Meteorological Applications of Temperature and Water Vapor Retrievals from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI)

Wayne F. Feltz Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison Madison, Wisconsin

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William L. Smith Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison Madison, Wisconsin

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Robert O. Knuteson Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison Madison, Wisconsin

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Henry E. Revercomb Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison Madison, Wisconsin

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Harold M. Woolf Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison Madison, Wisconsin

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H. Ben Howell Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison Madison, Wisconsin

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Abstract

The Atmospheric Emitted Radiance Interferometer (AERI) is a well-calibrated ground-based instrument that measures high-resolution atmospheric emitted radiances from the atmosphere. The spectral resolution of the instrument is better than one wavenumber between 3 and 18 μm within the infrared spectrum. The AERI instrument detects vertical and temporal changes of temperature and water vapor in the planetary boundary layer. Excellent agreement between radiosonde and AERI retrievals for a 6-month sample of coincident profiles is presented in this paper. In addition, a statistical seasonal analysis of retrieval and radiosonde differences is discussed. High temporal and moderate vertical resolution in the lowest 3 km of the atmosphere allows meteorologically important mesoscale features to be detected. AERI participation in the Department of Energy Atmospheric Radiation Measurement program at the Southern Great Plains Cloud and Radiation Testbed (SGP CART) has allowed development of a robust operational atmospheric temperature and water vapor retrieval algorithm in a dynamic meteorological environment near Lamont, Oklahoma. Operating in a continuous mode, AERI temperature and water vapor retrievals obtained through inversion of the infrared radiative transfer equation provide profiles of atmospheric state every 10 min to 3 km in clear sky or below cloud base. Boundary layer evolution, cold or warm frontal passages, drylines, and thunderstorm outflow boundaries are all recorded, offering important meteorological information. With important vertical thermodynamic information between radiosonde locations and launch times, AERI retrievals provide data for planetary boundary layer research, mesoscale model initialization, verification, and nowcasting. This paper discusses retrieval performance at the SGP CART site, as well as interesting meteorological case studies captured by AERI profiles. The AERI system represents an important new capability for operational weather- and airport-monitoring applications.

* Current affiliation: Atmospheric Sciences Division, NASA/Langley Research Center, Hampton, Virginia.

Current affiliation: National Environmental Satellite, Data, and Information Service, NOAA, University of Wisconsin—Madison, Madison, Wisconsin.

Corresponding author address: Wayne F. Feltz, CIMSS, Space Science and Engineering Center, University of Wisconsin—Madison, 1225 W. Dayton St., Rm. 239, Madison, WI 53706.

wayne.feltz@ssec.wisc.edu

Abstract

The Atmospheric Emitted Radiance Interferometer (AERI) is a well-calibrated ground-based instrument that measures high-resolution atmospheric emitted radiances from the atmosphere. The spectral resolution of the instrument is better than one wavenumber between 3 and 18 μm within the infrared spectrum. The AERI instrument detects vertical and temporal changes of temperature and water vapor in the planetary boundary layer. Excellent agreement between radiosonde and AERI retrievals for a 6-month sample of coincident profiles is presented in this paper. In addition, a statistical seasonal analysis of retrieval and radiosonde differences is discussed. High temporal and moderate vertical resolution in the lowest 3 km of the atmosphere allows meteorologically important mesoscale features to be detected. AERI participation in the Department of Energy Atmospheric Radiation Measurement program at the Southern Great Plains Cloud and Radiation Testbed (SGP CART) has allowed development of a robust operational atmospheric temperature and water vapor retrieval algorithm in a dynamic meteorological environment near Lamont, Oklahoma. Operating in a continuous mode, AERI temperature and water vapor retrievals obtained through inversion of the infrared radiative transfer equation provide profiles of atmospheric state every 10 min to 3 km in clear sky or below cloud base. Boundary layer evolution, cold or warm frontal passages, drylines, and thunderstorm outflow boundaries are all recorded, offering important meteorological information. With important vertical thermodynamic information between radiosonde locations and launch times, AERI retrievals provide data for planetary boundary layer research, mesoscale model initialization, verification, and nowcasting. This paper discusses retrieval performance at the SGP CART site, as well as interesting meteorological case studies captured by AERI profiles. The AERI system represents an important new capability for operational weather- and airport-monitoring applications.

* Current affiliation: Atmospheric Sciences Division, NASA/Langley Research Center, Hampton, Virginia.

Current affiliation: National Environmental Satellite, Data, and Information Service, NOAA, University of Wisconsin—Madison, Madison, Wisconsin.

Corresponding author address: Wayne F. Feltz, CIMSS, Space Science and Engineering Center, University of Wisconsin—Madison, 1225 W. Dayton St., Rm. 239, Madison, WI 53706.

wayne.feltz@ssec.wisc.edu

Introduction

In recent years it has become increasingly important to characterize the thermodynamic state of the earth’s planetary boundary layer (PBL) at higher temporal resolution than is currently possible with radiosondes. Improved meteorological model data assimilation schemes used in mesoscale models have created a necessity for frequent updates of primary meteorological parameters (Benjamin et al. 1995). In situ commercial aircraft measurements (Fleming 1996) and ground-based wind profilers (Weber et al. 1990) are currently being assimilated into the Rapid Update Cycle model (Benjamin et al. 1994), providing high temporal and spatial resolution measurements of temperature, moisture, and wind. New experimental atmospheric profiling technologies including Raman lidar (Goldsmith et al. 1994; Melfi et al. 1985), radio acoustic sounding systems (RASS) (May et al. 1990), and microwave radiometers (Han et al. 1994) can also provide moisture and temperature soundings at high temporal resolution.

A fully automated ground-based passive infrared interferometer called the Atmospheric Emitted Radiance Interferometer (AERI) provides 10-min temporal resolution atmospheric emitted radiance spectra of better than one wavenumber in spectral resolution. These radiances contain valuable information about the vertical thermal and moisture structure in the earth’s atmosphere to 3 km. The thermodynamic temperature and water information can be retrieved through inversion of the infrared radiative transfer equation (RTE), as explained by Smith et al. (1990, 1993, and 1998). High temporal resolution temperature and water vapor retrievals allow mesoscale meteorological features to be identified, including PBL evolution, frontal passages, and thunderstorm outflow boundaries (Feltz 1994, 1996). These features have important forecast implications. A description of retrieval accuracy compared to radiosondes and meteorological case studies is presented, illustrating AERI’s meteorological applications.

Retrieval technique

The AERI system (see Fig. 1) consists of a two-band, BOMEM-100 Michelson interferometer (of Quebec, Canada); two detectors (5–20 and 3–5 μm, henceforth referred to as band 1 and band 2, respectively); a scene-imaging scan mirror; and a two-point calibration system. Downwelling IR radiation enters the top of the instrument and encounters a scan mirror. As the IR radiation strikes the mirror, it is reflected toward a beam splitter, where a portion of the radiation is reflected onto one moving mirror, while another portion is transmitted onto another mirror moving in the opposite direction. The two beams are reflected back to the beam splitter, where they are recombined, creating an interference pattern called an interferogram. Constructive or destructive interference occurs depending on the optical path difference between the two when they are recombined. The interferogram is the summation of all interference patterns caused by every wavelength in the earth’s electromagnetic spectrum. The earth’s radiation pattern is the Fourier transform of the interferogram provided by the AERI system.

