Rapid estimation of photosynthetically active radiation over the West African Sahel using the...

8/11/2019 Rapid estimation of photosynthetically active radiation over the West African Sahel using the Pathfinder Land Dat

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JAG Volume 1 - Issue 3/4 - 1999

Rapid estimation of photosynthetically active radiation

over the West African Sahel using the Pathfinder Land

Data Set

Jonathan W. Seaquistl Lennart Olson1

1 Depa~ment of Physical Geography, University of Lund, Box 11 8z S-221 00, Lund, Sweden (e-mail: Jonathan.Seaquist~natgeo.lu.se)

KEYWORDS: angstrbm relation, CLouds from AVHRR,

CLAVR, global radiation, HAPEX Sahel, Niger, NOAA

AVHRR, normalised root mean square error, photosyn-

thetically active radiation, PAR, relative sunshine dura-

tion, West Africa

ABSTRACT

Photosynthetically Active Radiation (PAR) s important for assessing

both the impact of changing land cover on climate, and for model-

ling productivity on a regional scale, as well as its potential in areas

that are vulnerable to food shortfalls. A relatively simple method

that generates spatially comprehensive and representative values of

PAR at time scales of O-days .(dekads) or longer is described, tested

and implemented over a portion of West Africa. With simple equa-

tions to describe

the

geographical and temporal variation of global

radiation receipt at the top of the atmosphere, daily cloud flags

from the NOA~NASA AVHRR Pathfinder Land Data Set (PAL) are

used

in conjunction with an empirical formula developed by

Angstrdm and constants tailored to West African conditions to esti-

mate surface receipt of global radiation there. Ground observations

of PAR from the HAPEX Sahel experiment (at 1366 N and 253 E

from 1992) are used to parameterise the relative sunshine duration

variable in the angstrom relation so as to m~nimise errors between

observed and modelled PAR. Results indicate that PAR may be esti-

mated to within 20 percent of observed values for 28 out of 36 IO-

day summation periods over a year. End-of-year accumulated PAR is

estimated to within 1.96 percent. Normalised root mean square

errors (NRMSEs) and normalised mean absolute errors (NMAEs) of

15.69 percent and 12.46 percent, respectively, were obtained for

IO-day sums, with values of 10.96 percent and 8.74 percent,

respectively, for monthly sums. The spatial variability of end-of-year

PAR for 1992 is in accordance with what was expected. Though

more accurate methods exist for achieving this, the technique is

merited for its ease of application, using an accessible data set,

over areas where solar irradiation measurements are lacking.

INTRODUCTION

Over the last few years, a suite of models has been devel-

oped using satellite data (mostly from the National

Oceanic and Atmospheric Associations Advanced Very

High Resolution Radiometer - NOAA AVHRR) and other

auxiliary ground data that predict with varying degrees

of accuracy and generality Net Primary Production (NPP),

Above-Ground Net Primary Production (AGNPP) and

Above-Ground Gross Primary Production (AGPP) on a

regional or global basis. The models range from site spe-

cific black box approaches relating growing season

sums of the Normalized Difference Vegetation Index

(NDVI) to ground based NPP observations [Oxenstierna,

1990; Rasmussen, 1992; Lambin

et

a/,

1993; Field

et a/,

1995; Rasmussen, 1998a,b] to bio-geochemical models

that include explicit formulations of the laws governing

photosynthetic assimilation, allocation and respiration

[Prince, 1991; Potter et al, 1993; Field et al, 1995; Prince

& Goward, 1995; Haxeltine & Prentice, 19961. The para-

metric method of Monteith [I 3721 has recently gained

wide popularity as it easily accommodates spectral infor-

mation from satellite sensors with data on meteorologi-

cal conditions and vegetation type. Moreover, it com-

bines the ease of the black box approaches with the

mechanism of bio-geochemical models [Lambin et a/,

1993;

Bournan,

1995;

Moulin

et

a/,

19981.

The daily pro-

duction of above-ground GPP is given by:

AGPP=~[Ex(axND~+b)xPAR

II

i I

where

AGPP = Above-ground Net Primary Production (kg m-2)

E = biological efficiency of PAR conversion to dry matter

(g MJ-)

NDVZ =

Normalised Difference Vegetation Index (unitless)

PAR = Photosynthetically Active Radiation (MJ day-l)

a,b =

constants

This formula has been used by a number of researchers

to estimate end-of-growing-season NPP from regional to

global scales with satellite data; the NDVI computed from

channels 1 and 2 of the AVHRR onboard the NOAA satel-

lites is used to estimate FPAR (fraction of absorbed pho-

tosynthetically radiation, where FPAR = a x NDVI + bf.

