An uncooled microbolometer-array thermal infrared camera has been incorporated into a remote sensing system for radiometric sky imaging. The radiometric calibration is validated and improved through direct comparison with spectrally integrated data from the Atmospheric Emitted Radiance Interferometer (AERI). With the improved calibration, the Infrared Cloud Imager (ICI) system routinely obtains sky images with radiometric uncertainty less than 0.5 W/(m2 sr) for extended deployments in challenging field environments. We demonstrate the infrared cloud imaging technique with still and time-lapse imagery of clear and cloudy skies, including stratus, cirrus, and wave clouds.
©2005 Optical Society of America
The spatial distribution of clouds in the Earth’s atmosphere is an important variable in fields as diverse as climate studies , weather forecasting , atmospheric radiative-transfer modeling , solar power feasibility studies , Earth-space optical communication link availability analysis , and so forth. In these various fields clouds provide strong modulation of the short- and long-wave radiation reaching the Earth and escaping from its surface, determine the amount and duration of available solar energy, and contribute absorption and scattering that can dominate the link budget for an Earth-space optical communication path. Satellites are best for measuring global cloud statistics, but ground-based sensors provide valuable localized data with continuous time records, higher spatial resolution, and unique information about cloud bottoms that usually are not seen by satellites.
Thermal infrared sky imaging from the ground is well suited to measuring horizontal spatial cloud structure continuously at a fixed site because of the presence of a high-contrast thermal emission cloud signature during day and night. The contrast is lessened, however, as clouds become thinner, higher and colder, or as the atmospheric water vapor content rises. In the long-wave infrared spectral region (~8–14 μm), the dominant sources of atmospheric thermal emission are water vapor and clouds. Therefore, water vapor correction is necessary for consistent cloud measurement and is only practical if the infrared images are calibrated radiometrically so that the water vapor emission can be reliably estimated and removed . Furthermore, while cloud amount is the most basic parameter to be measured, in some instances there is further information about cloud type, cloud height, cloud emissivity, or other properties that can be obtained from radiometrically calibrated images.
The Infrared Cloud Imager (ICI) is a ground-based thermal infrared radiometric imaging system developed originally for deployment in the Arctic where in winter the radiation balance is especially sensitive to clouds and long-wave radiation. The system is based on a commercial microbolometer  camera that allows thermal imaging without cryogenic cooling that can be difficult or impossible in long-term deployments at remote sites. However, especially in their early offerings, it was not obvious that these detectors would provide sufficient sensitivity or stability to achieve high-quality radiometric imaging of the atmosphere, which emits with equivalent brightness temperatures ranging from approximately 0 to -100°C [8,9]. Encouragement was provided when a four-channel microbolometer camera was used to image clouds from the space shuttle , but the scene is much brighter for a downward-viewing thermal imager than for an upward-viewing one.
The purpose of this paper is therefore to describe the radiometric performance of the ICI system and to demonstrate that radiometric images having uncertainty within 0.5 W/(m2 sr) can be achieved routinely with a microbolometer camera deployed at remote field sites. In the remainder of this paper we describe the ICI system, provide validation and improvement of its calibration, and show examples of radiometric images obtained with the ICI under a wide variety of conditions in locations ranging from the Arctic to the midlatitudes.
2. Radiometric calibration of the Infrared Cloud Imager (ICI)
2.1 The ICI system
At the heart of the ICI system is a commercial uncooled microbolometer-array infrared camera (Amber Sentinel). Radiometrically calibrated sky images are recorded on 320 × 240 pixels in the 8–14 μm thermal infrared atmospheric window. The camera generates 30 frames per second, but usually we acquire one frame per minute (or slower) to avoid excessive data redundancy. Digital images are recorded with 12-bit resolution. The prototype camera lens has a full-angle field of view of approximately 18° × 13.5°, which will be increased to greater than 80° in future versions to capture most of the useful sky dome. As illustrated in Fig. 1, the camera views a beam-steering mirror mounted on a stepper motor controlled by the system computer. The stepper motor rotates the mirror such that the camera alternately views two large-area blackbody sources and the sky through a port that the computer opens for image collection when a precipitation sensor indicates that it is not raining or snowing excessively. One blackbody source is thermoelectrically controlled and maintained near 50°C (± 0.1°C) and the other floats at the ambient temperature of the optics-box interior (typically near 20°C, measured with accuracy ± 0.1°C). The controlled blackbody has a honeycomb surface with emissivity better than 0.99 and the ambient source has a flat surface with emissivity of approximately 0.97 (the reflected ambient radiance is assumed to compensate for the ambient-source emissivity, but the reflected radiance from the warm source is accounted for directly).
