1. Introduction
High spectral resolution infrared (IR) sounders such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) provide three-dimensional fields of atmospheric moisture and temperature with great vertical resolving power (Chahine et al. 2006; Clerbaux et al. 2007; Smith et al. 2009). Improved information about the clear-sky horizontal and vertical water vapor and temperature in the preconvection environment leads to substantial improvements in monitoring characteristics of the mesoscale environment such as atmospheric stability and boundary layer structure (Li et al. 2011). Such data are important for severe storm nowcasting and short-range forecasting, as well as other applications, although with limited temporal resolution and spatial coverage. Operational high spectral resolution IR sounder retrievals are performed with coarser spatial resolution microwave sounder observations to reduce the impacts due to clouds. On the other hand, the current Geostationary Operational Environmental Satellite (GOES) Sounder has fewer channels than the high spectral IR sounders on board the polar-orbiting satellites so that the vertical resolution of the GOES Sounder is limited. Therefore, the full spatial resolution [or single field of view (SFOV)] high spectral resolution IR soundings are needed for mesoscale applications, and are critical for measuring the degree of atmospheric instability, which is highly related to storm genesis.
Several potential applications from high temporal and high spectral resolution IR data were discussed by Sieglaff et al. (2009). They showed how the spectral “online” and “offline” absorption features in the IR window region of the spectrum are related to low-level temperature and moisture. Schmit et al. (2009) showed that the equivalent potential temperature differences between 800 and 600 hPa are among the indicators of thunderstorm potential. The advanced IR sounder is able to depict an unstable region similar to the “truth” field in a simulation using an International H2O Project (IHOP) case (Li et al. 2011). Equally important is that the atmospheric stability derived from an advanced IR sounder may suggest the region of stable air. This characteristic is important for reducing false alarms when forecasting the convective events. Full spatial resolution advanced IR soundings can also be assimilated in a regional numerical model to improve severe storm forecasts; for example, the hurricane track and intensity forecast can be improved when the AIRS full spatial resolution soundings are assimilated into a regional numerical weather prediction (NWP) model (Li and Liu 2009; Liu and Li 2010).
In this paper, applications of AIRS full spatial resolution IR soundings for three convective storms were investigated to reveal the advantages of using higher spatial and spectral resolution AIRS SFOV retrievals. In the first case (case A) the convective storm developed between 1700 and 2200 UTC across parts of Iowa, Minnesota, and Wisconsin on 28 August 2007. This is a linear mesoscale convective system that caused a large cluster of thunderstorms in north-central/northern Wisconsin. The convection produced several reports of hail (up to 2.5 cm in diameter) and wind gusts of 95–130 km h−1 according to National Oceanic and Atmospheric Administration/Storm Prediction Center (NOAA/SPC) reports. The second convection case (case B) was a cluster of severe thunderstorms that propagated southeastward across far northwestern South Dakota on 19 July 2010. This case produced a long-duration wind and hail event that resulted in a remarkably long and wide damage path. According to the NOAA/SPC storm reports, the largest hail size was 6.4 cm in diameter, and the maximum wind gust was 110 km h−1. The report also mentioned wind-driven hail duration of 15–30 min, which exacerbated the crop damage. In the third convective storm case (case C), a storm that occurred from 7 to 8 August 2010 in Zhou Qu County, Gansu Province, China, caused 1456 deaths and left another 309 people missing, according to the report of Xinhua News. From 1000 UTC 7 August to 0000 UTC 08 August 2010, the severe storm brought heavy precipitation to Zhou Qu County; the maximum rain rate was 77.3 mm h−1, which led to a massive mudslide in the urban area of Zhou Qu.
Case A indicates a stationary developing area of convection while case B represents moving convection associated with two high pressure supercells. Case C suggests that the full-spatial resolution retrievals may increase the likelihood of obtaining atmospheric stabilities in clear-sky FOVs over a wide cloud-covered region. In these three cases, the AIRS observed the atmospheric instabilities in the preconvection environments 1 ~ 3.5 h ahead of storm genesis. During the preconvection storm environment, the current regional NWP model has limited capability in forecasting the local storm genesis and development, while radar can provide important information only after the storm is initiated.