[Potter et a/, 1993; Guerif et a/, 1993; Ruimy et al, 1994;

Prince & Goward, 1995; Parueto et a/, 1997; Rasmussen,

1998a,b]. PAR is defined as the domain of incoming solar

radiation exploited by green plants to support photosyn-

thesis (0.4-0.7 pm). PAR regulates the Earths primary

productivity by determining the rate of carbon fixation in

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Estimating PAR over the West African Sahel

aquatic and terrestr ial plants [Frou in Pinker, 19951.

Determining its spatial and temporal distributio n is

important for a) assessing the impact of changing land

cover on climate, and b) modelling regional-scale pro-

ductivity potential in areas that are vulnerable to food

shortfalls (eg, the Sahel belt of Africa). PAR is usually

taken as a constant fraction of incoming short-wave solar

radiation. Though the PAR - insolation ratio varies

instantaneously from 0.45 to 0.50 as a function of

atmospheric water vapour, ozone, aerosol, cloud optical

thickness, and solar zenith angle, theoretical and experi-

mental evidence suggest that this ratio is constant

(approx imately 0.48) at time scales of a day and longer

[Froui n Pinker, 19951. The applicabi lity of this relative-

ly simple model hinges o n the retrieval of spatially com-

prehensive and frequent estimates of PAR. The most

practical approach for achieving this is to interpolate

among data values acquired at point locations from sur-

face pyranometers. Unfortunately, there is a paucity of

these data over large areas of the Earth s surface, [Eck

Dye, 19911. Eck Dye furt her state that a sparse, spa-

tially biased distributio n of pyranometer observations

leads to reduced confidence in those values correspond-

ing to under-represented areas. A common approach,

especially in developing countries, where there is a lack

of measurements, is to estimate incoming global irradia-

tion from the number of sunshine hours in a day [eg,

Guerif et a/, 1993; Rasmussen, 1998a,b] foll owed by

interpolation. This method underestimates global irradia-

tion as it does not take in to account the diffuse radiation

reaching the ground on cloudy days. Ruimy et a/ [I9941

used global sur face irr adiance derived on a monthly basis

from the French Meteorological Offices General

Circulation Model (GCM) and interpolated the data - to

obtain weekly estimates to match the time-step of their

NPP model. They acknowledge the drawback of using

simulated irradiance values in their work. The best way

forward, if the data and expertise are at hand, is to esti-

mate solar irradiance and PAR from geostationary weath-

er satellites (3-hourly observations by the Geostationary

Operational Enviro nmental Satellite, GOES, and 12:00

observations by the Total Ozone Mapping Spectrometer,

TOMS) in conjunctio n with physical models that predict

clear-sky, potential surface irradiance and that are

adjusted to actual surface irradiance due to the effect of

clouds detected by the satellite sensor [Dye Shibasaki,

19951. However, due to obli que viewing , the methods

only function at lower latitudes. These methods are data

intensive, and have been applied using information from

different sensors at differing spatial and temporal scales.

Normalised Root Mean Square Error s (NRMSEs) of

between 8 percent and 15 percent have been achieved

for daily sums of solar irradiance, with errors dr opping

down to around 3-6 percent for 5-day to monthly aver-

ages [see Just us et al, 1986; Pinker et al, 1995 for

reviews]. Less attention has been given to developing

JAG . Volume 1 - Issue 314 - 1999

satellite methods for estimating surface irradiance in the

PAR wavelength region [Eck Dye, 19911, though some

examples include Frouin et a/ [1989], Frouin Gautier

[1990], and Eck Dye [1991]. Prince Goward [I9951

for example, used the TOMS monthly incident PAR data

set prod uced by Dye Shibasaki [I9951 to drive the

GLObal Production Efficiency Model (GLOPEM), interpo-

lating between monthly means to produce IO-day esti-

mates to match t he time-step of their model.

