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.