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Estimation of Total Precipitable Water using AVHRR Imagery Final Project Report (Group C) 5/10/2013
Mohammad Danesh-Yazdi Yiwen Li
Project Description/Objectives
This project seeks to study and estimate the Total Precipitable Water (TPW) over the Twin Cities
metropolitan area in Minnesota. TPW is defined as the total atmospheric water vapor collected
by a vertical column of unit cross-sectional area between any two specified levels (Morris, 1992,
p. 1711). It is a useful measure of the moisture content in the air and thus is frequently calculated
by meteorologists to make appropriate weather forecast and to predict convective storms. The
study of precipitable water is also essential to the improved understanding of the Earth’s climate,
partly because water vapor plays a critical role in the greenhouse effect. Besides, the results of
TPW calculation are used to guide agricultural activities, manage natural resources, and control
natural disasters. For example, floods are caused by weather phenomena and events that deliver
extra precipitation to a drainage basin (Hirschboeck, 1991, p. 67); therefore, knowledge of the
precipitable water present in the atmosphere allows people to be aware of potential flooding
areas and to take precautions in order to prevent or reduce possible damage.
In consideration of the importance of precipitable water in various studies and applications
discussed above, we designed this project and focused on the Twin Cities metropolitan area,
bounded by coordinates -93.77W, -92.85W, 45.38N, and 44.62N, with the hope that the results
would contribute to researches in local environment and benefit the residents in the region.
Another expectation was to provide data of a specific area which might be conducive to the study
of global climate. In this respect, AVHRR (Advanced Very High Resolution Radiometer)
imagery has played a critical role in this project as it gave us access to images of the desired area
in different times and meanwhile provided other necessary ancillary information such as
Platinum Resistance Thermometers (PRT) and space count values to calibrate the thermal
channels and finally calculate the TPW.
The rest of this project is presented as follows. Open access to satellite data in recent decades
allows us to collect a series of satellite images of this region from 1998 to present. But it should
be noted that only cloudiness images are appropriate for this type of studies and this criterion
makes it a bit difficult to find a suitable image in the available library. After downloading usable
AVHRR image from NOAA’s website, the image is imported into Geomatica FreeView
software in order to get access to its header file and extract necessary information required for
the calibration process. Because the AVHRR imagery formats are not compatible with usual
remote sensing softwares, ENVI software is used to convert images into “.img” format. At last,
ERDAS and ArcGIS environments are used to perform the brightness and surface temperatures
calculations which are subsequently substituted into TPW equation. Final results are raster
images of brightness and surface temperatures and also TPW values across our case study.
Data Sources
1. A series of AVHRR images are downloaded from NOAA’s website:
http://www.class.ngdc.noaa.gov/saa/products/search?sub_id=0&datatype_family=AVHR
R&submit.x=24&submit.y=7
The latest instrument version is AVHRR/3 which first carried on NOAA-15 launched in
May 1998. This crosstrack scanning system collects five spectral bands of data at a
spatial resolution of 1.1 km. In this project, the fourth and the fifth bands are used to
calculate the TPW. Refer to Appendix A for more details of the images and AVHRR/3
channel characteristics.
2. Geomatica FreeView, a data viewing tool designed by PCI Geomatics Inc., permitted us
to load and read header file of the image downloaded from NOAA.
3. To convert NOAA’s images to ERDAS readable formats, we use ENVI, a geospatial
imagery analysis and processing application developed by Exelis Visual Information
Solutions.
4. We followed the steps on NOAA KLM User’s Guide, Section 7.1, to calibrate the
AVHRR thermal channels using ERDAS and ArcGIS environments. The Guide can be
viewed online via:
http://www.ncdc.noaa.gov/oa/pod-guide/ncdc/docs/klm/html/c7/sec7-1.htm
Procedure
Atmospheric water vapor adsorbs a portion of the reflected radiance signal recorded in the
visible and near infrared spectral bands captured by the AVHRR. Moreover, precipitable water
vapor between the sensor and surface differentially attenuates the adjacent wave bands in the two
channels (4 and 5) of the AVHRR sensor. For land, the difference between the two wave bands
varies both as a function of atmosphere water vapor and the temperature of the land surface. The
relationship between these parameters was explored by performing simulations using the
LOWTRAN-7 atmospheric radiative transfer code (Prince and Goward, 1995). According to this
study, water vapor simply alters the slope of the function of surface temperature and channel
4/channel 5 radiant temperature differences. As a result, TPW was derived as
(
)
where TPW is the total precipitable water vapor (cm), TE is the channel 4/channel 5 brightness
temperature (°K), and Ts is the surface temperature (°K). The major task is now to calculate the
correct brightness temperature of channels 4/5 and the surface temperature.