This work presents an effort to develop spatially compre-

hensive and representative values of PAR for use with a

Monteith -based NPP model at time-steps of 10 days

(dekads) or longer in Sahelian West Africa fro m cloud

cover information provided with the NOAA/NASA PAL

(Pathfinder Land Data Set) and an empirical formulation

derived by Angs trom [I 9241 to estimate irradi ance at the

Earths surface. The model was calibrated and tested

against PAR data generated by the HAPEX Sahel field

experiment that was carried out in S.W. Niger i n 1992.

Though the method is not intended to replace r igorous

satellite techniques outlined above, it is deemed to be a

quick, accessible and adequate means of obtaining PAR

estimates for use in bio-productivi ty models and over

areas where pyrano meter measurements are lacking .

DATA

The PAL data set (NOAA/NASA Pathfind er Land Data Set)

includes layers for the original 5 channels of the NOAA

AVHRR sensor, plu s information on solar zenith angle,

satellite look angle and azimuth angle. The data were

comp iled for the period 1981-1995 and are available as

daily images, as well as IO-day and monthly maximum -

value compo sites. Furthermo re, they were generated in a

consistent manner and included post-flight sensor cali-

bration and atmospheric corrections for Rayleigh scatter-

ing and ozone absorption [James Kallur i, 19941. The

data set also includes cloud flags produced by the Clouds

from AVHRR (CLAVR) algor ithm. For an array o f 2 x 2 pix-

els, the algorithm performs a series of 10 threshold and

uniformity tests using data from all five channels of the

AVHRR. If between 1 and 3 pixels within the window are

identified as cloudy, all four pixels are flagged as mixed

(values between 12 and 21). Otherwise, the wind ow is

designated as clear (22-30) or cloudy (l-l 1). Figure 1

shows a CLAVR image over Afri ca for July 1, 1992.

METHODS

The model has been constructed from equations describ-

ing the seasonal distributio n of solar radiation over the

surface of the Earth [eg, Linacre, 1968; Henderson-

Sellers Robinson, 1986; Monteit h Unswor th, 1990;

Prentice et al, 1992; Haxeltine Prentice, 1996; and

Guyot, 19981 and implemented in the computing lan-

guage FORTRAN. The seasonal and meridianal dist ribu -

tion of radiation is determined by the rotation of the

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N = daylength (seconds, from sunrise to sunset)

FIGURE

CLAVR data over Africa for June 1, 1992

Earth about its own axis and by its elliptical orbit about

the Sun [Monteith & Unsworth, 19901. The solar declina-

tion (6) refers to the angle between the Earths orbital

and equatorial planes, which varies between +23 30 in

June and -23 30 in December. Solar irradiance at the

top of the Earths atmosphere therefore depends on time

of day, time of year, and latitude, which may be

described by the angle between the direction of the Sun

and the vertical (solar zenith) for a point on the Earths

surface. The solar elevation angle is given by:

&21tt

24

PI

where

t = time of day (referring to the time when the Sun

reaches its zenith).

It may be shown by spherical trigonometry that

cosq = sin q7 in 6 cos tp cos 6 cos

e

where

IJJ= solar zenith angle

cp = latitude

8 = solar declination

8 = solar elevation angle.

At sunrise and sunset, the solar elevation, 8, is 0,

Equation 2 may be used to calculate the daylength:

L31

so

N %os-(tantan6)

L41

n

where

The solar constant refers to the amount of radiation

impinging on a horizontal plane just outside the Earths

atmosphere, and its value, Q,, is agreed to be about

1372 W m-z at mean Earth-Sun distance. As the orbit of

the Earth about the Sun traces an ellipse, the solar con-

stant varies throughout the year, and when multiplied by

the solar zenith angle and integrated over the daylight

hours, the total radiant exposure at the top of the atmos-

phere is obtained:

S,=NxQ,

+

[

1

where

S, = irradiance at the Earths surface (MJm-z day-l)

Q, = irradiance at the top of the Earths atmosphere (Wm-2)

do = Earth-Sun distance on a particular date

d

average Earth-Sun distance.

Figure 2 displays the variation in total daily receipt of

short-wave solar radiation at the top of the atmosphere

between latitudes IO N and 23 N.

