Using MetOp-A AVHRR Clear-Sky Measurements to Cloud-Clear MetOp-A IASI Column Radiances

Using MetOp-A AVHRR Clear-Sky Measurements to Cloud-Clear MetOp-AIASI Column Radiances

ERIC S. MADDY, THOMAS S. KING, AND HAIBING SUN

Dell, Inc., Fairfax, Virginia

WALTER W. WOLF, CHRISTOPHER D. BARNET, ANDREW HEIDINGER, ZHAOHUI CHENG,AND MITCHELL D. GOLDBERG

NOAA/NESDIS/STAR, Camp Springs, Maryland

ANTONIA GAMBACORTA, CHEN ZHANG, AND KEXIN ZHANG

Dell, Inc., Fairfax, Virginia

(Manuscript received 26 November 2010, in final form 25 February 2011)

ABSTRACT

High spatial resolution measurements from the Advanced Very High Resolution Radiometer (AVHRR)

on the Meteorological Operation (MetOp)-A satellite that are collocated to the footprints from the Infrared

Atmospheric Sounding Interferometer (IASI) on the satellite are exploited to improve and quality control

cloud-cleared radiances obtained from the IASI. For a partial set of mostly ocean MetOp-A orbits collected

on 3 October 2010 for latitudes between 708S and 758N, these cloud-cleared radiances and clear-sky subpixel

AVHRR measurements within the IASI footprint agree to better than 0.25-K root-mean-squared difference

for AVHRR window channels with almost zero bias. For the same dataset, surface skin temperatures re-

trieved using the combined AVHRR, IASI, and Advanced Microwave Sounding Unit (AMSU) cloud-

clearing algorithm match well with ECMWF model surface skin temperatures over ocean, yielding total

uncertainties #1.2 K for scenes with up to 97% cloudiness.

1. Introduction

Meteorological Operation (MetOp)-A, the first in

a series of three planned European Organization for the

Exploitation of Meteorological Satellites (EUMETSAT)

polar-orbiting satellites, was successfully launched in

October 2006 and carries a wide array of instruments

for measuring various atmospheric, oceanic, and sur-

face parameters. Included in the instrument suite are

several heritage instruments provided by the National

Oceanic and Atmospheric Administration (NOAA),

such as the Advanced Very High Resolution Radiom-

eter (AVHRR) and the Advanced Microwave Sounding

Unit (AMSU). In addition to these heritage instru-

ments, MetOp-A carries a new generation of advanced

instruments, which include the Infrared Atmospheric

Sounding Interferometer (IASI).

IASI is a cross-track-scanning Michelson interferom-

eter that measures 8461 channels at 0.25 cm21 spacing

between 645 and 2760 cm21 (3.6–15.5 mm) in a 2 3 2

array of circular footprints with a nadir spatial resolution

of roughly 50 km 3 50 km (with a corresponding single

footprint spatial resolution at nadir of roughly 12 km).

Spectral measurements from the IASI contain infor-

mation on the vertical temperature profile, surface pa-

rameters (e.g., temperature, emissivity, reflectivity),

clouds, and the vertical distribution of tropospheric and

stratospheric trace gases such as H2O, CO, CH4, CO2,

HNO3, and O3 (Cayla 1993; Maddy et al. 2009). In ad-

dition, comparisons with other high spectral resolution

spaceborne sounders, such as the Atmospheric Infrared

Sounder (AIRS) flying onboard the National Aeronautics

and Space Administration’s (NASA’s) Earth Observing

System (EOS) Aqua platform, have demonstrated the

excellent in-orbit calibration and performance of IASI.

Corresponding author address: Eric S. Maddy, Dell, Inc., Fairfax,

VA 22031.

E-mail: [email protected]

1104 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 28

DOI: 10.1175/JTECH-D-10-05045.1

� 2011 American Meteorological Society

While both AIRS and IASI have demonstrated im-

provement to forecast models, the accurate treatment of

clouds has long been a limiting factor to maximizing the

utility of IR sounder data (Le Marshall et al. 2006;

Collard and McNally 2009). This is true because clouds

have a considerable effect on observed IR radiances,

and at 12-km spatial resolution less than 10% of IASI

footprints are expected to be cloud free. Methods to

handle clouds are therefore required to optimally utilize

IR sounder data in numerical weather prediction (NWP)

models and for various other operational and research

purposes.

There are several approaches for handling the effect

of clouds in the IR, the most common of which include

the following: avoiding the clouds by screening for clear-

sky footprints, directly modeling the radiative effect of

the clouds using sophisticated radiative transfer and cloud

microphysical models, and estimating the clear-sky por-

tion of an IR scene by using a number of adjacent and

variably cloudy footprints coupled with an estimate of

the clear-sky radiance from a forecast model or collo-

cated satellite instrument that is less likely to be affected

by clouds. The last approach, termed cloud clearing, is

currently used at NOAA/National Environmental Sat-

ellite, Data, and Information Service (NESDIS) for

operational IASI processing and is briefly described in

the following.

NOAA currently operationally processes 100% of

IASI data from calibrated and apodized level 1C (L1C)

spectral measurements to geophysical level 2 (L2)

products and distributes these products to the NOAA/

Comprehensive Large Array-Data Stewardship System

(CLASS) (available online at http://www.class.ngdc.noaa.

gov/saa/products/welcome). The current algorithm used to

produce the L2 products from IASI is largely based on

the AIRS science team (AST) algorithm (Aumann et al.

2003), including the fast radiative transfer algorithm

(RTA) (Strow et al. 2003) and fast eigenvector regres-

sion (Goldberg et al. 2003; Zhou et al. 2008), as well as

cloud-clearing and physical retrieval methodologies

(Susskind et al. 2003), and is described in the IASI L2

Algorithm Theoretical Basis Document (ATBD).