We start with computation of brightness temperature of bands 4 and 5 from the AVHRR
imagery. Since quantitative radiometric applications of the AVHRR radiance measurements are
becoming increasingly important both in research and operational environments, it becomes
necessary to calibrate the sensors accurately in order that the AVHRR radiance measurements
meet the precise performance requirements necessitated by the accuracy requirements of the
products derived from these radiances. Here the post-launch calibration procedure is described in
detail for the thermal infrared channels (channels 3B, 4, 5) which is based on the work performed
at the NOAA/NESDIS Office of Research and Applications and on the material furnished by ITT
Aerospace/Communications Division, Fort Wayne, Indiana (the instrument manufacturer).
Steps to Calibrate the AVHRR Thermal Channels (HRPT Receiving Station Data Users)
Step 1. The temperature of the internal blackbody target is measured by four PRTs. In each
scanline, data words 18, 19 and 20 in the HRPT minor frame format contain three readings from
one of the four PRTs. A different PRT is sampled each scanline; every fifth scanline all three
PRT values are set equal to 0 to indicate that a set of four PRTs has just been sampled. The count
value CPRT of each PRT is converted to temperature TPRT by the formula
The coefficients d0, d1, d2, d3, and d4 vary slightly for each PRT. Values for the coefficients are
found in Appendix B, in Tables 1-4 for NOAA 15-18. To calculate the internal blackbody
temperature TBB, NESDIS uses the simple average
Step 2. The radiance NBB sensed in each thermal AVHRR channel from the internal blackbody at
temperature TBB is the weighted mean of the Planck function over the spectral response of the
channel. The spectral response function for each channel is measured in approximately 200
wavelength intervals and provided to NESDIS by the instrument manufacturer. In practice, a
look-up table relating radiance to temperature is generated for each channel. Each table specifies
the radiance for every tenth of a degree (K) between 180 and 340K. The tables are referred to as
“Energy Tables”. It has been found that the following two-step equation accurately reproduces
Energy Table equivalent blackbody temperatures to within ±0.01K in the 180 to 340 K range.
Each thermal channel has one equation, which uses a centroid wavenumber νc and an “effective”
blackbody temperature TBB*. The two steps are
where the first and second radiation constants are
c1 = 1.1910427 × 10-5
mW/(m2-sr-cm
-4)
c2 = 1.4387752 cm-K
The values for νc and the coefficients A and B for channels 3B, 4 and 5 are unique for each
spacecraft and are found in Appendix B, Tables 5-8.
Step 3. Output from the two in-orbit calibration targets is used to compute a linear estimate of the
Earth scene radiance NE. Each scanline, the AVHRR views the internal blackbody target and
outputs 10 count values for each of the three thermal channel detectors; these are found in words
23 to 52 in the HRPT data stream. When the AVHRR views cold space, 10 counts from each of
the five channel sensors are output and placed into words 52 to 102. Count values for each
channel are averaged together to smooth our random noise; often counts from five consecutive
scanlines are averaged because it takes five lines to obtain a set of all four PRT measurements.
The average blackbody count CBB and the average space count CS, together with blackbody
radiance NBB and space radiance NS, explained in the next paragraph, are used to compute the
linear radiance estimate NLIN
where CE is the AVHRR count output when it views one of the 2,048 Earth targets. The
Mercury-Cadmium-Telluride detectors used for channels 4 and 5 have a nonlinear response to
incoming radiance. Pre-launch laboratory measurements show that
o scene radiance is a slightly nonlinear (quadratic) function of AVHRR output count.
o the nonlinearity depends on the AVHRR operating temperature.
It is assumed that the nonlinear response will persist in orbit. For the NOAA KLM series of
satellites, NESDIS uses a radiance-based nonlinear correction method. In this method, the linear
radiance estimate is first computed using a non-zero radiance of space, the NS term in the above
equation. Then, the linear radiance value is input into a quadratic equation to generate the
nonlinear radiance correction NCOR
Finally, the Earth scene radiance is obtained by adding NCOR to NLIN
Values for NS and the quadratic coefficients b0, b1, and b2 are found in Appendix B, Table 9-12.
Step 4. Data users often convert the computed Earth scene radiance value NE into an equivalent
blackbody temperature TE. This temperature is defined by simple inverting the steps used to
calculate the radiance NE sensed by an AVHRR channel from an emitting blackbody at a
temperature TE. The two-step process is
The values for νc and the coefficients A and B are found in Appendix B, Tables 5-8.