Solar radiation passing through the atmosphere is atten-

uated by scattering and absorption by various atmos-

pheric constituents. Mie and Rayleigh scattering are

caused by aerosols, individual molecules, water droplets

and clouds. Absorption is due to ozone, water vapour,

carbon dioxide and oxygen, and depends not only on the

amount of the absorbent present but also on the atmos-

pheric path length. As a result of attenuation, solar radi-

ation reaching the ground consists of two components:

direct beam and diffuse beam irradiation, the former

rarely exceeding 75 percent of the solar constant

[Monteith & Unsworth, 1990; Guyot, 19981. The sum of

these two components is referred to as global solar radi-

ation. An empirical approach pioneered by Angstrom in

1924 is used to estimate solar irradiance at the ground.

The Angstrom relation has been widely used in hydro-

logical and agro-meteorological applications [Dunne &

%a---

FIGURE 2

Seasonal variation of top-of-atmosphere (TOA) total

daily global radiation between 1ONand 23N

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Leopold, 1978; Kowal & Kassam, 1978; Shuttleworth,

1993; Guyot, 19981 and is given by:

s* &[a*k( )]

where

St = global radiation received at the Earths surface (MJ

m-2 day-l)

So = global radiation received at the top of the Earths

atmosphere (MJm-2 day-l)

II = number of hours of sunshine in the day

N = daylength

a, = constant

b = constant.

The coefficients a, and b, are computed by comparing S,

on overcast days to give a,, while days with bright sun-

shine yield (a, + b, . These constants vary with local

atmospheric conditions (humidity, dust, cloud) and solar

declination (latitude and month) and thus vary both over

time and space. Cartledge [I9731 used this relation to

predict solar irradiation of the Brisbane region of

Australia, using sunshine records from a 56-year period,

yielding different coefficients for each month of the year.

Friend [I9981 computed the constants for the entire

globe using the Muller data set (166 stations). Davies

[I9661 carried out the same analysis for West Africa,

showing a, and b values to vary seasonally due to the

migration of the ITCZ (Inter-Tropical Convergence Zone).

Table 1 summarises a, and b, parameters computed for

different areas. Since the test site was in West Africa, the

constants from Davies [I9661 were used. If S, and N

(from the equations above) and a, and

b

(constants from

the literature) are known, then n must be obtained to

compute St (see Table 2).

CLAVR - PARAMETERISING THE ANGSTROM RELATION

The CLAVR flags provide an opportunity to factor in the

TABLE I

Some published Angstriim constants for Equation 6

(modified from Dunne & Leopold, 1978).

Location

World

World

World

S.E. England

Virginia,

U.S.A.

Canberra,

Australia

Brisbane,

Australia

Wageningen,

Netherlands

West Africa

as

0.23

0.29 cos

(latitude)

0.25

0.18

0.22

0.25 0.54

0.23 - 0.35

0.38 - 0.54

0.20 0.56

Spitters (1986)

-0.12 - 0.26 0.99 - 9.50

Davies (1966)

bs

0.48

0.52

0.5

0.55

0.54

Source

Black et al (1954)

Glover &

McCulloch (1958)

Friend (1998)

Penman (1948)

quoted by

Penman (1948)

quoted by

Penman (1948)

Cartledge (1973)


effect of cloud cover on estimates of incoming global

radiation by varying the relative sunshine duration (n/N)

of the Angstrgm relation (with a, and b, constants pro-

vided from Davies [ 19661) on a daily basis between three

states: clear, cloudy and mixed. Cloud cover observations

were restricted to one single afternoon acquisition per

day (roughly corresponding to 14:00 hours local time)

and were designated as either 100 percent clear (n/N =

I), 100 percent cloudy n/N = 0) or mixed (0 < n/N < 1).

One simplifying assumption was made: that if a pixel is

flagged as clear, cloudy or mixed during the acquisition

period, it holds for all daylight hours.

The operation was carried out on all CLAVR values

extracted for a portion of Sahelian W est Africa between

10N and 20N and 2W and 18E.

CALIBRATION

It was not known what proportion of the pixel is com-

posed of cloud cover for CLAVR pixels that were flagged

as mixed. In order to find the optimal

n/N

parameterisa-

tion for CLAVR pixels with mixed cloud, 11 trial runs

were made, stepping

n/N

values from 0 to 1 at incre-

ments of 0.1. Some 366 daily PAR images were generat-

ed for each increment, and subsequently integrated over

IO-, II- or 8-day periods to correspond to the composi-

tion periods of the dekadal MVC images from the PAL

data set for a total of 36 images per year. The model was

calibrated using daily observations of PAR (Photo-

synthetically Active Radiation) for the year 1992 taken

from the HAPEX Sahel Information website [http:// www.

ird.fr/hapex/data/climat92/daily/desc.htm]. The data were

collected at 13 40 N and 2 32 E. The corresponding

PAR values for each dekad and for each trial run (corre-

sponding to the images created for each n/N value) were

extracted. Three performance measures were used to

make a choice as to what value of

n/N

for mixed cloud

TABLE 2 Angstrdm constants for West African conditions from

Davies (1966), used in the model. Correlation coefficients are

given in the third column (r).

