Indian Journal of Radio & Space PhysicsVol. 17, December 1988, pp. 271-282

Atmospheric Profiles Retrieval from NOAA Satellites Data

RAJENDRA KUMAR GUPTA

National Remote Sensing Agency, Balanagar, Hyderabad 500037

With a conceptual introduction to temperature and moisture profile estimation through remote measurementsfrom satellite-based sensors, the radiative transfer equaJion of the measurement process together with the inherentproblem in its analytical solution is discussed. With the description of current sensipg systems on-board NOAA sa­tellites, the reasons for the synergistic use of 4.3, 15 micron and 5 mm absorption bands for tel;I1perature profile es­timation are discussed. With the discussions on currently practised statistical, physical, simultaneous and 31 retrie­val methods, and on the critical aspects of cloud effects, an account of intercomparisons for statistical, physical(iterative) and simultaneous retrieval methods, is presented. Current trends of using pattern recognition techniquesfor selecting initial guess profile, 1.1 Ian spatial resolution data in identifying partly cloudy measurement of 17.4km resolution sounder observation, and forthcoming totally microwave sensors have been discussed.

where

K( v,x) = {dB[ v, 1{x)]I d 1}{dI(v,x,O)/dx}, the kernelfunction1\ v,O) = l( v,O) - B[( v, 1\xo)]T{ v,xo,O)

- JX"B[ v, 1{x)]{d T! dx}dx°

l( v, 0) = B[ v, 1\xo)] T{ v, .to, 0)

Jo+ xlIB[v,T(x)][dT{v,x,O)/dx]dx ... (1)

where XV and x refer to transformed single-valuedfunction of surface pressure and pressure at a giv­en altitude, respectively; and T, T and B[ v, T(x)]

refer to temperature, atmospheric transmittanceand Planck's black body function, respectively.Use of Planck function to describe emission char­

acteristics is feasible only under local thermody­namic equilibrium condition and this, limits profilemeasurements to 45-50 km altitude.

First term on the right-hand side of Eq. (1) ref­ers to surface contribution while second termwithin integral refers to weighted contributionfrom the intervening atmospheric layers. Using de­viation (h) of atmospheric temperature (T) fromclimatic or forecast profile temperature (T) i.e.h(x) = T(x) - T(x), to make Planck function forh(x) moderately non-linear and applying Taylor'sapproximation, Eq. (1) becomes

,.i I Introduction

Satellite-based atmospheric profiles estimationinvolves measurements in absorption/ emissionbands wherein received-in signal is a weightedsynthesis of information from different atmospher­ic layers (Fig. 1) with predominant contributionfrom the layer bounded by the 70.7% fall-in-valuelimits with respect to peak level of contribution/weighting function. By judicious selection of meanwavelength and bandwidth of spectral bands, onecould obtain weighting functions peaking at differ­ent heights. Besides various spectroscopic consid­erations involved in radiative transfer equationformulation 1.2, the overlapping of weighting func­tions makes inversion of these measurements (forobtaining atmospheric profile) an ill-posed prob­lem in analytical terms.

For temperature profile measurements, absorp­tion bands of gases having constant number dens­ity up to mesopause (C02, N20) are used. Mea­surements undertaken in water vapour absorptionband would be related to water vapour concentra­tion as well as temperature of the layer; thus watervapour profile estimation would need temperatureprofile as 'a priori' input. The process of inversionis primarily similar for temperature as well as wa­ter vapour profile estimation and for this reasondiscussion is primarily restricted to temperatureprofile retrieval.

2 Radiative Transfer EquationThe intensity of radiation reaching satellite sen­

sor at frequency v at scan angle 0 denoted byl( v, 0) is given by

JXlIK( v,x)h(x)dx = r( v, 0)o

... (2)

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INDIAN J RADIO & SPACE PHYS, VOL. 17, DECEMBER 1988

Fig. I-Conceptual basis of satellite-based atmospheric pro­files: for a wavelength at which the atmosphere absorbsstrongly, radiation reaching space is maximum from some in­termediate height (b). The curves bounding the shaded areasindicate the amount of radiation emitted upwards from lower(a), intermediate (b) and high (c) base levels which reaches anygiven higher level. The curve at (d) represents the total con­tribution function for this wavelength. The dashed curves at(e) represent a family of total contribution functions corre-

sponding to different wavelengths.

+

parameters are continuous functions in horizontaldirection and retrieval accuracies could be im­

proved through applications of spatial linear filterSand Kalman filtero.

3 Current Sensing SystemTiros Operational Vertical Sounder (TOVS) is

the temperature and humidity profile sensing sys­tem on-board the current NOAA polar orbitingmeteorological satellites. TOVS consists of 20channels High Resolution Infrared Sounder-Ver­sion 2 (HIRS-2), 3 channels Stratospheric Sound­ing Unit (SSU) and 4 channels Microwave Sound­ing Unit (MSU) having 17.4, 147.3 and 109.3 kmSub Satellite Point (SSP) resolutions, respectively.The spectral and primary utility characteristics aredescribed in Table 1 while Fig. 2 (Ref. 7) depictsthe TOVS weighting functions. The instrumentcharacteristics have been presented in Table 2(Ref. 8). A look at Table 3 shows that 5 mm mic­rowave oxygen band is best for cloudy conditionsbut has better sensitivity for lower temperature,and very less energy compared to IR region whichresults in poor spatial resolution. The 300 K to200 Kenergy ratios for 4.3 and 15 micron are240 and 3, respectively, making 4.3 micron moresensitive for surface and lower troposphere tem­perature raRge; temperature sensitivity with re­spect to detector noise also supports 4.3 micronfor lower tropospheric region measurements. Veryhigh energy magnitude and high signal-to-noise ra­tio for 15 micron band as compared to that for4.3 micron band for the whole range of atmos­pheric temperatures makes it the base measure­ment band which is synergistically used with 4.3micron and 5 mm bands measurements. Similarlythe 3.7 micron window has higher sensitivity ascompared to 10.5-12.5 micron window especiallyunder broken clouds atmospheric situation. Emis­sivity variations for earth surface are in 0.95 to0.98 range for < 5 micron while for > 10 micronthese are in 0.85 to 0.95 range. For these reasonsthe mid-IR based surface temperature measure­ments play an important role in providing preciselower boundary value for temperature profily re­trieval.

Though having 1.1km SSP resolution, 5 chan­nels (0.55-0.68, 0.725-1.1, 3.55-3.93, 10.3-11.3and 11.5-12.5 micron) Adva~ced Very High Reso­lution Radiometer (AVHRR), an imaging sensoron the same satellite, is not of direct relevance forprofiles estimation; its higher spatial resolution ca­pabilities are being used in estimating lower boun­dary (surface) temperature and in identifying part­ly cloudy field-of-views'i-I'.

... (3)

HEIGIIT.HEIGtn'.

\\ \, '\ '\ \\ "\ ....

\ \'\ ,\ \ "\ )1/\ , ,}' ....

1\ ,I, \ I" .... /, /~,\" \" }I, ,

~ )"I ....\" ,,( '.I' ,

I ••••-',..<.I ", II I, ,,"

(dl TOTAL COIlTR'BUTIOII (o)TOTAL ~TIOIIFUR CT IOR FUIOC'TIOR_ DWFlR£RT

_loW: LEIKTIIS.

(e IUPl'ER MEIIIMTLEVEL

CONTRIBUTION

(blIIlTERIlEDIATE MEIIINTLEVEL COIlTRIBUTIOtl

(.) LOWER MEIGMTLEVEL CORTRIBUTION

[r]=[A][h]

where [r] is a measurement vector of order nand

[h] is a vector of order m (number of expected le­vels in the profile) while [A] is a transformationmatrix of order n Xm. The least square solution]would have [A7At I factor in the solution. Over­lapping nature of weighting functions results in lin­ear dependence between adjacent row and co­lumns of transformation matrix [A] making[A7A]- I ill-conditioned for inversion.

In atmospheric retneval case, constraint of n < mcould be overcome using 'a priori' information un­der optimal estimation method4• The atmospheric

By knowing 1(xo) from window channel, 1\ v,e) be­comes known and using 'a priori' climatic/ fore­cast profile and known transmittances the kernelfunction can be computed. Barring a few kernelfunctions like delta function, Eq. (2) would theor­etically have infinite solutions. The smoothness ofkernal function K( v,x) reduces the sensitivity ofequation for high frequency variations in h(x)

which results in a fundamental inherent instabilityand ambiguity in the inference of h(x).

In numerical solution mode, Eq. (2) could bewritten as

272

I II "tl'l" 'I' 'I '

GUPTA: ATMOSPHERIC PROFILES RETRIEVAL

Table I-Characteristics of TIROS operational vertical sounder (TOVS) channels

(a) High Resolution Infrared Sounder-Version 2 (HIRS-2)

HIRS Channelchannel centralnumber wavenumber

(cm-I)

Central

wavelength(,urn)

Principalabsorbing

constituents

Level of Purpose of the radiance observationpeak energycontribution

mbar

I 66815.00CO230Temperature Sounding. The 15 ,urn band channels provide better2

67914.70CO260sensitivity to the temperature of relatively cold regions of the at-

3

69114.50CO2100mosphere than can be achieved with the 4.3 ,urn band channels.

Radiance in channels 5, 6, and 7 are also used to calculate the4

70414.20CO2400heights and amounts of cloud within the HIRS fields-of-view.5

71614.00CO26006

73213.70CO/H2O8007

74813.40CO/HzO900

8

898II.lOWindowSurfaceSurface temperature and cloud detection9

10289.700.,25Total ozone concentration

10

12178.30HzO900Water vapour soundi'lg. Provides water vapour corrections forII

1364 7.30HzO700CO2 and window channels. The 6.7 ,urn channel is also used to

12

14846.70HzO500detect thin cirrus cloud.

. 132190 4.57NzO1000Temperature sounding. The 4.3 J.tm band channels provide better

142213 4.52N20950sensitivity to the temperature of relatively warm regions of the at-

15

2240 4.46CO/N2O700mosphere than can be achieved with the 15,um band channels.

16

2276 4.40CO/NzO400Also, the short-wavelength radiances are less sensitive to clouds

than those for the 15 ,urn region.17

2361 4.24CO25

18

2512 4.00WindowSurfaceSuiface temperature: Much less sensitive to clouds and H20 than19

2671 3.70WindowSurfacethe II ,urn window. Used with II ,urn channel to detect cloud

contamination and derive surface temperature undel partly cloudysky conditions. Simultaneous 3.7 and 4.0 J.tmdata enable reflect-ed solar contribution to be eliminated from observations.2014367 0.70WindowCloudCloud detection: Used during the day with 4.0 and II ,urn window

channels to define clear fields-of-view.

(b) Microwave Sounding Unit (MSU)

MSUchannel no.

FrequencyGHz

Principalabsorbing

constituents

Level of Purpose of the radiance observationpeak energycontribution

mbar

2

34

50.31

53.73

54.96

57.95

Window Surface

700

300

90

Surface emissivity and cloud attenuation determination

Temperature sounding. The mi~rl?wl}ve channels probe throughclouds and can be used to alleviate the influence of clouds on the 4.3and 15.um sounding channels.

(c) Stratospheric Sounding Unit (SSU)

SSUchannel no.

I2

3

Wavelength,urn

15.0

15.0

15.0

Principalabsorbing

constituents

Level of

peak energycontribution

mbar

15.0

4.0

1.5

Purpose of the radiance observation

Temperature sounding. Using CO2 gas cells and pressure modula­tion, the SSU observes thernal emissions from the stratosphere.

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INDIAN J RADIO & SPACE PHYS, VOL. 17, DECEMBER 1988

200

HIRS SHORTWAVE

nil \\-\. I

2nICOt' N20 CHANNELS~ft"' ...•..... - ....

3

3

4

4

!l

!l

66

88

10

10

20

20

30

30

40

40!lO

!lO60

60

80 100

200t 'AX~~,.\I

2'

300 400!lOO600

eO°tl

~ ~I 800'1000

.. lood00.20.4().60·.

100 •. .HIRS WATER VAPOUR AND

LONGWAVE WINDOW CHANNELS

I"'t~

Fig. 2-Normalized weighting function of HIRS, SSU and MSU channels of Tiros operational ver­tical sounder (Reproduced from Ref. 7 with the kind permission of US National Oceanic and At­mospheric Administration, National Environmental Satellite, Data, and Information Service, Wash-

ington, D.C. 20233, USA)

274

III 1 !jllil 'I' 'I I "II I:;

I T Ii·, I

-,~~'"-"-----------

GUPTA: ATMOSPHERIC PROFILES RETRIEVAL

Table 2-Instrument parameters for HIRS, SSU and MSU8

Instrument parameter HIRS-2SSU

CTOss-track scan angIe(deg from nadir)

±49.5:t40

Scan time (s)

6.432.0

Number of steps

548

Step angle (deg)

1.810

Step time (s)

0.14.0

Angular FOV (deg)

1.2510.0

Ground IFOV at nadir (km)

17.4147.3 MSU

±47.35

25.6

11

9.47

1.84

7.5

109.3

Ground IFOV at end of scan (km) 58.5km cross-track x 29.9kmalong-track

244km cross-track x I86.1km 323.1km cross-track x 178.8km

along-track along-track

Distance between IFOV centres(km along-track)

Swath width (km)

Calibration

42.0

± 1120

Stable 2 black bodies and

space background

62.3

±737

Stable black body and spacebackground

168.1

± 1174

Hot reference body and spacebackground each scan cycle

NE~N

(mW/m2.Sr.cm -I)3.00 for channel 1; 0.10 to 0.67for channels 2 to 12; .001 to 0.35,0.70 and 1.75 for NE~T of O.3K for channel0.006 for channels 13 to 19; channels 1 to 3, respectively band width of 200 MHz0.10% albedo for channel 20

Data precision (bits) 13 12 12

Table 3 -Comparisol' of characteristics of 4.3 ,urn, 15 ,urn and "5mm spectral regions used for the measurement of temperature pro­files

Spectral Energy (Relative Planck Temperature sensitivity Approxmate Cloud transmission forregion

radiance) for(relative to detectoroverall

temperaturenoise) at temperaturetemperatureWaterIce clouds

K

Ksensitivityclouds

200

300200300

4.3,urn

1.25300120-T126%1%

15,um

500015000106-1"1%1%

5mm

1141-T96%99.98%

4 Current Retrieval TechniquesThe U.S. National Oceanic and Atmospheric

Administration (NOAA)! National EnvironmentalSatellite Data and Information Services (NESDIS)uses statistical technique for operational retrievalof humidity and temperature profiles over theglobe. Its Developmental Laboratory at" Madison,Wisconsin, also supports profiles estimationthrough physical as well as single step simultane­ous retrieval techniques.

4.1 Statistical Technique

Here the solving of Eq. (3) is based on the as­sumption that the temperature at a particular at­mospheric level is statistically related to radiancemeasurements in all channels because the shape ofweighting functions is such that it contains varyingmagnitude contributions" from the whole of atmos­phere. This assumption further gets supported bythe fact that a particular level in the atmosphere isphysically as well as radiatively linked with other

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INDIAN J RADIO & SPACE PHYS, VOL. 17, DECEMBER 1988

distant levels in the atmosphere. The sought func­tion vector matrix [h], temperature profile of di­mension m, in Eq. (3) are related to measurementvector matrix [r] of dimension n through a trans­formation matrix [A] of order n x m. The elementsof matrix [A] could be obtained through regressilllnmethods by having sets of radiosonde profiles andcorresponding satellite measurements; and thesecoefficients need to be updated periodically. As si­milar sensors placed on different satellites do haveminor design variations, it is necessary to have co­efficients to cater to these variations. The statisti­cal method needs 'a priori' temperature and watervapour profiles. The atmospheric transmittanceswould vary with variations in the characteristics ofatmosphere. Thus the coefficients data base needto be generated for different atmospheric profilesto broadly cover five latitude zones: 90-600N, 60­300N, 300N- 300S, 30-600S and 60-900S.

US NOANNESDIS retrieval software confi­

gured around IBM 360/195, consists of four pri­mary modules namely, preprocessing, atmosphericradiance, stratospheric mapper and retrieval mo­dules. The preprocessing module and some of thecomponents of atmospheric radiance modulewould be of use for other techniques as well.

In the preprocessing module, the aim is to gen­erate sets of time-coincident HIRS, SSU and MSUdata after taking care of their varying instrumentcharacteristics (Table 2). Here, HIRS data are cor­rected for slant path effects, fluorescence effect in2360 cm - 1 channel, reflected sunlight effect (dur­ing daytime measurements) in 2500 cm-I channeland water vapour attenuation effect in IR windowchannels. The MSU data are preprocessed to cor­rect for side lobes, slant path, surface reflectivity(normalization to unit surface emissivity, surfaceemissivity is estimated using MSU channel 1 data)effects and liquid water attenuation. The ancillarydata such as solar zenith angle, terrain elevation,surface albedo (for daytime measurements) and in­itial guess values for surface skin temperature arealso needed. Calibration to convert digital countsinto radiances is also undertaken in this module.

One-third of software volume contains database for various coefficients. The band correctioncoefficients are used to compute Planck radiancesfor the first 19 HIRS channels with due consider­ations for instrument optical system transmittanceand channel dependent filter transmittance. At­mospheric transmittances coefficients generationuses information on mixing ratios for CO2, CO,02' N20 and CH4; continuum coefficients for for­eign and self-broadening in water vapour absorp-

276

tion bands, pressure induced nitrogen continuumeffects, vertical transmittances of atmosphere fortotal ozone amount in 257-480 Dobson unit range(total ozone in a vertical atmospheric column ismeasurable through HIRS channel 9); and regres­sion coefficients for slant view correction. Raw

spectral data and filter response are convoluted togenerate coefficients base for uniformly mixedgases (C02, CO, N20 and CH4) under 19 differentdry temperature profile conditions. Similarly, coef­ficients computation is done for water vapour us­ing 6 sets of water vapour concentration, tempera­ture and pressure profiles. Slant path correctioncoefficients are generated for 100 sets of 'a priori'global representative profiles of temperature andwater vapour for clear as well as varying cloudyconditions.

Calibrated, collocated and corrected SSU mea­surements together with preprocessing moduleoutputs and coefficients files (coefficients files arealso used by preprocessing module) are input toAtmospheric Radiance Module (ARM) which con­sumes the maximum computation time. In ARM,the aim is to arrive at true thermal emission for

the ensemblage of 9 scan spots and 7 scan lines(of HIRS) using all the 27 spectral channels dataof HIRS, SSU and MSU. Here, the data sets arevalidated with respect to presence of douds usingcriteria based on the comparisons of measured ra­diance and surface albedo, long w~e and short­wave IR window channel radiances, 11.1 micronchannel temperature and 50.31 GHz channel tem­perature, IR window measured surface tempera­ture and available in situ measured temperature,observed MSU channels radiance and the radi­ances computed for MSU channels from the set ofselected HIRS channels, etc.

The retrieval module uses eigenvector regres­sion technique for below 100 mbar region andmultiple linear regression technique for the regionabove 100 mbar for temperature and total ozonemeasurements. It uses the regression coefficientsderived from coincident radiosonde measurementsand satellite radiances. Here, the outputs are tem­perature profiles, water vapour mixing ratios, rela­tive geopotential thicknesses and total ozone con­centration in vertical atmospheric column. Underman-machine interactive version 12editing is carri­ed out with respect to horizontal inconsistencies,inconsistencies with other meteorological data, andby interactive doudy scene control. Statisticaltechniques are used by Meteorological Services ofUnited Kingdom, Canada, Australia 13 andChina 14.

II! i! 11111' 'I' 'I I I <II I'i'

III' q, I

1

GUPTA: ATMOSPHERIC PROFILES RETRIEVAL

4.2 Physical Retrieval Method

Here, heavy reliance is placed on the ability tomodel accurately the response of sensing instru­ments to atmospheric and surface conditions rath­er than on statistical relationship between radianceand temperature profile. HereI5 the computed ra­diances for TOVS channels from the initial guessprofile (as discussed under the statistical method)together with the skin/ cloud temperatures arrivedat using IR window channels are iterated with theobserved radiances unless an agreement betweenthe observed and computed radiances is achievedfor the cloud-insensitive MSU channels. Thereaf­

ter, the initial guess moisture profile is adjusted toreflect the existence of clouds assuming 100% rel­ative humidity at cloud level. Further, iterative ad­justments are carried out till the convergence isobserved between the computed and measured ra­diances in water vapour channels. With these asinputs, the temperature profile is further adjustedin an iterative manner to achieve convergence be­tween observed radiances and computed radiancesfor CO2 channels. Such obtained temperature pro­file could then be used for estimating water va­pour mixing ratios 16.

Susskind et all7 had used the difference be­tween the temperatures rather than radiances intheir relaxation method. The physical basis for thisis that the difference between the observed and

computed temperature is related to an equivalentdifference between true and estimated temperatureaveraged over a channel-dependent atmosphericlayer, surrounding the peak of the correspondingweighting function. Iterative physical method is inuse in USA, DFVLR, Fedreal Republic of Ger­many and University of Bologna, Italy.

4.3 Simultaneous Retrieval Method

In this method a single step direct solution isapplied which makes the technique computational­Iy efficient. Here, the integral of the radiativetransfer equation is integrated in parts and is treat­ed in the perturbation form where perturbation (0)

is with respect to 'a priori' estimated or mean con­dition. For temperature (T), the perturhation equa­tion could be written as

* JI''' araT (aBlaT)oT = oU------dp

II iJpiJU(aB/aT*)

f. 0 aT (aBlaT) .(aB/aT.) ()- .~:"Tap(aBlaT*) dp+ aT. iJB/aT* .. , 4

where 1'* is the brightness temperature, U is theprecipitahle water vapour, B is Planck radiance, T

is temperature at pressure level p, T, is surface­skin temperature. l' is the transmittance, and Po issurface pressure. The radiances observed in allchannels are used to solve for all parameters si­multaneously and this alleviates the problem ofthe interdependence of radiance observations up­on these three (u, T and T,) parameters. Imple­mentation details are available in Smith et al.IR

This method has shown significant improvementsin water vapour profile estimation and small, butsignificant improvements in temperature profileestimation; an observation also endorsed for simi­lar retrievals using geostationary satellites data I'i.Allen and Smith20 have shown that inclusion of

cloud parameters lead to small, large and verylarge improvements for clear atmosphere/lowcloud, moderate cloud and heavy cloud amount si­tuations, respectively.

4.4 Improved Initialization Inversion (31) Method

Statistical method has the advantage of gettingslow degradation of instrument responses implicit­ly accounted in the method through the periodicupdating of regression coefficients while it has li­mitations due to dependence on the applicabilityof statistical ensemble concept to the case understudy, and difficulties in accounting correctly forthe dependence of observed radiances on variousphysical parameters besides temperature profile21•

Physical retrieval method overcomes the above re­ferred to limitations of statistical method whereinstarting with a guess profile, the problem is solvedby iteration but this needs computation of trans­mittances and radiances for each iteration. To

economise on computation cost, simple atmos­pheric transmission models are used in physicalretrievals which results in inaccuracies. Scientists

at CNRS, France22 have suggested a physico-sta­tistical method wherein more physics in terms oflocal properties (like surface elevation, surfaceemissivities, viewing angle, percentage of water va­pour in the observed scene, etc.) is included ascompared to pure statistical method. In 31 meth­od, fast computation of transmittances is achievedthrough the use of Look Up Tables (LUTs) where­in Jacobian matrices consisting of partial deriva­tives of radiances/ equivalent brightness tempera­ture with respect to atmospheric temperature andwater vapour amounts are used; and care for thestructure of observed radiance prod file is providedin the inversion procedure. In this method, datasets are hierarchically arranged and consist of lati­tude zones, view angle, surface pressure, emissivity(one each over land and ocean) and large numberof atmospheric conditions profiles (525, 545 and

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INDIAN J RADIO & SPACE PHYS, VOL. 17, DECEMBER 1988

With measurements in IR atmospheric windowchannels one can estimate N* by

II (v, e) - N */2( v, e) where N*= N/N2I ( e)- *,

clear V, - 1- N (~)... I

... (6)

... (8)N*= Ij(w,e)- Iclear(w, 0)12( w, e) - Iclear( w, e)

where Ic\oud (v, e) and Ielear (v, e) denote the cloudand clear radiance components, respectively, of ra­diance reaching the satellite. Eliminating Ieloud

(v, e) from Eq. (5) and (6) one gets

where Ic\ear (w, Ii) could be computed using Eq. (1)and 'a priori' temperature profile at the first in~stance and thereafter using new adjusted profilesarrived at by the process of iteration. Here Ii( w, 0)

and 12( w, e) are the measured radiances in windowchannel for first and second FOY, respectively.Once N * gets known, Iclear( v, 0) for inital as well asfor various iteration steps could be computed fordifferent channels using Eq. (7). This method em­phasizes the necessity of accurate surface tempera­ture which has been discussed by Susskind andRosenberg2". Fig. 3 gives a diagrammatic presenta­tion of two FOYs approach. Here the distance be­tween the two spots in the I( w, 0) versus fi... v, e) di­agram depends only on the difference in cloudamount. As this distance becomes smaller, i.e.when cloud amounts in two FOYs are either verysimilar or are approaching near overcast, the smallrandom error in estimation could change the slopeof the line joining two points to a large extent.And this would cause more errors in clear' radi­ance estimation than expected in a linear way. Thesame thing would happen when one measures inthose channels which are more sensitivie toclouds. McMillin and Dean:'7 have discussed indetail the vulnerability of assumption of similartype clouds in two FOYs, 8 different tests for in~ferring about the clear radiances, and 6 tests to in­fer about the radiances belonging to partly cloudi­ness ongm.

6 Intercomparison of MethodsIn order to optimize and standardize TOYS

processing procedures towards providing accuratesoundings to international meteorological com­munity, efforts were undertaken:'~ for intercompar­ison studies. over Alpine Experiment (ALPEX),Tasman Sea and US sites. About eleven institutions. " (5)

137 conditions for polar, mid-latitude and tropicalzones, respectively). Eleven pressure surfaces in1013 to 725 mbar region (more in anticyclonic re­gion) are used to take care of detailed atmosphericstructures in lower troposphere. The method usesan average minimum distance classifier for assign­ing an input pattern to a particular class of featurespace (initial guess selection) from this elaborateset of initial guesses profiles. The maximum likeli­hood Baysian technique is used for obtaining theretrievals. The comparison with actual observ­ations resulted in the standard deviation of 0.15 to

0.17 for relative humidity between set of two pres­sure levels and of 1.2 to 2.2 for Sea Surface Tem­

peratures23. Use of cluster analysis technique toselect the initial guess has been discussed byThompson24 and Munteanu et af.25

5 Cloud Aspects

As first approximation one could considerclouds forming a single effective layer and coulddevelop the radiative transfer equation using thedirect reaching radiation (Ielear) and radiationreaching from the cloud layer (Ic\oud) consideringcloud as an absorption! emission system. For twolayers cloud distribution case, a further second si­milar functional transformation of single layer caseequation could be developed treating, output ofsingle layer case as input to second cloud layer.This single field of view approach has been re­viewed in Gupta2•

Due to opaqueness of clouds to IR radiation,very little intelligence from below cloud regiongets included in radiation emitted from it. Secon­dly, the observed radiances could arise due to var­ious combinations of cloud and temperature struc­tures and thus the range of valid solutions for tem­perature profile for a given set of radiances be­come wide. To overcome this, multiple field-of­view approach has been normally adopted whereinit is assumed that with high resolution spatial mea­surements it is feasible to assume that the scale ofhorizontal variability of temperature profile ismuch larger than the scale of horizontal variabilityof clouds. This approach uses two adjacent field­of-views (FOYs) assuming that the cloud propert­ies remain the same in these two FOYs and differ­

ence in radiance is caused only by the varyingamount of cloudiness in two FOYs. 1f N1 and N:.

are the fractions of cloud amounts in two FOYs,the radiances received in two adjacent FOYs,II( v, e) and 12 (v, e), respectively, are given by

278

I'II II 'w' 'I! 'I 1'1 I I II

I 1'''

GUPTA: ATMOSPHERIC PROFIlES RETRIEVAL

Fig. 3-Two field-of-view estimates of clear column radiancein cloud contaminated conditions assuming independent es­timate of clear value for window channel [lelear (w, 8)] (Repro­duced from Ref. 16 with the kind permission of World Mete­orological Organisation, 41 Avenue Giuseppe-Motta, Geneva,

Switzerland)

7 Discussion

As the structure of critical guess profile verymuch decides the shape and accuracy of retrievedprofile, improved initialization procedures involv­ing pattern recognition techniques and use of highresolution AVHRR data in estimating the initialvalue of surface temperature and partly cloudinesscondition in HIRS Instantaneous Field-of-View(IFOV) (Refs 10 and 22) are gaining momentum.Watts and Eyre30 have used 0.69 micron HIRS/channel 20 together with AVHRR data (for day­time measurements) for albedo check towards ob­taining improved clear radiances. Difference inAVHRR cl\annels 3 and 4 radiances as comparedto that for channels 19 and 8 of HIRS could beused for checking the part-presence of low cloudsin HlRS IFOVs. Hayden et ai.ll have found thatuse of AVHRR data had strong effect on the esti­mation of low level moisture field but did nothave dramatic effect in final products of tempera­ture and humidity profiles as current retrieval al­gorithms are highly biased with microwave chan­nels measurements. AVHRR instrument scanningcharacteristics are much different from the dwell­

ing type TOVS sensing system and Aoki'i has dis­cussed the collocating aspects for these sensors.Implementation of TOVS processing software onmicro-computer/personal computer31•32 has open­ed new opportunity for work using TOVS dataunder university environment in the developingcountries. Contribution of errors by slant viewingand clouds to retrieved temperature profile hasbeen discussed by Aoki3.\

Lipton and Yonder Harr34 have used principalcomponent image processing technique in watervapour retrieval and found that it slightly en­hanced the accuracy of retrievals. Conceptuallyphysical and 31 methods are superior but result-

nesses exhibited a tendency for the retrievals to befirst-guess dependent. Using 15° latitude intervalzonal climatology for 15 to 75°N region for themonths of January and July and US NOANNES­DIS operational sounding output as first guess,they found that physical method made substantialcorrections to climatological guess but made onlysmall correction to the operational sounding firstguess. The observation is that physical retrievalmethod offered little significant improvement overthe operational US NOANNESDIS statisticalmethod while use of 3 HIRS spot x 3 HIRS scanlines radiance horizontal resolution composites asopposed to 9 HIRS spot x 7 HIRS scan lines ra­diance resolution reduced the bias differences inthe lower and middle troposphere.

Cloud •.

Spot 2.Spot 1.

(Spot 2)

t•...C)

C

worked with the eighteen sets of atmosphericsoundings covering clear atmosphere as well ascloudy situations. Outputs over these test sites forstatistical, physical (iterative) and simultaneoussingle step retrievals were obtained in terms of fif­teen standard lev.el temperatures, geopotentialthicknesses for 1000 to 700 mbar, 700 to 500mbar, 500 to 300 mbar, 300 to 100 mbar and1000 to 500 mbar; and precipitable water within1000 to 400 mbar; 850 to 400 mbar, 700 to 400mbar and 500 to 400 mbar layers. These werecompared with similar measurements from RAOB(radiosonde, collocated within 150 km) andECMWF (European Centre for Medium RangeWeather Forecasting) forecast based data sets.Table 4 provides result of the intercom paris on ex­ercises.

Koehler ei at.2'i studied the impact of improvedhorizontal resolution on physical retrieval methodusing 3 HIRS spots x 3 HIRS scan lines as pri­mary data set instead of 9 HIRS spots x 7 HIRSscan lines. On comparing the retrieval output withradiosonde data interpolated through polynomialtransformations both in terms of time (4 observ­ations bracketing the time of satellite observation)and space, they found that temperature retrievalaccuracy statistics as well as geopotential thick-

279

INDIAN J RADIO & SPACE PHYS, VOL. 17, DECEMBER 1988

Table 4-0utcomes of intercomparison studies of TOVS profiles with respect to European centre for medium range weatherforecasting (ECMWF) analysis and radiosonde observations (RAOB)

Parameter oriented intercomparison results with respect to

280

Temperature profile

*Standard difference (SD) for both

physical and statistical methods wasless than 2 K between 850-400 mbar

(ALPEX), 800-300 mbar (over US loc­ations, small 'gradients in temperaturefield probably mitigated collocation, in­terpolation and varying resolutionHIRS and MSU data combarining er­rors) with respect to ECMWF datawhich itself has l.so K SD with respectto' radiosonde data due to 200 km gridresolution and inherent smoothing. Forthis smoothing reason the SD above850 mbar was generally larger with re­spect to radiosonde observations ascompared to that for ECMWF analy­SIS.

*SD with respect to radiosonde in 850­400 mbar was close to 2 K.

*SD for both methods was significantlylarger near 850 mbar and below zones.Ancillary data, and other constraintsplayed significant role.

*Bias-wise too cold (negative bias) nearsurface, 300 and 100 mbar, positive bi­as (too warm) near 200mbar and smallbiases in mid troposphere; reason isthe smoothing chara,cteristics of weight­ing functions. Strong influence of ancil­lary data and other constraints below850 mbar.

*Root mean square (RMS) error inALPEX region is little larger than 3 Kas compared to ECMWF analysis andjust over 4 K as compared to radio­sonde data in tropopause region. It wasjust below 2 K as compared toECMWF analysis and close to 2 K ascompared to radiosonde observationsin 850-400 mbar layer. RMS error wasnear 2 K in 800-300 mbar region overTasman sea site, and near/below 2 Kover US site.

*Correlation coefficients exceeded 0.98value with ECMWF standard level

temperatures.

Geopotemialthickness

*Cold bias in1000-700

mbar; RMS er­ror near 20 m

*RMS differ­ence for

-700' to 500

mbar regionwas 20m.

-500 to 300

mbar regionwas between30 and 40 mover ALPEX

region and25 moverTasman Sea

region.

-1000 to 500

mbar regionwith respectto ECMWF

analysis, was32, 29 and28 moverALPEX,Tasman Seaand US re­

gions, re­spectively.

Precipitable water

*Small difference in results of

Physical (iterative) and statisti­cal methods. One step simul­taneous physical retrievalshowed comparitively betterestimates in lowest levels.

*RMS error in 1000-400 mbarand 850-400 mbar columns (ascompared to ECMWF analy­sis) were

-Just over 40% in ALPEXcase

-Over 30% in 1000-400 mbarcolumn and over 40% in250-400 mbar column inTasman Sea and US cases.

f

II

GUPTA: ATMOSPHERIC PROFILES RETRIEVAL

wise no dramatic impact has been noticed. In In­dia, retrieval module for stratospheric region hasbeen developed by Murthy and AgarwaP5, simula­tion studies for HIRS water vapour channels havebeen carried out by Agarwal and LoganathanJh,and atmospheric temperature profiles have beenestimated from MSU data by Gohil et a/.J? FromNOAA- K onwards, say in early 1990s (Ref. 38), atotal microwave measurement based temperatureand humidity profile instrument known as Adv­anced Microwave Sounding Unit (AMSU) wouldbe available in place of SSU and MSU, and HIRS

availability would continue. It would consist oftwo units namely, 15 channels AMSU-A and5-channels AMSU-B catering to temperature andmoisture profile estimations, respectivelyJ9; thefrequency details and tentative weighting functionsfor AMSU (A and B) channels are presented atFig.4. The nadir IFOV for AMSU (A and B)would be 45 km and 15 km, respectively, withtemperature sensitivity ranging from 0.3 to 0.6 Kfor all channels execpt for channels 13 and 14(f± 10 MHz, f± 4.5 MHz where f= 57.29034GHz ± 322.2 MHz) where it would be 0.8 and

CHANNELS IN 23.8,31.4 i. 89 GHz NOT SHOWN

(b)

I~OGHz.

0.1 O.2 ct.~ 0.4 o.~WATER VAPOUR BURDENWEIGHTING FUNCTION.

....•NE....•.0-~ 0.1--Z,..,.

W.D

0E

0:::--::)

wCD

0:::0:::::) ::)(/) (/)0

W

0..

0:::

<tQ.

>0:::W~~

t\ "- ~...•...•..

183.3t7GHz.

0.1 0.2 0.3 0.4 0.5 0.6 0.7TEMPERATURE WEIGHTING FUNCTION.

Fig. 4- Weighting functions for advanced microwave sounding unit (AMSU) becoming availablefrom NOAA-K onwards and on Polar Orbiting Platforms: (a) Temperature weighting functions,and (b) Water vapour burden weighting functions (Reproduced from Ref. 39 with kind permissionof Dr. D H Staelin, Research Laboratory of Electronics, Massachusetts Institute of Technology

(MlT), Cambarridge, Massachusetts, (USA)

281

INDIAN J RADIO & SPACE PHYS, VOL. 17, DECEMBER 1988

1.2 K, respectively. AMSU would also continueon-board the Polar Platforms scheduled for launchfrom 1995 onwards servicing to data needs as welIas continuity tiIl 2005/2010 AD. Infrared mea­surements have better sensitivity in regions below700 mbar and joint use of HIRS and AMSU isexpected to improve the retrieval accuracies forthese levels in 0.5 to 1.0 K range. At present thejoint use of HIRS ano MSU gives errors of 2.08,2.26. 2.55, 2.73, 2.92 and 3.35 K (relative to ra­diosonde errors) for 700, 18U, 8-50, 920, 950 and1000 mbar Ievels-lli.

AcknowledgementAuthor is thankful to Prof. B L Deekshatulu,

Director, NRSA, and Shri A S Ramamoorthi, fortheir encouragement and permission towards mak­ing this contribution.References

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