The ICI atmospheric signal is an integral of the downwelling atmospheric emission spectrum shown in Fig. 2 over the spectral wave number range of approximately 714–1250 cm-1 (8–14 μm wavelength). This plot shows radiometric FTIR measurements of atmospheric emission  for a clear, dry atmosphere (bottom blue curve), cirrus clouds (middle green curve) and stratus clouds (top red curve). This part of the spectrum is a window band, which has high transmittance (low emission) relative to the highly absorbing surrounding regions (primary absorbers are water vapor on the right of Fig. 2 and carbon dioxide on the left). In the limit of very low, warm, optically thick stratus clouds, the downwelling atmospheric emission spectrum approaches a Planck curve for a blackbody at the cloud-base temperature (e.g. the top curve in Fig. 2). Each pixel of an ICI image provides a measure of the emitted radiance in a different part of the sky, from which clouds can be identified and classified by type through the magnitude of the residual radiance after the intervening water vapor emission is removed . Variations in the atmospheric water vapor content also cause the downwelling emission to change in a manner similar to that shown in Fig. 2 for clouds. Consequently, careful removal of water vapor emission in radiometrically calibrated images is required for consistent cloud detection in varying conditions .
One of the key questions in the ICI development was if the camera could be calibrated sufficiently for radiometric measurements of the weakly emitting clear sky and thin clouds. Cloud detection and classification depend less on absolute calibration than many radiometric sensing applications, but successful compensation of atmospheric emission between the sensor and the cloud still requires reasonably accurate sensor calibration (approximately 5%). The ICI calibration is based on measurements taken with the camera viewing the atmosphere and the two blackbody sources described previously. A linear fit of the band-integrated radiance (L) for two blackbody targets vs. digital number (DN) is used to derive a calibration equation of the form
which is applied with a unique gain (G = δL/δDN) and offset (C) each pixel to calibrate the sky image. Calibrations are performed with the automatic gain control of the camera turned off and a new calibration is performed for each image. The key to this calibration technique is linearity of the detector response (or linearization of the nonlinear response) because measurements of warm calibration targets are used to determine the line that is extrapolated downward to calibrate the much colder sky radiance.
Before each set of images, the camera’s internal offset correction is run to reduce pixel-to-pixel variations (a shutter covers the detector array and the offset voltage on each pixel is adjusted to minimize variation caused by charge buildup on the microbolometers). However, the internal correction does not always return the pixels to the same voltages. Therefore, a single blackbody calibration target was used in the earliest version of ICI to determine an offset-correction factor for each image, while relying on a laboratory gain measurement that was assumed to remain constant. Later a second blackbody calibration source was added to enable the system to continuously compensate for changes in the gain and offset of each pixel.
We also performed laboratory calibrations using a blackbody cone whose temperature was varied from 20 °C to -80 °C by adding dry ice to an isopropyl alcohol bath . Two independent calibrations twelve days apart yielded data that had mean r 2 values of 0.997 for linear least-squares fit calibration equations relating the band-average blackbody radiance to the mean camera signal. The laboratory calibrations had a radiance standard deviation of 0.58 W/(m2 sr), corresponding to brightness temperature uncertainties of 0.74 °C at the warm end (20 °C) and 3.5 °C at the cold end (-80 °C). The temperature data at the cold end have the largest uncertainty because of the nonlinear Planck function and because of measurement difficulties at temperatures near -60 °C . Maintaining this radiometric performance in the field, however, is challenging without the continuous two-source calibration.