Month a, b, r

January -0.041 0.878 0.99

February -0.040 0.878 0.92

March

0.069

0.796

0.96

April

0.084 0.821 0.97

May

0.123 0.723 0.93

June

0.194

0.611

0.87

July 0.293 0.640 0.93

August 0.204 0.602 0.86

September 0.259 0.497 0.92

October 0.174 0.616 0.92

November

0.068

0.740

0.94

December -0.119 0.989 0.88

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JAG Volume

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Issue 3/4 - 1999

pixels

would

minimize the errors between the observed

and predicted values: the Normalised Root Mean Square

Error (NRMSE), Normalised Mean Absolute Error

NMAE),

and the Index of Agreement IOA):

i(I: -0J

i l

NRMSE=

i(I: -oiy

i=l

NRMsE= ;

[71

where

i l

Pi = predicted value at dekad i

0; = observed value at dekad i

0 = mean of observed values

pi = pi -5

Oi = oi -5

N = number of observations

The NRMSE is the most widely used index. It is, however,

very sensitive to large, single outliers in the data, making

it more difficult to objectively assess data-model agree-

ment [Kirkby et a/, 19931. The NM E is less sensitive to

outliers but does not allow for compensations of positive

and negative discrepancies. Finally, the ZOA expresses

model-data agreement more directly and may be viewed

as a standardised measure of the mean square error. A

value of 0 represents no agreement, while 1 indicates

perfect agreement [Janssen & Heuberger, 19951. Figure 3

shows the sensitivity of all three error measures to dif-

fering values of

n/N

for the mixed cloud pixels by plotting

relative errors//OA against n/N values ranging from 0 to

1. The NRMSE reaches a minimum of 15.69 percent for

dekad integrals of PAR at n/N = 0.5 and 10.96 percent

for n/N = 0.4 when integrated at the monthly time-scale.

The

NMAE

reaches a minimum of 12.46 percent at

n/N =

0.5 for the dekad time scale, falling to 8.74 percent for

the monthly time scale and n/N = 0.4. IOA values

reached maxima of 0.71 (n/N = 0.5 dekad time scale) and

0.69 (n/N = 0.5 monthly time scale). All three perfor-

mance measures display minimum errors for n/Ns of

between 0.4 and 0.5.

RESULTS

Figure 4 displays time profiles of dekad PAR from the

model over which PAR observations from the HAPEX

Sahel observations that have been aggregated to corre-

sponding dekad periods are superimposed. There is a

general agreement between the observed and simulated

values, varying both in similar directions and being more

or less in step with each other. The simulated values

show higher amplitude variability, under- or overestimat-

ing PAR at certain times of the year. Figure 5 displays

observed - model comparison of accumulated PAR from

January to December 1992. Modelled accumulated PAR

is displayed for every other n/N value used for the cali-

bration procedure. All curves fan out from the onset to

produce vastly different results by the end of the run: n/N

= 0 yielded 2939.74 MJ m-2; n/N = 1.0 resulted in

4749.08 MJ m-2. It is clear that using

n/N = 0.4

generates

a curve that closely mirrors the yearly accumulation of

measured PAR, with a slight underestimation between

dekads 12 and 17, giving way to a slight overestimation

between dekads 26 and 36. The observed end-of-year

accumulated PAR is 3591.29 MJ m-2 as compared to

3663.27 MJ m-2 for the run corresponding to n/N = 0.4.

This yields a slight overestimation in PAR of

1.96

percent.

Figure 6 shows the relative discrepancies and relative

cumulative discrepancies of the simulated PAR values

from the observed PAR values. For individual dekads, the

relative discrepancies exceeded * 20 percent on only 8

occasions (22 percent of the observations), with the

0 0

0 02 0.4 06 08

3

m

IMned)

FIGURE3 Impact of choice of n/N value (relative sunshine dura-

tion parameter) in modelling PAR

140

.

