Feb 20, 1991 - AVHRR and Barry et al. [1987] for ... [1988] and Crane and Barry [1984]. ...... Svensson, J., E. Anderson, and N. Gustaffson, Towards an opera-.
JOURNAL
OF GEOPHYSICAL
RESEARCH,
VOL. 96, NO. D2, PAGES 2875-2887, FEBRUARY
20, 1991
Assessment of the Accuracy of Atmospheric Temperature Profiles Retrieved From TOVS Observations by the 31 Method in the European Arctic; Application for Mesoscale Weather Analysis CHANTAL CLAUD, NOELLE A. SCOTT, AND ALAIN CHEDIN Laboratoire de Meteorologie Dynamique, Ecole Polytechnique, Palaiseau Cedex, France JEAN-CLAUDE
GASCARD
Laboratoire d'Oceanographie Dynamique et de Climatologie, Paris, France
In recent years, polar orbiting satellites have greatly increased the number of meteorological measurementsin the Arctic, passingover the pole nearly once every hour. The Improved Initialization Inversion (3I) algorithm, which has been designed for retrieving meteorological parameters from observations of the satellites of the TIROS-N series, has been applied to 17 passes covering the European Arctic in order to check its ability to provide reliable temperature profiles in polar areas. Issues such as sea ice/cloud detection and better estimation of the tropopause level have been addressedand are discussed.Validation of temperature profiles through comparisonwith radiosonde reports and conventional analyses show the good quality of the retrievals. This quality is also illustrated by the early detection of two storms: one having gone undetected by the standard synoptic network, and one having been incorrectly described by conventional products, confirming the new opportunity offered by satellitesto improve our knowledge of polar meteorology.
1.
INTRODUCTION
In the past, available meteorological observations in polar areas were very sparse: only a few radiosounding stations and some local measurements to support special oceanographic field studies. The situation has now changedwith the advent of polar orbiting satellites providing global observations with a high temporal repetitivity. Among them, operational satellites of the TIROS-N series, equipped with a very high resolution imager, Advanced Very High Resolution Radiometer (AVHRR), and a multichannel vertical sounder, TIROS-N Operational Vertical Sounder (TOVS), provide information about these regions about every hour and are thus remarkably well adapted to the study of meteorological phenomena. In contrast to conventional observations, which give only a punctual description of the atmosphere and are affected by very local phenomena, satellite data (i.e., radiances) give a vertically and horizontally integrated measurement. Radiances emitted by the Earth-atmosphere system and measured by the TOVS radiometers can then be interpreted in terms of atmospheric parameters through a process called inversion of the radiative transfer equation. However, the precision, when calculating atmospheric parameters, is globally not as good as the precision of conventional observations. Because of the prevalent extreme conditions, polar environment is even more complex: specific elements such as sea ice or very low tropospheric temperatures have to be considered; clouds, which constitute a contamination for the radiances by absorbing, reflecting, or scattering radiation, are, at least for some of them, difficult to detect since they may have radiative properties similar to those of the surface. The multiple surface or upper level temperature inversions, as Copyright 1991 by the American Geophysical Union. Paper number 90JD02413. 0148-0227/91/90JD-02413505.00
well as the huge cyclonic activity, are a challenge when one tries to get a good description of the atmosphere. These difficulties have been underlined by different authors using TOVS observations in polar regions [see Lutz and Smith, 1988; Pedersen, 1987; Steffensen and Rasmussen, 1986; Svensson et al., 1987; Waren, 1988]. The 31 procedure, Improved Initialization Inversion, has been developed in the last few years at Laboratoire de Meteorologie Dynamique to analyze TOVS data [Chedin and Scott, 1985]. Further refinements have been made to cope with polar environment and in particular within the frame of the 1986 ARCTEMIZ campaign (Marginal Ice Zone of the European Arctic) [Claud et al., 1989a]. This campaign, which followed the MIZEX (Marginal Ice Zone Experiment), aimed at observing and