The current NOAA operational cloud-clearing meth-

odology uses the same fast eigenvector regression meth-

odology that is described in Goldberg et al. (2003) to

provide temperature and moisture geophysical profiles

as well as surface parameters using MetOp-A cloudy-sky

IASI spectral measurements and AMSU microwave

sounder brightness temperatures as inputs. These regression

output parameters are then matched with climatological

trace gas abundances (e.g., O3, N2O, etc.) and used as

inputs to an RTA (Strow et al. 2003) to produce a clear-

sky radiance estimate. This clear-sky radiance estimate

is then used to extrapolate cloud-cleared radiances

(CCs) from a spatial interpolation of multiple cloudy

infrared footprints in the IASI 2 3 2 array of footprints

collocated to the microwave footprint. The 2 3 2 array

of footprints is sometimes referred to as a field of regard

(FOR).

As the surface-leaving radiance in the 2 3 2 array of

IASI footprints becomes obscured because of increasing

cloudiness, the regression operator relies more heavily

on the microwave measurements to determine the atmo-

spheric profiles and surface temperature. Unfortunately,

broad vertical weighting functions and possible sidelobe

contamination limit the information content of micro-

wave sounders such as AMSU in the lower atmosphere.

In addition, because the clear-sky estimate is produced

via a radiative transfer model, accurate a priori as-

sumptions about infrared surface characteristics, such as

emissivity, are required to compute accurate radiances.

Therefore, scenes with low-altitude clouds where the

surface-leaving radiances are constrained entirely by

the microwave measurements can produce errant CCs

that, in turn, produce errant sounding products. Radi-

ances computed from the corrupted products can agree

with the measurements within the error budget, mak-

ing detection and removal of the errant scenes im-

practical. These and other limitations in using AMSU

for cloud clearing as applied to the AIRS cloud-clearing

algorithm were discussed in Barnet et al. (2005) and

form part of the impetus for the work described in this

paper.

In this paper we will describe future upgrades to the

operational cloud-clearing algorithm being used for

IASI processing within NOAA/NESDIS. Specifically,

our new cloud-clearing algorithm leverages off of the

MetOp-A AVHRR Clouds from AVHRR (CLAVR-x)

cloud mask (Heidinger 2010; Thomas et al. 2004) to

provide high-quality, high spatial resolution IR window

clear-sky scene radiance estimates required for cloud-

clearing inputs and quality assurance. For instance, Wang

and Cao (2008) showed that the mean difference between

collocated AVHRR and IASI for AVHRR channels 4

and 5 is generally less than 0.4 K, with a standard de-

viation of 0.3 K. Therefore, the direct use of AVHRR

clear-sky measurements decreases limitations of the

current algorithm to provide high-quality clear-sky ra-

diance estimates throughout the atmospheric column,

and especially near the surface to a high degree of ac-

curacy. In section 2 we describe the IASI–AVHRR

collocation procedures and the AVHRR cloud mask

products. In section 3 we fully describe our synergistic

IASI–AVHRR cloud-clearing algorithm and provide an

analysis of the performance of the new algorithm in

section 4.

SEPTEMBER 2011 M A D D Y E T A L . 1105

2. AVHRR–IASI collocation and AVHRRCLAVR-x cloud masking

AVHRR/3 is a six-channel imaging and scanning ra-

diometer that measures three solar channels in the visible–

near infrared region and three thermal infrared channels.

AVHRR has an instantaneous field of view of 1.3 mrad,

corresponding to a 1.1-km footprint at nadir. The cross-

track scan swath of the instrument extends 655.48 on

either side of nadir, providing a swath that extends be-

yond the IASI cross-track swath width of 648.38 on

either side of nadir. Two-point (deep space and internal

blackbody) calibration of the thermal IR channels is

performed on a scan-line-by-scan-line basis, and a pre-

launch nonlinearity correction has been performed on

the data (Sullivan 1999).

a. Collocation between AVHRR and IASImeasurements

A typical IASI spectrum and the spectral response

functions (SRFs) of AVHRR Channels 4 and 5 are

shown in Fig. 1. IASI’s spectral range fully overlaps

AVHRR longwave thermal infrared channels 4 and 5,

with nominal spectral centroids of 10.8 and 12 mm,

respectively. The complete spectral overlap between

IASI and AVHRR in the longwave IR window region

provides a unique opportunity to characterize subpixel

variability within the IASI footprints because these

split window thermal infrared channels are generally

used to derive sea surface temperature and other

surface properties. High spatial resolution AVHRR

measurements collocated within the IASI spatial

footprints therefore ideally enable the detection and

removal of the spectral fingerprint of clouds from IASI

spectra.

Collocation between IASI and AVHRR uses an al-

gorithm developed for use with AIRS and Moderate

Resolution Imaging Spectroradiometer (MODIS) data

on NASA’s Aqua satellite (Sun et al. 2006) and is an

extension of the algorithms described in Li et al. (2005).

Explained briefly, this algorithm finds the closest AVHRR

observation to the center of the IASI footprint and

performs an outward search to find all of the AVHRR

pixels falling within the IASI footprint. A weight, herein

termed the integrated point spread function (IPSF), is

assigned to each collocated AVHRR pixel, which de-

pends on the angular difference between the AVHRR

pixels and the center pixel. For instance, weights nearest

to the center of the IASI footprint are given a value of 1,

while weights on the edge of the IASI footprint are given

a weighting of 0.

b. CLAVR-x cloud masking

The CLAVR-x product (Thomas et al. 2004; Heidinger

2010) provides high spatial resolution (’1 km) cloud

masking in one of four categories, with 0 corresponding

to confidently clear, 1 corresponding to probably clear, 2

corresponding to probably cloudy, and 3 corresponding

to cloudy. In our processing we integrate various surface

parameters using the CLAVR-x mask to determine all-

sky (mask 5 0, 1, 2, 3), confidently clear-sky (mask 5 0),

and confidently and probably clear-sky (mask 5 0, 1)

AVHRR radiances as well as the average cloud-top

temperature and pressure and the standard deviation of

the cloud-top temperature from the CLAVR-x product.