I

1 6

11 16 21 26 31 36

Dsked

FIGURE4 Observed vs modelled PAR (n/N = 0.4) for 1366N

and 2O53E for the year 1992, located within the HAPEX Sahel

square in southwest Niger

209


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FIGURE5 Cumulative end of year (1992) PAR at 1366N and

253E for observed and modelled values with

n N

ranging

between 0 and 1 O

FIGURE6 Relative departures of modelled PAR from observed

PAR at 1366N and 253E

*

Mali

w

JAG Volume

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Issue 314

1999

majority of estimates falling within 15 percent. Relative

cumulative discrepancies exceeded f 10 percent only

once. Finally, Figure 7 displays the predicted spatial dis-

tribution of accumulated PAR for 1992 (for n/N = 0.4)

over the portion of West Africa encompassing most of

the Niger. There is a general tendency for PAR to

increase toward the north-north west, with lowest values

occurring in the southwest and southeast corners of the

image, where cloud frequenc ies are higher, especially

during the rainy season. Note the decreased PAR totals in

the high lands, (between 2000-3000 m) in the nort hwest

sector, where sig nificant amounts of cloud reduce the

amount of PAR. Areas around Lake Chad and along the

rivers (notably the Niger River) show decreased amounts

of PAR, due to increased evaporation, which generates

lower visibility and cloud. The northeast corner of the

map shows a PAR receipt in excess of 4300 MJ m-2,

which corresponds to an area of low elevation that con-

sists mainly of salt flats.

DISCUSSION

Kowal Kassam [I9781 state that the average gradient ,

in the receipt of irradiation along the north-south axis of

the West African Sahel between latitudes of roughly

1ON and 18 N is about 88 MJ m-2 year-l, though there

are anomalies. Average gradients computed from Figure

6 are 70 and 124 MJ m-2 year-1 f or the eastern part of

the image, more or less corrobor ating their values (which

are based on long -term averages).

CAUTIONARY NOTES

Though the calibration procedure established an accept-

PAR (Am7-2)

0

500

1000

1500 Km

FIGURE7

Map showing end of year accumulated PAR for 1992 over a portion of West Africa including most of

Algeria, Mali, Burkina Faso, Benin and Nigeria

Niger, and parts of

210


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Estimating PARover the West African Sahel

able relation between observed and simulated PAR for

one station, because validation data were not available it

is not known whether the same model parameters

(namely n/N produce viable results over the greater

region. The AngstrZjm equation is empirical, and its para-

meters are spatio-temporally variable. The Angstrijm

constants of Davies [ 19661 were derived from a number

of stations spanning Nigeria, Ghana, Chad and Niger, all

essentially located in the southern half of the West

African Sahel. The considerably sunnier and drier cli-

mates in the northern half of the sector may yield differ-

ing angstrijm coefficients, perhaps necessitating a re-cal-

ibration of the relative sunshine duration parameter used

in the model for mixed cloud pixels. Also, the accuracy

may be poorer over the highlands where cooler, cloudier

climatic regimes dominate. Nevertheless, the expected

distribution of accumulated PAR emerges.

Furthermore, the CLAVR flags are restricted to one single

afternoon acquisition per day, corresponding to the over-

pass of the NOAA satellite (about 14:00 local time).

Three cloudiness states are produced (cloudy, clear, and

mixed), which for utilitarian purposes were maintained

throughout each day represented in the model. This is a

drastic but necessary assumption. Realistically, atmos-

pheric conditions and cloud cover vary diurnally in a non-

random manner, especially during the rainy season,

when afternoon heating of the land surface may be

expected to produce convection clouds, often accompa-

nied by precipitation. If maximum cloudiness consistently

occurs in the afternoon, then the model may be expect-

ed to overestimate diurnal cloud effects, and thus under-

estimate average n/N values for mixed cloud pixels. Dye

& Shibasaki [I9951 report similar difficulties using the

12:00 TOMS observations upon which TOMS PAR fields

are based.

Further uncertainty revolves around the current PAL

CLAVR, which was intended as an experimental cloud

layer. The CLAVR algorithm is limited in its ability to

detect cloudy pixels from clear ones. The algorithm is

sensitive to surface heterogeneities and it has not been

validated for the entire globe. Moreover, the routines

using the thermal channels do not check for missing val-

ues or data gaps, which may lead to errors. CLAVR is also

based on top-of-the-atmosphere reflectances normalised

for solar zenith angle, leaving the effects of Rayleigh

scattering, ozone absorption, aerosols and water vapdur

unaccounted for. The risk is, therefore, one of overesti-

mation of amounts of cloud in the CLAVR flags.