Every 2 s an uncalibrated spectrum is obtained. The data are averaged over a 3-min time period (90 samples) to reduce radiometric noise and to decrease the volume of data. The scanning mirror operates on a 10-min cycle, leaving the remaining time for the mirror to view two blackbodies used for routine calibration. One of the most important steps for data integrity is absolute radiometric calibration traceable to the National Institute of Standards and Technology (NIST) standards. Revercomb et al. (1988) have developed a calibration technique that properly accounts for interferometric phase characteristics. The operational calibration system consists of“hot” and “ambient” high-emissivity blackbodies, which are kept at 333 K and at local air temperature, respectively. For the development of a nonlinearity correction algorithm, an external calibration source using liquid nitrogen was used. A more detailed description of the AERI instrument exists in a paper by Revercomb (1993).

Figure 2 illustrates the meteorological information contained in an AERI radiance spectrum. The upper plot contains an AERI band 1 spectra from a West Coast marine climate near Pt. Mugu, California. In the lower panel, two winter midlatitude AERI band 1 spectra are placed over one another, indicating diurnal differences in the radiance spectrum during the Spectral Radiance Experiment (Ellingson and Wiscombe 1996). The variations in atmospheric thermal structure in the PBL are noted by the 620–720-cm−1 CO2 emission region. Large differences in water vapor amounts between the maritime and continental atmospheres are noted with amplitude and magnitude differences of the water vapor lines in the 538–588- and 1250–1350-cm−1 spectral regions.

The temperature profile of the PBL can be extracted from radiance measurements within the 620–720-cm−1 region of the 15-μm CO2 band. As shown in Fig. 2, the relative tendency of the vertical gradient of temperature in the lower atmosphere can be seen within the radiance spectrum. Carbon dioxide is a uniformly mixed gas in the atmosphere. The center of the band is the most opaque region; therefore, it is receiving emission from CO2 molecules at the instrument face. As one moves in wavelength away from the center, emission from CO2 reaches the instrument from higher in the atmosphere due to the decrease in the absorption efficiency of CO2. Since the atmospheric emission from CO2 is directly related to the temperature of the atmosphere, evidence of a temperature inversion above the instrument (temperature increases with altitude) is indicated by radiance values increasing away from the center of the band and then abruptly decreasing (as in the lower panel of Fig. 2). Radiance decreasing outward from the center of the band indicates a steep lapse-rate situation (temperature decreases with altitude) in the vertical temperature profile. As weaker absorption is approached spectrally, the radiance values decrease because emission from cold space is sensed. These more transparent regions are called “atmospheric windows” (700–1250 cm−1).

Evidence of the Pacific marine trade inversion is noted in the top panel of Fig. 2. A more apparent nocturnal inversion is shown in the bottom panel. This radiative inversion is much more pronounced because the atmospheric emission occurs much closer to the instrument. In 6 h the atmosphere progresses from a steep adiabatic lapse-rate situation (decrease of 10°C km−1) to a well-developed nocturnal inversion. This boundary layer evolution is a common meteorological phenomenon seen in cross sections of AERI temperature retrievals (Fig. 7).

Large differences seen in amplitudes of the water vapor lines (538–588 and 1250–1350 cm−1) between the continental and tropical air masses are due to differences in absolute water vapor amounts. The more saturated the lines (lower amplitude), as in the maritime case, the higher the absolute water vapor content in the lower atmosphere. The high-amplitude lines in the continental polar air mass are due to decreased radiance emission between the lines, which result directly from the lack of water vapor in the atmosphere. Higher radiance values in the infrared atmospheric window (700–1250 cm−1) at Pt. Mugu indicate greater lower-tropospheric water vapor emission than do the lower radiance window values observed at Coffeyville, Kansas.

Figure 3 presents the IR spectral regions used in the physical retrieval algorithm to determine PBL temperature and water vapor profiles. A detailed mathematical presentation of the retrieval technique can be found in Smith et al. (1998).

The AERI retrieval is accomplished in two steps: 1) an initial temperature and water vapor profile is obtained by statistical regression based on a 2-yr collection of radiosondes and 2) an iterative recursive physical solution of the radiative transfer equation is conducted (using the results of step 1 as the initial profile) to yield a final measure of the temperature and water vapor profile.

The profile physical retrieval process requires the use of an initial temperature and a water vapor guess profile (Smith 1970). This initial profile serves as a first guess to constrain the numerical solution to a physically reasonable state, which is particularly important in atmospheric regions in which the profile information content of the radiances is weak (i.e., above the PBL in this case). The first-guess profile could be obtained from a nearby radiosonde observation or from a climatology of radiosonde data. In this application, a radiosonde climatology for the Southern Great Plains (SGP) is used to develop regression equations that can be used to provide a first-guess profile, for each retrieval, based on the actual radiance observations associated with that retrieval. The development of the “regression guess” relations is described as follows.

A fast forward model (Eyre 1991) calculation of spectral radiance, as would be observed by AERI, is performed for each radiosonde case to provide a radiosonde–radiance spectrum pair for the statistical regression analysis. A regression analysis is then applied to relate these theoretical calculations of radiance (for the spectral regions shown in Fig. 3) and the matching radiosonde temperature and water vapor profiles. The resulting regression equations allow the specification of an excellent initial profile of atmospheric state, as needed for a physical solution of the RTE, from every AERI observation. Total precipitable water (TPW) and/or surface humidity can also be used as a predictor in the regression. This allows the use of microwave radiometer-derived (Han et al. 1994) or GPS-derived (global positioning System) (Businger et al and Ware et al. 1996) TPW and/or surface observations to be used as additional information to better constrain the statistical retrieval used as the initial profile in the physical solution of the RTE.

A schematic of the retrieval technique can be seen in Fig. 4. In practice, microwave radiometer TPW may not be available for use in the AERI retrieval. To cope with this possibility, separate sets of regression equations are derived that exclude these data as predictors. Comparisons of retrievals conducted with and without the use of TPW observations indicate that the degradation of the resulting retrieval produced by the loss of these data is limited to the profile between 1500 and 3000 m. The additional information TPW offers impacts the atmospheric layer in which the AERI water vapor weighting functions are rapidly decaying. The surface mixing ratio is easily measured with a sensor on the AERI system or a nearby meteorological surface station. The surface temperature used as a first guess in the physical retrieval is the average of the AERI spectrum brightness temperature in the opaque CO2 region, 670–680 cm−1.

A physical iterative and recursive solution of the RTE provides the final temperature and moisture profile retrieval. The final solution, obtained through iterative application of an inverse of a simplified RTE, satisfies the observed AERI spectrum through minimal adjustment of the initial, regression-based profiles. During each iteration, temperature and water vapor mixing ratio adjustments are made to minimize the differences between the observed and the calculated spectrum. Details about the retrieval technique can be found in Smith et al. (1998).

Retrievals can be calculated for clear atmosphere beneath clouds. The three requirements to obtain an accurate temperature and water vapor retrieval to cloud base are

  1. a good initial profile (the statistical regression retrieval),

  2. a good definition of cloud-base altitude (obtained by a simultaneous ceilometer measurement, a concurrent micropulse lidar measurement, or the CO2–window channel radiative transfer procedure outlined below), and

  3. cloud emissivity (obtained by interpolation between“windows” for which emissivity can be calculated from the observed radiance given the cloud altitude obtained under requirement 2).

First-guess regression retrievals are performed only in clear or high-altitude cloud conditions. Retrievals obtained below middle and low clouds use a prior clear or high-cloud statistical retrieval as the initial profile. Work is currently being conducted to produce a regression-based first guess of atmospheric temperature and water vapor during all cloud conditions. The pressure of the cloud base is specified using the initial temperature profile and a cloud-base height supplied by simultaneous ceilometer or micropulse lidar measurements. In the absence of these direct measurements of cloud-base altitude, the pressure of the cloud base is specified from semitransparent CO2 absorption channel radiances and neighboring window channel radiances (Smith and Platt 1978). Since CO2 is a uniformly mixed gas, the CO2 depth from the surface to the cloud is directly proportional to the difference between the cloud pressure and the surface pressure. The CO2 depth is derived from the radiance emission CO2 line amplitude observed in the AERI spectrum. The effective cloud emissivity spectrum needed for calculating the cloud contribution to the observed radiance spectrum is estimated by linear interpolation of values extracted for window wavelengths across the spectrum used for the profile retrieval.