2.2 Validation of the ICI calibration
While laboratory calibrations can provide useful insights, what ultimately matters is the radiometric performance in field deployments. The ICI was developed to operate at the Poker Flat Research Range (PFRR) near Fairbanks, Alaska, but it has been deployed also at sites operated by the Atmospheric Radiation Measurement (ARM) program (www.arm.gov) in Barrow, Alaska (Jan-Apr 2002, Feb-May 2004) and Lamont, Oklahoma (Feb-Apr 2003), where a variety of validation data are available.
Early calibration comparisons at PFRR during the winter of 2000-2001 suggested that the early ICI prototype calibration was somewhat low. As an example, the ICI measured a persistent stratus cloud brightness temperature of -14.4 ± 0.55 °C on 2000 Sep. 30 at 0154 UTC, while a radiosonde at 0000 UTC suggested a cloud-base temperature of -13 ± 1 °C. The same radiosonde gave a cloud-top temperature of -22 ± 1 °C, in good agreement with -23 °C measured by the Advanced Very High Resolution Radiometer (AVHRR) satellite channel 4 (10.3–11.3 μm) at 0045 UTC. In a clear-sky comparison on 2000 Dec. 14, the ICI brightness temperature was -85.8 ± 2 °C at 1453 UTC, compared with a radiosonde-based radiative transfer calculation of -79 ± 3 °C This comparison has only modest value because the radiosondes were launched from the Fairbanks airport (~50 km SW of PFRR). Later comparisons were similar, but calculations based on radiosondes are problematic because of spatial and temporal mismatches, inaccurate water vapor measurements in dry air, unknown cloud emissivity, and imprecise determination of cloud temperatures.
The most significant limitation in the ICI radiometric calibration and its validation is that the detailed shape of the camera’s spectral response function is unknown (the specifications state simply that the bandwidth is 8–14 μm). The optical elements are attached to the detector array in a manner that prevents us from measuring their transmittance. After repeated failures to obtain details from the manufacturer, we have been using a rectangular 8–14 μm bandwidth.
A direct validation of the ICI calibration (Figs. 3 and 4), which enabled adjustment of the unknown bandwidth, came from comparisons of the ICI and the Atmospheric Emitted Radiance Interferometer (AERI), an FTIR spectrometer operated at the ARM sites, whose radiometric calibration uncertainty is within 1% of the ambient radiance . Figure 3 is a plot of the mean ICI image radiance and band-integrated AERI radiance from a deployment at the ARM site near Barrow, Alaska in February-April 2004. Matching data from two sensors with different fields of view and temporal sampling periods is particularly difficult in variable sky conditions, so we restricted the comparison to data with high spatial uniformity. However, we still see some scatter in the plot that is likely related to variable clouds.
Figure 3 includes 220 individual ICI images and corresponding AERI measurements, chosen from a three-day data period based on a requirement that the ICI image standard deviation is less than 0.6 W/(m2 sr). The original field calibration of the ICI data is consistent over time, but has a cold bias at both the cold and warm ends (larger at the warm end). From numerous calculations and measurements, this bias appears to not be caused by blackbody emissivity or temperature errors, but rather from a combination of the uncertain bandwidth and a possible camera nonlinearity. The error is not correlated with the camera temperature.
Figure 4 is a plot of the same data from Fig. 3, recalibrated with a rectangular bandwidth of 8.5–14 μm (which produces a zero mean error between the cold ICI and AERI data) and adjusted for a nonlinear gain. Recalibrating the ICI data repeatedly with different bandwidths showed that varying the bandwidth over reasonable ranges allows great control over the cold data, with little effect on warm data. For identifying and classifying clouds in infrared images, it is the cold calibration that matters most because of the low signal from high, thin clouds (in contrast to the much larger radiance of low clouds, which makes them effectively impossible to miss, even with a significant calibration error ). Nevertheless, it is desirable to achieve a good calibration over the full range to maximize the quantitative value of the measurements.