Another potential shortcoming is the fundamental differ-

ence in spatio-temporal scales represented by the mod-

elled estimates (constrained by the one observation per

day, 8 km x 8 km resolution of the CLAVR flags) and

ground observations used for calibration or validation.

211


The satellite estimates are spatial averages at specific

times, whereas surface measurements are temporal aver-

ages at specific sites [Frouin & Pinker, 19951.

STRENGTHS

Figure 8 displays cloud frequencies for each dekad (with

mixed cloud states classified as 100 percent cloud) over

1992 for the pixel corresponding to the HAPEX Sahel site

used for the calibration. Most dekads from April through

to September (roughly corresponding to the rainy sea-

son) reveal cloud frequencies between 7 and 10, while at

other t imes of the year cloud frequencies vary between 1

and 6. As we expected, cloud amounts are higher during

the rainy season, thus lending qualitative su.pport to the

use of CLAVR as a source of reliable cloud data.

*/

EB

d

6

14

2

0

16

FIGURE 8 Cloud frequency at 1366N and 253E computed

from daily CLAVR mages

Other advantages include the fact that ttie method is

easily implemented using equations that are well estab-

lished in the literature. Furthermore, the CLAVR fields are

widely accessible to the public, and easily incorporated

into the modelling procedure, thus providing the ability

to partition incoming solar irradiation between its direct

and diffuse components. Provided regional Angstrijm

coefficients can be established, the method has the abil-

ity to represent regional variation in PAR with accuracies

that improve over longer integration periods. Finally, the

error permitted for PAR estimations to furnish satisfacto-

ry accuracy for use in biomass models depends both on

the amount of PAR and on the biomass. Accuracies of 20

percent over land and 35 percent over water are suffi-

cient when actual daily PAR exceeds 300 W m-2 [Frouin &

Pinker, 19951. The overall discrepancies

this study indicate that PAR estimates

African Sahel fall below the 20 percent

land.

obtained from

for the West

threshold over

CONCLUSIONS

A technique for generating spatially comprehensive and

representative values of PAR has been established using


8/9


the CLAVR flags from PAL for time scales of IO days or

longer, calibrated with data from the HAPEX Sahel exper-

iment in S.W. Niger. Though more accurate methods

exist for generating total PAR values on a daily basis -

using a combination of radiation transfer models and

data from other satellite sensors - the technique has

merit in that it requires a mjnimum of data and can be

rapidly implemented using an easily accessible data set.

Regional PAR estimates may be generated for use in bio-

mass models that require PAR accuracies within 20 per-

cent over land. Data model comparisons using the

NRMSE, NMAE,

and

ZOA

confirm that overall accuracies

are within this 20 percent threshold. Furthermore, simu-

lated end-of-year accumulated PAR may be achieved at

accuracies within 1.9 percent of observed values.

However the technique must be applied judiciously. Its

reliance on empirical formulae such as the Angstrom

relation imposes geographical constraints. Furthermore,

the method relies on one cloud observation per day,

leading to an underestimation of diurnal cloudiness

cycles. It is also known that the CLAVR algorithm is not

entirely efficient in detecting amounts of cloud.

Unfortunately, due to lack of ground observations of sur-

face solar irradiance or PAR, the model could not be ade-

quately validated over the larger region.

ACKNOWLEDGEMENTS

This work was funded by the Swedish International

Development Agency (SIDA) and the Swedish Space

Agency. The CLAVR files were provided by the Pathfinder

programme (NASA-EOSDIYNOAA, Goddard Space Flight

Center, Greenbelt, MD 20771, USA) with ancillary infor-

mation found on the FEWS Information Server (EROS

Data Center, USGS, Sioux Falls, SD 57198, USA).

Calibration data were downloaded from the HAPEX

Sahel Information System [http://www.ird.fr/hapex]. Lars

Harrie and Lars Eklundh of the Department of Physical

Geography, Lund University, Sweden, provided valuable

suggestions for this manuscript.

