NASA Technical Reports Server (NTRS) 20030020801: Evaluation of Methods to Estimate the Surface Downwelling Longwave Flux during Arctic Winter

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306 J 0 11 K N A I. 0 I- 4 P I' I. I I. I) hl I.. 1' I' 0 K 0 I. 0 C; Y vcit I hll 1 I
Evaluation of Methllds to Estimate the Surface Downwelling Longwave Flux during
Arctic Winter
(Manuwript ricched ?I March 2001. in linal form R August 2001)
ABSTRACI
Surface lonpuiive radiation fluxc\ doininate the energy budget oln ighttime polar regions. yet little I\ known
about the relati\e a( curacy of exisling satellite-based techniques to estimate this parameter. We compare eight
methods to estimate the downwellinp longwave radiation flux and to validate their performance with measure-
menth from two fielcl programs in thc Arctic: the Coordinated Eastern Arctic Experiment (CEAREX ) conducted
in the Barents Sea curing the aiituniii and winter of 19XX. and the Lead Erpcrirneni performed in the Heaulort
Sea in the spring of 1092. Five of thc eight methods fiere developed for satellite-derived quantities. and three
are simple parainetc ritations bared 1111 surface observations. All of the algorithms require information ahout
cloud fraction. which i\ provided from the NASA-NOAA Televition and Infrared Observation Sarellite (TIROS)
Operational Vertical Sounder (TOVS) polar pathtinder data\et (Path-P): some techniques ingest temperature and
moisture profile3 (a1 .o frnm Path-PI: tme-half of the methods wsiime that clouds itre opaque and have ;I con\tant
geometric thickness of 50 hPa. and three include no thichncrs information whatsoever. With a somewhiit limited
\alidation datawt. tie following priin;irq conclusions result: I ) all methods exhibit approximately the sumc
correlations with mtasurements and rms differences. hut the hiares range from -34 W m (16% ot'the mean)
to nearly 0: 2) the t rror analysis dcwihed here indicates that the assumpticin of a SO-hPa cloud thicknes\ 13
too thin hy a factor tmf 2 on average in! 1~1)I;iin ighttime conditions: 3)c loud-overlap techniques. which eflecti\ely
increase mean clou~lth ickness. \igiiilicsiitly improve the rcsulLr: 4) simple Arctic-specific paranietcriratiom
pertcirrncd poorly. r rnbahty hecauw they wcrc devclopcd with wrface-observed cloud fraction\ H hciritr the
ed here used s;rtellite-dciivcd effective cloud fractions: and 5)t he \ingle algorithm that include3 an
cloud th ckncs< exhihit* ihe snialleat differenccs Iron1 obrervaticins.
1. Introduction tion.;. including a long-term dataset (or studies of var-
iability and change and use as forcing fields for mod-
In this investigation we cxtend the uork of Key et
eling studies. We also extend the work of Key et al.
al. (1996) and evaluate the ability of several existing
ll997) in evaluating the sensitivity of DLF to pertur-
methods to estimate the surface downwelling flux of
bations in various atmospheric parameters with the in-
longwave radiation (DLI:) cwer snow- mid ice-covered
tent of identifying variables thitt require improved re-
surfaces, particularly at night. The algorithnis examined
trieval accuracy.
by Key et al. (1996) arc a 1 simple parameteriLntions
empirically derived from si riace measurements in the Polar regions are notoriously problematic for global
Arctic; here we also evalu itc several techniques that surface radiation budget (SRB ) algorithms based on sat-
ingest retrievals from satell te data. Our goal is to de- ellite data. because several factors coniplicaie the re-
termine which method(s) should be used in computing trieval process and few validation data are available.
DLF from wtellite retricva s for a variety ol' itpplica- Rccause the high latitudes are recognixed as climatically
senaitive areas. there is a strong dcniand from the sci-
entific community for reliable. long-term surface radi-
Ctirrr\poiidriix urtrhor uddt-t,,u: Dr. Jcnniter l+.mcis. htituts 111 ation datasets for the polar regions. We employ a com-
Marine and Coastal Sciences. Kuigcr\ Univzr\ity. 71 Dtidlcy Rtl..
New Rrunswick. NJ OXYOI-Xfi?l. bination of satellite retrievals from the National Aero-
E-mail: fraiicis(~'inics.rutg~rs.cdu nautics and Space Administration (NASA)-National
_Ice_---
.\
MARC" ?ON)? ('HIACCHIO ET AI-. 307
Oceanographic and Atmosp ieric Administration Schweiger 2000). The TOVS insti-ument, which has
(NOAA) Television and Inti-are 1 Observation Satellite flown on NOAA polar-orbiting sensors since 1978. com-
Operational Vertical Sounder (1O VS) pcilar pathtinder prises the High-Resolution Infrared Radiation Sounder
(hereinafter, Path-P) dataset (F'riincis and Schwciger (HIRS). the Microwave Sounding Unit (MSU), and the
2000) with surface observations from radiometers. ceil- Stratospheric Sounding Unit (SSU). Data from SSU are
ometers, and surface observers to evaluate the perfor- not used to create the Path-P dataset. HIRS measures
mance of several existing algorithins to estimate surface radiances in 20 channels from the visible to infrared
longwave fluxes in the Arctic. wavelengths with a resolution of 17 km at nadir, and
*. Longwave radiation doniinstey the Arctic surface en- MSU has four channels in the oxygen absorption band
ergy budget for almost one-half of the year when in- near 50 GHz. The Path-P dataset was produced using
solation is absent or weak. During the polar winter, tur- the improved initialiration inversion (31) processing 91-
bulent fluxes are small in sca-ii.e-covered regions, ex- prithni for TOVS radiances. developed by the Atmo-
cept over cracks in the ice when vertical air-ocean teni- spheric Radiation Analysis Group at the Laboratoire de
perature and moisture gradients are large. In contrast to MetCorologie Dynmique in Palaiseau. France (ChCdin
lower latitudes, 31 which low-le\ el temperature and wa- ct al. 1985). The 21 algorithin was modified to improve
ter vapor content largely go\wri the DLF. clouds play retrieval accuracy over sea ice and snow [Francis
the most importnnt role in polnr regions. Sensitivity (lY94); Scott et al. (19YY)I. Path-P products are pro-
studies by Key et al. (19971. Fiouin et al. (1988). and vided daily on a (100 km): grid and include temperature
Chiacchio (2001) show that DI,F is most sensitive to and moisture profiles, surface skin temperature, cloud
cloud traction. cloud thickness (or liquid water path). fraction and height. and a variety of other parameters.
and cloud-base height. Of thcse xiranieters. passive sat- The 31 algorithm has at its core ii comprehensive library
ellite sensors can be used to cstiriate only cloud fraction of global atmospheric profiles (.- I8(K)) that provides
and cloud-top height during pol;ir night conditions. and the tirst guess to this physical-statistical technique and
even these have much larger uiicertainties than do es- consequently is able to capture the strong surface-based
timates from other parts ol' thc globe. Algorithms to and elevated temperature inversions that are nearly ubiq-
detect clouds and to diagnose their properties often tail uitous in all seasons hut summer in the Arctic region.
over snow- and ice-covered miis because cd frequent Validation of surface and 900-hPa temperatures with
surface-based tcinpcrature inver-;ions that confound sat- radiosonde data reveals small mean errors ( 1.4 and 2.5
ellite cloud-detection algorithm.; and introduce unccr- K). Retrieved inversion strength, however, is often less
tainty into satellite-retrieved temperature profiles. Short- than radiosonde values owing to the coarse vertical rcs-
wave channels, especially ncw misors that measure ra- olution of the temperature profile. ilnd the cap may be
diances in the I .6-pm wavelen;:th region. add consid- misplaced in the vertical by il few lens of hectopascals.
erable inforniation, but historica \ isible data iire limited The cloud fraction variable (labclcd FCLD in the Path-
by the lack of contrast between clouds and snow. Efforts P dataset) is an y[pc.riiv cloud fraction A, E. which is
are underway to infer polor cloud characteristics beyond the product of the fraction A* of Ihe sky covered by
fraction and cloud-top height. but they are still exper- cloud and the cloud emissivity E. Cloud emissivity rang-
imental. es between 0 an I: therefore A, E is always less than or
Several algorithm and paramcterizations exist for es- equal to A,, which is the quantity reported by human
timating DLF from satellite-dcrib*edi ntormation. hut in- observers. This distinction is significant in polar regions
tercomparisons and validation for polar-night conditions because optically thin clouds-ven in the infrared-
have not been performed. The ahjective of this inves- are common, especially in winter. Hereinafter we ab-
tigation is to conipnrc DLF valuer. computed with eight breviate effective cloud fraction as A,, . and "cloud frac-
different methods quantitatilcly to validate result5 with tion" denotes the fraction of the sky covered by cloud
measurements from surface-bas XI instruments and hu- (A,) . See Schweiger et al. (2001) for additional infor-
man observers. to identify prob;.ble causes for errors in mation and validation results for the Path-P data.
each method, iind to niakr rcc,onrnendations ;IS to which
algorithni(s) provides the bot ehnates of DLF in the
h. Vtrlicltiiiori c1atri.vcJt.v
Arctic night.
A significant problem in studying cloud or surface
characteristics in this region is the paucity of measure-
2. Data sources and tools ments. especially in winter when DLF is the dominant
component of the surface energy budget. Observations
, (1. Sriirllitc,-(l~).iI'Pnp rocliic'i.\
from two tield experiments are used in this study. The
Several of the methods undt r investigation require tirst is the drift phase of the Coordinated Eastern Arctic
temperature protiles. humidity r rofiles, surface teinper- Experinient (CEAREX: CEAREX Drift Group 1990).
ature, cloud tinction. and/or cloud height. For this study, which was conducted in the eastern Arctic Basin from
atmospheric state inforniation is obtained froin the September of 1988 through January of I989 (Fig. I).
NASA-NOAA TOVS Path-P dataset (Francis and The experiment included two research vessels and an
308 _I 0 II I< N A I. 0 I- ,\ I' P I. I E I> M k I 1: 0 K 0 I 0 (i Y VOI I'htl 41
westward from 24 March 1992 until 2.5 April I992 (Fig.
I ). The main objectives of the experiment were to study
the cracklike openings (leads) in sea ice formed by the
ice deformation and to understand the effects of leads
on the polar ocean and atmosphere. In addition to ra-
diation measurements. we use cloud-base height retriev-
als from a lidar ceilometer to compare with Path-P-
derived values. The vertical resolution of the ceilometer
retrievals was 30 111. and the instrument could observe
cloud bases up to 8 kni. These ceilometer estimates are
not considered to be reliable for absolute validation.
however, owing to reported problems in detecting thin.
low-level, ice clouds (0.Pe rsson 1999, personal com-
munication). Thirty-second ceilometer values are av-
eraged over 24 h to be consistent with Path-P products.
c. Rudiutiw trirrtsjrr tttodrl
Sensitivity tests and calculations of surface fluxes are
performed using a forward radiative transfer model
called Streamer, which w x assenihled hy J. Key and A.
Schweigcr (Key and Schweiger 1998). Streamer is a
array of surface canips at which a variety of nieteoro- publicly available, highly flexible package that can be
logical and oceanographic measurements were made. In used to calculate shortwave and longwave radiances and
this study we use data obtained aboard the R/V fo- fluxes for a wide variety of atmospheric and surface
Inrbjiirn. which include downward infrared fluxes (be- conditions. Absorption and scattering by gases is pa-
tween 4 and SO pm) from an Eppley Laboratory. Inc., rameterized for 24 shortwave and 105 longwave bands.
pyrgeometer. which has a nominal instrument error of Built-in data include water and ice cloud optical prop-
5 W In (CEAREX Drift Group 1990). Owing to the erties. aerosol profiles. iind seven standard-atmosphere
lack of solar radiation during CEAHEX. as well as the profiles. or users ciin provide their own. Each scene can
low sun angles and large cloud fractions during the 1992 include up to IO cloud types, up to ID overlapping cloud
Beaufort Sea Ixad Experiment (LeadEx). we assume sets of up to 10 clouds each. and up to three surface
errors in radiometer measurements resulting from solar types. Also. spectral albedos for various surface con-
contamination are small. The radiometer domes required ditions are included. The number of streams used in the
cleaning hourly to remove frost and precipitation; only calculation can be varied; two are used in this study.
measurements follhwing a cleaning were used to com- Modeled fluxes for standard atmospheres were com-
pute daily-average flux vnlues, which we compare with pared with calculations by approximately 37 other mod-
daily-average satellite-derived DLFs. The differing els presented in the report of the lntercomparison of
space scales 01 Palh-P data and surface point nieasure- Radiation Codes in Clirnatc Models pro.@ (Ellingson
ments is a possihle source of error. Schweiger et al. et al. 199 I ). Streamer-computed fluxes were within 5%
(200 1 ) analyzed correlations between time-averaged (I standard deviation) ofthe mean of all models (Francis
point measurements of cloud fraction and spatially av- 1997).
eraged satellite vnlues and found that correlations werc
low for timescale4 shorter than 2 days and peak at X
3. Sensitivity of DLF to atmospheric parameters
days. Because clouds are the dominant factor in deter-
mining DLF in the Arctic winter, we wsume these cor- Sensitivity studies are performed to determine the
relations also apply to DLE They speculate that the lack likely errors in DLF resulting froin uncertainty in cloud
of strong correlation at short timescales may be caused parameters and from published uncertainties in Path-P
by the differing perspectives of satellite versus surface products. DLF is calculated using Strcamer for typical
observations (view from above or below). Another proh- winter and summer Arctic conditions and with expected
able cause is that smaller variations occur at short time- rim errors (in parentheses) for each of the following
scales, which may cause this signal to be lost in noise, state variables under clear conditions: surface skin tem- .
whereas large variations may occur at long timescales perature (23 K). temperature profile ( 2.3 K at all lev-
and so are more detectable above the noise. els), and moisture protile (230%a t all levels). In ad-
Data from LeadEx (LeadEx Group 1993) are also dition. we estimate the sensitivity of DLF to varying
used to validate DLF computed from each of the eight bulk cloud properties: fraction, thickness, base height.
algorithms. This tield program was conducted in the liquid water content (LWC). and effective droplet radius
Beaufort Sea ;it ii camp on the pack ice that drifted r, . Each calculation includes omnc amounts for a stan-
E 20
uV
dard subarctic winter protile, and the carhon dioxide
-20
concentration is fixed at 340 ppmv. Clouds in these tests
are composed of spherical water droplets with a nominal
cloud thickness of SO hPa. an LWC of 0.20 g rn ', and
;I typical r, of 8 pm (Curry and Ehert 1991). We feel
justified in considering only water clouds, because liq-
uid water was detected in over one-half of the clouds
during the Surface Heat Budget of the Arctic (SHEBA)
experiment in every month except December (lntrieri
et at. 1001 ) and because phase alone has a ncgligihle
-1-7etf cct on DLF (Francis 1999).
Result> of sensitivity tests for temperatures and water
vapor are shown in Fig. 3 and are summarized in Table
1
I. These results iirc consistent with those of' Key et at.
4 ( 1997) and show that errors in DLF arising Croni doc-
c, -10 ........................................... umented uncertainties in satellite-derived temperature
3 and moisture protile\ will be well within the 10 W ni
I
9 -20 threshold that has been suggested by the World Climate
Rcsearcli Progratiime (WCRP) ;is thc target accuracy
- m L d for surface Hux estimates (Raschke et at. 1990).W c also
-6 -4 -2 0 2 4 6 use Streamer to test the sensitivity of DLF to uncer-
tainties in TOVS-retrieved surface-based inversions. We
Pmrtwbotbn to Sfc. r.mp.mtur. (K)
calculate DLF for ii typical winter surfacc-hased inver-
I sion (13 K difference between the surface and top of
the inversion at 900 hPa) and for a temperature profile
with the inversion smeared out and ;I positive lapsc rate
throughout. A typical SO-hPa-thick water cloud is placed
with its top at 900 hPa (top of the inversion). The dif-
ference in DLF hctwc.cn these two model runs is less
than 3 W 111 (inversion run is smaller). Because this
scenario is likely a worst case. we conclude that any
errors in TOVS-retrieved invewion strength, height. or
even existence would not result in the niagnitude of DLF
deticiencies we ohserve in many of the algorithms.
Figure 3 shows the computed sensitivity of DLF to
cloud fraction and geometric thickness. A 30% error in
low-cloud fraction would result in DLF errors in excess
of the WCRP threshold. but DLF is less sensitive to
uncertainties in high-cloud fractions. DLF is highly scn-
sitive to cloud thickness-more so for low clouds: an
error of IO W rn : could arise from iissuming a cloud
is only 30 hPa thicker than its actual value. This is an
important point in our later discussion of the assumption
in some ;tlpithnis that clouds are a constant SO hPa
thick.
Figure 1 shows the sensitivity of' DLF to cloud LWC
for varying droplet effectivc radii and cloud-base
heights, with and without a surface-hased teniperature
0.00 0.05 0.10 0.15 0.20
0.0 0.2 0.4 0.6 0.8 1.0 L e[9 rn+l
Cloud &action
c!
6
0.00 0.05 0.10 0.15
L* Ism7
0 20 40 0 80 100
80
Cloud thickness [mbl 2.-860 mb- - -- - -- - -
e- --a
FIG. 3. Senhitivity 01 ULF to perturbation\ in (a) clwd frwtiun
and (hl cloud thickneir. with cloud-hose heights at XIWI and 400 hPa
in typical Apr ci)nditii)m in the central Arctic. Cloud.\ are coinpored
of hphcrical ~atedrr oplet?.w ith o nonliniil cloud rhickiwb of 50 hPa.
an LN'C' 01 0.20 g rn '. and a typicill equivalent radius r, 01 X pm.
inversion. We use temperature and water vapor profiles
typical for April. and LWC is varied from 0.0 to 0.2 g
m '. In each of these experiments. it is apparent that
the DLF is extremely sensitive to the LWC in thin. high - - - - . - - . . . -E .- - -
clouds that contain less than 0.02 g m ' of water and 0
in low clouds with less than 0.05 g m For thicker
I.
clouds. DLF ih no longer sensitive to LWC: that is, the
cloud is optically thick in the infrared. Cloud droplet
cffcctive radius in this size range, however. has a neg-
ligible effect on DLE This result is consistent with re-
sults by Francis (1999) that show little sensitivity of
infrared cloud radiative forcing by water clouds (rv =
10 pni) versus ice clouds (r, = 50 pm). Figure 4b shows below the cap of a surface-based temperature inversion.
that DLF is more sensitive to the LWC of low clouds The results of these sensitivity tests for bulk cloud pa-
than of high clouds and that. for optically thick clouds, rameters are also generally consistent with those of Key
an error of IO W m-: would rcsult from an error in et al. (1997). although we test some different variables
cloud-hasc height of approximately I SO hPa. Figure 4c and, in some cases, over ii more widely varying range
illuhtrates thc effect of a cloud base lying above and of values. We are interested only in the sensitivity of
DLF to individual variiihles, givc n thai part 01' our goal GEWEX SRH and CERES projects ohtained from re-
is to identify which satellite-relrieved quantities lack analysis datasets (either the European Centre for Me-
sufficient accuracy for DLF ciilcu alions. For an analysis dium-Range Weather Forecasts or NASA's Data Assim-
of the overall uncertainty in DIJ see Key et ill. (1997). ilation Oftice). In this sttidy. we instead use the Path-P
From the results of these te\ts, ue conclude that DLF products. hecause they iire helicvcd to be more accurate
is mosl sensitive to errors in cli~uJ fraction and to LWC in Arctic conditions (Francis 1994). The primary as-
in thin clouds and that IILI-' is rc.l;itiwly insenhitive to suniptions of the Gupta ( 1989) technique are that clouds
droplet size. Known uncertai tit ic s in satel I itc-retrieved exist in ;I single. SO-hPa-thick layer nnd they are opaque.
temperature and water vapor profiles result in IILF cr- The method parameteri7es DLF ;is
treosrtss ,w siethei nC h1i0a cWch tiio1 (22. 0F0o1r ) .l urthcr details on rcncitivity DLF = DLFL,,I( - A ) + DLF<,,,,A,, (1)
where DLF,,, is the downwitrd longwave flux for clear
sky, DLFL,!, is the downward Hux for cloudy sky. and
4. Methodology
A, is the cloud fraction. for which we use .4cLf rom Path-
We evaluate the ability of sc'vetiil methods to estimate P. This equation is then simplified a<
DLF by comparing daily-mean-t alculated viilues from
+
a (100 km)' grid hox to surl';icc,-tiieasured DLF from D1.F = C, (-,A,.,. (2)
two tield experiments in the Arctic. Some of the algo- where C, = DLF,,, and C': = (DLF,,,, - DLFL,,)t.h at
rithms. such as those of Gupt;~( 1 989) ;ind Frouin et al. is. the therrnnl emission from the cloud base. The C,is
(1988). are being used globally. and we compare their parameterized in terms of the effective emitting tem-
performance with Arctic-specif : algorithms. such as perature T, of the atmosphere (estimated from the sui--
that of Francis ( 1997). We iilso evalunte thrce simple lace and lower tropospheric temperatures) and the sur-
empirical parameterimticins tlevc~lopctll or Arctic con- frtce-to-7OO-hPa water vapor hurdeii W, (mm):
ditions: Marshunova ( 1966).% ill naii ( 1972). and May-
kut and Church ( 1973). Thcse par;uneteri/;itions are c, = .ji w,) 7;.. (3)
among the longwave flux method5 cxaniined hy Key et
where .r i\ empirically dctermined to he 3.7. Further.
al. (IY96). yet we do not coiisitler the Schmctz et al. f( W,) is exprcssed ;IS
( 1986) method. bccausc it WIS developed for daylight
conditions. In this section we deszribe each mcthod and
the required input data. A suiiirn;iry ol' the assumptions
where V = In(U',) and the As are regression coefticients.
and required informetion for cat:h mcthod i> given in
Equation (4) is then fit to fluxes and meteorological
Table 2.
profiles from tive sites in the United States to obtain
the regression cod'licients A,, = 1.70 X 10 '. A =
(I. C'Lcpt~r I IYXO) 2.093 X 10 '.A, = -3.748 X IO '. and A, = 1.184
x 10 &'.
This algorithm was used to ;c iieriite a global 8-yr
The p:iramctcrization of c', contains the terms TI,
SRB dataset (Gupta ct al. IWVI and also is included
(clnud-hase tc.mprr;iture) and W, (w;iter vapor below the
among other algorithms in hotti the Clouds and the
cloud). To determine TLl3t,h e cloud-hasc hcight must hc
Earth's Radiant Energy Systern (CERES, and the
calculated from the sutcllite-retrieved cloud-top height.
WCRP-Global Energy and CVa er Cycle Experiment
assuming ;I cloud thickness of SO hPa and ii positive
(GEWEX) SRB projects. This riethod requires inputs
atmospheric lapse rate. Radiosonde d;it;i and a forward
for water vapor and temperature Imtiles. cloud fraction,
radiative transler model are used to determinc the re-
and cloud-top pressure. which (iupta ( 1989) obtained lalionship between C,a nd TLb:
from NOAA operationid TOV S retrievals. and the
c, = (B,, + R,W< 4- B:W' - 8,W,')' (5) creogmiopnu t(eF t,h,.e Fp ,,,r. obanabdi lFiii,ef so r( Chi,g,,h,,. ) mfoidrd elaec, ha ncdl oluodw )t:y p4e)
using the following equations:
Tests by Gupta et al. (1991) using products from the
Intrrnational Satellite Cloud Climatology Project C,, = F,,ll. (7)
(ISCCP) dataset show that (5)w orks well except when C, IFh,/( I - F,,)I, and (8) c
P, - P,.h5 200 hPa (P,i s the surface pressure, and Pch
is the pressure of the cloud base), which is a significant C,, F, /( I - F,, - F,,,): (9)
problem in the Arctic where low. thin clouds predom-
-5) calculate clear-sky probabilities (e.g., 1 - C,, for high
inate.
clouds); 6) calculate fractional values for combinations
of conditions (clear. high alone, high over middle, high
h. Froirin rf c~l.I IYXXI over low, high over middle over low. etc.); 7) calculate
fluxes fur each case (DLF,) with appropriate cloud-buse
This algorithm comprises two techniques (hereinafter
height (SO-hPa thickness assumption) and A,, using
FI and F2) for determining DLF during nighttime. The
Streamer; and 8) multiply fluxes for each case by their
FI method requires temperature profiles, water vapor
corresponding fractional values C,:
protiles. and cloud properties (cloud-top height and
cloud fraction), which we obtain from the Path-P dataset DLF*,,,,=, DLF,C, + DLF,C, -I- . . . + DLF,C,. (10)
(Frouin et at. (198X) used operational NOAA TOVS
retrievals]. In this algorithm. clouds are assumed to exist where DLF,,,,, is the new Hux valuc from the cloud-
in one opaque layer that is SO hPa thick. The temperature overlap method.
and water vapor protiles (from Path-P) are input to
Streamer io compute the clear-sky DLE To estimate the
c. FrctrrcYs (1997)
cloudy-sky flux, we use the Path-P cloud effective fruc-
tion and cloud-top height and assign a cloud-base height The only assumptions in this method are that clouds
that is SO hPa lower than thc cloud-top height. Using exist in one layer and that cloud fraction is always 100%.
the Path-P temperature protile. the cloud-base height is with all the variability in A,., occurring in \he emissivity.
matched with the level of the corresponding temperature Clouds may be optically thin and may have varying
level to obtain the cloud-base temperature. This infor- geometric thicknesses. This technique ingests Path-P at-
mation is input to Streamer to calculate the cloudy-sky mospheric temperature and moisture protiles, effective
DI,E The cleiir-sky and cloudy-hky fluxes are combined cloud fraction. cloud-top height. and surface tempera-
according to ( I ). ture. Differences between brightness temperatures (TB)
tn F2. DLF is pararneterixd as a function afthe clear- in several pairs of HlRS channels are used to estimate
sky flux DLF,,,: cloud type (positive or negative internal lapse rate).
DLF = DLF<,,+ <.A,, (6) phase, thickness, and LWC of Arctic clouds. Cloud
phase is inferred by sohtracting TBs in two pairs of
which is colculated iis for F1. Again, we use A,,ci n placc channels: HlRS 10 (8.3 pm) and HIRS 8 ( I I. I pm).
of cloud fraction. The coefficient c depends on latitude. and HlRS 18 (4.0 pm) and HlRS 15, (3.7 pm). For
season, an3 cloud typc, which is determined by Frouin exaniple, in the first pair. the absorption coefficients k;,,,
et al. ( 1988) by siinulating DLF in varying cloud con- for water and ice are different. At 8.3 ,urn. k,,, is similar
Jitions using the Stephens ( 1978) model. A value at' 66 for wafer and ice, hut at I I. I pm the difterence in k,,,,
W rn is selected for this study. which corresponds IO is large. thereby differentiating ice and water clouds.
subarctic winter conditions and liquid water clouds. Cloud thickness is estimated using TB differences in
-
Polar clouds rarely exist as a single layer (e.g.. two pairs of channels: HlRS 6 (13.7 pm) HlRS 15
Schweiger and Key 1992): thus. we investigate the ef- (3.46 pm). and HlRS 14 (4.52 Gm) - HIRS 7 (13.7
fects of multilayeriiig by applying il cloud-overlap tech- pm). The first pair is for mid- and high clouds (top
nique to the FI method using TOVS Path-P data as height >750 hPa), and the second pair is used for low
input. This random-overlap technique is adopted from clouds. Because the weighting function peak of HIRS
Tian and Curry ( 1989) and is heing ksted at the NASA h is at a lower altitude than that of HIRS 15. its TB is
Langley Research Ccnter for the WCRP-GEWEX SRB warmer in B cloud-free sky. When a thin cloud is present.
program. the difference in TB decreases. To determine the cloud
The overlap method comprises thc following steps: thickness from the differences in TB, the base fraction,
1) obtain cloud-top height and A,,, for each satellite re- a value between 0 and I, is determined by setting end-
trieval in ;I 2.5" x 2.5" region; 2) categorize the cloud point thresholds and interpolating linearly between them
type for each retrieval hased on the ISCCP cloud-height by matching calculated DLFs to ohserved quantities.
definitions (high: top pressurc below 440 hPa, middle: The liquid or ice water content (IWC) is estiniaced
between 440 and 680 hPa. and low: greater than 680 using empirical relationships between mean cloud tem-
hPa): 3) dcterniinc the fraction of each cloud type in a perature and LWC or IWC for water or ice clouds. All
this informiition is input to Streinier to compute DLF tion 3 and listed in Table 2. Fluxes calculated from daily-
See Francis ( 1997) for further di.tails of this ;iIgorithin average input data are compared to daily-average mea-
The following three algorithras are simple. enipiri- sured fluxes from the CEAREX and LeadEx held pro-
cally derived parameteri;lation\. For this study the P;rth- grams. Scatterplots that illustrate direct comparisons of
.
P effective cloud fraction is useG in pl:ice ofc.loud frac- the computed and measured fluxes are shown in Fig. 5.
tion in the relationships. iind ;I summary ot the comparison statistics is presented
in Table 3.
The rms differences and correlation coefticients are
d. Murshitnocci ( 1966) remarkably similar for all eight methods; hence. they
This mcthod is an empirical y derived parameteri- are listed in decreasing accuracy according to bias. All
zation to estimate DLF h a d on surface temperature. the methods exhihit negative bias, that is. calculated
near-surface vapor pressure. and cloud fraction. A sini- fluxes are too small. although it is negligihle for the
ple cloud factor is defined th;it i icludes the cloud frac- Francis ( 1997) algorithm. which is one ofthe four spe-
tion and a coefficient: cifically designed for polar conditions. The cloud-over-
lap niethtd applied to FI clearly improves the results:
DLF = Dl,F,,,(I t xA, ). (11) the bias is reduced froin -34 (16%) to - I1 (5%) W
where m :. Results from the other three Arctic-specific mcth-
ods IMarshunova (1966): iMaykut and Church (1972):
DLF,,, = trT30.67 i 0.OSe"'). (12) Zillman ( I972)I. although only simple parameterim-
.r is a coefticient derived usin: time-varying surface tions. are disappointing. These same three paranieteri-
temperatures T,, and P is nc;ir-! urface vapor pressure. ziitions were evaluated by Key et al. ( 1996) using wl-
In this study .r = 0.26 afrer an ;iniJysis by Jacohs ( 1978). idation data from two land stations: Resolute, Northwest
Effective cloud fractions and sk 11 temperatures are ob- Territories, Caniida. and Barrow. Alaska. When coni-
tained from Path-P data. and (' i i calculated I'rom Path- pard with Key et al. (1996). the Maykut and Church
P moisture profiles. (1973) algorithm exhibited ii bias one-half as large iis
in our study. probably because it was developed with
data fi-om Barrow. Alaska. The Zillrnan ( 1972) parain-
e. Zillrriiitr ( 19721 etcridon. developed with data from the Southern
This parameterization is a function of both the cloud Ocean was. not surprisingly. the least accurate of the
amount and the near-surface air temperature. The Path- three in both evnluotions. Our results yielded ;I larger
P surface skin temperatures are used in this \tudy. be- negative hias for the Marshunova (1966) method. again
cause our analysis of CEAREX measurements reveals probably because the parameterization was developed
that the surface skin temperalure rarely differ< from thc using diitii from Arctic coastal stations such as Resolute
2-in air temperature hy morc ttiiii 2 K except in pro- and Barrow rather. than observations from within the
longed clear winter conditioris. Arctic Ocean.
+
DLF - DLF,,, [trTf0.96(1 - 9.2 X 10 '*T;)A1,, ( 13)
h. Sources of' error
where DLFL,,= rrTi (9.2 X IO "Tt). This rclationship
was derived hy Zillman ( 1973) fiom nieasurciiients over 1 ) AT\IIOSP)tEKI(' PROFII.ES
Antarctic sea ice obtained from Pease ( I975 ).
The reported bias in Path-P temperature profiles is
approximately I K (Schweiger el al. 2001 1. which mans-
,f: Mtrxkur titid Clrurch (I97.J) lates to an error in DLF of about 3 W in in summer
and 1.5 W m in winter. The bias in water vapor pro-
The relationship was developt:d with year-round sur-
lilcs, as compared with r;idiosonde data from the SHE-
face temperature and cloud fra-tion data collected in
BA tield program. is about 10%. which would produce
Barrow. Alask;i. over a 5-yr period, during which the
an error in DLF of approximatcly 3 W in :. Wc con-
surface temperature ranged froni 144 to 277 K. The DLF
sequently conclude that errors in satellite-retrieved tein-
is paruneteri;led as
perature and water vapor profiles do riot contribute sig-
DLF = DLFJ I -t 0.22~275). (14) nificitntly to the apparent biases in computed DLF.
where DLF,,, = 0.7855crT:.
2) Cl.OU0 PKACfION
5. Results and discussion Because all of these algorithms rcquire infomiation
about cloud fractii)n and because DLF in polur regions
u. Perfiit-niut~c.co~f DLF d,yoririltt1.s is sensitive to cloud traction. this variable may xcotint
Downwiird longwave fluxes ;it the surfact: ;ire coni- for much of the error in computed DLE As already
puted using each of the cight mithods descrihed in sec- mentioned. the Path-P product used in the analy\es is
314 J 0 1' I< N A 1 . 0 F A PPI. I ED M ET EO K 0 1. 0 Ci Y
Gupta l19891 Frouin et at. r19881 #l
Frouin et at. [1988] #1 w/OL Frouin et 01. [ 19881 #2
x
1
200 200
3
0 rn
, 0
-50 150 0
E 0 0
' V
100 100
100 150 200 250 300 100 150 200 250 300
Ueosured nux [wmJ] Msmursd Flux [Wm-)
r
Francis 19971 Marshunova 19661
,0
100 150 200 250 300 100 150 200 250 300
Measured Flux [Wm-'1 Ueosured Flux [Wm-q
Maykut and Church [1973]
x
G 1 M
J
loot4 .
loo 150 200 250 300 100 150 200 250 300
uworurod nux [Wm4J uoosured nux [Wmq