In summary, the AERI system records an interferogram and, through an FFT and radiometric calibration procedure, computes atmospheric infrared radiance spectra. The radiance spectrum contains detailed information on lower-tropospheric (PBL) temperature and water vapor. A retrieval technique developed by Smith et al. (1993, 1998) inverts the IR RTE to solve for both vertical temperature and moisture profiles through the PBL from the AERI radiances observed in clear sky or the atmosphere below cloud base.

Retrieval analysis

The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program has provided an opportunity to develop and operate the AERI system continuously from March 1993 to the present. The AERI-01 instrument has been automated by integrating an all-weather hatch (which closes during precipitation events to protect the instrument) and a Stirling cycle detector cooler. The cooler allows the detector in the interferometer to be cooled electrically instead of with liquid nitrogen, which is the conventional method of cooling ground-based detectors. This automation and NIST-traceable calibration has allowed a continuous flow of AERI radiances from the SGP CART site in which real-time retrievals may be performed. A fully automated retrieval algorithm, as described by Smith et al. (1998), has been implemented at the Pacific Northwest National Laboratory for the ARM SGP CART site. Since the temperature and water vapor retrievals have been collected continuously, insight into retrieval performance during long periods of time has been gained through comparisons with radiosonde observations.

Figure 5 illustrates AERI retrieval rms temperature and water vapor differences with radiosondes for a 6-month period at the SGP CART site (221 retrieval–radiosonde matches) during clear and cloudy cases.

The retrieval–radiosonde differences demonstrate that temperature retrieval reproducibility is better than 0.6–1.35 K (see Fig. 5a, solid line) from the surface to an altitude of 3 km, while water vapor mixing ratio retrieval reproducibility is better than 0.8–1.4 g kg−1 (see Fig. 5b, solid line). A significant contributor to water vapor differences is radiosonde-to-radiosonde calibration error, which is estimated to have a standard deviation of 4%–6% of absolute water vapor. The lower vertical resolution of the AERI retrieval algorithm levels is another significant contributor. These differences will be addressed again below. The dashed lines in Figs. 5a and 5b show AERI regression retrieval (i.e., physical retrieval first guess) differences from radiosondes before the physical retrieval algorithm reduces the spectral residuals. Improvement in temperature is noted at all altitudes, while most of the water vapor information is added in the first 1500 m of the atmospheric column. A mean bias is presented in Fig. 5 (solid line with dots), indicating the average differences between radiosonde and AERI retrievals to be near zero from the surface to 1500 m for both temperature and water vapor. AERI retrievals on average are moister and warmer than radiosonde measurements from 1500 to 3000 m. The regression retrieval bias appears to have the same features, indicated (dashed line with triangles) in Fig. 5, as the physical retrieval bias. The AERI physical retrieval algorithm adjusts the regression retrieval mean bias for both temperature and water vapor toward zero at all levels. While the physical retrieval tries to correct the regression retrieval to fit the observed radiances, a mean difference of as great as 0.5 K and 0.7 g kg−1 relative to radiosondes still exists above 1500 m. It is believed that further work on the fast forward model will reduce the bias aloft as a result of adding more retrieval levels to the model.

Figure 6 shows the physical retrieval sensitivity to adding the microwave radiometer TPW and Surface Meteorological Observation System surface moisture information as input into the statistical regression first-guess retrieval (consult Fig. 4). Note that for temperature these additional data have negligible impact (Fig. 6a). For moisture (Fig. 6b), improvement in rms differences as compared to radiosondes occurs in the lowest 1500 m when a well-calibrated surface moisture value is inserted at the surface point in the first guess for the physical retrieval (dotted–dashed line). The rms differences are reduced by as much as 0.2 g kg−1. Using TPW as a predictor in the regression retrieval improves rms differences from 1500 to 3000 m by 0.1 g kg−1 at the highest altitudes. Final physical AERI retrievals have nearly the same rms differences in the lowest 1500 m, indicating that AERI physical retrievals are insensitive to additional TPW predictor information within the first guess for the lower 1500 m of the PBL. The best overall agreement between radiosonde and AERI physical retrieval moisture profiles occurs when both a surface moisture measurement and microwave TPW are used (black solid line) in the regression retrieval process.

It should be noted that radiosondes are assumed to provide the closest measure of “truth” for the lower troposphere. However, radiosonde sensors have problems (especially water vapor) mentioned in numerous research papers (e.g., Schmidlin 1988; Pratt 1985; Wade 1994). The rapid ascent of radiosonde balloons in the first kilometer can miss strong temperature inversion magnitudes and altitudes (Mahesh et al. 1997). Surface points in the radiosonde data are also found to be suspect because adequate environmental acclimation was not performed with the radiosonde before launch prior to September 1996. Finally, column (AERI) versus point (radiosonde) measurement inconsistencies may cause discrepancies between the profiles. In general, radiosonde rms errors are on the order of 0.5°C for temperature (Pratt 1985; Schmidlin 1988) and a standard deviation of 6% for absolute water vapor amounts (H. Revercomb 1997, personal communication), respectively, indicating that the actual errors in the AERI retrievals are significantly smaller than the estimates shown in Fig. 5. Since the normal frequency of radiosonde launches is 12 h, the AERI will provide better resolution of the PBL features than operational radiosonde measurements. Also note that the vertical resolution of the AERI retrievals is somewhat inferior to the point-measuring radiosondes. However, the high time frequency of the AERI sounding provides some compensation in terms of providing significant thermodynamic structure information of the PBL.

A seasonal breakdown of the annual retrieval statistics is presented in Fig. 7. Retrieval accuracy, based on comparisons to radiosondes, was split into four seasons of three months each. The solid lines are the standard deviation of the radiosondes, while the dashed lines are AERI retrieval rms differences compared to radiosondes. The standard deviation of the radiosonde temperature and water vapor profiles represents the variance of the meteorology during each season.

The rms temperature differences were lowest in the summer and showed a weak maximum in the winter (see Fig. 7). The larger rms temperature differences during the winter can be partly explained by frequent airmass transitions in the Southern Great Plains, indicated by the 6–7-K variance of the radiosondes in the winter plot. Strong arctic cold fronts pass through the SGP CART site area, followed by rapid warm-air advection on the west side of the arctic high pressure system (due to its relative close proximity to the Gulf of Mexico). During the summer, radiational cooling may cause temperature changes in the lowest 500 m of the atmosphere. However, cold frontal passages are relatively rare between June and August in northern Oklahoma. AERI temperature retrieval rms differences with radiosondes are likely to be larger because of the point-versus-volume sampling differences and temporal offsets during winter airmass transitions, while the homogeneity of summertime air masses allows better agreement between the profiles. Fewer radiosondes are launched during the winter months because there were no intensive operational periods (periods during which radiosondes are launched every 3 h), as existed in other seasons.

Greater rms differences for water vapor versus radiosondes are indicated during the summer than during the winter due to extreme seasonal absolute water vapor differences. The 2 g kg−1 differences at 2 km in the summer are due probably in large part to radiosonde-to-radiosonde calibration errors, while another factor is the saturation of water vapor lines in high absolute water vapor environments, causing a rapid decline in water vapor information (weighting functions). A new AERI system will be installed in the tropical western Pacific within the next year. With this tropical radiance data, more transparent water vapor spectral regions will be included in the physical retrieval algorithm in order to avoid line saturation. It is believed that with better-known spectroscopy AERI retrievals will be successful to higher vertical distances even in very high absolute water vapor amounts. When rms differences are compared to the standard deviation of the radiosondes (solid line) present during the spring and fall, excellent retrieval skill is shown. Major water vapor oscillations during these months occurred, while differences between radiosonde profiles and AERI retrievals remained at 1 g kg−1.