Using the 8.5–14 μm bandwidth instead of 8–14 μm increases the cold ICI measurements by approximately 1 W/(m2 sr), but leaves a residual error of 2.4 W/(m2 sr) at the warm end. A likely cause of the warm error is a slightly nonlinear camera response (although we are still investigating the effect of a potential offset caused by viewing the nearby blackbody sources with the camera focused at infinity, and the combination of this and an unknown bandwidth could contribute to the calibration error). By evaluating the difference between ICI and AERI data over a much larger data set than shown in Fig. 3, we developed a mildly quadratic equation to calculate corrected radiance (Lc) from the radiance (L) generated by the linear ICI calibration:
The data from Fig. 3 were plotted in Fig. 4 after all the Barrow 2004 ICI data were recalibrated with the 8.5–14 μm bandwidth and the nonlinearity correction (eq. 2). In Fig. 4 there is no systematic bias between ICI and AERI data, but there are brief periods with larger differences that we attribute to different clouds within the sensor fields of view. With the correction shown in Fig. 4, the mean residual error is approximately 0.5 W/(m2 sr).
3. Radiometric sky images
In this section we show images recorded by the ICI in Alaska, Montana, and Oklahoma to illustrate the ICI capabilities in varying conditions. Fig. 5 shows the results of water vapor correction and cloud detection with a threshold filter. Figure 5a is an ICI radiance image [W/(m2 sr)] from Lamont, Oklahoma, 2003 April 16, 1643 UTC, and Fig. 5b is the water-vapor corrected version with the same scale. Fig. 5c is a binary cloud-detection image indicating clouds in dark red and clear sky in dark blue. The successful water vapor correction and cloud detection is encouraging, given the reasonably high value of 2.3 cm precipitable water vapor. The water vapor correction is based on MODTRAN radiative transfer calculations using the precipitable water vapor amount measured by a ground-based microwave radiometer or a radiosonde launched within three hours of the image. Further details about the water vapor correction and cloud statistics from ICI data are given in .
Figure 6 is a collection of ICI images displayed in brightness temperature (°C), with dark blue = -80°C and dark red = +20°C, obtained from a curve fit to the spectrally integrated Planck radiance function. These images were selected to cover a wide range of conditions, from an extremely dry, clear Arctic atmosphere (Fig. 6a) to a humid, midlatitude atmosphere with low stratus clouds (Fig. 6i). Figure 6g is an interesting image, showing a serendipitously imaged flock of birds flying below thick cirrus (the brightness temperature is -6 to -2.9 °C for the birds and -22 to -15°C for the clouds). Table 2 lists some key parameters for each image, along with the cloud emissivity derived from each ICI image when sufficient information was available. The listings in Table 2 use the following notation:
NSA = North Slope of Alaska ARM site near Barrow, Alaska (71.32°N, 156.62°W);
PFRR = Poker Flat Research Range near Fairbanks, Alaska (65.12°N, 147.47°W);
MSU = Montana State University in Bozeman, Montana (45.67°N, 111.05°W);
SGP = Southern Great Plains ARM site at Lamont, Oklahoma (36.62°N, 97.5°W);
pwv = precipitable water vapor [cm];
cl = cloud liquid [mm];
H cld = height of cloud above ground level [m];
T a = air temperature at surface [°C];
T cld = air temperature at cloud height [°C];
T b_cld = brightness temperature of cloud measured by the ICI [°C];
T b_sky = brightness temperature of clear sky measured by the ICI [°C];
L cld = radiance of cloud measured by the ICI [W/(m2 sr)];
L sky = radiance of clear sky measured by the ICI [W/(m2 sr)];
ε = cloud emissivity derived from ICI radiance.
The cloud emissivity values listed in Table 2 were derived with a simple radiative transfer formulation that includes the cloud emission plus the atmospheric transmittance and emission below the cloud. If the downwelling radiance measured by the ICI (L cld) is written as the downwelling atmospheric emission below the cloud (L a) + cloud emission transmitted through the air below the cloud (τεL bb), then the emissivity can be expressed as
where τ is the transmittance of the atmospheric path from cloud to ground, and L bb is the blackbody radiance evaluated at the air temperature for the cloud height (all radiances are integrated over the bandwidth). Taking into account cloud reflectance and cloud transmittance can improve the accuracy for thin clouds, but the values given here demonstrate the utility of the ICI data for retrieving cloud parameters. The resulting emissivities are spread over a reasonable range from 0.16 for thin, Arctic cirrus to greater than 0.99 for low, midlatitude stratus (the emissivity should be essentially 1.0 for cl > 0.1 mm ).