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RESUME

La radiation active photosynthetique (PAR) est importante pour

evaluer a la fois Iimpact du changement de couverture des

terres sur le climat et pour rrfodeliser la productivite a une echel-

le regionale, aussi bien que son potentiel dans des regions

sujettes a des penuries alimentaires. Une methode relativement

simple qui produit des valeurs de PAR &endues et representa-

tives a des echelles de temps de 10 jours (decades) ou plus

longues est d&rite ; cette methode est testee et completee sur

une partie de IAfrique de IOuest. Avec des equations simples

pour decrire la variation geographique et temporelle de la radia-

tion globale recue au sommet de Iatmosphere, chaque jour des

nuages enregistres par NOAA NASA AVHRR Pathfinder Land

Data Set (PAL) sont utilises conjointement avec une formule

empirique developpee par Angstrom et adaptee aux conditions

dAfrique de IOuest pour @valuer la reception de surface de

radiation globale a cet endroit. Des observations au sol de PAR a

partir de Iexperience HAPEX du Sahel (a 13.66 N et 2.53 E

de 1992) sont utilisees pour etablir le parametre de la variable

de duree relative densoleillement dans la relation Angstrom

ainsi que pour minimiser les erreurs entre le PAR observe et

modelise. Les resultats indiquent que PAR peut @tre estime

jusque dans les 20% des valeurs observees pour 28 des 36

periodes de sommations de 10 jours sur une annee. Le PAR

accumule en fin dannee est estime a 1.96 pour cent. Des

erreurs moyennes quadratiques normalisees (NRMSEs) et des

erreurs moyennes absolues normalisees (NMAEs) de 15.69 pour

cent et 12.46 pour cent, respectivement ont ete obtenues pour

des sommes de 10 jours, avec des valeurs de 10.96 pour cent et

8.74 pour cent, respectivement pour des sommes mensuelles.

La variabilite spatiale de fin dannee PAR pour 1992 est en

concordance avec ce qui etait escompte. Bien quil existe des

methodes plus precises pour accomplir cela, la technique est

meritoire pour sa facilite dapplication, en utilisant une serie de

donnees accessibles, dans une region ou manquent les mesures

de Iirradiation solaire.

RESUMEN

La radiacidn fotosinteticamente activa (PAR) es importante para

evaluar tanto el impact0 de 10s cambios de la corteza terrestre

sobre el clima y para planear la productividad a escala regional,

coma su potential en areas que son vulnerables a deficits de ali-

mentos. Se describe un metodo relativamente sencillo que gene-

ra valores de PAR de conjunto y representativos a nivel espacial

a escalas de tiempo de 10 dias o m6s y que ha sido estudiado y

puesto en prdctica en una region del Africa occidental. Con

ecuaciones sencillas que describen la variaci6n geogrdfica y tem-

poral de la radiaci6n global recibida en la parte alta de la atmos-

fera, se utilizan las rachas de nubes diarias de NOAA/NASA

AVHRR Pathfinder Land Data Set (PAL) conjuntamente con una

formula empirica desarrollada por Angstrom y unas constantes

adaptadas a las condiciones del oeste de Africa para calcular la

recepci6n en la superficie de la radiation global en aquel punto.

Se utilizan las observaciones de PAR en el suelo del experiment0

de HAPEX Sahel (a 13,660 N y 2,530 E desde 1992) para obte-

ner 10s pardmetros de la duracidn relativa de la Iuz solar variable

en la relation de Angstrom, de forma que se reduzcan al mini-

mo 10s errores entre el valor de PAR observado y el modelado.

Los resultados indican que se puede calcular la PAR con un error

inferior al 20% para 10s valores observados en 28 de 10s 36 peri-

odos de adicion de 10 dias a lo largo de un aiio. La PAR acumu-

lada a final de afio se calcula con un error del 1.96%. Se obtu-

vieron errores de la media cuadratica normalizada (NRMSE) y

errores de la media absoluta normalizada (NMAE) de 15,69% y

12,46% respectivamente, para el acumulado de 10 dias, con

valores de 10,96% y 8,74% respectivamente, para resultados

mensuales. La variabilidad espacial de PAR al final del ario para

1992 concuerda con lo esperado. Aunque existen metodos mas

precisos para conseguir esto, la tecnica merece la pena por su

fdcil aplicacion, utilizando un conjunto de datos accesibles, en

zonas donde se carece de medidas de la radiaci6n solar.

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