The robust retrieval procedure has allowed boundary layer meteorological phenomena to be remotely sensed. Meteorological case studies are presented below.

Meteorological case studies

Atmospheric airmass transitions lead to rapid changes in radiative emission and absorption properties. Increases (decreases) of water vapor cause greater (lesser) emission in spectral regions sensitive to this constituent during warm-air advection events (drylines). Emission from CO2 regions will suddenly decrease during a cold frontal passage. Outflow boundaries from thunderstorms, important for redevelopment of new convection (Purdom 1976), are readily detectable by AERI retrievals.

Several instrument measurements are used for validation of AERI retrievals. Raman lidar provides accurate high vertical and temporal resolution profiles of water vapor. Currently, Raman lidar is not practical for widespread implementation due to high production costs, although it has proven to be a valuable research tool. Profiles of virtual temperature (in clear sky and through clouds) can be obtained by RASS. The RASS instrument does have measurement limitations in high winds and very stable atmospheres but has the advantage of profiling through clouds. The RASS instrument usually can be put into a wind-profiling mode that allows excellent measurement of boundary layer wind structure. Currently, at SGP CART, the RASS is in temperature-profiling mode on the hour for 10 min and then switches to wind-profiling mode in between the soundings. The best way to characterize the lower-tropospheric thermodynamics would be to use both AERI and RASS together in a synergistic manner. This will be the subject of a future paper. Microwave radiometers provide integrated total precipitable water amounts, but currently, a robust vertical profiling capability has not been developed. Integrated TPW amounts have been used in data assimilation models with success (Kuo et al. 1993). Table 1 compares AERI, RASS, radiosonde, Raman lidar, and microwave radiometer measurement capabilities. A few examples of these events are examined below.

Boundary layer evolution

The mean structure of PBL development and decay for midlatitude continental clear-sky conditions has been outlined by Stull (1988). During the morning, as the solar angle increases, convective plumes of warmer air begin to rise from the earth’s surface. The boundary layer height at which this motion ceases is determined by the amount of solar heating and the synoptic weather conditions existing at this time. Surface heating provides the main forcing, kinetically working to create a thicker PBL during the daytime. As the sun sets, surface forcing subsides and the characteristics of the convectively mixed layer persist aloft. This is a neutrally stratified layer in which turbulent forcing is weak; it is sometimes referred to as the residual layer. This nocturnal event is not in contact with the earth’s surface but instead is decoupled as the bottom of the residual layer radiatively cools. This cooler layer at the bottom of the residual layer is called the stable boundary layer. The radiative cooling process produces a nocturnal inversion that is frequently evident with AERI-retrieved profiles in Oklahoma (Fig. 8).

An example of PBL evolution as viewed by AERI retrievals is shown in Fig. 8. All stages of boundary layer evolution described above are present in the lower panel, which shows potential temperature on 5 November 1996. A stable boundary layer due to radiative cooling exists between 0000 and 1600 UTC. The atmosphere begins to convectively mix due to solar heating at 1600 UTC and allows the PBL height (convectively mixed layer) to rise to 1 km by 2200 UTC, indicated by constant vertical potential temperature. By 2300 UTC the radiationally cooled layer appears again near the surface, while a residual layer is present above this cooling (uniform potential temperature). Note how poor the sampling of these diurnal events is from traditional 0000 and 1200 UTC radiosonde launches.

Figure 9 compares AERI retrievals (solid lines) from the cross section presented in Fig. 8 to radiosonde profiles (dashed lines) at 0530, 1130, 1430, and 2030 UTC. The panel on the left shows minimum temperatures of 277 K (4°C) during the night at 1130 UTC warming to 297 K (24°C) at 2030 UTC at the surface. Inversion strength and tendencies were captured very well by AERI retrievals. The right panel in Fig. 9 indicates dramatic mixing in the lower boundary layer of more than 20 K of potential temperature in the lowest 200 m of the boundary layer. This is valuable meteorological information for nowcasting convective temperatures and high temperatures in a forecast region. AERI profiles (lines) are compared to RASS (symbols) in Fig. 10 at a 3-h time interval. The eight-profile comparison provides insight into how well the boundary layer evolution can be captured with both systems and radiosonde data (if present within 30 min of AERI and RASS data). Note the growing intensity of the nocturnal inversion between 0000 and 2100 UTC. Both the passive and active remote sensing techniques compare well. The only differences occur at 1800 UTC when the RASS and AERI agree to 400 m but diverge after this point and when the RASS misses the near-surface radiative inversion between 0000 and 0900 UTC. AERI retrievals may become smoothed out through inversions above 500 m. This may be alleviated somewhat when a higher vertical resolution fast model is implemented. RASS profiles vary in altitude as atmospheric stability and wind affect the acoustic sound propagation and reception. It is important to note that boundary layer height and stability can be provided by AERI retrievals in clear conditions and to cloud base every 10 min. Boundary layer model verification and data assimilation are potential uses for these data.

Cold frontal passages

Many cold frontal passages have been recorded with AERI retrievals. Below, several examples will be shown using radiosonde, Raman lidar (Goldsmith et al. 1994;Melfi et al. 1985), and RASS (Martner et al. 1993) data for validation. The rapid temperature and moisture transition during these events can contribute to the development of severe weather in the Southern Great Plains. Nowcasting regional atmospheric convective instability is crucial in forecasting convective initiation and intensity accurately.

An example of a frontal passage at the SGP CART site on 12 September 1996 is shown in Fig. 11. A comparison of an AERI temperature time cross section to radiosonde time cross section is presented. Notice the rapid cooling of the atmosphere from 0 to 1.5 km at 0600 UTC in both cross sections. It should be mentioned that the radiosonde launches at the SGP CART site were quite frequent (every 3 h) as compared to normal National Weather Service synoptic radiosonde launches (every 12 h at 0000 and 1200 UTC). Individual radiosonde profiles are compared to AERI retrievals in Fig. 12 (matching the long dashed lines in the bottom panel of Fig. 11). The left panel indicates a rapid airmass temperature transition between 0530 and 0830 UTC by 10°C. The AERI profiles (solid lines) compare very favorably to the radiosonde profiles (dashed lines). The left panel presents the temperature profiles in terms of potential temperature. Note the accuracy to which AERI retrievals can provide PBL height information. At 0830 UTC the PBL maximum altitude is approximately 300 m. With solar heating present, the PBL had grown to an altitude of 900 m at 2100 UTC. (In Fig. 12, the respective arrows point to the inversion location.) This is useful high temporal PBL height information for both boundary layer atmospheric research and for operational weather forecasting. Since the AERI retrievals can be compared only at 3-h intervals with radiosonde profiles, a comparison to RASS profiles is shown in Fig. 13. The profiles progress with a 1-h time interval from right (0400 UTC) to left (0900 UTC) as the virtual temperature decreases due to the frontal passage. The 0600 UTC RASS data were not available and therefore are missing in Fig. 13. Good agreement, within 1°C, exists between the two measurement systems, having observed the same atmospheric state by two physically independent means.

A time–height cross section showing the water vapor mixing ratio amounts and trends is presented in Fig. 14. AERI retrievals (passive), Raman lidar (active), and radiosonde (in situ) data show the inherent differences when using the different measurement techniques. AERI retrievals and the Raman lidar water vapor profiles indicate an increase in absolute water vapor at approximately 0600 UTC to a similar magnitude and depth. AERI and Raman lidar also indicate an elevated layer of moisture between 1600 and 2100 UTC at 1 km. Note that the radiosondes were launched before and after the frontal passage (large dashed lines), missing the relatively rapid increase in water vapor amount, while the AERI and Raman mixing ratio tendency agree quite well. The elevated water vapor layer is only hinted at in the radiosonde cross section, while both passive and active systems indicated its presence. The AERI water vapor vertical resolution degradation with altitude is apparent when comparing the Raman lidar cross section to the AERI cross section at 1 km between 0600 and 1400 UTC. Raman lidar profiles have proven to be a very useful validation tool for AERI water vapor retrievals due to the high vertical resolution and similar time resolution. Currently, high production costs for the Raman system have been prohibitive to practical widespread operational use. AERI water vapor retrievals can provide fairly accurate measurements of local variations of water vapor in the PBL every 10 min. This information may be valuable for forecasting rapid prefrontal atmospheric destabilization due to latent heat advection by high absolute moisture amounts.

It is important to note that Figs. 11 and 14 were constructed from AERI retrievals for both clear and cloudy conditions. The cloud-base altitudes ranged from 5 to 12 km during the day, shown with the lowest clouds being observed near the time of frontal passage.

Many other frontal passages have been retrieved by AERI radiances. Figure 15 presents three more days of pre- and postfrontal passage AERI retrievals (solid lines) and radiosonde (dashed lines) used for validation. An advantage of an AERI system is its capability to retrieve temperature and water vapor vertical profiles during rapid airmass transitions. All three of these cold frontal passages have different thermodynamic transitions in both magnitude and depth. All were retrieved well from AERI radiances.

Note that the sharp increase in temperature on 30 October 1996 in between 700 and 1000 m was missed by the retrieval calculation. It is believed that this information is contained in the AERI radiances. An increase in the retrieval algorithm-level spacing will allow retrieval of inversion intensity above the surface to improve. The current AERI retrieval algorithm does a calculation at 20 levels between 0 and 3 km. Research is under way to produce a retrieval with 200 levels between the surface and 3 km.

Warm-air advection

Warm-air advection (WAA) events occur quite often in the southern plains in the spring and fall of each year. Usually the event is marked by a dramatic return of relatively warm air and moisture from the Gulf of Mexico on the west side of a surface high pressure ridge. This return of moisture and heat plays an important role in destabilizing the atmosphere both in the southern plains and the upper Midwest, providing latent heat energy for production of mesoscale convective complexes (Maddox 1980). These storm systems produce severe weather and copious amounts of rainfall in the southern plains and the Midwest. Real-time monitoring of the return flow vertical moisture and temperature profiles should give a forecaster some knowledge of the magnitude and depth of the boundary layer instability.

Figure 16 presents three days of different WAA events at the SGP CART site in Lamont, Oklahoma. The top row of figures shows temperature profiles before and after the return flow for each day, while the bottom row shows water vapor mixing ratio profiles for the same times as the temperature panels above. The AERI temperature retrievals compare very favorably with concurrent radiosonde profiles. The only exception to this is on 29 April 1997 (middle profile) when the inversion at 1500 m is not captured in the AERI retrieval. [When more retrieval algorithm levels are added, inversions such as these may be better resolved (see Smith et al. 1998).] In contrast, the nocturnal inversion intensities on all three days are captured very well. AERI water vapor retrievals also show good agreement to radiosonde measurements. Note the 1 g kg−1 agreement to 1500 m in all cases. However, the decrease in vertical resolution with altitude is noticeable above this point. The most dramatic example of WAA is on 21 April 1996 (left profile) when mixing ratio values increase from 3–4 to 10–11 g kg−1 in an 18-h period. The boundary layer also warmed by 4°C during this same time. This has important implications on the degree of convective instability in the atmosphere. In fact, severe convection developed at 2300 UTC near the SGP CART facility due to the high instability present and low-level forcing due to a frontal passage. It should be made clear that warm-air advection may or may not be accompanied by large increases in moisture. Often, the moistening occurs in narrow bands, not well sampled by the synoptic rawinsonde network.

AERI retrievals can also monitor the integrated TPW in the boundary layer to 2 km. Figure 17 presents these results for the three days described in the previous figure. The solid line presents the AERI TPW every 10 min, while the stars are radiosonde TPW when launched. AERI and radiosonde TPW amounts agree to within 1 mm in most cases. AERI boundary layer TPW amounts for 13 May 1997 show a mesoscale oscillation in moisture between 0800 and 2000 UTC. This same feature is also indicated by the radiosonde measurements; however, the temporal resolution of the AERI measurements captures a maximum at 1900 UTC.

GOES and AERI synergism

Currently, a new GOES and AERI radiance physical retrieval algorithm is being developed to take advantage of up-looking and down-looking emission measurements. GOES can observe temperature and water vapor information vertically in the upper and midtroposphere, while AERI can provide this information within the PBL. This instrument synergism would allow for continuous monitoring of atmosphere stability during clear conditions. Stability indexes would be available every 10 min instead of every radiosonde launch time. This information could be valuable for mesoscale model validation and should improve lead time for the onset of severe convection. With deployment of four new AERI systems in the Southern Great Plains, data assimilation of AERI and GOES high temporal resolution retrievals into a mesoscale model will be tested for forecast impact. This is a subject of a future paper.

Conclusions and future directions

AERI spectra can provide high temporal profiles of temperature and water vapor to 3 km of the earth’s atmosphere. This information can be used to monitor lower-tropospheric atmospheric stability, boundary layer evolution, and airmass transitions. This is important for numerical model validation, data assimilation, and forecast meteorological nowcasting. The instrument is well calibrated and fully automated, making it a valuable atmospheric monitoring tool. The retrieval algorithm has reached a state of operational maturity and is currently producing automated retrievals at Pacific Northwest Laboratories for the Atmospheric Radiation Measurement program.

Future plans include combining ground-based AERI radiances with spaceborne GOES radiances to produce full tropospheric retrievals. With the possibility of reducing the number of radiosondes in the near future, a combined ground-based–spaceborne temperature and water vapor passive retrieval algorithm is a viable option. Another area of research to be investigated is synergistic use of a Raman–DIAL (differential absorption lidar) lidar system and an AERI. The active lidar system can provide high vertical resolution water vapor profiles. If water vapor is known, water vapor emission measured in the AERI radiances can be used to provide more vertical temperature information in the atmosphere, thereby improving and extending vertically AERI temperature retrieval skill. The possibility of a dual temperature (passive) and water vapor (active) retrieval algorithm would take the best aspects of both lidar and the interferometer to better characterize the thermodynamic state of the atmosphere. The coupled use of AERI and wind profilers will reproduce the major functionality of radiosondes in the lower troposphere with a time resolution up to every 10 min. Lower-tropospheric wind profilers used in conjunction with AERI would provide extremely valuable high-resolution information on moisture flux in the lower troposphere. No one remote sensing instrument can measure every meteorological variable important to forecasting and model assimilation. It is only through integrated ground- and space-based remote sensing techniques that full tropospheric profiling will be possible.

Acknowledgments

The author would like to gratefully acknowledge Rosalyn Pertzborn, Leanne Trebilcock, Robert Rabin, David Tobin, Scott Curtis, and William Raymond for their helpful editing and suggestions. This research was supported by the DOE Atmospheric Radiation Measurements Program Grant DE-FG-02-92ER61365.

REFERENCES

  • Benjamin, S. G., K. J. Brundage, and L. L. Morone, 1994: The rapid update cycle. Part I: Analysis/model description. NOAA/NWS Tech. Proc. Bull. 416, 16 pp. [Available from National Weather Service, Office of Meteorology, 1325 East-West Highway, Silver Spring, MD 20910.].

  • ——, D. Kim, and T. W. Schaltter, 1995: The rapid update cycle: A new mesoscale assimilation system in hybrid theta-sigma coordinates at the National Meteorological Center. Preprints, Second Int. Symp. on Assimilation of Observations in Meteorology and Oceanography, Tokyo, Japan, Japanese Meteorological Agency, 337–342.

  • Businger, S., and Coauthors, 1996: The promise of GPS in atmospheric monitoring. Bull. Amer. Meteor. Soc.,77, 5–18.

  • Ellingson, R. G., and W. J. Wiscombe, 1996: The Spectral Radiance Experiment (SPECTRE): Project description and sample results. Bull. Amer. Meteor. Soc.,77, 1967–1985.

  • Eyre, J. R., 1991: A fast radiative transfer model for satellite sounding systems. ECMWF Tech. Memo. 176, Reading, United Kingdom, 1–28. [Available from ECMWF Library, Shinfield Park, Reading RG29AX, United Kingdom.].

  • Feltz, W., 1994: Meteorological applications of the Atmospheric Emitted Radiance Interferometer (AERI). M.S. thesis, Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, 87 pp. [Available from University of Wisconsin—Madison, Schwerdtfeger Library, 1225 W. Dayton, Madison, WI 53705.].

  • ——, L. Smith, R. O. Knuteson, H. Revercomb, and B. Howell, 1996:AERI temperature and water vapor retrievals: Improvements using an integrated profile retrieval approach. Proc. Sixth ARM Science Team Meeting, San Antonio, TX, Department of Energy, 81–83.

  • Fleming, R. J., 1996: The use of commercial aircraft as platforms for environmental measurements. Bull. Amer. Meteor. Soc.,77, 2229–2242.

  • Goldsmith, J. E. M., S. E. Bisson, R. A. Ferrare, K. D. Evans, D. N. Whiteman, and S. H. Melfi, 1994: Raman lidar profiling of atmospheric water vapor: Simultaneous measurements with two collocated systems. Bull. Amer. Meteor. Soc.,75, 975–982.

  • Han, Y., J. Snider, E. Westwater, S. Melfi, and R. Ferrare, 1994: Observations of water vapor by ground-based microwave radiometers and Raman lidar. J. Geophy. Res.,99, 18 695–18 702.

  • Kuo, Y., Y. Guo, and E. R. Westwater, 1993: Assimilation of precipitable water measurements into a mesoscale meteorological model. Mon. Wea. Rev.,121, 1215–1238.

  • Maddox, R. A., 1980: Mesoscale convective complexes. Bull. Amer. Meteor. Soc.,61, 1374–1387.

  • Mahesh, A., V. P. Walden, and S. G. Warren, 1997: Radiosonde temperature measurements in strong inversions: Corrections for thermal lag based on an experiment at the South Pole. J. Atmos. Oceanic Technol.,14, 45–53.

  • May, P., R. G. Strauch, K. P. Moran, and W. L. Ecklund, 1990: Temperature soundings by RASS with wind profiler radars: A preliminary study. IEEE Trans. Geosci. Remote Sens.,28, 11–20.

  • Melfi, S. H., and D. N. Whiteman, 1985: Observation of lower atmospheric moisture structure and its evolution using a Raman lidar. Bull. Amer. Meteor. Soc.,66, 1282–1292.

  • Pratt, R. W., 1985: Review of radiosonde humidity and temperature errors. J. Atmos. Oceanic Technol.,2, 404–407.

  • Purdom, J. F. W., 1976: Some uses of high-resolution GOES imagery in the mesoscale forecasting of convection and its behavior. Mon. Wea. Rev.,104, 1474–1483.

  • Revercomb, H., H. Buijs, H. B. Howell, D. D. LaPorte, W. L. Smith, and L. A. Sromovsky, 1988: Radiometric calibration of IR Fourier transform spectrometers: Solution to a problem with the high-resolution spectrometer sounder. Appl. Opt.,27, 3210–3218.

  • ——, F. A. Best, R. G. Dedecker, R. P. Dirkx, R. A. Herbsleb, R. O. Knuteson, J. F. Short, and W. L. Smith, 1993: Atmospheric Emitted Radiance Interferometer (AERI) for ARM. Preprints. Fourth Symp. on Global Change Studies, Anaheim, CA, Amer. Meteor. Soc., 46–49.

  • Schmidlin, F. J., 1988: WMO international radiosonde comparison, phase II final report, 1985. Instruments and Observing Methods Rep. 29, WMO/TD 312, Geneva, Switzerland, 1–113. [Available from WMO Secretariat, 41 Ave. Giuseppe Motta, Case Postale No. 2300, CH-1211 Geneva 2, Switzerland.].

  • Smith, W. L., 1970: Iterative solution of the radiative transfer equation for the temperature and absorbing gas profile of an atmosphere. Appl. Opt.,9, 9.

  • ——, and C. M. R. Platt, 1978: Comparison of satellite-deduced cloud heights with indications from radiosonde and ground-based laser measurements. J. Appl. Meteor.,17, 1796–1802.

  • ——, and Coauthors, 1990: GAPEX: A Ground-based Atmospheric Profiling Experiment. Bull. Amer. Meteor. Soc.,71, 310–318.

  • ——, R. O. Knuteson, H. E. Revercomb, F. Best, R. Dedecker, and H. Howell, 1993: GB-HIS: A measurement system for continuous profiling of boundary layer thermodynamic structure. Preprints, Eighth Symp. on Meteorological Observations and Instrumentation, Anaheim, CA, Amer. Meteor. Soc., 180–183.

  • ——, W. F. Feltz, R. O. Knuteson, H. E. Revercomb, H. B. Howell, and H. M. Woolf, 1998: The retrieval of planetary boundary-layer structure using ground-based infrared spectral radiance measurements. J. Atmos. Oceanic Technol., in press.

  • Stull, R. B., 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic.

  • Wade, C. G., 1994: An evaluation of problems affecting measurement of low relative humidity on the United States radiosonde. J. Atmos. Oceanic Technol.,11, 687–700.

  • Ware, R. H., and Coauthors, 1996: GPS sounding of the atmosphere from low earth orbit: Preliminary results. Bull. Amer. Meteor. Soc.,77, 19–40.

  • Weber, B. L., and Coauthors, 1990: Preliminary evaluation of the first NOAA demonstration network wind profiler. J. Atmos. Oceanic Tech.,7, 909–918.

Fig. 1.
Fig. 1.

Picture of the operational AERI at the DOE ARM SGP CART site in Lamont, Oklahoma.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 2.
Fig. 2.

An AERI band 1 radiometric observation of radiance vs wavenumber, showing two different climates (marine and continental) along with the parts of the spectrum used in the retrieval.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 3.
Fig. 3.

AERI spectral bands 1 and 2, indicating regions used for the physical temperature and water retrieval algorithm.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 4.
Fig. 4.

A schematic of the AERI retrieval process at the SGP CART site. Microwave radiometer total precipitable water and surface water vapor data are used only to improve the first guess and are not necessary to do retrievals. The AERI physical retrieval algorithm will change the first guess to fit the observed radiances without any auxiliary data.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 5.
Fig. 5.

AERI retrieval statistics for cases from June to December 1996 compared to radiosonde (221 matches). (a) AERI retrieval temperature rms differences from radiosonde for both the regression first guess (long dashed) and final physical retrieval (solid black). Also displayed are mean differences for the same matches for first-guess (dashed triangles) and physical retrievals (solid circles). (b) The same comparison except for the water vapor mixing ratio.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 6.
Fig. 6.

AERI retrievals compared to radiosonde rms statistics for (a) temperature and (b) mixing ratio, indicating sensitivities to providing auxiliary surface moisture information and microwave radiometer TPW. The statistics were calculated using the same 221 matches shown in Fig. 5. The best agreement between AERI physical retrievals and radiosonde moisture measurements occur when both a surface moisture measurement and microwave TPW are used [solid black line in (b)]. A good measure of surface moisture improves the lower 1500 m of the physical retrievals [dotted–dashed line (b)], while microwave radiometer TPW slightly improves the profiles from 1500 to 3000 m [long dashed line in (b)]. The additional moisture information provided to the first guess has little impact on the temperature profile rms differences [panel (a)].

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 7.
Fig. 7.

(a) AERI temperature retrieval seasonal rms differences (dashed) and radiosonde sample std dev (solid). The number of matches used for each season are indicated within the parentheses for each season. (b) Same as (a) except for mixing ratio.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 8.
Fig. 8.

AERI temperature and potential temperature time cross section for 5 November 1996. This figure presents a boundary layer evolution event calculated by the AERI retrieval algorithm. Note the strong radiative inversion development at 0000 UTC between 0 and 500 m in altitude and the rapid decay due to solar heating between 1600 and 2200 UTC. Inversion development begins for the next evening at 2200 UTC.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 9.
Fig. 9.

A comparison of AERI retrievals (solid) and radiosonde temperature (dashed) profiles from the cross section presented in Fig. 8 from 0 to 1000 m. The left panel contains temperature profile comparisons, while the right panel shows the same profiles in terms of potential temperature.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 10.
Fig. 10.

A comparison of AERI retrievals (solid) and RASS virtual temperature (symbols) profiles from the cross section presented in Fig. 8 from 0 to 1500 m. This figure contains a higher temporal resolution (hourly) temperature retrieval comparison between passive and active retrieval techniques.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 11.
Fig. 11.

An AERI and radiosonde time cross section comparison of temperature for a cold frontal passage at SGP CART on 12 September 1996.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 12.
Fig. 12.

A comparison of AERI retrievals (solid) and radiosonde temperature (dashed) profiles from the temperature cold frontal cross section presented in Fig. 11 from 0 to 2000 m. The left panel contains temperature profile comparisons, while the right panel shows the same profiles in terms of potential temperature. Note the good agreement between the two profile types throughout the frontal passage event.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 13.
Fig. 13.

AERI (solid) and RASS (symbols) virtual temperature profiles are compared throughout the frontal passage from 0400 to 0900 UTC. Good agreement exists (within 1°C) between the RASS and radiosonde profiles using completely different retrieval methods.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 14.
Fig. 14.

A three-panel comparison of AERI (passive), Raman lidar (active), and radiosonde (in situ) water vapor time cross sections for the 12 September 1996 frontal passage. Radiosonde profiles are available at the large dashed lines in the lower panel. Data in between the launches are interpolated.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 15.
Fig. 15.

AERI temperature profiles (solid) compared to radiosonde temperature profiles (dashed) for three frontal passages.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 16.
Fig. 16.

AERI (solid) compared to radiosonde (dashed) temperature and water vapor mixing ratio profiles for three warm-air advection events at the DOE ARM SGP CART site. The solid lines are AERI retrievals, and the dashes lines are concurrent radiosonde profiles. AERI can provide the profiles shown at the radiosonde launch times every 10 min.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Fig. 17.
Fig. 17.

Radiosonde (stars) and AERI (line) total precipitable water (cm) comparisons to 2 km for the three cases shown in Fig. 16. Note the high time resolution AERI can provide in retrieving PBL total precipitable water.

Citation: Journal of Applied Meteorology 37, 9; 10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2

Table 1.

Comparison of atmospheric instrument measurements.

Table 1.
Save
  • Benjamin, S. G., K. J. Brundage, and L. L. Morone, 1994: The rapid update cycle. Part I: Analysis/model description. NOAA/NWS Tech. Proc. Bull. 416, 16 pp. [Available from National Weather Service, Office of Meteorology, 1325 East-West Highway, Silver Spring, MD 20910.].

  • ——, D. Kim, and T. W. Schaltter, 1995: The rapid update cycle: A new mesoscale assimilation system in hybrid theta-sigma coordinates at the National Meteorological Center. Preprints, Second Int. Symp. on Assimilation of Observations in Meteorology and Oceanography, Tokyo, Japan, Japanese Meteorological Agency, 337–342.

  • Businger, S., and Coauthors, 1996: The promise of GPS in atmospheric monitoring. Bull. Amer. Meteor. Soc.,77, 5–18.

  • Ellingson, R. G., and W. J. Wiscombe, 1996: The Spectral Radiance Experiment (SPECTRE): Project description and sample results. Bull. Amer. Meteor. Soc.,77, 1967–1985.

  • Eyre, J. R., 1991: A fast radiative transfer model for satellite sounding systems. ECMWF Tech. Memo. 176, Reading, United Kingdom, 1–28. [Available from ECMWF Library, Shinfield Park, Reading RG29AX, United Kingdom.].

  • Feltz, W., 1994: Meteorological applications of the Atmospheric Emitted Radiance Interferometer (AERI). M.S. thesis, Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, 87 pp. [Available from University of Wisconsin—Madison, Schwerdtfeger Library, 1225 W. Dayton, Madison, WI 53705.].

  • ——, L. Smith, R. O. Knuteson, H. Revercomb, and B. Howell, 1996:AERI temperature and water vapor retrievals: Improvements using an integrated profile retrieval approach. Proc. Sixth ARM Science Team Meeting, San Antonio, TX, Department of Energy, 81–83.

  • Fleming, R. J., 1996: The use of commercial aircraft as platforms for environmental measurements. Bull. Amer. Meteor. Soc.,77, 2229–2242.

  • Goldsmith, J. E. M., S. E. Bisson, R. A. Ferrare, K. D. Evans, D. N. Whiteman, and S. H. Melfi, 1994: Raman lidar profiling of atmospheric water vapor: Simultaneous measurements with two collocated systems. Bull. Amer. Meteor. Soc.,75, 975–982.

  • Han, Y., J. Snider, E. Westwater, S. Melfi, and R. Ferrare, 1994: Observations of water vapor by ground-based microwave radiometers and Raman lidar. J. Geophy. Res.,99, 18 695–18 702.

  • Kuo, Y., Y. Guo, and E. R. Westwater, 1993: Assimilation of precipitable water measurements into a mesoscale meteorological model. Mon. Wea. Rev.,121, 1215–1238.

  • Maddox, R. A., 1980: Mesoscale convective complexes. Bull. Amer. Meteor. Soc.,61, 1374–1387.

  • Mahesh, A., V. P. Walden, and S. G. Warren, 1997: Radiosonde temperature measurements in strong inversions: Corrections for thermal lag based on an experiment at the South Pole. J. Atmos. Oceanic Technol.,14, 45–53.

  • May, P., R. G. Strauch, K. P. Moran, and W. L. Ecklund, 1990: Temperature soundings by RASS with wind profiler radars: A preliminary study. IEEE Trans. Geosci. Remote Sens.,28, 11–20.

  • Melfi, S. H., and D. N. Whiteman, 1985: Observation of lower atmospheric moisture structure and its evolution using a Raman lidar. Bull. Amer. Meteor. Soc.,66, 1282–1292.

  • Pratt, R. W., 1985: Review of radiosonde humidity and temperature errors. J. Atmos. Oceanic Technol.,2, 404–407.

  • Purdom, J. F. W., 1976: Some uses of high-resolution GOES imagery in the mesoscale forecasting of convection and its behavior. Mon. Wea. Rev.,104, 1474–1483.

  • Revercomb, H., H. Buijs, H. B. Howell, D. D. LaPorte, W. L. Smith, and L. A. Sromovsky, 1988: Radiometric calibration of IR Fourier transform spectrometers: Solution to a problem with the high-resolution spectrometer sounder. Appl. Opt.,27, 3210–3218.

  • ——, F. A. Best, R. G. Dedecker, R. P. Dirkx, R. A. Herbsleb, R. O. Knuteson, J. F. Short, and W. L. Smith, 1993: Atmospheric Emitted Radiance Interferometer (AERI) for ARM. Preprints. Fourth Symp. on Global Change Studies, Anaheim, CA, Amer. Meteor. Soc., 46–49.

  • Schmidlin, F. J., 1988: WMO international radiosonde comparison, phase II final report, 1985. Instruments and Observing Methods Rep. 29, WMO/TD 312, Geneva, Switzerland, 1–113. [Available from WMO Secretariat, 41 Ave. Giuseppe Motta, Case Postale No. 2300, CH-1211 Geneva 2, Switzerland.].

  • Smith, W. L., 1970: Iterative solution of the radiative transfer equation for the temperature and absorbing gas profile of an atmosphere. Appl. Opt.,9, 9.

  • ——, and C. M. R. Platt, 1978: Comparison of satellite-deduced cloud heights with indications from radiosonde and ground-based laser measurements. J. Appl. Meteor.,17, 1796–1802.

  • ——, and Coauthors, 1990: GAPEX: A Ground-based Atmospheric Profiling Experiment. Bull. Amer. Meteor. Soc.,71, 310–318.

  • ——, R. O. Knuteson, H. E. Revercomb, F. Best, R. Dedecker, and H. Howell, 1993: GB-HIS: A measurement system for continuous profiling of boundary layer thermodynamic structure. Preprints, Eighth Symp. on Meteorological Observations and Instrumentation, Anaheim, CA, Amer. Meteor. Soc., 180–183.

  • ——, W. F. Feltz, R. O. Knuteson, H. E. Revercomb, H. B. Howell, and H. M. Woolf, 1998: The retrieval of planetary boundary-layer structure using ground-based infrared spectral radiance measurements. J. Atmos. Oceanic Technol., in press.

  • Stull, R. B., 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic.

  • Wade, C. G., 1994: An evaluation of problems affecting measurement of low relative humidity on the United States radiosonde. J. Atmos. Oceanic Technol.,11, 687–700.

  • Ware, R. H., and Coauthors, 1996: GPS sounding of the atmosphere from low earth orbit: Preliminary results. Bull. Amer. Meteor. Soc.,77, 19–40.

  • Weber, B. L., and Coauthors, 1990: Preliminary evaluation of the first NOAA demonstration network wind profiler. J. Atmos. Oceanic Tech.,7, 909–918.

  • Fig. 1.

    Picture of the operational AERI at the DOE ARM SGP CART site in Lamont, Oklahoma.

  • Fig. 2.

    An AERI band 1 radiometric observation of radiance vs wavenumber, showing two different climates (marine and continental) along with the parts of the spectrum used in the retrieval.

  • Fig. 3.

    AERI spectral bands 1 and 2, indicating regions used for the physical temperature and water retrieval algorithm.

  • Fig. 4.

    A schematic of the AERI retrieval process at the SGP CART site. Microwave radiometer total precipitable water and surface water vapor data are used only to improve the first guess and are not necessary to do retrievals. The AERI physical retrieval algorithm will change the first guess to fit the observed radiances without any auxiliary data.

  • Fig. 5.

    AERI retrieval statistics for cases from June to December 1996 compared to radiosonde (221 matches). (a) AERI retrieval temperature rms differences from radiosonde for both the regression first guess (long dashed) and final physical retrieval (solid black). Also displayed are mean differences for the same matches for first-guess (dashed triangles) and physical retrievals (solid circles). (b) The same comparison except for the water vapor mixing ratio.

  • Fig. 6.

    AERI retrievals compared to radiosonde rms statistics for (a) temperature and (b) mixing ratio, indicating sensitivities to providing auxiliary surface moisture information and microwave radiometer TPW. The statistics were calculated using the same 221 matches shown in Fig. 5. The best agreement between AERI physical retrievals and radiosonde moisture measurements occur when both a surface moisture measurement and microwave TPW are used [solid black line in (b)]. A good measure of surface moisture improves the lower 1500 m of the physical retrievals [dotted–dashed line (b)], while microwave radiometer TPW slightly improves the profiles from 1500 to 3000 m [long dashed line in (b)]. The additional moisture information provided to the first guess has little impact on the temperature profile rms differences [panel (a)].

  • Fig. 7.

    (a) AERI temperature retrieval seasonal rms differences (dashed) and radiosonde sample std dev (solid). The number of matches used for each season are indicated within the parentheses for each season. (b) Same as (a) except for mixing ratio.

  • Fig. 8.

    AERI temperature and potential temperature time cross section for 5 November 1996. This figure presents a boundary layer evolution event calculated by the AERI retrieval algorithm. Note the strong radiative inversion development at 0000 UTC between 0 and 500 m in altitude and the rapid decay due to solar heating between 1600 and 2200 UTC. Inversion development begins for the next evening at 2200 UTC.

  • Fig. 9.

    A comparison of AERI retrievals (solid) and radiosonde temperature (dashed) profiles from the cross section presented in Fig. 8 from 0 to 1000 m. The left panel contains temperature profile comparisons, while the right panel shows the same profiles in terms of potential temperature.

  • Fig. 10.

    A comparison of AERI retrievals (solid) and RASS virtual temperature (symbols) profiles from the cross section presented in Fig. 8 from 0 to 1500 m. This figure contains a higher temporal resolution (hourly) temperature retrieval comparison between passive and active retrieval techniques.

  • Fig. 11.

    An AERI and radiosonde time cross section comparison of temperature for a cold frontal passage at SGP CART on 12 September 1996.

  • Fig. 12.

    A comparison of AERI retrievals (solid) and radiosonde temperature (dashed) profiles from the temperature cold frontal cross section presented in Fig. 11 from 0 to 2000 m. The left panel contains temperature profile comparisons, while the right panel shows the same profiles in terms of potential temperature. Note the good agreement between the two profile types throughout the frontal passage event.

  • Fig. 13.

    AERI (solid) and RASS (symbols) virtual temperature profiles are compared throughout the frontal passage from 0400 to 0900 UTC. Good agreement exists (within 1°C) between the RASS and radiosonde profiles using completely different retrieval methods.

  • Fig. 14.

    A three-panel comparison of AERI (passive), Raman lidar (active), and radiosonde (in situ) water vapor time cross sections for the 12 September 1996 frontal passage. Radiosonde profiles are available at the large dashed lines in the lower panel. Data in between the launches are interpolated.

  • Fig. 15.

    AERI temperature profiles (solid) compared to radiosonde temperature profiles (dashed) for three frontal passages.

  • Fig. 16.

    AERI (solid) compared to radiosonde (dashed) temperature and water vapor mixing ratio profiles for three warm-air advection events at the DOE ARM SGP CART site. The solid lines are AERI retrievals, and the dashes lines are concurrent radiosonde profiles. AERI can provide the profiles shown at the radiosonde launch times every 10 min.

  • Fig. 17.

    Radiosonde (stars) and AERI (line) total precipitable water (cm) comparisons to 2 km for the three cases shown in Fig. 16. Note the high time resolution AERI can provide in retrieving PBL total precipitable water.

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