional polarized radiance data at POLDER 0.670 and 0.865 µm channels.

A natural correlation between water vapor content and aerosol loading is obtained in the spatial

distribution on a global scale.

The WA (Water vapor- aerosol) index, i.e. the product of water vapor content, aerosol optical

thickness and Angstrom exponent, is an indi-

cator for a bio-active zone of the atmosphere-

surface system.

Figure

Fig. 1

Fig. 2

captions

Multi-directional viewing provided by the POLDER sensor.

The atmospheric transmission of water vapor (denoted by the thin curves) at altitudes 0 , 3 and 6 km

above sea level, and the response functions of POLDER-near infrared wavelength bands at 0 .865 iiin

and 0.910 ,um (denoted by the solid curves).

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Monthly averaged distribution of water vapor content during the ADEOS-working period. The squares denote the measurements made by NASA/AERONET represented by the same color scale as that for the POLDER sensor results shown at the bottom.

A scattergram between water vapor content derived from the POLDER sensor and from AERONET data over eight months from November 1996 to June 1997.

An illustration of the surface model for a polarization simulation of the Earth atmosphere- surface model.

Global maps in December, 1996 (left hand side) and June, 1997 (right hand side) for (a) aerosol optical thickness at 0.55 pm.

(b) Angstrom exponent.

(c) The WA-index, which denotes the product of aerosol optical thickness, Angstrom exponent and atmospheric water vapor content.

(d) The Normalized Differential Vegetation Index.

POLDER POLDER

nadir

7 ^glint

over land

(1)O.

U cd

z O . H

C"r O U

0.

a) c 0

o.

POLDER 0.865 µm

band

0.91 µm band

n

V. 7

Wavelength (p.m)

(a) November 1 996 (e) March, 1997

(h) Deeemher 1 WA

mwmaii....000.mmaril, 1997 _r....11%1111111111111=

sammion=1110;;1997 ^{111111e' '(g)} May, 1997

..41W7544111111111111111111111111

(d Fehniary,

— •

1 997

(h) June, 1997

Nov., 1996 - June, 1997

I ^a)

WW

F-~Ml --i

O

AERONET data (kg/m`)

aQ

--- ^ Lambert

NDVI

vegetation

soil

—^ BPDF

T. Cox & Munk

sea-surface

--

December, 1996 June, 1997

(a) Optical th ickness of aerosols (0.55µm)

(n) Angstrom exponent

(Sc) wA mciex

~U)

N LI V1

Fig. 6

Acknowledgements

ADEOS/POLDER data was provided by CNES. AERONET date was provided by Drs. B.Holben (NASA/GSF(

and D.Tanre (Lielle/France). This work was supported in part by a grant from the ADEOS scientific program (NASDA-PSPC-29321) of NASDA, by a Grant-in-Aid for Scientific Research (No.13573017) from JSPS and by Grants-in-aid for Young Scientists (A) (No. 14702069 ) and Scientific Research on Priority Areas (No.

14048201, 14048224) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT).

The authors acknowledge their computer work by Mrs.K.Hirata and R.Imamura.

References

[1] Bouffies, S., F.M. Breon, D.Tanre and P.Dubuisson, Atmospheric water vapor estimate by a differen-

tial absorption technique with the polarization and directionality of the Eartth reflectances (POLDER)

instrument", Journ. Geophys. Res. 102, 3831-3841, 1997.

[2] Breon, F.M., POLDER Level 1 Product Data Format and User Manual, CNES, PA. MA. 19. 1332. CEA, 1997.

[3] Cox, C., and W. Munk, Measurements of the roughness of the sea surface from photographs of the sun's glitter, J. Opt. Soc. Amer., 44, 838-850, 1954.

[4] Deuze, J.L., F.M. Breon, C.Devaux, P.Goloub, M.Herman, B.Lafrance, M.Maignan, A.Marchand, F.Nadal, G.Perry and D.Tnare, Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements, Journ. Geophys. Res. 106, 4913-4926, 2001.

[5] Frouin, R., P.Y.Deschamps, and P.Lecomte, Determination from space of atmospheric total Water vapor amount by differential absorption near 940nm: theory and airborne verification, J. Appl. Meteorol. 29,

448-60, 1989.

[6] Goloub, P., D. Tanre, J.L. Deuze, M. Herman, A. Marchand, and F.M. Breon, " Validation of the first algorithm applied for deriving the aerosol properties over the ocean using the POLDER / ADEOS

measurements", IEEE Trans. Geosci. Remote Sensing, 37, 1586-1596, 1999.

[7] Hegg, D.A., J.M.Livingston, P.V.Hobbs, T.Novakov and P.Russell, Chemical apportionment of aerosol column optical depth off the mid-Atlantic coast of the United States, direct aerosol radiative forcing off

the US Mid-Atlantic coast: comparison of values calculated from sunphotometer and in situ data with

those measured by airborne pyranometer, Journ. Geophys. Res. 102, 25293-25303, 1997.

[8] Kaufman, Y.J. and B.C.Gao, Remote sensing of water vapor in the near IR from EOS/MODIS, IEEE Trans. Geosci. Remote Sens., 30, 871-884, 1992.

[9] Kneizys, EX., E.P. Shettle, L.W. Abreu, J.H. Chetwynd, G.P. Anderson, W.O. Gallery, J.E.A. Selby, and S.A. Clough, Users guide to LOWTRAN 7, AFGL-TR-88-0177, Air Force Geophysics Laboratory,

Hanscom AFB. MA, 1988.

[10] Mukai, S., and I. Sano, " Retrieval algorithm for atmospheric aerosols based on multi-angle viewing of ADEOS/POLDER", Earth, Planets, Space, 51, 1247-1254, 1999.

[11] Nadal, F., and F-M., Breon, Parameterization of surface polarized reflectance derived from POLDER

spaceborne measurements, IEEE Trans. Geosci. Remote Sensing, 37, 1709-1718, 1999.

[12]

[13]

[14]

[15]

[16]

Rind, D., E.-W.Chiou, W.Chu, J.Larsen, S.Oltmans, J.Lerner, M.P.McCormick, and L.McMaster, Posi- tive water vapor feedback in climate models confirmed by satellite data, Nature, 349, 500- 503, 1991.

Russell, P.B., P.V.Hobbs, and L.L.Stowe, Aerosol properties and radiative effects in the US Mid- At- lantic haze plume: An overview of the Tropospheric Aerosol Radiative Forcing Observational Experi- ment (TARFOX), Journ. Geophys. Res., 104, 2213-2222, 1999.

Sano, I., and S. Mukai, Algorithm description of system flow for global aerosol distribution, Appl. Math.

& Comp., 116, 79-91, 2000.

Sano I., S.Mukai, and Y.Okada "Aerosols all over the ocean and land", Proc. IEEE/IGARSS, vol.7, pp.3102-3104, 2001

Vesperini, M., F-M Breon, and D.Tanre, " Atmospheric water vapor content from spaceborne POLDER

measurements", IEEE Trans. Geosci. Remote Sensing, 37, 1613-1619, 1999.