understanding the phenomena causing sea ice fluctuations, as well as the influence of these fluctuations on oceanic and atmospheric environments. Experiments have been conducted in the European Arctic (Greenland, Norwegian, and Barents seas), where many atmospheric and oceanic exchanges between mid-latitude and polar areas take place. Because some atmospheric phenomena originating or developing in these areas are not yet well understood, a special effort has been produced during this campaign to simultaneously obtain local and global measurements in the atmosphere. The objective of this paper is to assessthe accuracy of 31 algorithmproducts(for example, seaice/open water discrimination, cloud detectionresults, temperatureprofiles)usingthe best corroborativeinformationavailable. After a descriptionof the 3I systemin section2, section 3 shows how the problems arising when using a retrieval scheme as 31 to determine temperature profiles in polar environment have been addressed.Then, in section4, results of applicationof 31 to 17 different satellitepassesare presentedand discussed.Comparisons with radiosondesand analyses are shown. Finally, because of the good quality of the results, the ability of the
2875
2876
CLAUD ET AL.' RETRIEVAL OF TEMPERATURE PROFILES FROM TOVS km
2g.g km
170.8 km
___t 166.1
-- '•0•.3 km
• 7.5ø
km
113 km 125.4 km
150.3 km
9.47ø (360ø138) HIRS/2
CALIBRATION
PERIOD
-42 km
1119.9
47.37 ø 1173.6
km
km
Fig. 1. Horizontal resolutionsand scan patternsfor HIRS 2 (small ellipses)and MSU (large ellipses). The central arrow is the subsatellitetrack. The black squaredelineatesan area of about 100 by 100 km, a 3I box. Two consecutive boxes share two scan lines. The ten divisions,from nadir to limb, correspondto the ten sampledviewing angles[from Chedin et al., 1985].
method to delineate mesoscalephenomenain an early stageis then demonstrated
for two storms.
algorithm; when a cloudy situation is identified, cloud sensitive channels to be used in the second step have to be corrected
for the influence
of clouds.
2. The inversion uses a pattern recognition approach to select an initial guessfrom a vast data set, called the TOVS The 3I method is a physico-statisticalmethod providing retrieval of temperature and humidity profiles, microwave initial guessretrieval (TIGR). This data set consistsof 1207 groundemissivity,geostrophicwind gradient,surfacetemper- situations, characterized by temperature, water vapor, and ozone vertical profiles, as well as radiances and transmission ature, cloud amount, and cloud top temperaturefrom TOVS observations.This method has been documentedin detail by profiles which have been calculated in advance for all TOVS channels and all possible observation conditions (viewing Chedin and Scott [1985] and Chedin et al. [1985]; further improvementsare describedin the work by Chedin [1988]. In angle, surface emissivity ...) [Scott and Chedin, 1981]. this paper we will concentrateon temperatureprofile determi- These situations are supposed to represent all situations nation. Hence a summaryof the determinationof temperature which can be found all over the world and are separatedinto three air masstypes: 137 tropical situations, 545 mid-latitude profilesis presentednow. situations, and 525 polar situations [Moine et al., 1987]. The 31 method uses as input two types of data: The "best" initial guess solution in the TIGR data set is 1. Satellite observations:radiancesprovided by the High retrieved using the observed clear column radiances. The Infrared Resolution Radiation Sounder (HIRS 2), radiometer selected set of observed radiances (or equivalent brightness with 19 channels in the infrared and one in the visible and the temperatures) and correspondinga priori information on the Microwave Sounding Unit (MSU), with four channels censituation observed are compared with each equivalent set tered at about 55 GHz, as well as latitude and longitude of archived in TIGR, and the closest is retained. The retrieval the field of view, and the local and solar zenithal angles. 2. So-called ancillary data: surface mean elevation and of the exact solution uses a maximum probability estimation percentage of surface water for every field of view. No other procedureto minimize the deviations between the brightness temperatures associated with the initial guess and the obsurface data are used in the inversion. 2.
THE 3I SYSTEM
The spatial resolution of 31 is a compromisebetween the spatialresolutionof the HIRS 2 and MSU observationsand is of about 100 km by 100 km (referred to as a box, see Figure 1). The inversion process can be split into two steps: 1. Preliminary processing:all input parameters are interpolated to the samegrid; then, an air massclassificationuses the satellite measuredradiancesto determine the type of air mass observed (tropical, mid-latitude, polar, see second step). This classification is followed by a cloud detection
served ones. Use is made of the Jacobian associated with the
retrieval initial guessin the TIGR data set. 3.
ADAPTING
THE 3I SYSTEM FOR USE
IN ARCTIC
3.1.
ENVIRONMENT
Surface Conditions
Most of the the maritime area is covered with sea ice,
someof it being seasonal.Sea ice often inducestemperature
CLAUD ET AL.: RETRIEVAL
80-
r,:•' ••,
-
/9'
_
•...,- ---..-/!
.....'" .......
.......
> tO 40 "''"'"...'""' ...... '
o
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O I d
OF TEMPERATURE
!
F
E•STEUR•SL••RC•_I I
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A
I
M
!
d
C•NTRAL ARCTIC I
d
I
A
t
S
I
0
i
N
li
D
Fig. 2. Monthly mean Arctic cloud cover (in percentage). Four different areas are distinguished: West Eurasian Arctic, East Eurasian Arctic, Canadian Arctic, and Central Arctic [from Huschke, 1969].
inversions in the lower atmosphere (see 3.4) and causes problems with interpretation of satellite data becauseit may be confused with clouds, which have similar radiative properties in the infrared and visible frequency ranges. Therefore a procedure has been developed to discriminate between sea ice and open water directly from MSU observations;this is performed through the determination of the microwave surface emissivity, which varies drastically from open water (values ranging from 0.45 to 0.65) to sea ice (values greater than 0.7) at these frequencies [Hollinger et al., 1984]. 3.2.
Cloudiness
Compared to other regions of the globe, only a few data are available
about the nature and the distribution
of clouds
in the Arctic. The existing climatologies [London, 1957; Voskresenskiy and Chukanin, 1959; Vowinckel, 1962; Huschke, 1969] are based on a few surface and airbone observations and may not be extremely reliable. As a matter of fact, the observation from the surface of cloud amount and type is even more difficult in polar areas than in other latitudes, because of (1) the long polar night, during which distinction between cloud types is difficult; and (2) the low temperatures, which produce clouds of rather low density. Satellite observations, with their more complete spatial and temporal coverage, overcome many of the problems associated with surface observations. Imagers, such as AVHRR about TIROS-N series satellites or the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP), have provided cloudinessinformation, which, unfortunately, is for limited periods (see World Meteorological Organization (WMO) [1987] for AVHRR and Barry et al. [1987] for DMSP). On the other hand, data from the NIMBUS 7 satellites have provided a global 6-year climatology, but this climatology is not relevant in polar areas because of difficulties in discriminating between sea ice or snow and clouds [Hwang et al., 1988]. In addition, the satellite-derived cloud results may show considerably different estimates from each other as discussedby McGuffie et al. [1988] and Crane and Barry [1984]. However, most of the climatologies agree on the fact that cloudinessis important in this region at any time of the year (see Figure 2) and that low clouds are a quasi-permanent feature of sea ice covered areas. These low clouds present a
PROFILES FROM TOVS
2877
temperature which is not very different from the surface and therefore are difficult to detect. Since a priori knowledge of the presence of sea ice may be obtained through the computation of the microwave surface emissivity, a new cloud detection test dealing with stratus has been achieved for daylight situationsusing two HIRS 2 channels. For daylight situations, radiance measured at 3.7/•m (HIRS 2 channel 19) correspondsto a combination of surface and solar radiation, whereas radiance at 11 /•m (HIRS 2 channel 8) comes from the surface only. The difference between these two radiances, normalized by the solar zenith angle, thus correspondsto the solar radiation and is greater in presence of clouds (or fog) which have an albedo larger than that of ice [Kidder and Wu, 1984]. Consequently, a threshold on this difference may readily allow the detection of stratus. During night a large difference between brightnesstemperatures of these two same channels indicate the presence of stratus. This is causedby the emissivity of stratusor fog being greaterat 11/•m (emissivityof 0.99) than at 3.7/•m (emissivity in the range 0.8-0.9) [Saundersand Kriebel, 1988]. 3.3.