Figure 7 shows a movie of altocumulus wave clouds observed with the ICI at Lamont, Oklahoma on 2003 April 05 at 1930-1956 UTC. This kind of wave cloud usually is caused by a disturbance in an elevated layer of stable air. The images were recorded at one frame per minute and are played back in the movie at two frames per second. The conditions were: T a = 14 °C, pwv = 1.24 cm, H cld = 2383 m AGL, T cld = -18.7 to -33 °C, and L cld = 17-23 W/(m2 sr). From the cloud height and angular width in the image, the spatial wavelength is calculated to be approximately 374 m, corresponding to a velocity of 12.5 m/s (the rawindsonde measurement was 14 m/s). In this case the ICI was oriented with the top of the image pointing south, so that the cloud motion is nominally from southwest to northeast (the rawindsonde measured a southwesterly wind coming from azimuth angle 215°).
The sensitivity of the ICI to thin clouds is demonstrated by measurements on 2005 March 1–3 at the Montana State University campus in Bozeman, Montana. We were operating the ICI and a dual-polarization cloud lidar while launching radiosondes. The sky was entirely devoid of visible clouds for most of this time and at night stars were sharply visible. However, the lidar observed a persistent scattering layer at 7–8 km AGL that produced a depolarization ratio of 0.18–0.24 . After water vapor correction (using the precipitable water vapor of 0.39 cm obtained from the integrated radiosonde profile), the ICI residual radiance images showed a relatively uniform layer of 5–6 W/(m2 sr), well above the typical cloud-identification threshold range of 1.5–3 W/(m2 sr). This layer could have been a subvisual cirrus cloud, or possibly a layer of Asian dust. A back-trajectory analysis indicated that the air over Bozeman on this night came from China ten days earlier. Asian dust is often observed over the Pacific Ocean and the United States during the northern hemisphere spring months . Either way, the layer was optically very thin, likely with optical depth of 0.03 or less , and the ICI detected it consistently on both March 1 and 3. This measurement suggests a high sensitivity to even very thin cloud layers, at least under low-humidity conditions.
4. Discussion and conclusions
The ICI technique has been developed sufficiently that continuous data can be collected in long-term deployments. For example, the ICI operated with no downtime during a five-week deployment in the harsh environment of Barrow, Alaska during March-April 2004 and has operated for multiple months at a time at Poker Flat Research Range. The water vapor correction of continuously recorded ICI images requires a continuous (or frequently updated) measurement of the precipitable water vapor content, which ideally is provided by a ground-based microwave radiometer, but which also can be provided by a combination of radiosonde profiles and surface meteorological measurements with a modestly reduced accuracy (the latter approach is being studied currently).
The ICI radiometric calibration using two blackbody sources and a matrix formulation that provides individual gain and offset values for each pixel provides consistent accuracy, even as the instrument housing temperature varies over a range of 0–30°C. Radiometric uncertainty of 0.5 W/(m2 sr)] (approximately 2% of the ambient radiance) is achieved in comparisons of radiance obtained from the improved ICI calibration and the AERI. The ICI’s uncooled microbolometer detector array provides adequate sensitivity to detect even thin, cold, Arctic cirrus with brightness temperatures down to approximately -80 °C, below which detector noise and inter-pixel fluctuations begin to become problematic. One significant advantage of the ICI system is that it provides a consistent measurement of clouds continuously throughout day and night. With the improved calibration, it is possible to go beyond cloud statistics and derive cloud emissivity and similar parameters from ICI images.