2. AIRS full spatial resolution soundings
Satellite-based high spectral resolution (or advanced) IR sounding measurements are a principal source of atmospheric water vapor and temperature data over areas where conventional in situ observations are relatively sparse. Hyperspectral IR sounders, such as the AIRS on board the National Aeronautics and Space Administration’s (NASA) Earth Observing System (EOS) Aqua platform and the IASI on board the European Organisation for the Exploitation of Meteorological Satellites’s (EUMETSAT) MetOp-A satellite, are providing unprecedented global atmospheric temperature and moisture profiles with high vertical resolution and accuracy. Along with the Advanced Microwave Sounding Unit (AMSU), which provides atmospheric temperature profiles in most cloudy regions, AIRS is able to provide soundings (Susskind et al. 2003) with much better vertical resolution and higher accuracy [1 K for temperature, 15% for water vapor mixing ratio; Tobin et al. (2006)]; such improvements are very useful for global numerical weather prediction and climate applications (Reale et al. 2008). Since the AIRS–AMSU soundings are based on the AMSU footprint, which has a spatial resolution of approximately 50 km at nadir, the application of AIRS–AMSU soundings on mesoscale weather forecasts might be limited. Although the GOES Sounder provides 10-km spatial resolution soundings (Ma et al. 1999; Li et al. 2008), with relatively broad width and fewer spectral channels, it does not provide enough vertical resolution and accuracy compared with the advanced IR soundings. Therefore, full spatial resolution AIRS soundings are needed for mesoscale applications. The SFOV retrieval approach was first developed for the processing of ultraspectral data obtained by the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounding Test Bed-Interferometer (NAST-I; Zhou et al. 2002; Smith et al. 2005). For satellite sounding applications, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) hyperspectral IR sounder retrieval (CHISR) algorithm has been developed to retrieve the atmospheric temperature and moisture profiles from the advanced IR sounder radiance measurements in clear skies and some cloudy-sky conditions on a SFOV spatial-resolution basis. The CHISR algorithm has three steps: the first step is the IR sounder subpixel cloud detection using a high spatial resolution imager cloud mask product [e.g., the AIRS cloud mask can be derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product (Li et al. 2004b,a, 2005b,a), while the Advanced Very High Resolution Radiometer (AVHRR) cloud mask can be used for IASI cloud detection], the second step is to perform an eigenvector regression on the hyperspectral IR radiance measurements for a first guess of temperature and moisture profiles (Weisz et al. 2007; Liu et al. 2008), and the final step is to update/improve the first guess by performing a one-dimensional variational data assimilation (1DVAR) retrieval algorithm with a quasi-Newtown iteration technique. Radiance measurements from all good IR channels are used in the sounding retrieval process. The retrieved profiles have root-mean-square differences (RMSD) of 1 K in temperature and less than 2 g kg−1 in moisture ratio when compare with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis dataset (Kwon et al. 2012), The CHISR algorithm can be applied to hyperspectral IR sounder radiances for atmospheric profiles in noncloudy preconvection environments with full spatial resolution (approximately 12–14 km at nadir) for nowcasting purposes. These full spatial resolution soundings are crucial for measuring the degree of atmospheric instability, which is highly related to the storm genesis, and can be used to analyze the preconvection environment. The results will be compared with the NASA AIRS science team product AIRS–AMSU (Chahine et al. 2006) and the current GOES Sounder retrievals (Ma et al. 1999; Li et al. 2008).
3. Warning information from AIRS full spatial resolution soundings
a. Linear mesoscale convective system in the Upper Midwest on 28 August 2007 (case A)
A linear convective system was developing across parts of Iowa (IA), Minnesota (MN), and Wisconsin (WI) on 28 August 2007. The individual cumulonimbus towers built up in northeastern Iowa between 2100 and 2200 UTC, and can be identified from GOES visible imagery in Fig. 1. The larger cluster of thunderstorms in north-central/northeastern Wisconsin produced several reports of hail (up to 2.5 cm in diameter) and wind gusts of 90–130 km h−1 (NOAA/SPC storm reports). The high spatial resolution MODIS observation provides useful information on clouds in cloudy areas while the high spectral resolution AIRS measurements can provide atmospheric thermodynamic structures in clear skies. A MODIS 11.0-μm IR image around 1910 UTC depicted cloud-top brightness temperatures as cold as −79°C in north-central Wisconsin, with numerous cloud to ground lightning strikes. AIRS observed the atmospheric instability at ~1910 UTC 28 August 2007 in the preconvection environment, around 3 h before the storm genesis. Figure 2a shows the clear-sky convective available potential energy (CAPE) (colored regions) from AIRS SFOV soundings overlain on the AIRS 11-μm brightness temperature image (black and white) at 1910 UTC 28 August 2007. The local area of northeastern Iowa has CAPE values exceed 4000 J kg−1 (magenta color), whereas Fig. 2c is the calculated CAPE from the NASA AIRS science team product AIRS–AMSU retrieved profiles. Figure 2b shows the GOES-12 Sounder derived product images (DPIs) of CAPE at 1700 UTC for comparison. It should be noted that the CAPE images from the AIRS CHISR algorithm (Fig. 2a), the NASA AIRS science team product AIRS–AMSU retrievals (Fig. 2c), and GOES Sounder DPIs (Fig. 2b) are 3, 3, and 5 h, respectively, ahead of the GOES-12 visible image in Fig. 1.
GOES-12 visible images showing a linear mesoscale convective system at 2155 UTC 28 Aug 2007. Note the picturesque shadows cast by the individual cumulonimbus towers building in northeastern IA.
Citation: Weather and Forecasting 27, 2; 10.1175/WAF-D-10-05057.1
(a) The clear-sky CAPE (color regions) from AIRS SFOV soundings overlain on the AIRS 11-μm brightness temperature image at 1910 UTC 28 Aug 2007. (b) The clear-sky CAPE (color regions) from GOES-12 DPIs overlain on a GOES-12 window channel brightness temperature image at 1700 UTC 28 Aug 2007. (c) Corresponding NASA AIRS science team product AIRS–AMSU sounding CAPE data at 1910 UTC 28 Aug 2007. (d) The reported high wind, tornado, and hail locations between 1700 and 2350 UTC 28 Aug 2007.
Citation: Weather and Forecasting 27, 2; 10.1175/WAF-D-10-05057.1
CAPE (J kg−1; energy per unit mass) provides the integration of the positive area on a skew T sounding. The positive area is that region where the theoretical parcel temperature is warmer than the actual temperature at each pressure level in the troposphere. The theoretical parcel temperature is the lapse rate(s) a parcel would take if raised from the lower surface level. The high CAPE values mean a storm will build vertically very quickly. The updraft speed depends on the CAPE environment as well. The operational significance of CAPE suggests the hail potential increases when CAPE exceeds above 2500 J kg−1, and large hail requires very large CAPE values. The CAPE can be used together with other products such as the lifted index (LI), convective inhibition (CIN), K index (KI), total precipitable water (TPW), and derived atmospheric motion vectors (AMVs) to improve the severe storm nowcasting. In this study, we compared the CAPE values from atmospheric temperature and moisture profiles from the CHISR algorithm, AIRS–AMSU, and GOES Sounder retrievals, respectively, at the each observational spatial resolution. This will help to evaluate the atmospheric stabilities in the preconvection environment, instead of the profile information at various vertical resolutions.
The colored area in Fig. 2 denotes the CAPE values; most areas have significant CAPE values, while the area west of Iowa and north-central Wisconsin are extremely unstable. Although Fig. 2b (GOES-12 DPI CAPE) shows the atmospheric instability at an earlier time, the CAPE from SFOV AIRS soundings in Fig. 2a does reveal more severe instability and concise areas when compared with the low spatial resolution NASA AIRS science team product AIRS–AMSU retrievals in Fig. 2c for the development of this linear mesoscale convection. Nevertheless, CAPE images from different retrievals suggest that certain regions are unstable, in particular the NASA AIRS science team product AIRS–AMSU soundings in Fig. 2c. Figure 2a (full spatial resolution AIRS retrievals) does provide higher correlation to the convection-induced high wind, tornado, and hail locations in Fig. 2d when CAPE exceeds 4000 J kg−1.
b. Large hail damage across northwestern South Dakota on 19 July 2010 (case B)
A cluster of thunderstorms developed and swept southeastward across far western South Dakota (SD) on 19 July 2010 from 1815 to 2345 UTC. This case caused approximately 4.7 × 106 m2 of corn field damage according to NOAA/SPC reports. The enhanced GOES-13 10.7-μm brightness temperature image in Fig. 3 shows the cluster of severe thunderstorm at 2210 UTC. Note that the darker red color enhancement of the coldest brightness temperature is as cold as −63°C, and the “enhanced V” storm-top signature in western South Dakota. This is a typical severe thunderstorm but it nonetheless produced a long-duration wind and hail event that resulted in a remarkably long and wide damage path. Figure 4a shows the clear-sky LI (color regions) at SFOV spatial resolution overlain on the AIRS 11-μm brightness temperature image (black and white) at 1920 UTC 19 July 2010 in the preconvection environment, while Fig. 4b is the LI from the NASA AIRS science team product AIRS–AMSU retrievals. The local northwestern South Dakota area has LI values less than −15 (dark red color) from AIRS SFOV soundings, and the AIRS observation time was approximately 1 h ahead of the peak of the severe storm in Fig. 3. The LI from GOES-13 DPIs (not shown) suggests that the GOES-13 Sounder has an instrumental striping problem that might cause the incorrect cloud mask.
The enhanced GOES-13 10.7-μm images showed a cluster of severe thunderstorms at 2210 UTC 19 Jul 2010. Note that the darker red color enhancement of the coldest brightness temperature is as cold as −63°C and the typical severe enhanced-V storm-top signature in northwestern SD.
Citation: Weather and Forecasting 27, 2; 10.1175/WAF-D-10-05057.1
(a) The clear-sky LI (color regions) from AIRS SFOV soundings overlain on the AIRS 11-μm brightness temperature image at 1920 UTC 19 Jul 2010. (b) The corresponding NASA AIRS science team product AIRS–AMSU sounding LI at 1920 UTC 19 Jul 2010. The remarkably long and wide damage path is located in western SD. Although (b) indicates the unstable area, (a) gives a more precise area and may reduce the false-alarm area, as in eastern MN and IA.
Citation: Weather and Forecasting 27, 2; 10.1175/WAF-D-10-05057.1
LI (K) provides an estimate of the atmospheric stability in cloud-free areas and is one of the most popular satellite-derived products. The LI (Galway 1956) expresses the temperature difference between a lifted parcel and the surrounding air at 500 hPa. The parcel is lifted dry adiabatically from the mean lowest 100-hPa level to the condensation level, and then wet adiabatically to 500 hPa. When an air parcel is lifted adiabatically from the surface, the atmosphere is considered potentially unstable if the parcel’s temperature at 500 hPa is less than its environment (i.e., LI < 0). In general, the LI has the following meteorological implications for storm development: LI > 0 K for stable, −3 K < LI < 0 K for marginally unstable, −6 K < LI < −3 K for moderately unstable, −9 K < LI < −6 K for very unstable, and LI < −9 K for extremely unstable. The likelihood of severe thunderstorm development increases as the LI becomes more negative, with LI values less than −6 K indicating very favorable thermodynamic conditions for severe thunderstorm development. Similar to CAPE comparisons in case A, LI comparisons are applied for this case, and the LI values are also derived from the integration of retrieved profile information.
Although Fig. 4b and GOES-13 Sounder DPIs reveal the low atmospheric stability in the same region in Fig. 4a, the low LI areas in eastern North Dakota span to eastern South Dakota, where there was no convection in the following hours. This is the advantage of distinguishing stable and low-stability areas by applying SFOV soundings in regional short-term forecasting and nowcasting. Using the high spectral resolution AIRS soundings at its full spatial resolution, we may have an advantage not only in the accuracy of the atmospheric thermodynamic profiles but also in the finer vertical resolution at each observation point. These will reduce the false-alarm possibility, as presented in this case.
c. Supercell convective system on 7 August 2010 (case C)
The storm occurred on 7–8 August 2010 in Zhou Qu County, Gansu Province, China. The NASA EOS Aqua satellite’s overpass time is at about 0635 UTC on 7 August 2010 over the Zhou Qu area; the preconvection environment shows clear sky surrounded by some high ice clouds. Figure 5 shows the MODIS cloud-top phase (top panel) and cloud-top pressure (bottom panel) images from granules around 0635 UTC 7 August 2010. Zhou Qu city is located in the center of the circled area. The eastern areas of Zhou Qu are covered by low water clouds, while most western areas of Zhou Qu contain clear skies with some isolated high ice clouds with cloud tops higher than 300 hPa. The area surrounding Zhou Qu city had clear skies, high ice clouds, and low water clouds. The high spatial resolution MODIS provides useful information on clouds in cloudy areas while the high spectral resolution AIRS data can provide atmospheric thermodynamic structures in clear skies. From 1000 UTC 7 August to 0000 UTC 8 August 2010, the severe storm brought heavy precipitation to Zhou Qu County. AIRS observed the atmospheric instability at ~0635 UTC 7 August 2010 in the preconvection environment, 3.5 h before storm genesis. Figure 6 shows the clear-sky lifted index (LI) (color regions) from AIRS SFOV soundings overlain on the AIRS 11-μm channel brightness temperature image (black and white) at 0635 UTC 7 August 2010.
The MODIS (top) cloud phase and (bottom) cloud-top pressure images from 0625 to 0645 UTC 7 Aug 2010.
Citation: Weather and Forecasting 27, 2; 10.1175/WAF-D-10-05057.1
(a) The clear-sky LI (color regions) from AIRS SFOV soundings overlain on the AIRS 11-μm channel brightness temperature image at 0635 UTC 7 Aug 2010. (b) The corresponding NASA AIRS science team product AIRS–AMSU sounding LI at 0635 UTC 7 Aug 2010. Three remarkably unstable AIRS SFOVs in (a) are in the Zhou Qu area (34.19°N, 104.41°E), which is located in the center of the circle in Fig. 5.
Citation: Weather and Forecasting 27, 2; 10.1175/WAF-D-10-05057.1
From Fig. 6, it can be seen that the AIRS data provide clear-sky soundings in Zhou Qu and the surrounding areas. The colored areas denote the LI values; most areas are stable with some areas moderately unstable, while the local Zhou Qu area (34.19°N, 104.41°E) has extremely unstable LI values of less than −10 K (red color). The warning information from AIRS SFOV retrievals is 3.5 h ahead of the time when the rainfall starts. The LI in this case is an indicator of likely storm development in this area, together with other satellite and ground measurements as well as the NWP forecast; the LI in the preconvection environment is very useful for storm warning and nowcasting. Figure 6 also shows the advantages of using the full spatial resolution of the high spectral AIRS soundings in the wide cloud-covered area, because this increases the possibility to have cloud-free soundings. As opposed to the satellite-based LI product, the radar does not provide atmospheric dynamic structure information about the prestorm environment, but once the convective storm has developed, the radar data provide very good information about the precipitation and storm intensity.
4. Summary
The derived atmospheric instability (e.g., LI and CAPE) from AIRS full spatial resolution soundings provided earlier warning information, approximately 1 h before a local genesis of a severe thunderstorm (case A), and 3 h before severe convection (moving synoptic systems in cases B and C). IASI has a sounding capability similar to that of AIRS, full spatial resolution IASI soundings will also be processed so that AIRS and IASI can be used together to improve the temporal coverage for storm warning. This study also demonstrates the needs of an advanced IR sounding system from the geostationary orbit that provides high temporal resolution. Although the vertical resolution from the advanced IR sounder has improved from the legacy GOES Sounder, the near-surface resolution is still not necessary high enough to adequately capture convective parameters. Atmospheric sounding measurements from the high spectral resolution IR sounders, however, still provide critical preconvection atmospheric destabilization information that can be used together with other measurements and regional numerical weather prediction models for nowcasting the storm genesis and development.
Acknowledgments
The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. government position, policy, or decision. The authors thank Drs. Jinlong Li, Danyu Qin, Elisabeth Weisz, and Zhenglong Li for processing the AIRS, MODIS, and FengYun-2 data. CYL appreciates three anonymous reviewers’ valuable suggestions and comments to make this manuscript a giant improvement from its early version. This study is partly supported by NOAA Grant NA06NES4400002 and 863 Program 2009AA12Z150.
REFERENCES
Chahine, M. T., and Coauthors, 2006: AIRS: Improving weather forecasting and providing new data on greenhouse gases. Bull. Amer. Meteor. Soc., 87, 911–926.
Clerbaux, C., and Coauthors, 2007: The IASI/MetOp I mission: First observations and highlights of its potential contribution to GMES. COSPAR Inf. Bull., 2007, 19–24.
Galway, J. G., 1956: The lifted index as a predictor of latent instability. Bull. Amer. Meteor. Soc., 528–529.
Kwon, E.-H., Li J. , Li J. , Shon B. J. , and Weisz E. , 2012: Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval. Adv. Atmos. Sci., 29, 263–273, doi:10.1007/s00376-011-1119-z.
Li, J., and Liu H. , 2009: Improved hurricane track and intensity forecast using single field-of-view advanced IR sounding measurements. Geophys. Res. Lett., 36, L11813, doi:10.1029/2009GL038285.
Li, J., Menzel W. P. , Sun F. , Schmit T. J. , and Gurka J. , 2004a: AIRS subpixel cloud characterization using MODIS cloud products. J. Appl. Meteor., 43, 1083–1094.
Li, J., Menzel W. P. , Zhang W. , Sun F. , Schmit T. J. , Gurka J. , and Weisz E. , 2004b: Synergistic use of MODIS and AIRS in a variational retrieval of cloud parameters. J. Appl. Meteor., 43, 1619–1634.
Li, J., Liu C.-Y. , Huang H.-L. , Schmit T. J. , Wu X. , Menzel W. P. , and Gurka J. , 2005a: Optimal cloud-clearing for AIRS radiances using MODIS. IEEE Trans. Geosci. Remote Sens., 43, 1266–1278.
Li, J., and Coauthors, 2005b: Retrieval of cloud microphysical properties from MODIS and AIRS. J. Appl. Meteor., 44, 1526–1543.
Li, J., Li J. , Schmit T. J. , Otkin J. , and Liu C.-Y. , 2011: Warning information in preconvection environment from geostationary advanced infrared sounding system—A simulation study using the IHOP case. J. Appl. Meteor. Climatol., 50, 766–783.
Li, Z., Li J. , Menzel W. P. , Schmit T. J. , Nelson J. P. III, Daniels J. , and Ackerman S. A. , 2008: GOES sounding improvement and applications to severe storm nowcasting. Geophys. Res. Lett., 35, L03806, doi:10.1029/2007GL032797.
Liu, C.-Y., Li J. , Weisz E. , Schmit T. J. , Ackerman S. A. , and Huang H.-L. , 2008: Synergistic use of AIRS and MODIS radiance measurements for atmospheric profiling. Geophys. Res. Lett., 35, L21802, doi:10.1029/2008GL035859.
Liu, H., and Li J. , 2010: An improvement in forecasting rapid intensification of Typhoon Sinlaku (2008) using clear-sky full spatial resolution advanced IR soundings. J. Appl. Meteor. Climatol., 49, 821–827.
Ma, X. L., Schmit T. J. , and Smith W. L. , 1999: A nonlinear physical retrieval algorithm—Its application to the GOES-8/9 sounder. J. Appl. Meteor., 38, 501–513.
Reale, O., Susskind J. , Rosenberg R. , Brin E. , Liu E. , Riishojgaard L. P. , Terry J. , and Jusem J. C. , 2008: Improving forecast skill by assimilation of quality-controlled AIRS temperature retrievals under partially cloudy conditions. Geophys. Res. Lett., 35, L08809, doi:10.1029/2007GL033002.
Schmit, T. J., Li J. , Ackerman S. A. , and Gurka J. J. , 2009: High spectral and temporal resolution infrared measurements from geostationary orbit. J. Atmos. Oceanic Technol., 26, 2273–2292.
Sieglaff, J. M., Schmit T. J. , Menzel W. P. , and Ackerman S. A. , 2009: Inferring convective weather characteristics with geostationary high spectral resolution IR window measurements: A look into the future. J. Atmos. Oceanic Technol., 26, 1527–1541.
Smith, W. L., Sr., Zhou D. K. , Larar A. M. , Mango S. A. , Howell H. B. , Knuteson R. O. , Revercomb H. E. , and Smith W. L. Jr., 2005: The NPOESS Airborne Sounding Testbed Interferometer—Remotely sensed surface and atmospheric conditions during CLAMS. J. Atmos. Sci., 62, 1118–1134.
Smith, W. L., Sr., and Coauthors, 2009: Evolution, current capabilities, and future advance in satellite nadir viewing ultra-spectral IR sounding of the lower atmosphere. Atmos. Chem. Phys., 9, 5563–5574, doi:10.5194/acp-9-5563-2009.
Susskind, J., Barnet C. D. , and Blaisdell J. M. , 2003: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens., 41, 390–409.
Tobin, D. C., and Coauthors, 2006: Atmospheric Radiation Measurement site atmospheric state best estimates for Atmospheric Infrared Sounder temperature and water vapor retrieval validation. J. Geophys. Res., 111, D09S14, doi:10.1029/2005JD006103.
Weisz, E., Li J. , Zhou D. K. , Huang H.-L. , Goldberg M. D. , and Yang P. , 2007: Cloudy sounding and cloud-top height retrieval from AIRS alone single field-of-view radiance measurements. Geophys. Res. Lett., 34, L12802, doi:10.1029/2007GL030219.
Zhou, D. K., and Coauthors, 2002: Thermodynamic product retrieval methodology for NAST I and validation. Appl. Opt., 41, 6957–6967.