The next parameter required in TPW calculation is the surface temperature. It is actually the skin
temperature of the land surface, i.e., the kinematic temperature of the soil plus the canopy
surface (or, in the absence of vegetation, the temperature of the soil surface). The procedure to
derive surface temperature is called Split Window Technique which uses a linear combination of
the thermal channels in both channels to produce an atmospherically corrected thermal image.
The general equation for the split window technique for a two thermal channel can be written as
As described by Coll and Caselles (1997), coefficient used to generate surface temperature maps
are
Finally, we have all the required parameters to estimate the total precipitable water. It also
should be noted that residual cloud was further identified and removed where < 0.683
K and Ts > 292 K or > 0.683 K and Ts < 292 K.
Results
As mentioned in the previous section, several parameters are required to calibrate the raw
AVHRR images so that correct brightness temperature can be found. Most of these parameters
are satellite dependent and obtained from the header file of each image. In AVHRR imagery, the
header file of an image is not available as an external text file; so we used Geomatica FreeView
environment to import any specific image and get access to its header file. Here is an example of
a header file and its containing information.
“Geomatica FreeView ”
After finding a cloudiness image, the next step is to assign the right coordinate system to the
images. The spatial reference used for the images was NAD_1983_UTM_Zone_15N which is
appropriate for Twin Cities area.
Cloud free region
Then the Shapefile of Twin Cities boundary was downloaded from MetroGIS Data Finder
website (http://datafinder.org/catalog/index.asp) and both vector and raster maps were imported
into ERDAS Imagine as shown below.
As it is seen, that portion of the image containing Twin Cities area is cloud free which is suitable
for our purpose. Now we should extract the Twin Cities area from the image for further analysis.
To do so, the Clip tool in ERDAS is used and the selected area is saved into an AOI layer. Here
is the schematic of clipped image depicting Twin Cities area.
Twin-Cities Area
Now, this image was imported into ArcMap to perform the brightness and surface temperatures
and also TPW computation using Map Algebra tool.
Channel 4 brightness temperature
Channel 5 brightness temperature
Surface temperature
Total precipitable water
Discussion
It is seen from the brightness and surface temperature maps that the western and north eastern
parts of the study area have higher temperature than the south eastern part at the specific time
and date during which the image was captured. As we expect, this actually resulted in observing
more precipitable water in the south eastern part than the western and north eastern regions, but
this does not guarantee that we detect the same behavior for other time and dates. Moreover, it is
very essential to bear in mind that total precipitable water vapor is a meteorological parameter
and changes even from one day to another subsequent day; thus, any conclusion about vegetation
or land cover pattern based on the derived results may be misleading. Of course, promising
results may be obtained if the same algorithm is used for a large data set so that one can detect a
specific trend in precipitation distribution and its effect on land cover pattern.
References
[1] Hirschboeck, K. K. (1991). Hydrology of floods and droughts. In R. W. Paulson, E. B.
Chase, R. S. Roberts & D. W. Moody (Eds.), National water summary 1988-89 (pp. 67-88)
United States Government Printing Office.
[2] Morris, C. G. (1992). Academic press dictionary of science and technology Gulf Professional
Publishing.
[3] Inference of surface and air temperature, atmospheric precipitable water and vapor pressure
deficit using Advanced Very High-Resolution Radiomete satellite observations: comparison with
field observations, S.D. Princea,, S.J. Goetza, R.O. Dubayaha,b, K.P. Czajkowskia, M.
Thawleya, Journal of Hydrology 212–213 (1998) 230–249.
[4] Determination of precipitable water and cloud liquid water over oceans from the NOAA 15
advanced microwave sounding unit, Norman Grody, Jiang Zhao, Ralph Ferraro, Fuzhong Weng,
and Reinout Boers, JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 106, NO. D3, PAGES
2943–2953, FEBRUARY 16, 2001.
[5] César Coll Vicente Caselles (1997), A split-window algorithm for land surface temperature
from advanced very high resolution radiometer data: Validation and algorithm comparison,
Journal of Geophysical Research, Volume 102, Issue D14, pages 16697–16713, 27 July 1997.
[6] Becker, F., and Li, Zhao-Liang (1990), Towards a local split window method over land
surface, Int, J. Remote Sens., 3:369-393.
[7] Prince SD, Goward SN. Global primary production - a remote-sensing approach. Journal of
Biogeography. 1995;22:815–835.
Appendix A. Image Example and AVHRR/3 Channel Characteristics
Figure 1. One of the images downloaded from NOAA’s website and displayed on Geomatica
FreeView. This is a multispectral color composite image with 5 bands.