The ICI development and deployments at Poker Flat Research Range were funded by the Japanese Communications Research Laboratory, now the National Institute of Information and Communication Technology (www.nict.go.jp), as part of a joint Japan - U.S. program to study the Arctic atmosphere. Hector Bravo (NOAA/ETL, www.etl.noaa.gov) and Erik Edqvist (Uppsala University, Sweden) built the original ICI hardware. Dr. Gary Wick (NOAA/ETL) provided the AVHRR data. Erik Meheil (University of Colorado) conducted the early ICI calibration measurements and participated in the 2000–2001 PFRR deployment; Erik Edqvist (Uppsala University, Sweden) continued the early calibration measurements and was the key participant in the 2002 NSA deployment. Participants in later deployments were Montana State University (MSU) graduate students Michael Obland (SGP 2003, NSA 2004), Nathan Seldomridge (NSA 2004), Nathan Pust (NSA 2004), and Brentha Thurairajah (SGP 2003), and MSU undergraduate student Paul Nugent (PFRR 2005). MSU graduate student Nathan Seldomridge provided the MSU lidar measurements. We gratefully acknowledge the contributions of the support teams at PFRR and the ARM sites in Oklahoma and Alaska. Financial support beyond that provided by NICT in Japan came from the NOAA Arctic Research Office and the Office of Biological and Environmental Research of the U.S. Department of Energy as part of the Atmospheric Radiation Measurement program.
References and links
1. R. D. Cess and P. M. Udelhofen, “Climate change during 1985-1999: cloud interactions determined from satellite measurements,” Geophys. Res. Letters 30, 19–1–4 (2003). [CrossRef]
2. K.-N. Liou, “Influence of cirrus clouds on weather and climate processes: a global perspective,” Mon. Weather Rev. 114, 1167–1199 (1986). [CrossRef]
3. R. F. Cahalan, W. Ridgeway, W. J. Wiscombe, T. L. Bell, and J. B. Snider, “The albedo of fractal stratocumulus clouds,” J. Atmos. Sci. 51, 2434–2455 (1994). [CrossRef]
4. C. H. Whitlock, D. E. Brown, W. S. Chandler, R. C. DiPasquale, N. A. Ritchey, S. K. Gupta, A. C. Wilber, D. P. Kratz, and P. W. Stackhouse, “Global surface solar energy anomalies including El Nin̂o and La Nin̂a years,” J. Solar Energy Eng. 123, 211–215 (2001). [CrossRef]
5. D. M. Erickson, D. H. Tsiang, and M. Jeganathan, “Upgrade of the Atmospheric Visibility Monitoring system,” in Free Space Laser Comm. Technol. XI, G. S. Mecherle, ed., Proc. SPIE 3615, 310–315 (1999). [CrossRef]
6. B. Thurairajah and J. A. Shaw, “Cloud statistics measured with the Infrared Cloud Imager (ICI),” IEEE Trans. Geosci. Rem. Sens. 43 (to be published, September2005). [CrossRef]
7. P. W. Kruse, Uncooled thermal imaging: Arrays, systems, and applications (SPIE press, 2001), ch. 4. [CrossRef]
8. J. A. Shaw and L. S. Fedor, “Improved calibration of infrared radiometers for cloud-temperature remote sensing,” Opt. Eng. 32, 1002–1010 (1993). [CrossRef]
9. J. A. Shaw, H. M. Zorn, J. J. Bates, and J. H. Churnside, “Observations of downwelling infrared spectral radiance at Mauna Loa, HI during the 1997–1998 ENSO event,” Geophys. Res. Let. 26, 1727–1730 (1999). [CrossRef]
10. R. S. Lancaster, J. D. Spinhirne, and K. F. Manizade, “Combined infrared stereo and laser ranging cloud measurements from shuttle mission STS-85,” J. Atm. Ocean. Technol. 20, 67–78 (2003). [CrossRef]
11. R. O. Knuteson, H. E. Revercomb, F. A. Best, N. C. Ciganovich, R. G. Dedecker, T. P. Dirkx, S. C. Ellington, W. F. Feltz, R. K. Garcia, H. B. Howell, W. L. Smith, J. F. Short, and D. C. Tobin, “Atmospheric Emitted Radiance Interferometer. Pt. II: Instrument Performance,” J. Atmos. Oceanic. Technol. 21, 1777–1789 (2004). [CrossRef]
12. N. L. Seldomridge, “Dual-polarization cloud lidar design and characterization,” M.S. Thesis, Montana State University, Bozeman, Montana (2005)., http://www.montana.edu/etd/available/seldomridge_0805.html.