Temperature Profiles
Temperature profiles in high-latitude regions are characterized by low tropospheric temperatures and a tropopause level lower than for other latitudes (about 8 km in FebruaryMarch, and 9.5 in July-August), with the tropopause height increasing in anticyclonic conditions and decreasing in cyclonic conditions. On occasion the tropopause becomes indistinct [Vowinckel and Orvig, 1970]. Accordingly, special attention has been paid to a better estimation of the pressure range in which the tropopause level can be expected. For the proximity recognition when searching for the closest situation in the TIGR data set, not only HIRS 2 and MSU brightnesstemperaturesare used, but also an estimation of the mean temperature of the lower part of the stratosphere, referred to as T(LS), obtained through a regressionof the type:
T(LS)reg=ao + al TBHIRS1 + a2 TBHIRS2 + a3 TBHIRS3 + a4 TBMSU2 + a5 TBMSU3 + a6 TBMSU4 where TBHIRSi is the brightness temperature of HIRS 2 channeli and TBMSUi is the brightnesstemperature of MSU channel i. Regressioncoefficientsai are obtained from the data archived in each TIGR data subset for all observing conditions (viewing angle, . . .). Previously, the regressioncoefficientswere calculatedconsidering the predicted variable to be the mean temperature between 200 and 305 hPa, which are two levels of the TIGR stratification.
In order to take into account
the fact that in
high-latitude regions, tropopause levels can also be found between 400 and 300 hPa, the predicted variable is now the mean temperature between 222 and 380 hPa (two TIGR stratification levels). Another striking feature concerns the temperature inver-
sions.Very frequent and occurringin all seasons,they can be subdividedinto three types: surface inversions, subsidence inversions, and advection inversions (see Figure 3). Over sea ice, surface inversionsare almost systematic,the surface sea ice temperaturebeing colder than the air above. In addition, Vowinckel and Orvig [1970] point out that, especially over Greenland, mixed layers are often found near the surface and isothermallayers are found above the principalinversionlayer.
2878
CLAUD ET AL.: RETRIEVAL OF TEMPERATURE PROFILES FROM TOVS
%
F
M
A
M
J
J
A
S
0
N
TABLE
D
1.
Characteristics of Analyzed Situations
IOO
•
NIo INViRSION I I• Orbit
ß
8o
z
6o
4o
I
2o
Fig. 3.
Time,
Number
Frequency distribution of different inversion types [from Vowinckel and Orvig, 1970].
7617 7632 7646 7660 7674 7687 7702 7716 7730 7745 7759 7772 7787 7801 7813 7843 6678
Date
Satellite
June 5, 1986 June 6, 1986 June 7, 1986 June 8, 1986 June 9, 1986 June 10, 1986 June 11, 1986 June 12, 1986 June 13, 1986 June 15, 1986 June 16, 1986 June 17, 1986 June 18, 1986 June 19, 1986 June 20, 1986 June 21, 1986 Dec. 31, 1987
NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA9 NOAA10
clones associated
Profiles with low tropospheric temperatures and with inversions (surface and/or above) are present in the TIGR data set. On the other hand, there is no profile with a very large isothermal layer. This is illustrated in Figure 4, showing a few representative TIGR temperature profiles that belong to the polar air mass type. However, the poor vertical resolution of the present satellite sounders also limits the ability of detecting inversions. Consequently, the possibility of resolving an inversion will mainly depend on its thickness. 3.4.
Cyclonic Activity
The meteorological situation in this region results mainly from the relative position and strength of the Islandic Low and the Greenland Anticyclone. During summer, many cy-
with
the arctic
UTC
HIRS 2 Lines Number
0957 1124 1117 1107 1056 0905 1034 1023 10!4 1132 0941 1111 1059 0729 0718 1026 0917
front
move
73 70 72 73 70 74 72 72 71 70 72 72 73 74 69 70 110
northward
or
eastward following the Siberian coast. During autumn and winter, very strongcyclonicactivity takes place in the 6reenland Sea. In other words, at almost any time of the year the region is characterizedby a large cyclonic activity with many mesoscalesystems. Among them, especially during wintertime, shallow mesoscale disturbancescalled polar lows can develop rapidly during a cold air outbreak and represent a challengein terms of forecastingbecause of their large spatiotemporal variability and relatively small scale. They often developfrom upper level cold core disturbancesthat shouldbe detectableby means of TOVS data. Rasmussenand Lystad [1987] suggestthat TOVS data be used for early detection of polar lows, even before the formation of any cloud or surface pressurefalls. However, TOVS observationsshouldalso provide informationduring the life of the polar low. The size of suchphenomena(from 100to 600 km) bringsup the problemof the optimal spatialresolutionat which one has to work. The 100-kmby 100-kmbox resolutionseemsreasonablyadaptedto the detectionof such phenomena. 4.
RESULTS
OF 3I METHOD
TO TOVS
4.1.
APPLIED
OBSERVATIONS
Description of the Cases
Table 1 presentscharacteristicsof the analyzed situations. 17 consecutive days from June 5 through 21, 1986, were acquired from the Troms0 Telemetry Station and calibrated and navigatedat the Centre de Meteorologie Spatiale in Lan-
lO
nion. However, observations taken on the 14th of June were
not usablebecauseof problems when storingthem. All these passes correspond to the experimental phase of the ARCTEMIZ campaign.The TIROS-N seriessatelliteproviding theseobservationswas National Oceanicand Atmospheric Administration9 (NOAA9). Each pass consistsof about 70 HIRS 2 lines, i.e., 8 min storing,and approximatelycoversthe
lOO
lOOO
180
220
260
300
TEMPERATURE ( K )
Fig. 4. Vertical temperature profiles of a few representative polar air mass TIGR situations. The temperature is given in Kelvin, and the pressure in hPa.
area between 65 ø and 90øN latitude and between 40øW and 70øE
longitude. A winter situation (December 31, 1987) was also studied. Corresponding calibrated-navigated NOAA10 observations were obtained from the Norwegian Meteorological Institute (DNMI). The 10-min pass covers an area centered on the
CLAUD ET AL.' RETRIEVAL OF TEMPERATURE PROFILES FROM TOVS
2879
80N
75
SPITZBERGEN
65
40
60E
Fig. 5a. Determination of the sea ice edge from the microwave surface emissivity computation in 3I boxes. Orbit 7660, June 8, 1986, NOAA9. Dark dots represent boxes with an emissivity larger
75
than 0.7.
Atlantic Ocean ranging from the Arctic to the north of Spain and was chosen because of the simultaneouspresence of a mid-latitude disturbance over Great Britain and a polar low over northern
4.2.
Scandinavia.
Open Water/Sea Ice Discrimination Results
Figure 5a displays the surface microwave emissivity as inferred from TOVS observations for an ARCTEMIZ pass (June 8, 1986, orbit number 7660) [Claud et al., 1989a]. These resultscompare well with the sea ice map provided by the Norwegian Meteorological Institute for the period from 5 to 11 June, deduced from AVHRR images and surface analyses (see Figure 5b). The threshold of detectability for sea ice concentration (i.e., fractional area of the ocean
covered by sea ice) is about 7/10. A border correspondingto an emissivity of 0.7 can easily be drawn, almost parallel to the Greenland coast, and then going from Spitzbergen to New
2O
Zemble
and from
New
Zemble
to the USSR.
40
60E
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