AVHRR/3 Channel Characteristics
Channel
Number
Resolution at
Nadir
Wavelength
(um) Typical Use
1 1.09 km 0.58 - 0.68 Daytime cloud and surface mapping
2 1.09 km 0.725 - 1.00 Land-water boundaries
3A 1.09 km 1.58 - 1.64 Snow and ice detection
3B 1.09 km 3.55 - 3.93 Night cloud mapping, sea surface
temperature
4 1.09 km 10.30 - 11.30 Night cloud mapping, sea surface
temperature
5 1.09 km 11.50 - 12.50 Sea surface temperature
Figure 2. AVHRR/3 Channels
Appendix B
Table 1. NOAA-15 AVHRR/3 conversion coefficients
PRT d0 d1 d2 d3 d4
1 276.60157 0.051045 1.36328E-06 0 0
2 276.62531 0.050909 1.47266E-06 0 0
3 276.67413 0.050907 1.47656E-06 0 0
4 276.59258 0.050966 1.47656E-06 0 0
Table 2. NOAA-16 AVHRR/3 conversion coefficients
PRT d0 d1 d2 d3 d4
1 276.355 5.562E-02 -1.590E-05 2.486E-08 -1.199E-11
2 276.142 5.605E-02 -1.707E-05 2.595E-08 -1.224E-11
3 275.996 5.486E-02 -1.223E-05 1.862E-08 -0.853E-11
4 276.132 5.494E-02 -1.344E-05 2.112E-08 -1.001E-11
Table 3. NOAA-17 AVHRR/3 conversion coefficients
PRT d0 d1 d2 d3 d4
1 276.628 0.05098 1.371 E-06 0 0
2 276.538 0.05098 1.371 E-06 0 0
3 276.761 0.05097 1.369 E-06 0 0
4 276.660 0.05100 1.348 E-06 0 0
Table 4. NOAA-18 AVHRR/3 conversion coefficients
PRT d0 d1 d2 d3 d4
1 276.601 0.05090 1.657 E-06 0 0
2 276.683 0.05101 1.482 E-06 0 0
3 276.565 0.05117 1.313 E-06 0 0
4 276.615 0.05103 1.484 E-06 0 0
Table 5. NOAA-15 AVHRR/3 thermal channel temperature to radiance coefficients.
νc A B
Channel 3B 2695.9743 1.621256 0.998015
Channel 4 925.4075 0.337810 0.998719
Channel 5 839.8979 0.304558 0.999024
Table 6. NOAA-16 AVHRR/3 thermal channel temperature to radiance coefficients.
νc A B
Channel 3B 2700.1148 1.592459 0.998147
Channel 4 917.2289 0.332380 0.998522
Channel 5 838.1255 0.674623 0.998363
Table 7. NOAA-17 AVHRR/3 thermal channel temperature-to-radiance coefficients.
νc A B
Channel 3B 2669.3554 1.702380 0.997378
Channel 4 926.2947 0.271683 0.998794
Channel 5 839.8246 0.309180 0.999012
Table 8. NOAA-18 AVHRR/3 thermal channel temperature-to-radiance coefficients.
νc A B
Channel 3B 2659.7952 1.698704 0.996960
Channel 4 928.1460 0.436645 0.998607
Channel 5 833.2532 0.253179 0.999057
Table 9. NOAA-15 radiance of space and coefficients for nonlinear radiance correction
quadratic.
Ns b0 b1 b2
Channel 4 -4.50 4.76 -0.0932 0.0004524
Channel 5 -3.61 3.83 -0.0659 0.0002811
Table 10. NOAA-16 Radiance of Space and coefficients for nonlinear radiance correction
quadratic.
NS b0 b1 b2
Channel 4 - 2.467 2.96 - 0.05411 0.00024532
Channel 5 - 2.009 2.25 - 0.03665 0.00014854
Table 11. NOAA-17 Radiance of Space and coefficients for nonlinear radiance correction
quadratic.
NS b0 b1 b2
Channel 4 -8.55 8.22 -0.15795 0.00075579
Channel 5 -3.97 4.31 -0.07318 0.00030976
Table 12. NOAA-18 Radiance of Space and coefficients for nonlinear radiance correction
quadratic.
NS b0 b1 b2
Channel 4 -5.53 5.82 -0.11069 0.00052337
Channel 5 -2.22 2.67 -0.04360 0.00017715
Appendix C
NOAA’s website allowed us to view the details of the images before we downloaded them. For
example, we selected appropriate images based on the location of Twin Cities in the image and
also their cloudiness condition.
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