1. Introduction
Shallow (i.e., up to a few kilometers deep) moist (cloudy) mesoscale maritime convection (SMMMC) has garnered ample interest in the recent literature. A common justification for its study is the role that SMMMC plays in the general circulation and climate through the associated heat, moisture, momentum, and cloud radiative fluxes. For example, see the review of the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) by Randall et al. (2003). While the focus of the present research is open-cell SMMMC over the northeastern Pacific Ocean, a geographically and phenomenologically broad review is provided due to the interrelatedness of the various SMMMC types.
Studies investigating tropical and subtropical SMMMC include the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS) experiment (Lenschow et al. 1988), the First International Satellite Cloud Climatology Project Regional Experiment (FIRE) (Albrecht et al. 1988), the Atlantic Stratocumulus Transition Experiment (ASTEX) (Albrecht et al. 1995), the DYCOMS-II field study (Stevens et al. 2003), the East Pacific Investigation of Climate (EPIC) 2001 field experiment (Bretherton et al. 2004), the Rain in Cumulus over the Ocean (RICO) study (Rauber et al. 2007), and the Variability of the American Monsoon System Ocean–Cloud–Atmosphere–Land Study (VOCALS; information online at http://www.eol.ucar.edu/projects/vocals/). An additional body of SMMMC-focused work has examined cold-air outbreak regimes at middle to high latitudes [e.g., the Air Mass Transformation Experiment (AMTEX), Lenschow and Agee (1974); the Convection and Turbulence Experiment (KonTur), Brümmer et al. (1985); and the “ARKTIS” experiments, Brümmer (1999)]. Finally, although not of great relevance to the present research, for completeness, Arctic mixed-phase SMMMC clouds were a focus of the Surface Heat Budget of the Arctic Ocean (SHEBA) program (Uttal et al. 2002) and the Mixed-Phase Cloud Experiment (M-PACE; Verlinde et al. 2007).
Atkinson and Zhang (1996) present an excellent review of SMMMC. They classify its organization as rolls, open cells, and closed cells, with fields of each often occurring over rather large areas. Figure 1 in Atkinson and Zhang (1996) shows an example of rolls, which are manifested by alternating long bands of cloudy (usually stratocumulus) and clear air. Figure 3 in Atkinson and Zhang (1996) is an example of closed cells, which are pockets of stratocumulus surrounded by clear air.
Figure 2 in Atkinson and Zhang (1996) presents an example of open cells, which are pockets of clear air surrounded by cumulus clouds. A close examination of Fig. 2 in Atkinson and Zhang (1996) and many of the satellite images in the literature reveals examples of asymmetric open cells and open cells whose clear portions are not completely surrounded by clouds; that is, they are crescent or arc shaped. The relevance of that observation will be made evident below.
Rolls are generally attributed to thermodynamic instability in the presence of vertical wind shear and inflection point instability (Asai 1970). Further, the two instabilities are not mutually exclusive (LeMone 1973). Rolls caused by thermodynamic instability in a sheared environment align more or less with the mean boundary layer vertical wind shear vector while those caused by inflection point instability align perpendicular to the vertical wind shear vector at the inflection point (Asai 1970; Asai and Nakasuji 1973).
SMMMC organized as rolls comes in two flavors: narrow mode and wide mode (see Young et al. 2002 for review). The former mode primarily inhabits the low latitudes and has a relatively small aspect ratio (approximately 3), which is independent of boundary layer depth (Zi). The latter mode tends to occur during cold-air outbreaks and often has a relatively large aspect ratio (order as large as 10), which scales linearly with Zi.
Increasing roll aspect ratio, and the transformation to cells, has been associated with boundary layer sensible heating, latent heating, and mixing (Chlond 1988). Mixing increases the boundary layer static stability and reduces the vertical wind shear, thus impacting the organization. All else being equal, then, rolls broaden to closed cells. However, if the corresponding heating appreciably increases the boundary layer depth and decreases the static stability of the capping inversion, cumuliform clouds can be preferred and open cells may result. The parameterization of the above-mentioned processes remains an area of active research. For example, Weckwerth et al. (1997, 1999) suggested the ratio of −Zi to the Monin–Obukhov length as a predictor of aspect ratio and organization while Brümmer (1999) discounted the same and instead argued for the combination of multilayer Rayleigh numbers and a shear Reynolds number. In general, the open- and closed-cell aspect ratio order of magnitude is 10, but specific values in the literature tend to be larger than those of wide-mode rolls.
Documented in maritime regions throughout the world, open and closed cells are generally attributed to thermodynamic instability (e.g., Atkinson and Zhang 1996). So it is that lower-tropospheric static stability promotes seasonal and large-scale geographic variabilities to the fields of open and closed cells that often occupy expansive portions of the subtropics (e.g., Agee 1987; Atkinson and Zhang 1996; Painemal et al. 2010). There, closed cells are generally preferred during the summer, and to the immediate west of continents where the static stability of the capping inversion is climatologically strong. In contrast, open cells are generally preferred during the winter, and to the west of the closed cells where the static stability aloft is expected to be weaker. The former convection is forced, at least in part, from cloud-top cooling via radiation and entrainment (e.g., Albrecht et al. 1988; Moyer and Young 1994), while the latter convection is surface based.
Similar to wide-mode rolls, the frequency of open-cell convection at middle to high latitudes is primary forced by transient synoptic-scale phenomena. For example, Eastman and Warren (2010) show that open cells and rolls occur more frequently during the low Arctic cold season, probably because of the increased presence of cold-air outbreaks. Similarly, Agee (1987) shows the preference for open cells in the climatologically favored regions for cold-air outbreaks at middle to high latitudes. Representative research documenting SMMMC in maritime cold-air outbreaks includes the work of Agee and Dowell (1974), Sheu and Agee (1977), Miura (1986), Bond and Fleagle (1988), Brümmer (1999), and Schröter et al. (2005).
It is rather common for wide-mode rolls and open cells to form in adjacent regions during a maritime cold-air outbreak. The typical cloud pattern evolution in a cold-air outbreak is for the wide-mode rolls to form just offshore (Young et al. 2002) and the open-cell convection to form hundreds of kilometers downwind (Brümmer 1999). At times, there is also a final transition to a small region of closed-cell convection immediately behind the cold front (Agee 1987).
Brümmer (1999) used aircraft observations to study the roll to open-cell evolution in cold-air outbreaks. In a manner similar to that described above, he linked the evolution to an increase in boundary layer heating and a corresponding weakening capping inversion via sensible and latent heating. The inversion weakening allows for open-cell clouds to inhabit a deeper layer than their upwind neighbors, the rolls, do. In fact, the open-cell clouds were sufficiently deep such that the integrated latent heating was greater than the corresponding sensible heat flux from the ocean, opposite of that found for the roll clouds. Brümmer (1999) also clearly shows that boundary layer vertical wind shear decreases steadily, on average, from the roll regimes to the cell regimes.
2. Motivation
Much of the observational research referenced above has relied on traditional space-borne remote sensing (e.g., visible and infrared images from operational satellite missions) to document the cloud forms of interest. Newer, higher-resolution remote sensing systems allow moist mesoscale maritime convection to be reexamined. A particularly useful combination of modern remote sensing systems for this venture is the Moderate Resolution Imaging Spectroradiometer (MODIS) (King et al. 1992) and the Synthetic Aperture Radar (SAR) (Beal et al. 2005). The MODIS instruments were launched into earth orbit by the National Aeronautics and Space Agency (NASA) on board Terra [Earth Observing Satellite (EOS) AM] in 1999 and Aqua (EOS PM) in 2002. These satellites are in a sun-synchronous polar orbit. The instruments capture data in 36 spectral bands, in wavelengths from 0.4 to 14.4 μm, and at several band-dependent pixel spacings: 250 m, 500 m, and 1 km. MODIS has a swath width of 2330 km.
An example of a space-borne SAR is that on board the Canadian Space Agency’s Radarsat-1 satellite, which was launched into a sun-synchronous orbit in 1995. The radar is horizontally–horizontally polarized and C band (approximately 5 cm in wavelength). The so-called ScanSAR Wide B product of the Alaska Satellite Facility (ASF) is commonly used in SAR meteorology. These data have pixel spacings as small as 50 m and swath widths of approximately 450 km (Jackson and Apel 2004).
Of interest to marine meteorologists is the ability to produce SAR-derived wind speed (SDWS) images (Beal et al. 2005). As demonstrated in Beal et al. (2005), these 10 m MSL wind speed images possess a higher resolution than those from satelliteborne scatterometers. Thus, SDWS images reveal sea surface signatures of many mesoscale, or even microscale, phenomena that would otherwise go unobserved (Beal et al. 2005; Sikora et al. 2006).
Using nearly coincident MODIS observations, Young et al. (2007) documented the SDWS signature of open-cell convection over the Gulf of Alaska. In short, a field of open-cell convection appears as a pattern of pockmarked squalls and lulls within the SDWS imagery. Figure 1 shows a typical example of a field (hereinafter referred to as an event) of open-cell convection within an SDWS image of the northeastern Pacific Ocean. Highlighted within Fig. 1 is one of the larger open cells. Figure 2 shows a MODIS true color image from the same event, zoomed in to focus on a single open cell. The overpass time and aerial coverage differences between the SAR and MODIS made it impossible to match individual open cells. However, given that the overpass time difference was small, it is likely that the extent and character of the open-cell convection changed little between the images. Thus, the general characteristics of any one cell can be compared between the SDWS and MODIS images (Young et al. 2007).
In examining Fig. 1, the squall appears as an area (often arc shaped) of stronger wind with a sharp gradient along its leading edge. Upwind of the squall is an area of weaker wind (i.e., the lull). The sides and rear of the lull (i.e., its crosswind and upwind boundaries) are marked by a sharp wind speed gradient as well. In Fig. 2, an arc of largest cumulus is found along the downwind edge of the nearly cloud-free center, while smaller cumuli ring its sides and rear (ring clouds). Thus, the arc of largest cumulus corresponds to the leading edge of the squall and the ring clouds correspond to the sides and rear of the lull. The nearly cloud-free center corresponds to the lull.
Young et al. (2007) suggested the need for a comprehensive remote-sensing-based study of middle- to high-latitude open-cell convection. Given the ready availability of SDWS images of the northeastern Pacific Ocean (as demonstrated in the next section), a logical step beyond Young et al. (2007) is a comprehensive SDWS-based study of open-cell convection in that vicinity. The present research satisfies that goal via an 8-yr (1999–2006) climatology of the frequency of open-cell convection and of the thermodynamic and kinematic environments of its formation.
3. Data and procedures
The data employed in the present research of open-cell convection 1) capture its near-surface wind signature remotely (via SDWS images) and 2) characterize its thermodynamic and kinematic environments (via reanalysis data). SDWS images were obtained from the Johns Hopkins University Applied Physics Laboratory’s (JHUAPL) comprehensive online archive (http://fermi.jhuapl.edu/sar/stormwatch/web_wind/). Those SDWS images were based primarily on ASF’s Radarsat-1 ScanSAR Wide B product. SDWS images from other ASF Radarsat-1 SAR products (e.g., Full Resolution Standard Beam) and other satellite SARs [e.g., the European Space Agency’s Advanced SAR aboard the Environmental Satellite (Envisat)] were occasionally present within the SDWS image archive and therefore also employed. Pixel spacing for those SAR products (and the resulting SDWS images) varied, but was typically subkilometer (Jackson and Apel 2004).
SDWS was produced by JHUAPL using the C-band Model (CMOD4 or CMOD5 depending on the age of the image) algorithms, which relate backscatter to wind speed (Stoffelen and Anderson 1997; Hersbach et al. 2007). For the CMOD algorithm to accurately estimate wind speed from the SAR backscatter, the wind direction must be known a priori (Beal et al. 2005). The required wind direction data were obtained by JHUAPL from the U.S. Navy’s Navy Operational Global Atmospheric Prediction System (NOGAPS) global numerical weather prediction model. The SDWS signature of open-cell convection, as described above, held for NOGAPS wind directions that were both mainly parallel and mainly perpendicular to the radar look direction.
Reanalyses were obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis project through the National Oceanic and Atmospheric Administration (NOAA)/Climate Diagnostics Center’s Web site (http://www.cdc.noaa.gov/cdc/reanalysis/reanalysis.shtml). The NCEP–NCAR reanalysis project resulted in an extensive database where many model-assimilated atmospheric variables are available with 6-h temporal resolution. Most variables used in the present research came from one of the 17 upper-air pressure levels on a 2.5° latitude × 2.5° longitude global grid. The exceptions are surface fluxes, which came from a Gaussian grid measuring 192 × 94 points to maintain maximum accuracy (Kalnay et al. 1996).
For the period 1999–2006, 616 events of open-cell convection were observed in the SDWS image archive of the northeastern Pacific Ocean following the methodology of Young et al. (2007). Figure 3 shows the location centers of the 616 events from that 8-yr SDWS image collection, rounded to the nearest 0.25° (overlaps are not identified). That domain was restricted by the data archived by JHUAPL rather than by the geographic limits of occurrence of open-cell convection. Additionally, due to the intermittent overpass schedule of the SARs, the identified events likely do not represent every occurrence of open-cell convection in the region during the study period. Finally, because the SARs often made multiple overpasses during the same synoptic situation, the 616 events should not be looked upon as 616 different synoptic situations.
For each SDWS-detected event center location from the 8-yr collection, the nearest surface and upper-air reanalysis data from NCEP–NCAR reanalysis project archive were retrieved (surface sensible and latent heat fluxes, sea surface temperature, and air temperature and wind vector at the near surface and at 925, 850, 700, and 500 hPa). Those data were used to produce the thermodynamic and kinematic climatology.
4. Results
The temporal distribution [year, month, day of month, and hour of day (UTC)] for the 616 events within the 8-yr SDWS image collection is revealed in Fig. 4. Year-to-year variations evident within Fig. 4 were the result of the frequency of northeast Pacific Ocean image acquisition as well as natural variability. Figure 4 shows that open-cell convection was predominantly a cool-season phenomena. This annual cycle reflects the expected synoptic setting of middle- to high-latitude open-cell convection, cold-air outbreak. Figure 4 also shows that there was no bias by day of the month, thus providing a check on the uniformity of the SDWS image archive procedure. Finally, because Radarsat-1 did not pass over the gulf regions at any other times, the hour of observation is split into two narrow windows with nearly 75% around 0300 UTC and the remaining 25% around 1500 UTC. The tails reflect the other satellite SARs employed.
As with any convective phenomena, buoyancy and vertical wind shear are of particular interest to the study of open-cell convection because they affect the organization of the convective elements (e.g., Johnson et al. 2005; Zurn-Birkhimer et al. 2005; Markowski and Richardson 2010). Therefore, buoyant forcing via surface fluxes of latent and sensible heat for the open-cell convection events from the 8-yr SDWS image collection were obtained from the NCEP–NCAR reanalysis project dataset. In addition, the temperature and wind profiles bounded by the lower-troposphere standard levels were generated using this dataset. Those profiles are used to assess the vertical wind shear and static stability. All corresponding data can be found within Table 1. The data are presented as first quartile, median, and third quartile values because many of the distributions were nonnormal.
Table 1 shows that the surface sensible and latent heat fluxes were usually upward for the identified open-cell events, with median values of 15 and 52 W m−2, respectively. These values are slightly smaller than the corresponding means reported for open-cell cases from the AMTEX, KonTur, and ARKTIS experiments [see Table 2 of Atkinson and Zhang (1996) and Table 3 of Brümmer (1999)] but are still supportive of surface-based convection.
For the temperature difference data found within Table 1, the near-surface air temperature–sea surface temperature difference was typically negative, in keeping with the sign of the surface sensible heat flux data. Aloft, the median event exhibited conditional instability in the 925-hPa to near-surface layer, the 850–925-hPa layer, and the 500–700-hPa layer. In the intermediate layer (700–850 hPa), median static stability was nearly moist adiabatic.
Previously reported thermodynamic profiles for open-cell convection, from the surface upward, consist of a superadiabatic surface layer, a conditionally unstable or dry-adiabatic layer below the clouds, a moist-adiabatic cloud layer, and a capping inversion (Agee and Dowell 1974; Agee and Lomax 1978; Brümmer 1999). The lapse rate results provided herein, pertaining to the cloud layer and below, are consistent with previous findings. However, the lapse rate results provided herein imply that rather deep (i.e., deeper than a few kilometers) open-cell convection was possible for the northeast Pacific Ocean events, with conditional instability existing to at least 500 hPa. This may be due to weakening or the elimination of the frontal inversion via sustained mixing along long overwater trajectories and/or due to enhanced Ekman pumping and increased boundary layer depth in the vicinity of the semi-permanent Aleutian low. The authors note that for the Gulf of Alaska case reported by Young et al. (2007), the largest cumulus clouds in the arc region topped out at about 500 hPa.
Table 1 also shows first-quartile, median, and third-quartile data for the magnitude of the vector wind difference over the 925-hPa to near-surface, 850–925-hPa, 700–850-hPa, and 500–700-hPa layers. Those data are meant to represent the magnitude of the vertical wind shear vector spanning the region from the surface to the expected tops of the highest open-cell clouds. The vertical wind shear vector showed no preferred direction (not shown), most likely because of a wide distribution of cyclone-relative positions for the set of events. As can be seen, vertical wind shear is largest in the lowest and uppermost layers, with relatively small vertical wind shear at the midlevels.
The magnitudes of the vertical wind shears provided here are in keeping with those reported for the boundary layer of open cells in Brümmer (1999). Interestingly, though, the distribution of vertical wind shear provided herein (large low level and small midlevel) is similar to that found by LeMone et al. (1998) and Johnson et al. (2005) to form arcs of radar-detected tropical deep moist mesoscale maritime convection (DMMMC) perpendicular to the low-level vertical wind shear vector (see Fig. 4 in Johnson et al. 2005). Recall that the open-cell convection over the northeastern Pacific Ocean described herein is often characterized by arcs of deep cumulus and arc-shaped squalls. Of course, the limits of LeMone et al.’s (1998) and Johnson et al.’s (2005) lower and midlevels (800–1000 hPa and 400–800 hPa, respectively) are geared to troposphere-spanning convection rather than to the shallower convection of cold-air outbreaks. Nevertheless, the similarities are intriguing.
5. Discussion and future work
There exists an extensive literature archive on DMMMC. