• Bretherton, C. S., and Coauthors, 2004: The EPIC 2001 stratocumulus study. Bull. Amer. Meteor. Soc., 85, 967977.

  • Browning, K. A. & , and G. A. Monk, 1982: A simple model for the synoptic analysis of cold fronts. Quart. J. Roy. Meteor. Soc., 108, 435452.

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  • Hawkins, J. D., , T. F. Lee, , J. Turk, , C. Sampson, , J. Kent & , and K. Richardson, 2001: Real-time internet distribution of satellite products for tropical cyclone reconnaissance. Bull. Amer. Meteor. Soc., 82, 567578.

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  • Kim, S.-W., , E.-S. Chung, , S.-C. Yoon, , B.-J. Sohn & , and N. Sugimoto, 2011: Intercomparisons of cloud-top and cloud-base heights from ground-based lidar, Cloudsat and CALIPSO measurements. Int. J. Remote Sens., 32, 11791197.

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  • Kummerow, C., and Coauthors, 2001: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor, 40, 18011820.

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  • L'Ecuyer, T. S. & , and J. Jiang, 2010: Touring the atmosphere aboard the A-Train. Phys. Today, 63, 3641.

  • Lee, T. F., and Coauthors, 2007: NPOESS online satellite training for users. Bull. Amer. Meteor. Soc., 88, 1316.

  • Matrosov, S. Y., 2010: CloudSat studies of stratiform precipitation systems observed in the vicinity of the Southern Great Plains atmospheric radiation measurement site. J. Appl. Meteor. Climatol., 49, 17561765.

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  • Miller, S. D., and Coauthors, 2006: NexSat: Previewing NPOESS/VIIRS imagery capabilities. Bull. Amer. Meteor. Soc., 87, 433446.

  • Minnis, P., , P. W. Heck, , D. F. Young, , C. W. Fairall & , and J. B. Snider, 1992: Stratocumulus cloud properties derived from simultaneous satellite and island-based instrumentation during FIRE. J. Appl. Meteor, 31, 317339.

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  • Mitrescu, C., , S. Miller, , J. Hawkins, , T. L'Ecuyer, , J. Turk, , P. Partain & , and G. Stephens, 2008: Near-real-time applications of CloudSat data. J. Appl. Meteor. Climatol., 47, 19821994.

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  • Mitrescu, C., , T. L'Ecuyer, , J. Haynes, , S. Miller & , and J. Turk, 2010: CloudSat precipitation profiling algorithm— Model description. J. Appl. Meteor. Climatol., 49, 9911003.

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  • Posselt, D., , G. L. Stephens & , and M. Miller, 2008: CloudSat: Adding a new dimension to a classical view of extratropical cyclones. Bull. Amer. Meteor. Soc., 89, 599609.

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  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 17711790.

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  • Winker, D. M., , M. A. Vaughan, , A. Omar, , Y. Hu, , K. A. Powell, , Z. Liu, , W. H. Hunt & , and S. A. Young, 2009: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Oceanic Technol., 26, 23102323.

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  • Winker, D. M., and Coauthors, 2010: The CALIPSO mission: A global 3D view of aerosols and clouds. Bull. Amer. Meteor. Soc., 91, 12111229.

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  • View in gallery

    A-Train constellation. Credit: NASA.

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    (middle) Visible GOES-11 Geocolor image, 2230 UTC 7 Apr 2010. Red line marks ascending CloudSat overpass path. (top) Northern and (bottom) southern portions of CloudSat radar reflectivity profile.

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    (top) GOES-11 visible image, 2125 UTC 30 Dec 2007. Red line marks ascending CALIPSO overpass path. (bottom) CALIPSO attenuated backscatter profile.

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    (top) GOES-11 Infrared Geocolor image, 1207 UTC 24 Apr 2010. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

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    (top) GOES-11 Infrared Geocolor image, 1030 UTC 4 Jan 2008. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

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    (top) GOES-11 Infrared Geocolor image, 1030 UTC 3 Dec 2007. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

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    Surface isobars (hPa) and frontal analysis over northern Europe, 0000 UTC 12 Nov 2010. Data and graphic from UK Met Office.

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    (top) Meteosat-8 Infrared Geocolor image, 0100 UTC 12 Nov 2010. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

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    (top) GOES-11 Infrared Geocolor image, 1045 UTC 20 Nov 2009. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

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    (top) GOES-11 Infrared Geocolor image, 1000 UTC 20 Oct 2007. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

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    Hydrometeorological Prediction Center (HPC) radar/weather depiction, 1200 UTC 2 Dec 2009. Courtesy of National Centers for Environmental Prediction (NCEP).

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    (top) GOES-11 infrared Geocolor image, 1900 UTC 2 Dec 2009. Red line marks ascending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

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    (top) Aqua MODIS visible image, 0355 UTC 15 Sep 2009. Red line marks ascending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

  • View in gallery

    (top) GOES-11 infrared image in black and white, 2130 UTC 25 Jun 2010; corresponding AMSR-E precipitation rates (h–1) in color. Red line marks ascending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

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Meteorological Education and Training Using A-Train Profilers

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  • 1 Naval Research Laboratory, Monterey, California
  • 2 Science Systems and Applications, Inc., Hampton, Virginia
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NASA A-Train vertical profilers provide detailed observations of atmospheric features not seen in traditional imagery from other weather satellite data. CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) profiles vividly depict the vertical dimension of otherwise two-dimensional features shown in mapped products. However, most forecasters have never seen these profiles and do not appreciate their capacity to convey fundamental information about cloud and precipitation systems. Here, these profiles are accompanied by weather satellite images and explained in the context of various meteorological regimes. Profile examples are shown over frontal systems, marine stratocumulus, orographic barriers, tropical cyclones, and a severe thunderstorm.

CORRESPONDING AUTHOR: Thomas F. Lee, 7 Grace Hopper Ave., Monterey, CA 93943, E-mail: thomas.lee@nrlmry.navy.mil

NASA A-Train vertical profilers provide detailed observations of atmospheric features not seen in traditional imagery from other weather satellite data. CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) profiles vividly depict the vertical dimension of otherwise two-dimensional features shown in mapped products. However, most forecasters have never seen these profiles and do not appreciate their capacity to convey fundamental information about cloud and precipitation systems. Here, these profiles are accompanied by weather satellite images and explained in the context of various meteorological regimes. Profile examples are shown over frontal systems, marine stratocumulus, orographic barriers, tropical cyclones, and a severe thunderstorm.

CORRESPONDING AUTHOR: Thomas F. Lee, 7 Grace Hopper Ave., Monterey, CA 93943, E-mail: thomas.lee@nrlmry.navy.mil

Profiles from CloudSat and CALIPSO, atmospheric profilers within the NASA A-Train constellation, offer detailed observations of clouds, providing understanding that neither satellite imagers nor traditional sounders can convey.

The potential for training forecasters and educating students is immense using data from the National Aeronautic and Space Administration's (NASA's) two A-Train (L'Ecuyer and Jiang 2010) profilers, CloudSat (Stephens et al. 2002) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). In particular, the vertical profiles can provide crucial insights into two-dimensional features observed on satellite images and other traditional meteorological products. Launched on 28 April 2006, CloudSat is the first capability of its kind, a NASA Earth observation satellite that uses radar to infer vertical profiles of cloud properties. CloudSat flies in formation in the A-Train with several other satellites [Aqua, Aura, CALIPSO, and the French Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL)] whose orbits occur in the same path, one behind the other (Fig. 1). The examples of Posselt et al. (2008) suggest how effective use of CloudSat data can validate traditional conceptual models of midlatitude weather systems. Limited training has appeared on the World Wide Web, from the Cooperative Program for Operational Meteorology, Education and Training (COMET) Tropical Meteorology Textbook (www.meted.ucar.edu/tropical/textbook_2nd_edition/) and the Virtual Institute for Satellite Integration Training (VISIT; http://rammb.cira.colostate.edu/training/visit/). There are also examples on the Colorado State University Atmospheric Science web site (http://cloudsat.atmos.colostate.edu/). This article juxtaposes CloudSat profiles with corresponding satellite images to illustrate the education and training potential in a variety of atmospheric environments. An additional example covers the use of a CALIPSO profile to observe stratus and stratocumulus tops over and off the West Coast of the United States.

Fig. 1.
Fig. 1.

A-Train constellation. Credit: NASA.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

CloudSat's main sensor is the Cloud Profiling Radar (CPR), a 94-GHz nadir-viewing instrument that measures the returned backscattered energy by clouds as a function of height along the orbital track (Stephens et al. 2002). The CPR has a 240-m vertical range resolution between the surface and 30 km. Because of surface contamination from ground clutter, the usefulness of cloud information is quite limited near the surface. CloudSat observations provide a single row of pixels along its flight path with footprint size of 1.4 km × 1.7 km.

CloudSat produces accurate, high-resolution cloud heights and cloud vertical profiles (Kim et al. 2011). Unfortunately, it is capable of quantitatively profiling lightly precipitating cloud systems only (Mitrescu et al. 2010). For higher precipitation rates, complications arising from increased extinction and multiple scatter factors make quantitative precipitation analysis almost impossible. Despite these limitations CloudSat profiles can show precipitation features such as melting layers (or “bright bands”) (Matrosov 2010), deep convective towers, orographic cloud systems, and multiple cloud layers. The capability to distinguish between convective and stratiform precipitating systems also exists.

Until 17 April 2011, when CloudSat experienced major battery problems and data became unavailable, the Naval Research Laboratory (NRL) posted products in near-real time on its NexSat web portal (Miller et al. 2006). Our near-real-time processing scheme is described in detail in Mitrescu et al. (2008). Product latency of about 4 h severely limited many nowcasting applications; however, missions such as the reconnaissance of oceanic tropical cyclones still benefited in spite of the delay. Data from future missions, if delivered much more promptly, could enable these profiles to be integrated into the forecast process.

The A-Train constellation, a configuration of clustered satellites in an early afternoon orbit, has several other instruments that are potentially useful for user training and education (Fig. 1). The Aqua satellite has the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) instrument that provides two-dimensional images of precipitation rates. CloudSat profiles may be used for detailed examination of the clouds responsible for the precipitation observed in AMSR-E retrievals (discussed later in conjunction with Fig. 14). The Moderate Resolution Imaging Spectroradiometer (MODIS), also onboard Aqua, yields detailed high-resolution true color images. The CALIPSO (Winker et al. 2009) instrument is a cloud lidar that can give important information about cloud height, cloud phase, and aerosol characteristics. Its capacity to profile cirrus, aerosols, and marine stratocumulus is unprecedented (Winker et al. 2010). Although these various A-Train satellite instruments are orbiting on separate platforms, together they comprise a single “virtual satellite” capability for which observations nearly coincide in time and space.

Examples and discussions in this article demonstrate how three-dimensional understanding can be improved by using the near-simultaneous display of profiles with the contemporaneous geostationary satellite imagery. For imagery, the geostationary Geocolor product is mainly used. This product is a visible and longwave infrared (0.63 and 10.8 μm) composite available 24 hours a day (Miller et al. 2006). It provides a single-channel visible image during the daytime, against the NASA “blue marble” background, and single-channel infrared image at night, against a background of nighttime lights. On the CloudSat profiles, temperature contours from the Navy Operational Global Atmospheric Prediction System (NOGAPS) are overlain for additional context in the vertical. To arrive at valid times corresponding to A-Train profiles, the NOGAPS data were interpolated between very short-term forecast times. Additionally, terrain height contours allow observation of orographic influences on clouds. For descriptions of the accompanying weather situations, daily weather maps archived by NOAA were consulted (www.hpc.ncep.noaa.gov/dailywxmap/).

EXAMPLES FOR TRAINING AND EDUCATION.

Cloud heights in the low/midtroposphere.

Identification of “open” vs. “closed” cell convection is a familiar exercise for satellite meteorology students. Figure 2 (7 April 2010) is an illustration of how CloudSat can sample the cloud vertical structure in both regimes. Note that open cells appear as bright dots in the Geocolor image composed of visible data northwest of a frontal system moving into the Pacific Northwest. Far south of the frontal system closed cells produce a near overcast in the trade wind regime. The CloudSat profile reveals the height of the open cells at about 4 or 5 km. To the south, the closed cells have only a height of approximately 1 or 2 km. The height of the closed cells in this example approaches 1.2 km, the lower limit of cloud tops detectable by CloudSat (Mitrescu et al. 2010).

Fig. 2.
Fig. 2.

(middle) Visible GOES-11 Geocolor image, 2230 UTC 7 Apr 2010. Red line marks ascending CloudSat overpass path. (top) Northern and (bottom) southern portions of CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

CloudSat crosses a very shallow frontal band separating the air masses, with cloud heights comparable to the closed cells to the south. Nearly all the sampled band lies under the freezing-level height (according to NOGAPS temperature contours), suggesting that resulting precipitation, if any, should be from “warm rain” processes. This shallow frontal band is compared to much deeper systems later in the article. While experienced image interpreters may infer the shallow nature of the front in this region based on the image alone, new forecasters and students would benefit from the CloudSat comparison.

Marine stratocumulus and continental stratus clouds are usually too low to be well observed with CloudSat. However, CALIPSO profile data offer a powerful alternative to observe these cloud tops in comparison with imager products. In Fig. 3 (30 December 2007) both cloud system types occur under the same A-Train overpass. Topographically constrained stratus appears over California's Central Valley with marine stratocumulus to the south. The continental stratus has tops at approximately 1.2 km above mean sea level (MSL) over most of the valley, sloping upward to approximately 1.5 km above the slopes at the southern side of the valley and to nearly 2 km over the mountains at the northern side. To the south, the marine stratocumulus clouds have lower tops, sloping from about 0.7 km to the north to 1.0 km to the south. The north-to-south increase in height shown in this region is representative of summary statistics for stratocumulus prepared from CALIPSO data (Winker et al. 2010). Such variations in marine stratocumulus are tied to the height of the marine boundary layer (e.g., Bretherton et al. 2004). The tops of the stratocumulus clouds as observed by CALIPSO are difficult to derive from other weather satellite data (Minnis et al. 1992).

Fig. 3.
Fig. 3.

(top) GOES-11 visible image, 2125 UTC 30 Dec 2007. Red line marks ascending CALIPSO overpass path. (bottom) CALIPSO attenuated backscatter profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

Stability of frontal systems.

Figures 4 (24 April 2010) and 5 (4 January 2008) demonstrate CloudSat's ability to diagnose the stability of frontal systems. The Geocolor image in Fig. 4 [Geostationary Operational Environmental Satellite (GOES) infrared data] shows a frontal system in the North Pacific Ocean with CloudSat transecting the overrunning clouds in the warm sector. These clouds have a depth of approximately 10 km. Contrast this cloud depth with the much shallower frontal system from an earlier example (Fig. 2). Centered within the profile is an elongated bright band between about 1–2 km, representing the melting process. Bright bands are extremely common on CloudSat profiles within stratiform precipitation systems. As expected, the bright band lies just under the NOGAPS 0°C isotherm. This long and well-defined melting layer suggests a stable precipitation regime with steady warm sector precipitation. The bright band slopes downward at approximately 46°N (moving northward along the profile). The NOGAPS model also shows this decline in the 0°C level. Such changes in melting layer height are common in CloudSat reflectivity profiles, suggesting fronta l discontinuities where the slope changes dramatically.

Fig. 4.
Fig. 4.

(top) GOES-11 Infrared Geocolor image, 1207 UTC 24 Apr 2010. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

On 4 January 2008 a deep trough and associated polar air mass moved southeastward across the West Coast of the United States. The Geocolor image in Fig. 5 (top) depicts a CloudSat overpass through the associated cold frontal band. In contrast to the stable precipitation regime shown in Fig. 4, this CloudSat profile reveals embedded convection in the frontal band and the absence of an easily defined stable bright band. The Geocolor image confirms the unstable character of the precipitation with embedded convective cells off the northern California and Oregon coasts. Also of note is the orographic cloud tied to the Cascades on the CloudSat profile. This type of cloud is virtually impossible to detect on the nighttime longwave infrared image. Such orographic signatures are common in CloudSat data and will be discussed further in the next section.

Fig. 5.
Fig. 5.

(top) GOES-11 Infrared Geocolor image, 1030 UTC 4 Jan 2008. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

Orographic influences on clouds.

The strong effect of mountains on a coastal frontal system can be seen in Fig. 6 (3 December 2007). Precipitation from a cloud band apparent on the GOES-11 longwave infrared image over Washington is corroborated by significant backscatter on the CloudSat profile. Significantly, precipitation and, to a large degree, clouds are absent from the lee of the Cascades, illustrating a strong rain shadow effect. Such comprehensive depictions of rain shadows are often not possible from ground-based weather radars because of the interference of terrain. The use of visible and, especially, longwave infrared satellite images is also limited due to the obscuration by higher clouds.

Fig. 6.
Fig. 6.

(top) GOES-11 Infrared Geocolor image, 1030 UTC 3 Dec 2007. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

Cloud layers.

Cloud layering due to a split front (Browning and Monk 1982) is marked by an extensive region of saturated ascending air aloft, on the order of 100 km ahead of shallow clouds from a surface cold front. On 12 November 2010, a split front was located over north-central Europe (Fig. 7). According to this model the trailing edge of the upper front is marked by sharply falling humidity aloft and sharp cloud boundaries on satellite images. The subsequent advance of the surface front is marked by low-level increases in clouds and often precipitation. In this case the cloud bands associated with surface and upper fronts are separated by approximately 200 km (Fig. 8). A CloudSat transect through a bright (low infrared temperature) cloud band reveals the vertical structure of the upper front (from 2 to 10 km) but little indication of precipitation at the surface. Surface-based radar composites at this time (not shown) confirm little or no precipitation in the vicinity of the CloudSat profile through the upper frontal band, but significant precipitation in the western half of Poland in the vicinity of the surface frontal band.

Fig. 7.
Fig. 7.

Surface isobars (hPa) and frontal analysis over northern Europe, 0000 UTC 12 Nov 2010. Data and graphic from UK Met Office.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

Fig. 8.
Fig. 8.

(top) Meteosat-8 Infrared Geocolor image, 0100 UTC 12 Nov 2010. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

If the orientation of the transect is favorable, CloudSat can show detailed frontal structure over hundreds of kilometers. A Geocolor infrared image and CloudSat profile show a frontal cloud band off the West Coast of the United States (Fig. 9; 20 November 2009). Letter A (north) on the CloudSat profile shows orographic cloud enhancement (reds and yellows on the profile above elevated terrain) over the Cascade Mountains and Vancouver Island. On either side of point B farther to the south, the CloudSat profile reveals an elongated cloud composed of jet stream cirrus. Without a CloudSat profile a meteorologist might infer deep cloud and precipitation along this axis. At point C, CloudSat samples the eastern portion of a convective complex to the west of the frontal band. At point D to the south, CloudSat profiles the low-level frontal band, revealing cloud heights at about 4 km.

Fig. 9.
Fig. 9.

(top) GOES-11 Infrared Geocolor image, 1045 UTC 20 Nov 2009. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

CloudSat is useful in mountainous terrain especially when high clouds obscure orographic effects at low levels. On 20 October 2007 strong zonal flow prevailed across California. A cirrus feature associated with a jet streak appears over the Sierra Nevada in the Geocolor infrared image (Fig. 10; 20 October 2007). The CloudSat profile reveals two major cloud systems affecting the area: 1) clouds associated with the westerly jet streak aloft and 2) a lower layer underneath, composed of residual clouds following the passage of a weak cold front. The lower-level cloud is partly contained within the California Central Valley but slopes upward over terrain of the Sierra Nevada to the summit where it terminates.

Fig. 10.
Fig. 10.

(top) GOES-11 Infrared Geocolor image, 1000 UTC 20 Oct 2007. Red line marks descending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

Severe weather diagnosis.

In general, heavy rain or hail attenuates the CloudSat signal near the ground sufficiently to obscure low-level cloud structure. However, “reflectivity spikes” are sometimes observed in the upper portions of convective cloud systems and serve as potential indicators of severe weather. On 2 December 2009, a strong cold front and associated convective cloud band brought severe thunderstorms and seven confirmed tornadoes over south-central Georgia, causing structural damage and injury (Figs. 11 and 12). The most intense convection appears in the CloudSat profile over northern Florida and Georgia with cloud tops generally around 12 km (Fig. 12). Also, at 31.5°N, an overshooting top appears that extends upward through the cloud system (resembles an upward pointing red arrow) with the top at approximately 14 km. This feature occurred very close (in time and space) to tornado reports and warnings in southeastern Georgia.

Fig. 11.
Fig. 11.

Hydrometeorological Prediction Center (HPC) radar/weather depiction, 1200 UTC 2 Dec 2009. Courtesy of National Centers for Environmental Prediction (NCEP).

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

Fig. 12.
Fig. 12.

(top) GOES-11 infrared Geocolor image, 1900 UTC 2 Dec 2009. Red line marks ascending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

Tropical cyclones.

CloudSat helps delineate cloud structure in the core regions of tropical cyclones (Mitrescu et al. 2008). The profile displayed in Fig. 13 (15 September 2009) provides an example over Typhoon Choi-Wan, carrying maximum winds of about 125 kt. The prominent central eye becomes wider with increasing height in both the MODIS image from the Aqua satellite and the CloudSat profile. CloudSat reveals that the eye is cloud free over nearly the entire extent of the column, except that surface clutter prevents observation of lowest 1 km above the surface. The profile reveals information that the infrared image cannot. For example, on the northern side of the storm, precipitation is more stratiform, as revealed by the uniform melting layer at about 4 km. On the southern side, however, precipitation is characterized by a number of convective turrets. This example illustrates how the A-Train can constitute a “virtual satellite” with sensors on different satellites (Aqua and CloudSat) being easily colocated in time and space.

Fig. 13.
Fig. 13.

(top) Aqua MODIS visible image, 0355 UTC 15 Sep 2009. Red line marks ascending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

CloudSat can also be used to observe the periphery of tropical cyclones. AMSR-E precipitation retrievals (Kummerow et al. 2001; colors in Fig. 14) show heavy precipitation rates of approximately 1 inch per hour (25 mm h−1) near the center of eastern Pacific Hurricane Celia (25 June 2010). Near the western periphery of the storm along the CloudSat overpass path, however, the precipitation rate drops to approximately 0.10 inches per hour (2.5 mm h−1). Based on the AMSR-E/GOES product alone, forecasters might believe that the storm canopy is responsible for this precipitation. However, the CloudSat profile shows a large cloud-free gap between the canopy aloft (tops about 13 km) and a layer of stratiform clouds just above the surface (tops at about 2.5 km). This critical information suggests that the lower layer is responsible for the precipitation observed by AMSR-E. Because of the demonstrated usefulness of microwave imagers to fix position and intensity (Hawkins et al. 2001; Lee et al. 2007), products like those shown in Figs. 13 and 14 are now used routinely by forecasters along with visible and infrared images. (See https://www.meted.ucar.edu/training_module.php?id=159.)

Fig. 14.
Fig. 14.

(top) GOES-11 infrared image in black and white, 2130 UTC 25 Jun 2010; corresponding AMSR-E precipitation rates (h–1) in color. Red line marks ascending CloudSat overpass path. (bottom) CloudSat radar reflectivity profile.

Citation: Bulletin of the American Meteorological Society 93, 5; 10.1175/BAMS-D-11-00120.1

CONCLUSIONS.

Within each class of phenomena there is endless variability, and the cases here are not presented as typical or representative. To promote the use of A-Train profiles in training materials, the profiles must be displayed in the context of other products such as satellite images, weather maps, and ground radar plots. Without this coupling, meteorologists will not be able to relate cloud vertical structure to the horizontal structure of weather systems.

With infrequent refresh over a specific area and a latency of several hours, CloudSat seldom captures evolving meteorological events in a way that could benefit forecasters. However, data from future profilers may be delivered faster, enabling incorporation into the forecast process, especially poleward of about 50°N or S where temporal refresh increases. Even without real-time use, forecasters can greatly increase their knowledge of their area of responsibility by viewing profiler products from recent and historic weather events. They can gain insight into a number of phenomena. The stability of precipitation regions is one example. The meteorologist can acquire knowledge of the influence of orography on local cloud and precipitation patterns, relating upslope and downslope patterns to variations in wind flow and stability. Over oceans they can distinguish various types of cloud and precipitation regimes based on frontal type, cloud depth, and stability. CloudSat profiles over severe weather may supplement information from ground-based radar and other observations. Additionally, CALIPSO profiles of fog and stratus can provide accurate tops that are nearly impossible to infer from visible and infrared images. Stratus clouds are often assumed to be of uniform altitude; these profiles reveal important slopes that are important to understanding how fog and stratus evolve. The focus of this article was on midlatitude weather systems. A-Train profilers also have important applications for education and training in diverse regions, including the Arctic, the Antarctic, and the tropics.

ACKNOWLEDGMENTS.

The authors gratefully acknowledge the support of the Joint Polar Satellite System (JPSS). Rafal Iwanski (Institute of Meteorology and Water Management, Krakow Poland) provided radar plots. Dr. Derek Posselt from the University of Michigan provided useful suggestions.

REFERENCES

  • Bretherton, C. S., and Coauthors, 2004: The EPIC 2001 stratocumulus study. Bull. Amer. Meteor. Soc., 85, 967977.

  • Browning, K. A. & , and G. A. Monk, 1982: A simple model for the synoptic analysis of cold fronts. Quart. J. Roy. Meteor. Soc., 108, 435452.

    • Search Google Scholar
    • Export Citation
  • Hawkins, J. D., , T. F. Lee, , J. Turk, , C. Sampson, , J. Kent & , and K. Richardson, 2001: Real-time internet distribution of satellite products for tropical cyclone reconnaissance. Bull. Amer. Meteor. Soc., 82, 567578.

    • Search Google Scholar
    • Export Citation
  • Kim, S.-W., , E.-S. Chung, , S.-C. Yoon, , B.-J. Sohn & , and N. Sugimoto, 2011: Intercomparisons of cloud-top and cloud-base heights from ground-based lidar, Cloudsat and CALIPSO measurements. Int. J. Remote Sens., 32, 11791197.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors, 2001: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor, 40, 18011820.

    • Search Google Scholar
    • Export Citation
  • L'Ecuyer, T. S. & , and J. Jiang, 2010: Touring the atmosphere aboard the A-Train. Phys. Today, 63, 3641.

  • Lee, T. F., and Coauthors, 2007: NPOESS online satellite training for users. Bull. Amer. Meteor. Soc., 88, 1316.

  • Matrosov, S. Y., 2010: CloudSat studies of stratiform precipitation systems observed in the vicinity of the Southern Great Plains atmospheric radiation measurement site. J. Appl. Meteor. Climatol., 49, 17561765.

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