Atomic oxygen modeling in the upper thermosphere

0032.~633/XX $3.00-1-0.00 Pergamon Press pie

ATOMIC OXYGEN MODELING IN THE UPPER THERMOSPHERE A. E. HEDIN

Planetary Atmospheres Branch/614, NASA/Goddard Greenbelt, MD 20771, U.S.A.

Space Flight Center,

(Received injinal.form IS April 1988) Abstract-The historical development and features of empirical models of atomic oxygen in the Earth’s thermosphere are reviewed. The primary data base for models has historically been either satellite drag or $2situ mass spectrometer measurements, and each of these have their own particular problems of calibration. The latest models based on drag data and those based on mass spectrometer data agree on average to within about IS%, strong evidence that the absolute values are reasonably well known in the upper thermosphere. Comparison of different models with various data sources generally also shows residuals of at least 15% These residuals are the result ofunmodeled magnetic storm, e.u.v., and geographical variations and smaller scale variations caused by gravity waves. However, our data bases are growing old. Maintaining even our present level of accuracy in predicting atomic oxygen density in the new solar cycle will require new satellite missions, a revival of appropriate satellite drag analyses, or improved methods of monitoring from the ground.

2. HISTORICAL

1. INTRODUCTION

DEVELOPMENT

2.1. Standard atmospheres Empirical models of the upper atmosphere can be divided into several historically related series (Fig. 1). The U.S. Standard Atmospheres (COESA, 1962, 1966, 1976) trace back to early efforts to establish atmospheric standards by the International Civil Aviation Organization (ICAO) and the Air Research and Development Command (ARDC) of the U.S. Air Force, starting before the era of satellite measurements. The history of these models is provided in some detail by Minzner (1976). These modeis provide altitude profiles of temperature and density for one or a small number of places and geophysical conditions taken to be typical or average for the atmosphere and include elaborate definitions of defining constants and equations. The U.S. StandarJ Atmosphere, 1976, also includes densities for N,, 0, 02, Ar, He and H in the thermosphere. While helpful in providing an initial indication of thermospheric conditions, they are neither intended nor suitable for detailed comparison with satellite or rocket data taken globally and under a wide variety of conditions.

Atomic oxygen is the predominant species in the upper thermosphere (200600 km) and is readily ionized by e.u.v. radiation from the sun. Thus its absolute density and variations are important in determining the structure of the thermosphere and ionosphere and the content and magnitude of airglow and aurora1 emissions. Knowledge of atomic oxygen density is of practical importance in determining the drag on artificial satellites and estimating the degradation of satellite surface materials that are easily oxidized by exposure to atomic oxygen. The atmospheric drag on artificial satellites was itself exploited to determine oxygen density early in the satellite era as accumulating data allowed the first empirical modelling efforts. Subsequently other techniques for determining atomic oxygen were developed, the major one being the satellite-borne mass spectrometers. This paper will review the principal models of atomic oxygen in the upper thermosphere, inter~ompare the results of some recent models, and compare these models with data to assess the current accuracy with which we can retrospectively predict atomic oxygen density. The background of a number of models and their intercomparison has also been discussed by Hickman ef al. (1979), Barlier et al. (1979), and Barlier and Berger (1983). Atomic oxygen near the lower thermosphere peak will not be discussed in detail here.

2.2. CIRA The Committee on Space Research (COSPAR) has issued a series of COSPAR International Reference Atmospheres (CIRA, 1961, 1965, 1972) which, in the first tissue, synthesized the existing data and model 907

908

A. E.

HEDIN

HistoricalDevelopmentof EmpiricalThmmwhme Models PrimarilyTotal Density Data from SatelliteDrag

Y.SW -

PrimarilyTanp & Corm. Cati from Ground and In-situ Instr.