For instance, for pixels determined to be confidently

clear sky by the CLAVR-x cloud mask, we calculate the

clear AVHRR radiance in AVHRR spectral band i in

each IASI footprint RclrA

ias follows:

RclrA

i5 �

nclrAVHRR

l51IPSFlR

clr,lA

i. (1)

In Eq. (1), Rclr,lAi

is the radiance of the confidently clear-

sky AVHRR pixel l, nclrAVHRR is the number of confi-

dently clear-sky AVHRR pixels collocated to the IASI

footprint, and IPSFl is the integrated point spread

function for pixel l. We have also assumed that the IPSF

has been normalized to unity.

Example collocations for the single day of IASI and

AVHRR data obtained on 3 October 2010 are shown in

Fig. 2. IASI measurements R at wavenumber n (Rn) are

spectrally averaged onto the AVHRR SRF for channel

i, SRFi,n using

RAi

5 �n

SRFi,nRn, (2)

FIG. 1. IASI spectrum for Air Force Geophysics Laboratory

(AFGL) U.S. Standard Tropical Atmosphere, 1976 (black) and

overlaid AVHRR SRFs (red) for AVHRR channels 4 and 5.


and plotted against the spatially collocated and aver-

aged [using Eq. (1)] all-sky and confidently clear-sky

AVHRR measurements. The number of successful col-

locations, that is, those corresponding to cases where

both the IASI and AVHRR quality assurance (QA) flags

indicate highest quality, is 1 268 749. The correlation be-

tween the all-sky AVHRR measurements and IASI mea-

surements for this set of cases is very high, giving a value

of 0.998 for both channels considered. A summary of

statistics for the IASI–AVHRR collocations for both

all-sky and clear-sky (92 347 cases) are provided in Table 1

for AVHRR channels 4 and 5.

Similar to the findings discussed in Wang and Cao

(2008), the differences between AVHRR and IASI for

our nonuniform and clear-sky scenes show temperature-

and scan-dependent biases. These temperature-dependent

biases suggest possible problems with nonlinearity in

AVHRR calibration (Wang and Cao 2008). In what

follows, we have performed a brightness temperature–

dependent bias correction to the AVHRR measure-

ments RAi,

R9Ai

5 aAi

1 (1 1 bAi)RA

i, (3)

to make them better agree with the IASI measurements.

The correction coefficients are listed in Table 2. We

have also found scan angle–dependent biases between

IASI and AVHRR that are symmetric about nadir and

are on the order of ’0.2 K for both AVHRR channels 4

and 5. At this point, we have not attempted to correct

these scan angle–dependent differences because they

are much smaller than the sidelobe corrections required

to use the AMSU. Wang and Cao (2008) discuss these

scan angle–dependent differences and the possible causes

for the scan angle dependence in more detail.

3. A review of cloud-clearing methodology

The two-spot, adjacent footprint cloud-clearing meth-

odology assumes that the spectral radiance in two adjacent

footprints, denoted RFOV

jn , differ only in the product of the

cloud fraction and cloud emissivity Nj�jn according to

RFOV

jn 5 (1 2 Nj�jn)Rclr

n 1 Nj�jnRcldn , (4)

where RFOVjn is the measured radiance in footprint j, and

Rclrn and Rcld

n are the true clear-sky and true cloudy-sky

column radiances, respectively, for footprint j 5 1, 2. By

defining a new parameter h 5 N1�1n/(N2�2

n 2 N1�1n), and

assuming the cloud emissivities are equal in footprints 1

and 2 (i.e., �1n 5 �2

n), we can simultaneously solve both

equations for Rclrn to enable estimation of the cloud-

cleared radiance Rccn , giving

Rccn 5 R

FOV1

n 1 h(RFOV

1n 2 R

FOV2

n ). (5)

With those substitutions, the problem of determining

the cloud-cleared radiance Rccn in the two adjacent

footprints then reduces to the determination of the pa-

rameter h. The authors note that Rccn is not guaranteed to

be exactly equal to the true clear-sky scene radiance Rclrn

from Eq. (4) because measurements are susceptible to

instrument noise and there is a possibility that our

FIG. 2. Collocations of AVHRR BTs and IASI BTs for 3 Oct

2010. IASI data were spectrally convolved onto the AVHRR

channel 4 SRF, while AVHRR was spatially convolved onto the

IASI footprints. Collocations for all cases are shown (black dots),

while collocation for cases determined by the CLAVR-X cloud

mask to be clear are also shown (red). Results for AVHRR channel

5 are similar.

TABLE 1. Bias, standard deviation (std dev) and correlation co-

efficient r between spatially convolved AVHRR measurements

and spectrally convolved IASI measurements for AVHRR chan-

nels 4 and 5 for both all-sky and clear-sky cases.

All-sky cases Clear-sky cases

AVHRR

channel Bias (K)

Std dev

(K) r Bias (K)

Std dev

(K) r

4 20.163 0.422 0.998 20.321 0.109 0.999

5 20.203 0.417 0.998 20.425 0.117 0.999

TABLE 2. Slope and offset coefficients between AVHRR and IASI

measurements.

AVHRR channel aAi(K) bAi

[K (K)21]

4 2.08706 28.19407 3 1023

5 2.36485 29.94639 3 1023


assumption that the two footprints differ only in their

respective cloud fractions is not true (e.g., water vapor

or surface variability between the two adjacent foot-

prints).

Smith (1968), Chahine (1974), and McMillin and Dean

(1982) showed that a single channel or small subset of

channels can remove the radiative effect of clouds from

entire spectrum provided that an independent estimate

of the clear-sky radiance for the two footprints Rclrn is

given. In our notation, the solution for adjacent spot,

single-channel cloud clearing is given by solving Eq. (5)

for h, yielding

h 5Rclr 2 RFOV

1

RFOV1 2 RFOV

2

. (6)

Chahine (1977) and Chahine et al. (1977) showed that

the formulation of the cloud-clearing equations in the h

notation enables determination of clear-sky IR spectra

affected by J 2 1 cloud formations in J footprints. Joiner

and Rokke (2000) employed the h notation in a varia-

tional context to cloud clear Television and Infrared

Observation Satellite (TIROS) Operational Vertical

Sounder data with J 5 3. Susskind et al. (2003) used the

3 3 3 array of AIRS pixels collocated to an AMSU

footprint and Chahine’s h methodology to determine as

many as 4hs to cloud clear AIRS spectra. The AIRS

approach provides the basis for the operational IASI–

AMSU cloud-clearing algorithm currently employed by

NOAA/NESDIS, and therefore shares many of the

benefits and limitations of the AIRS algorithm.

As described in section 1, the coupled AIRS–AMSU

or IASI–AMSU algorithm relies heavily on the AMSU

measurements to provide inputs to a forward model,

which in turn provides the clear-sky estimate Rclrn . The

IR forward model requires the complete atmospheric

state, including temperature, moisture, and trace gas

profiles as well as IR surface properties, such as surface

emissivity and reflectivity in order to compute Rclrn . AMSU

measurements are generally only sensitive to tempera-

ture profiles, surface temperature, and moisture profiles

requiring one to make assumptions regarding the IR

surface properties and trace gas profiles. Therefore, any

biases the AMSU geophysical profiles, either assumed

surface parameters or trace gas profiles, and/or biases in

the forward model itself directly affect the determination

of h and the error characteristics of the inferred Rccn .

Clear-sky radiance estimates from collocated high spa-

tial resolution imager measurements (e.g., from MODIS)

can also be used to remove the effects of clouds from IR

sounder measurements. For instance, Smith et al. (2004)

and Li et al. (2005) showed that the collocated AIRS IR

sounder and Aqua MODIS imager measurements

enable direct calculation of high-quality cloud-cleared

radiances without the use of a forward model to estimate

Rclrn . Their methods rely on the high spatial resolution

MODIS measurements and cloud mask to estimate clear-

sky measurements in MODIS IR spectral bands spa-

tially collocated and averaged onto the AIRS footprints.

The use of IR spectral bands covering the spectral do-

mains sampled by the AIRS instrument enables direct

comparison of the clear-sky MODIS measurements to

AIRS, and therefore does not require a priori assump-

tions about the geophysical state (i.e., surface properties,

trace gas concentrations, and/or water vapor abundances)

to enable calculation of clear-sky radiances.

In a similar fashion, in the following we utilize the

spatially averaged and collocated AVHRR clear-sky ra-

diances aggregated onto the IASI footprints and denoted

RclrAi

to produce h and Rccn . We expand on the method-

ology described in Li et al. (2005) by providing an error

analysis of Rccn with respect to the composite input var-

iables to the CC algorithm. Explicit treatment of the

errors induced through cloud clearing leads to an im-

proved quality control scheme and a more optimal se-

lection of footprints enabling a reduction in the noise in

the cloud-clearing algorithm. In addition, our use of the

h notation has the advantage that multiple cloud for-

mations (e.g., type, height, etc.) can be cleared and will

be the subject of a future publication.

a. A description of the AVHRR/IASI cloud-clearingalgorithm

Although there are various methods to determine h

given RclrAi

and RFOV

j

Ai, the method of least squares enables

a simple solution to our problem. The least squares

problem for determining h from the clear-sky AVHRR

pixels can be written as the minimization of the objective

function given in Eq. (7):

J(h) 5 �N

chan

i

1

s2i

fRclrA

i2 [R

FOV1

Ai

1 h(RFOV

1

Ai

2 RFOV

2

Ai

)]g2.

(7)

In Eq. (7), Nchan corresponds to the number of AVHRR

channels used, which is 2, and s2i is the sum of the ex-

pected variance in the AVHRR clear-sky radiance in

channel i and the expected variance in the spectrally

averaged IASI radiance in channel i. For simplicity we

assume that the s2i 5 1:0 radiance unit squared; how-

ever, we have found that modifying these weights does

not affect the quality of the cloud-cleared radiances to

a large degree. Taking the derivative of Eq. (7) with

respect to h,


›J(h)

›h5 �

i

2

si

[(RFOV

1

Ai

2 RclrA

i)(R

FOV1

Ai

2 RFOV

2

Ai

)

1 h(RFOV

1

Ai

2 RFOV

2

Ai

)2], (8)

and setting the result equal to zero enables minimization

of this objective function. Solving the previous equation

for h yields the least squares solution for h and is given by

h 5

�i

1

s2i

(RclrA

i2 R

FOV1

Ai

)(RFOV

1

Ai

2 RFOV

2

Ai

)

�i

1

s2i

(RFOV

1

Ai

2 RFOV

2

Ai

)2. (9)

From Eq. (9), we can see that the magnitude of extrap-

olation parameter h linearly depends on the contrast

RclrAi

2 RFOV1

Aiand nonlinearly depends on the contrast

between the adjacent footprints RFOV1

Ai2 R

FOV2

Ai. In the

next section we further develop the mathematics that

enable the characterization of the response of Eqs. (5)

and (9) to the contrast between various input variables

to the algorithm and to the uncertainties in the input

variables themselves.

b. Cloud-clearing error estimates

The potentially large corrections (e.g., many tens of

kelvins) that are required by cloud clearing warrant

quantification of the uncertainties in the cloud-clearing

process. Calculating the differential of Eq. (5) as

dRccn 5 (1 1 h)dR

FOV1

n 2 hdRFOV

2n 1

›Rccn

›h

��~R

FOVjn

dh

5 (1 1 h)dRFOV

1n 2 hdR

FOV2

n

1 ( ~RFOV

1

n 2 ~RFOV

2

n )dh, (10)

where ~RFOVj

n 5 RFOV

jn 1 dR

FOVj

n , enables estimation of

the bias in the cloud-cleared radiance dRccn . The bias is

composed of two terms—the first arising from fact that

noise in Rccn is a linear combination of instrument noise

in RFOV1n and R

FOV2n , and denoted dR

FOV1n and dR

FOV2n

respectively; and the second arising from biases in h.

Generally speaking, the magnitude of dh, which is a

function of the uncertainty in the composite variables

used to determine h (e.g., RclrAi

and RFOV

j

Ai), and the contrast

between the footprints used to derive h modulates the

magnitude of the spectral correlation of dRccn . We also note

that self-apodization of the spectra resulting from in-

strument FOV geometry and instrument imperfections

as well as user-selected apodization of the interfero-

gram (e.g., either Gaussian or Blackmann apodization)

to reduce sidelobes also introduces spectral correlation

to the IASI random noise. Because the first two terms of

Eq. (10) are linearly proportional to h, we can therefore

expect that the magnitude of h itself will also effect the

magnitude of the spectral correlation in dRccn . In addi-

tion, because h depends on the squared reciprocal of the

contrast between footprints, dh will be a strongly non-

linear function of the contrast between footprints, mul-

tiplied by the uncertainties in the two footprints used for

cloud clearing.

On a case-by-case basis it is not generally possible to

estimate the bias in Rccn ; however, calculating the co-

variance of Eq. (10) and taking the expectation of the

result enables us to statistically estimate some of the

terms of the error covariance of Rccn . It can be shown that

the error covariance of the cloud-cleared radiance Scce

takes the form

Scce 5 (1 1 h)2S

FOV1

e 1 h2SFOV

2e 1 E[( ~R

FOV1

n 2 ~RFOV

2

n )

3 dhdhT( ~RFOV

1

n 2 ~RFOV

2

n )T]

1 cross-correlation terms, (11)

where E(�) denotes the statistical expectation. Although

small differences in the calibration between the IASI

footprints exist (see Collard and McNally 2009), statis-

tically, the expectation of noise in the IASI footprints

should be equal to the spectral error covariance of

the IASI instrument, which we denote as Se (i.e.,

SFOV1e 5 S

FOV2e 5 Se). We can therefore rewrite Eq. (11) as

Scce 5 [(11h)2

1 h2]Se 1 E[( ~RFOV

1

n 2 ~RFOV

2

n )dhdhT

3 ( ~RFOV

1

n 2 ~RFOV

2

n )T] 1 cross-correlation terms.

(12)

It is clear from Eq. (12) that the cloud-clearing process

amplifies the spectral error covariance of the IASI

measurements Se by a factor of

a(h)25 (1 1 h)2

1 h2, (13)

the square root of which we will term the amplification

factor. From Eq. (13), we can also see that as h / 0 (i.e.,

the footprints are cloud free), then a(h) / 1.

The error analysis described by Eqs. (11) and (12) is

complicated by the cross-correlation terms [e.g.,

dRFOV1n dhT(R

FOV1n 2 R

FOV2n )T] that will be difficult to es-

timate. Nonetheless, from these equations it is clear that

a well-designed cloud-clearing algorithm should mini-

mize both the random noise amplification and the spec-

tral error correlation introduced by the cloud-clearing

process. This can be accomplished for each set of IASI

footprints and collocated AVHRR clear-sky pixels ag-

gregated onto the IASI footprint by selecting


(i) cases where the contrast between footprints

RFOV1

Ai

2 RFOV2

Ai

is large in order to minimize

a(h) } h } 1/(RFOV1

Ai2 R

FOV2

Ai)2, and also the spec-

tral correlation as given in Eq. (10); and

(ii) cases where the distance from the IASI radiance to

the clear-sky estimate is small (i.e., RFOV1

Ai’ Rclr

Ai), in

order to minimize a(h) } RclrAi

2 RFOV1

Ai.

A description of our cloud-clearing algorithm is given in

the next section.

c. Cloud-clearing algorithm implementation

The use of AVHRR enables characterization of sub-

pixel cloud variability within the IASI footprints and,

more importantly, enables detection of cloud-free or

clear-sky footprints. As described in the section 3b, cloud

clearing amplifies the random components of noise in

the IASI measurements; therefore, our approach to

handle clouds is two pronged. If AVHRR determines

that any of the IASI footprints are clear sky, then we

average those clear-sky footprints and skip cloud clearing

(this is commonly referred to as ‘‘hole hunting’’); other-

wise, we perform cloud clearing. The steps of our algo-

rithm are presented in more detailed in the following and

a schematic of the algorithm is shown in Fig. 3:

(i) Aggregate the collocated clear-sky AVHRR radi-

ance for each channel RclrA

ionto the IASI footprints.

FIG. 3. Schematic of the cloud-clearing algorithm illustrating that the radiance in each footprint RFOVjn is as-

sumed to be a linear combination of a clear-sky radiance Rclrn and cloudy-sky radiance Rcld

n with the relative

weighting described by the cloud fraction N j in each footprint j. The collocated AVHRR measurements for

channel Ai that are determined to be clear by the CLAVR-x cloud mask for pixel l and denoted Rclr,lAi

in the figure

are averaged and used as an estimate of the clear radiance RclrAi

. For clarity, these subpixel measurement locations

are shown for only one footprint and the size of the subpixel footprints is exaggerated. To compare apples to

apples, the IASI spectral measurements are also spectrally integrated onto the AVHRR bandpasses. The cloud-

clearing algorithm cycles through various combinations of the IASI footprints (e.g., f j 5 2, k 5 1g, f j 5 2, k 5 3g,f j 5 4, k 5 2g, etc.), estimates h( j, k) using Eq. (9), and produces a cloud-cleared radiance via Eq. (5) for each

combination. The algorithm then selects the optimal combination of footprints; i.e., the ones that minimize the

figure of merit fom( j, k) described in section 3c. It is not possible to tell from this general example which footprints

would be used in our algorithm; however, the algorithm would likely not choose footprints 1 and 3 to perform

cloud clearing because the cloud fraction in these two footprints is very similar (i.e., N3 ’ N1), and hence

h } 1/(RFOV1

Ai2 R

FOV2

Ai)2 / ‘.


If fewer than 3% of all collocated AVHRR pixels

are masked clear by the CLAVR-x mask, then

reject the current case.

(ii) If any IASI footprint is determined by AVHRR to

be clear-sky, then Rccn is set equal to the average

IASI spectral radiances in those clear-sky foot-

prints. In this case a(h) is equal to 1/ffiffiffiffiffiffiffinclr

p, where

nclr is the number of clear-sky IASI footprints

within the IASI 2 3 2 array. Proceed to step 9.

(iii) If no clear-sky IASI footprints are found, then sort

the j IASI footprints by 1/Nchan

�Nchan

i RFOVj

Ai

.

(iv) For each sorted cloudy IASI footprint ( j 5 1, 2, . . . ,

4) select a neighboring footprint (k 5 2, 3, 4, j 6¼ k

order matters), giving a total of six possibilities.

We order the FOVs such that the warmest FOV

always corresponds to RFOV1

Aiin order to minimize

the noise amplification.

(v) For each pair ( j, k) calculate h( j, k) using Eq. (9).

(vi) Calculate Rcc( j, k) from Eq. (5).

(vii) Calculate x2( j, k) 5 �i1/s2i f[Rclr

Ai2 Rcc

Ai( j, k)]g2

and a[h( j, k)].

(viii) Define a figure of merit for each pair of footprints

( j, k) with

fom( j, k) 5

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi[x2( j, k)]2

1 a[h( j, k)]2q

.

(ix) Select the pair of footprints ( j9, k9) with fom( j9, k9) 5

min fom( j, k).

(x) Apply quality control to the selected cloud-

cleared radiance Rccn ( j9, k9) by requiring that

x2( j9, k9) # 5.0 K and a[h( j9, k9)] # 10.

The quality control thresholds for x2( j, k) and a[h( j9,

k9)] are very liberal and were selected such that we

maximize the number of cases that get through our

cloud-clearing algorithm.

4. Performance of the cloud-clearing algorithm

In this section we perform an analysis of the results of

the cloud-clearing algorithm by comparing the cloud-

cleared radiances Rccn ( j9, k9) to the subpixel clear-sky

AVHRR measurements RclrAi

. To test the accuracy of

the cloud-cleared radiances over a large range of atmo-

spheric conditions, we selected a subset of 66 night gran-

ules from five partial IASI orbits on 3 October 2010. For

the analysis that follows, the results have been restricted to

latitudes between 708S and 758N. The authors note that

the polar orbit of MetOp-A samples the poles more than

the middle or low latitudes. A restriction to investigate

latitudes between 708S and 758N was made due to a lower

availability of subpixel clear-sky estimates from AVHRR

at higher latitudes and a lower acceptance rate of the IASI

L2 retrievals.

Figure 4 shows the AVHRR CLAVR-X cloud mask

for the several partial MetOp-A orbits that form our

dataset. In creating the figure, we restricted viewing

angles from AVHRR to be within those viewed by IASI.

It is worthwhile to note that for this dataset the

CLAVR-X cloud mask determined ’10% of the single

FOV IASI footprints to be clear sky, ’2.5% of the 2 3 2

array of IASI footprints to be clear sky, and ’39% of the

2 3 2 array of IASI footprints to be completely covered

with clouds (i.e., they are overcast).

Figure 5 illustrates the improvement in yield result-

ing from the use of a cloud-clearing algorithm and also

FIG. 4. CLAVR-X cloud mask for several partial MetOp-A orbits on 3 Oct 2010. For this

dataset, ’10% of the single FOV IASI footprints are clear, ’2.5% of the 2 3 2 IASI FORs are

clear, and ’39% of the 2 3 2 IASI FORs are completely overcast.


the performance of the algorithm. The top left panel of

Fig. 5 shows the AVHRR channel 4 brightness tem-

perature for any IASI FOR where at least one footprint

was determined to be clear sky. The top right panel

shows the spectrally convolved cloud-cleared brightness

temperature for the equivalent AVHRR channel 4 SRF

where RccAi

met the quality control thresholds described in

section 3c. The bottom right panel illustrates the ability of

the cloud-clearing algorithm to fit the subpixel clear-sky

AVHRR measurement in AVHRR channel 4 by showing

the difference between RccAi

and RclrAi

, where each are con-

verted to a brightness temperature. The bottom left panel

shows the coldest brightness temperature in the IASI

FOR spectrally convolved for AVHRR channel 4.

Apparent from the bottom right panel, the cloud-

clearing–hole-hunting algorithm fits the AVHRR subpixel

clear-sky scenes extremely well. The probability distri-

bution function and cumulative distribution function for

the difference between the subpixel clear-sky AVHRR

measurements for AVHRR channel 4 and IASI cloud-

cleared radiances are also shown in Fig. 6. For this da-

taset of partial orbits, the root-mean-squared difference

(RMSD) and bias between the RccAi

and RclrAi

for AVHRR

channel 4 is 0.2225 and 20.1429 K. Likewise, the root-

mean-squared difference and bias between the RccAi

and

RclrAi

for AVHRR channel 5 is 0.2376 and 20.1648 K.

One area for possible improvement of the algorithm is

for land cases. Over land the bottom right panel shows

larger departures especially over high surface terrain,

such as the U.S. and Canadian Rockies and the Andes

range in South America. These larger differences over

land surfaces could be due to a number of factors, in-

cluding differences in the subpixel AVHRR footprint

versus the IASI footprint aggregate surface emissivity,

surface temperature, water vapor amount, or other geo-

physical variability not properly accounted for by our

FIG. 5. (top left) Map of AVHRR measurements averaged onto the IASI footprints where the any of 2 3 2 IASI footprints comprising

the IASI FOR were determined to be clear sky. (top right) Map of the cloud-cleared IASI measurements spectrally averaged onto the

AVHRR SRF for AVHRR channel 4. (bottom left) Map of the coldest IASI footprint (FOV) in the IASI 2 3 2 array. (bottom right) Map

of the difference between the IASI cloud-cleared radiances and the clear estimate RclrAi

for AVHRR channel 4.


algorithm. Nevertheless, if we only consider land cases

in this partial set of orbits we find the RMSD and bias for

AVHRR channel 4 is 0.3251 and 20.0347 K and the

RMSD and bias for AVHRR channel 5 is 0.3285 and

20.03213 K.

In the previous section we developed equations to de-

scribe the error characteristics of Rccn and argued that a

successful algorithm should minimize both the difference

between the clear estimate and cloud-cleared radiance

and also the noise amplification resulting from cloud

clearing. Figure 7 compares the amplification factor

a[h( j, k)] resultant from two systems that use different

figures of merit (foms) to select optimal footprints for

cloud clearing. The black curves in Fig. 7 correspond to

a system that uses a fom that minimizes the root-mean-

squared agreement between the subpixel clear-sky radi-

ances observed by AVHRR and IASI cloud-cleared

radiances x2( j, k) and the amplification factor a[h( j, k)],

while the red curves correspond to a system that uses a

fom that minimizes only the RMSD agreement x2( j, k).

The data plotted in this figure are for only those cases

that passed our quality control described above. While

the probability distribution functions (PDFs) and cu-

mulative distribution functions (CDFs) of a[h( j, k)] for

each fom agree well for a[h( j, k)] # 1, for larger am-

plification factors we see that the black CDF curves (fom

uses both quantities) approaches 1 much faster than the

red CDF curve. This means that our algorithm will limit

the random noise amplification much better than an al-

gorithm that does not consider a[h( j, k)].

It is also worthwhile to note that for this ensemble,

roughly 48% of our 2 3 2 IASI FORs include at least

one clear-sky FOV; that is, a(h) # 1. This means that

over the scale of the IASI FOR, which is ’50 km, if 3%

of the 1-km AVHRR clear-sky pixels collocated to IASI

footprints are found to be cloud free (i.e., the FOR is not

overcast), then 18% of the atmosphere is cloud free over

a scale of 50 km, 30% of the atmosphere is cloud free

over a scale offfiffiffi2p

/2 3 50 km, 35% of the atmosphere is

cloud free over a scale of 25 km (roughly half an IASI

footprint), and 48% of that the scene is cloud free over

a scale of the IASI footprint size, 12 km. Thus, 3% of the

AVHRR pixels collocated to an IASI footprint is ’six

1-km AVHRR pixels. This finding is extremely impor-

tant to note in the context of the development of future

IR sounders with high spatial resolution, and also for

cloud model parameterizations and studies of cloud spa-

tial scaling; however, the use of dataset with more days

and geographical scenes would be required to provide

robustness to these findings.

Assuring good accuracy of Rccn ( j9, k9) relative to Rclr

Ai

does not guarantee good performance of the radiances

themselves. Because IASI is a thermal sounder, the ability

of our algorithm to remove the effect of clouds is directly

dependent on the thermal contrast between clouds and

the surface-leaving radiances. We would therefore ex-

pect that the largest degree of difficulty for the algorithm

would be for undetected low clouds. To test the quality

and usefulness of Rccn we also run the NOAA operational

IASI retrievals using Rccn as inputs to produce estimates of

the geophysical state observed by IASI. To determine the

quality of our Rccn for near-surface properties, we com-

pared the ECMWF model output ocean skin tempera-

ture to retrieved ocean surface skin temperature for two

system configurations. The first configuration used the

FIG. 6. PDF (solid) and CDF (dotted) of the difference between

the IASI cloud-cleared radiances and the clear estimate RclrAi

for

AVHRR channel 4 (928.15 cm21) for the five partial MetOp-A

orbits on 3 Oct 2010.

FIG. 7. PDF (solid) and CDF (dashed) of the amplification factor

as calculated from Eq. (12). The PDF and CDF when the figure of

merit used in the algorithm to decide footprints j9 and k9 consists of

only the x2 term (red curves), and the PDF and CDF when the

figure of merit includes both x2 and a(h) (black curves) are shown.


current operational AIRS-based IASI plus AMSU cloud-

clearing algorithm and L2 processor described in section 1,

while the second configuration used the combined

AVHRR, IASI, and AMSU cloud-clearing algorithm

described in this paper. In the combined AVHRR, IASI,

and AMSU system configuration the L2 processor was

told that the cloud-cleared radiances were clear with

nominal IASI NEDN. As discussed in section 3b, the

process of cloud clearing amplifies the random compo-

nents of IASI spectral noise. We would therefore expect

that the performance of the retrieval algorithm that the

AVHRR, IASI, and AMSU cloud-cleared radiances

would be suboptimal due to the fact that noise amplifi-

cation is not explicitly handled.

The benefits of adding AVHRR to the cloud-clearing

algorithm are shown in Fig. 8. Here, the blue curves cor-

respond to surface temperature retrievals from cloud-

cleared radiances that utilized information from AVHRR,

IASI, and AMSU, while red curves correspond to surface

temperature retrievals from cloud-cleared radiances that

utilized information from IASI and AMSU only. Quality

control for the IASI retrievals is based on a series of

threshold tests for various retrieval convergence criteria

and coarse data quality checks. To ensure our comparison

was fair, we used a common rejection for each system

so that the same ensemble is considered in each PDF

(section 3c). The percent of cases accepted by both sys-

tems was 42.7% and statistics are summarized in Table 3.

Generally speaking the system that utilizes AVHRR

to quality control and produce cloud-cleared radiances

shows a much smaller, if nonexistent, cold tail in the

differences between retrieved ocean surface skin tem-

peratures and ECMWF model ocean surface skin tem-

peratures. This cold tail, which is a tendency for the

retrievals to be colder than the model, is a direct result of

cloud contamination in the cloud-cleared radiance. Add-

ing AVHRR to the cloud-clearing algorithm also tightens

the PDF toward a more Gaussian shape with a smaller

standard deviation, which again illustrates the high qual-

ity and low noise (correlated and random) of the cloud-

cleared radiances produced using our approach.

5. Conclusions

In this paper we have developed a methodology that

enables the determination of high-quality cloud-cleared

radiances from high spectral resolution sounders using

collocated high spatial resolution imager measurements.

Building on the results of Smith et al. (2004) and Li et al.

(2005) and examining the propagation of errors through

the cloud-clearing algorithm lead us to an improved

quality control scheme and more optimal selection of

footprints (a combination cloud-clearing–hole-hunting

approach) that demands low-magnitude noise amplifi-

cation for the cloud-cleared radiances. In addition, by

formulating the problem in the h notation, our approach

has the advantage that multiple cloud formations can be

cleared from the spectra simultaneously, which will be

the subject of a future publication.

When faced with real data from MetOp-A, the com-

bination AVHRR, IASI, and AMSU algorithm suc-

cessfully removes the effects of clouds from the IASI

radiances for ’42% of cases attempted. Considering that

’39% of cases were rejected because the 2 3 2 array of

IASI footprints was determined by the CLAVR-x cloud

mask to be completely covered with clouds, a 42% yield

over the entire ensemble corresponds to a ’ 70% 5

100[42/(100 2 39)]% success rate for the cloud-clearing

algorithm. In addition, after correcting for some

FIG. 8. PDF of the difference between retrieved ocean skin

temperatures and ECMWF analysis modeled skin temperatures

for the five partial MetOp-A orbits on 3 Oct 2010. The surface

temperature retrievals where the cloud-clearing algorithm utilized

information from AVHRR, IASI, and AMSU (blue), and surface

temperature retrievals where the cloud-clearing algorithm utilized

information from IASI and AMSU only (red) are shown. Quality

control for each system was common so that the same ensemble

was used in each system configuration.

TABLE 3. Bias, standard deviation (std dev), correlation co-

efficient r, and % outliers between ECMWF model ocean surface

skin temperatures and retrieved skin temperatures that utilized

either AVHRR, IASI, and AMSU cloud clearing or the AIRS-

based IASI plus AMSU cloud clearing. We define an outlier as the

% of cases falling outside j3 Kj about the mean difference.

AVHRR 1 IASI 1

AMSU IASI 1 AMSU

Bias (K) 0.1530 22.013

Std dev (K) 1.168 2.010

r 0.9870 0.9667

% outliers 1.93 10.73


calibration differences between AVHRR and IASI,

these cloud-cleared radiances agree with subpixel clear-

sky AVHRR radiances to better than ’0.2-K RMSD,

with almost no bias for either surface sensitive AVHRR

window channel. Although the dataset used to test the

algorithm was not global in extent, the dataset included

a wide variety of atmospheric conditions, so it is ex-

pected that the algorithm performance should extend to

global conditions.

To guarantee the performance of the cloud-cleared

radiances, we ran our cloud-cleared radiances through

the operational L2 processor for IASI assuming that the

radiances were clear. For the partial set of MetOp-A

orbits considered on 3 October 2010 between 708S and

758N latitude, surface temperature retrievals run using

the combined AVHRR, IASI, and AMSU algorithm

agree with the ECMWF model surface skin tempera-

tures to better than 0.2 K in the mean, with a standard

deviation of ’1.2 K, and demonstrate the high accuracy

and precision of these cloud-cleared radiances for chan-

nels spanning the atmospheric column and including the

surface. Relative to the current operational system,

these statistics represent a ’2-K improvement in the

bias and a ’1-K improvement in the random component

of error for the surface temperature retrieval. As noted

in section 4, we did not handle the noise amplification of

the cloud-cleared radiances for the AVHRR, IASI, and

AMSU algorithm, and therefore we would expect that the

algorithm performance is better than that reported here.

Another interesting finding that has implications for

the design of future IR sounding instruments as well as

the understanding of cloud size and spatial scaling fol-

lows from the spatial scaling of cloud-free pixels over the

IASI 2 3 2 array of footprints. We found for this en-

semble that in the 61% of cases that at least 3% of the

1-km AVHRR pixels collocated onto the IASI foot-

prints were determined to be cloud free over the 50-km

IASI 2 3 2 array, all four footprints were cloud free 18%

of the time, three footprints were cloud free 25% of the

time, two footprints were cloud free 35% of the time,

and at least one of the 12-km footprints were cloud free

50% of the time. This indicates that there is no simple

progression in the probability of finding clear-sky pixels

in smaller FOVs and also that there is a potential to run

the IASI retrievals at a higher spatial resolution than a

50-km AMSU footprint.

We anticipate that the next release of the IASI op-

erational retrievals will incorporate the AVHRR data

into the cloud-clearing algorithm.

Acknowledgments. This work was supported by NOAA

Office of System Development (OSD) Product Systems

Development and Integration (PSDI) funding. The au-

thors wish to thank ECMWF for the model data,

EUMETSAT, and Murty Divakarla for discussions re-

lated to this work. The views, opinions, and findings

contained in this paper are those of the authors and

should not be construed as an official National Oceanic

and Atmospheric Administration, National Aero-

nautics and Space Administration, or U.S. Government

position, policy, or decision.

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Documents

Using MetOp-A AVHRR Clear-Sky Measurements to Cloud-Clear MetOp-A IASI Column Radiances