I

ICAO 1960

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USA62

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JACCHIAWALKEFI-

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1970

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1990

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1995 FIG. 1. CHART

ILLUSTRATING

HISTORICAL

CONTEXT

-- __

AND CONNECTIONS

I

MSIS95 BETWEEN

THERMOSPHERIC

EMPIRICAL

MODELS.

suggestions, and then, in latter issues, essentially adopted previously published models as shown in Fig. 1. The Harris-Priester model (Harris and Priester, 1962) for example, was an attempt to adapt a theoretical framework for atmospheric predictions and was adopted for CIRA (1965). These models will not be discussed in detail except as they relate to other models. 2.3. Jacchia models With the detection of atmospheric drag effects in early artificial satellites (Jacchia, 1959 ; Priester, 1959),

data became available which led to the well-known series of Jacchia models starting with 560 (Jacchia, 1960). The most widely used of these are 565 (Jacchia, 1965), J70 (Jacchia, 1970) 571 (Jacchia, 1971) and culminating in 577 (Jacchia, 1977), as indicated in Fig. 1. The 565 model was the earliest comprehensive global model based on satellite drag (orbital decay) and had a lower boundary at 120 km. Following the work of Nicolet (1961), height profiles of the principal constituents were calculated as a function of exospheric temperature assuming diffusive equilibrium and fixed

Atomic oxygen modeling in the upper thermosphere

boundary conditions at 120 km which essentially determined the proportion of the total density to be attributed to atomic oxygen and the other constituents. The 565 modd introduced an exponential form for the temperature profile that was shown by Walker (1965) to be closely approxi~ted by theoretical temperature profiles (Bates, 1959) which allow the hydrostatic equation to be explicitly integrated to provide density as a function of altitude. This model was the first to include the five principal types of thermospheric density variations (diurn~, seasonal, semiannual, solar activity and magnetic activity) by using ad hoc formulas with a few adjustable parameters to calculate variations in exospheric temperature which gave density variations in agreement with available measurements. The 570 model used a lower boundary at POkm and introduced more complicated temperature profiles which required numerical integration of the hydrostatic equation. While still basically adhering to the concept of describing density variations by variations in exospheric temperature, the model added a winter helium bulge of approximately a factor of three as well as formulas to describe density variations, but not temperature variations, in the lower thermosphere. The 571 model has atomic oxygen densities at 150 km about 50% larger than given in 570, resulting in the highest atomic oxygen density at this altitude for any of the Jacchia or the MSIS models to be discussed later. This increase was driven by the goal of reaching consistency with an analysis of rocket results by von Zahn (1970). The model introduced a major departure in the description of the semiannual effect by abandoning semiannual temperature variations and using height-dependent density variations. The 577 model extensively revised the 571 model with the goal of incorporating early satellite mass spectrometer results. The method chosen for the diurnal variation was to introduce a separate heightde~ndent pseudo-t~~rature for the various constituents. The magnetic activity effect included horno~u~ height changes which introduced com~si~on~e~nd~t variations, and the seasonallatitude variations were extended to include molecular nitrogen and atomic oxygen as well as helium. Magnetic coordinates were recommended for calculating the latitu~~~d~t magnetic activity e&ct in exospheric temperature. The Jacchia models have been incorporated into a number of other models. The 560 model was combined with an ARDC model for early orbital analysis and called DENSEL or Lockheed-Jacchia (Lockheed Missiles and Space Co. Rep. SS-T61-43, 1961). Bruce (1966) modified the J65 model for computational

909

efficiency and more realism near the lower boundary. The J’70 model was incorporated into the GRAM model (Justus et al., 1980) as the engineering reference model for NASA and the 571 model was chosen for the 1972 CIRA model.

The OGO-6 satellite mass spectrometer (Cuban and Pinkns, 1968) provided the iirst extensive meas~ments of the densities of N,, 0 and He in the thermosphere. Their snm provides an independent dete~ination of total density for comparison with satellite drag methods. The observed variations in composition were quite different from the 565 and J70 model predictions, so a new approach was introduced (see Fig. 1) for the UGU-6 model (Hedin et al., 1974) based on the 565 framework of Bates-type temperature profiles for easy integration of the hydrostatic equation, diffusive equilibrium, and a lower boundary at 120 km. The exospheric temperature was inferred from the N, density rather than total density, based on the concept that N, is generally closer to diffusive equilibrium than any of the other constituents (Mayr and Volland, 1972 ; Mayr et al., 1974). The densities of 0 and He were not fixed at 120 km, but varied with local time and other geophysical parameters to fit the measurements. These variations were assumed to represent the effects of mixing processes and global wind systems in causing departures from diffusive equilibrium in the lower thermosphere. Spherical harmonics were chosen to represent the exospheric temperature and the density variations at 120 km based on the concept that spherical harmonics are approximate eigenfunctions of the thermosphere (Volland and Mayr, 1972), thus requiring fewer parameters for a given level of accuracy and facilitating future increases in complexity. The MSIS-77 model (Hedin et al., 1977a) followed the format of the GGO-6 model with an expanded data base of composition and density data from five satellites and temperature data from four groundbased incoherent scatter stations whose data coverage complements those of satellites. This allowed the N2 density at 120 km to also be varied to fit the measurements. Subsequently, variations in U.T./lon~tude (Hedin et al., 1979) were added to describe the effects of magnetically controlled energy inputs and momentum interactions with the neutral atmosphere. The MSIS-83 model (Hedin, 1983) was based on an expanded data base of seven satellites providing temperature as well as density and composition data and five incoherent scatter stations providing lower thermosphere density as well as temperature. The

910

A.

E. HISDIN

model lower boundary was dropped to the mesopause (near 85 km) using an extensive set of rocket measurements, incoherent scatter, and satellite U.V. absorption measurements to define major variations below 120 km. Temperature profiles were chosen so that the hydrostatic equation can be integrated analytically and density is based on the temperature profiles. Atomic oxygen densities near the lower thermosphere peak are based on the U.S. Standard Atmosphere 1976. A formula was introduced to calculate magnetic activity variations using the time history of 3 h magnetic activity indices. The MSIS-86 (Hedin, 1987) model was based on added data from Dynamics Explorer and revisions to the U.T./longitude formulas for the polar regions from analysis of these data (Hedin and Carignan, 1985). Atomic nitrogen was added to the list of species covered (N,, 0, He, Ar, 02, H and N). This model was selected for inclusion in 1986 CIRA as the upper thermosphere portion. Variations of the MSIS model formulation were adopted for other models as shown in Fig. 1. These include ESRO-4 (von Zahn et al., 1977), Ml and M2 (Thuillier et al., 1977a,b), DTM (Barlier et al., 1978), AEROS (Kohnlein, 1979) and C (Kohnlein, 1980). 2.5. Advantages/disadvantages of the model types The Jacchia series of models should ideally be best if satellite drag is the primary quantity desired for satellite geometries and orbits similar to those used in generating the model. Since atomic oxygen is the major constituent over much of the satellite range, oxygen should be predicted reasonably well under many conditions. However, since composition (including atomic oxygen) and tem~rature are dependent on auxiliary data or assumptions, the model results are not always in accord with mass spectrometer measurements and may be less accurate. Absolute densities, determined from the satellite drag method, depend on knowledge of the drag coefficient which is assumed to be known with uncertainties on the order of lo%, based largely on theoretical considerations (Cook, 1965). Laboratory tests of the underlying assumptions are limited (Herrero, 1985) and inaccurate assumptions about composition (such as magnitude of the winter helium bulge) may lead to inaccurate drag coefficients and thus inaccurate density. The formulation of these models has particular difficulty coping with minor constituent variations. The cumbersome pseudo-temperatures of 577 help with predictions of diurnal phases but not diurnal amplitudes as a function of altitude. The MSTS series of models should be best for composition and temperature, since these measurements

constitute the primary data base. Absolute densities are dependent on indi~dual instrument laboratory calibrations which have uncert~nties on the order of 15%. However, the model represents an average of measurements from several independent instruments, so it should have an absolute accuracy better than any individual instrument. As seen later, the total densities predicted by the two model series, each dependent on completely different methods of determining absolute density, agree within about 15% over their common ranges of validity. The MSIS database also extends to lower altitude and thus the later MSIS models contain features not described by the Jacchia models. Lower thermosphere density variations are derived from temperature variations rather than treated independently as in the Jacchia models. 3. MODEL CO~P~ISO~S

In the upper the~osphere oxygen density dependence on long-term solar activity is very similar for several recent models. Figure 2 shows an example at 500 km for the MSIS-86, MSIS-83, 577 and J70 models with maximum differences of 20%. The differences at 150 km are somewhat larger, resulting in part from lack of lower altitude data in the Jacchia models and in part from the model formulation. Examples of the diurnal variation are show in Fig. 3. Here we see that at 500 km 570 has an earlier maximum than the other models as it was constructed with the assumption that all species peaked near 14 h local time regardless of altitude. Maximum differences among the models are about 25%. At 150 km the MSIS models reflect the phase shift to morning locat times of the diurnal variation and increasing importance of the semidiurnal variation which is not present in the Jacchia models. Differences in the J70 and MSIS-83 model predictions for the seasonal distribution of atomic oxygen are shown in Fig. 4 by contouring the ratios of MSIS83 to 570 as a function of altitude and season. The models agree to within 10% around 500 km where oxygen is usually the dominant constituent, but differ significantly near 200 km where the N2 density is significant and oxygen is depleted in the summer. Differences are less between the MSIS and the 577 model. The predictions of total density by various models, however, do not differ significantly between 200 and 500 km (as shown in Fig. 4 for the ratio of MSIS-83 to 570 total densities), illustrating that agreement in total density does not always mean agreement in oxygen density. A common thread through the above examples is that oxygen (and total density) shows a broad general

Atomic

oxygen

_

_______MS,~_~~

_

-MSIS-66

modeling

911

in the upper thermosphere

0 Density 500

kn

150

70

110

150 10.7cm

FIG. 2. ATOMIC OXYGENDENSITY

190

15-20%. Intercomparisons of a number of models over a wider range of conditions have been reported by Hickman et al. (1979), Barlier et al. (1979), and Barlier and Berger (1983) and these show that larger than average disagreements between models are not hard to find when we focus on particular geographical and geophysical conditions.

230

SOLAR FLUX

FROM FOUR MODELSVS 10.7 cm FLUX AT ATTHEEQUATORAND 6h LOCALTIME.

agreement over the range of validity for the Jacchia and MSIS models. This is true both on average and in an approximate way for the principal variations. Since the absolute density for the two model series is based upon completely different and independent approaches, barring coincidental errors of the same magnitude and sign for each model, the absolute oxygen density and its large scale variation in the upper thermosphere are reasonably well known. Comparisons with individual data sets suggests the average uncertainty is on the order of

km

150 AND

500 km DURINGEQUINOX

4. COMPARISONS

BETWEEN MODELS

AND MEASUREMENTS

4.1. General Comparisons of individual data sets used in generating an MSIS model with the model itself (Hedin, 1983, 1987) show that some data sets deviate on average up to 17% from the model. It is likely that an important factor is differences in preflight calibration of the mass spectrometers or gauges and/or lack of instrument stability as well as differences in geophysical conditions. The standard deviations between data and model range from 15% to over 30%. Again, some part of these differences must be attributed to instrument errors and some to real geophysical conditions not included in the models. Comparisons of total density measurements with a number of models have been reported by Hickman et al. (1979) and Prag (1983). Comparisons of in situ

A. E. HEDIN

912

0

Density

150

0

6

12

LOCAL FIG.

3. ATOMICOXYGEN

TIME

km

18

DENSITY FROM FOUR MODELS vs LOCAL SOLAR TIME AT EQLIINOXATTHEEQUATOR.

data sets and different models have been made by Barlier et al. (1979) and Barlier and Berger (1983). They concluded that models have improved very little in an overall sense in predicting total densities since 571. 4.2. DE 2 comparisons Data from two satellites (DE 2 in a polar orbit and AE-E in a near equatorial orbit) will be used to illustrate in more detail the type of deviations that can be experienced between measurements and models. Densities from both of these satellites happen to be below average with respect to the models. Figure 5 shows 2 day averages (and with error bars the standard deviations within those 2 day periods) of data to model ratios of atomic oxygen for the time history of the DE 2 emission. Data used are an 18,000 point sample (original along track resolution of 16 s or about 130 km) between 300 and 500 km including

24

lhrs)

150 AND 500 km DURING

all magnetic activities. The average residual (logarithm of the data to model ratio) (AVG), overall standard deviation for individual point residuals (SD), and standard deviation of the 2 day averages (SDA) are given on the plots. Of all the models shown, only the MSIS-86 model used the DE 2 data in its generation. Not surprisingly the standard deviations grow larger with model age. Since the local time and latitude of perigee (about which data are concentrated) change systematically throughout the flight, it is not easy to divide the discrepancies between geographical effects and day to day or solar effects. However, it is likely that day-to-day changes in e.u.v. as well as magnetic storm effects are a significant factor in the day-to-day variability. These e.u.v. changes are not completely correlated with the 10.7 cm solar flux that is used in the model to describe them (Hedin, 1984). If the DE 2 equinox data and data with high mag-

Atomic oxygen modeling in the upper thermosphere

netie activity (A, > 19) are excluded, then likely seasonal/latitude effects can be observed in a plot of residuals against latitude (Fig. 6). Summer data from both hemispheres have been plotted at positive latitudes. Because of the pure polar DE 2 orbit, most of the solstice data occurred near dawn or dusk local time and the seasonal plots were divided into these two groups. In Fig. 6 the residuals for data sampled between 300 and 500 km are averaged over 10” latitude intervals (17,000 points in the afternoon and 15,600 points in the morning). The seasonal variations in the local afternoon are better represented by all the models, but again the residuals get progressively worse with the older models. For MSIS-86 the systematic seasonal errors are a relatively small factor in the overall data/model variance, but the seasonal factor increases in importance with the older models. The Jacchia models show marked systematic departures, with the I70 model having about 40% too much oxygen in the summer at both local times consistent with Fig. 4.

913

Magnetic activity effects are illustrated with DE 2 data in Fig. 7 using a sample of 15,000 points between 300 and 500 km. The residuals were averaged in bins of 10” in latitude and 10 A, units to facilitate the contouring of residuals on a Ap and latitude grid. All models show significant discrepancies at higher magnetic activities. While .I77 has the least extremes, the standard deviation between the bin averages increases systematically with model age. Here the standard deviation between bin averages is a significant factor in the overall standard deviation for all models and indicates that improved modelting of gross magnetic a~tivi~yllatitude efIects would provide a si~ifi~nt improvement in overall model accuracy.

The A&E satellite obtained data from about 140 to 500 km. Figure 8 shows residuals plotted against locat time, averaged into 2 h intervafs, for ~titudes below 200 km (2500 points) and between 400 and 500 km (I 1,600 points) and for all magnetic activity levels.

914 DE-2

0

I

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C

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a-O.14 = O.IS

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= 0.12

0.60

T

1

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FIG. 5.DE2

0 / 570

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+-0.18 * 0.29 = 0.21

0.60

ATOMICOXYGEN amh RESIDUALS (LOGARITHM OFT~IERAT~O~FMEA~UREDTOMODEL AVERAGEDIN 2 DAYINTERVALsFORFOURMODELSVSYEAR(ANDFRACTIONTHXEOFf.

DENSITY)

Error bars indicate the standard deviation within each 2 day period. Also indicated is the average residual (AVG) and standard deviation of the residuals for individual points (SD), and standard deviation of the 2 day averages (WA).

Atomic

_.--

oxygen modeling

I

I

I

I AVG

MORNING

LOCAL

TIME

915

in the upper thermosphere

so SDA

=-0.12 = 0.13 = 0.03

AFTERNOON DE-2

LOCAL

0

/

AVG SD SD*

TIHE

0

DE-2

LOCAL 0

/

AVG SD

TINE

=-0.07 = 0.22

AFTERNOON

MSIS-83

-

DE-2

LOCAL

0

/

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I I MORNING

=-0.11 = 0.15 = 0.03

MIS-36

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=-0.07 + 0.13

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0.04

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LOCAL

TIME

0

-O.do-

DE-2

0

/

-

577

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/

AVG SD SOA

= 0.02 = 0.21 = 0.10

AVG SD SDA

= 0.25 = 0.16

577

t MORNING 0 2

0.40

-0.10

DE-2

LOCAL 0

/

s-O.14 = 0.27 = 0.13

r

J70

AFTERNOON DE-2

0

LOCAL /

TIME

I

I

I

I

I

I

0

46

-4s

0

46

LAT-SEASON

LAT-SEASON

FIG. 6. DE 2

ATOMIC

MODELS VS LATITUDE

OXYGEN (SUMMER

SOLSTICE PLOTTED

DATA

=-0.09

J70

-4s

I -so

AVG so BOA

TIME

RESIDUALS

AT POSITIVE

AVERAGED

LATITUDES)

IN

10”

FOR DATA

LATITUDE NEAR

4

AND

INTERVALS

16 h

FOR FOUR

LOCAL

TIME.

916 DE-2

1

0

DE-2

/

D

MSIS-86

/

J77

(

H

\

/O?$i$=i

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0

/

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--o.ro,~-~&&

-

3 s z A

AVG

SD

o.oo-

SDA

-46.00

=-0.18 =

0.28

=

0.21

/ --0

f

-

-ao.oo’

0

2s

50

MAGNETIC

FIG. I.

CONTOURS

OF

DE 2

ATOMIC

OXYGEN ACTIVITY

Data were averaged

RESIDUALS (A,)

into 10” of latitude

7s

INDEX

loo

lApI

FOR FOUR

MODELS

AS A FUNCTION

AND LATITUDE.

and 10 units of A, before contouring.

OF MAGNETIC

Atomic

I 130

TO

200

oxygen modeling

s-0.13 = 0.12

SD 2 AE-E I- 0.40a

0

/

MSIS-66

-0.m

SDA = 0.02

I

I

I

I

AE-E 0.40

0

/

TO

500

/

AE-E 0

km

AVG

30

USIS-

AVG =-0.14

__

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400 TO 600 km AE-E

WSIS-53

0

,

=-0.19 = 0.21

SD* = 0.04

I I

I

130 TO 200 km :

400

I

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917

in the upper thermosphere

AVG =-0.18

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AVG SD SDA

J77

=-0.30 = 0.17 = 0.10

--

400

TO

AE-E

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I I

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km

AVG

=-0.15

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400

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~-0.11 = 0.22 = 0.07

II I I B LOCAL

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AE-E ATOMlCOXYGENRESlDUALSAVERAGEv1N 2h LOCALTIMEINTERVALSFORFOURMOVELSVSLQCAL TIMEFORVATA BETWEEN 130 AND 200km ANDBETWEEN 400 AND SOOkm.

I I4

918

A. E.

The low altitude data were taken during low solar activity in 1976 and late 1975. The high altitude data were taken during high solar activity in 1979 and 1980. At higher altitudes all the models are about equal in representing the data. Errors in local time are on the order of 5% or less, thus representing a minor portion of the overall variance between data and model. At lower altitude, the Jacchia models have systematic discrepancies of the type illustrated in Fig. 3 of up to plus or minus 20%. 4.4. Incoherent scatter Atomic oxygen during daytime in the upper thermosphere has also been estimated from incoherent scatter measurements. Initial comparisons with OGO6 indicated reasonable agreement during parts of the day, but discrepancies are larger toward dawn or dusk (Hedin and Alcayde, 1974). An extensive analysis of incoherent scatter results by Fontanari et al. (1983) shows average oxygen densities at 200 km which are within about 20% of the MSIS models and with a similar seasonal variation. However, the diurnal and semidiurnal variations are several times larger than in the MSIS models. The exceptional agreement at higher altitudes, illustrated in Figs. 3 and 8, between models based on in situ mass spectrometer measurements and satellite drag based models suggests the physical basis of the incoherent scatter interpretation must be examined and tested very closely before concluding the current model diurnal variations are wrong. 5. WAVES

The presence of ubiquitous waves in the thermosphere (Potter et al., 1976) is often suggested as

O.DO -30

I

I

I

-45

HEDIN

part of the reasons for data/model residuals. Figure 9 shows an example of oxygen Auctuation amplitudes determined from DE 2 data. At higher latitudes, standard deviations between individual measurements taken at about 8 km intervals along the satellite track and a smoothed density profile are typically about 6% and occasionally twice as large. Since data to model residuals are rarely better than 15%, waves are currently not the major factor limiting model accuracies. Wave activity will, however, determine the minimum level of model accuracy that can be reached in the future.

6. FUTURE ACCURACY

Thermospheric variations do not necessarily repeat themselves from year to year in a way which is currently predictable. The semiannual variation is a well known and important variation (+ 25% typically) in the thermosphere whose magnitude has changed by more than a factor or two from year to year (WulfMathies et al., 1975 ; Walker, 1978) for unknown reasons. There is evidence that long-term variations in the ionospheric density are not perfectly correlated with sunspots or 10.7 cm flux (Chiu, 1975 ; Smith and King, 1981) and similar effects could occur in the neutral density. The diurnal temperature variation may have become systematically larger in the current solar cycle (Hagan and Oliver, 1985). Thus there is good reason to be concerned that after many years without an infusion of new data the models will become increasingly inaccurate. To maintain even the current level of accuracy will require new in situ satellite missions, a revival of appropriate satellite drag

I

I

0 MAGNETIC

I 45

I

90

LATITUDE

FIG. 9. CONTOURS GIVING THE PROBABILITY OF OCCURRENCE OF A FUCTUATION AMPLITIJDE IN DE 2 ATOMIC OXYGENDEN~~~A~LARGEA~ORLARGERTHANTHE~RD~NATEATAPARTI~ULARMAGNETI~LATI~ZTDE(AB~~ISSA).

Atomic analyses,

or advancement

of promising

oxygen modeling ground-based

techniques.

7. SUMMARY

The development of the two principal thermospheric model types, Jacchia and MSIS, has been reviewed. The advantages of the Jacchia models for describing drag and the MSIS models for composition and temperature were pointed out. The overall general agreement between oxygen and total densities between the two models, based on independently calibrated data, strongly argues for the basic correctness of the absolute values and gross variations. Detailed comparisons between the models and with two example satellite data sets suggests that diurnal variations (at least near the equator) are fairly well represented in all the models at higher altitudes, but discrepancies between data and the Jacchia models develop at lower altitudes. Seasonal variations are also well represented in the MSIS models but not as well in the Jacchia models. Magnetic activity variations remain a critical area needing improvement for all models. Wave activity, except under exceptional situations, is not currently a limiting factor to model accuracy.

REFERENCES

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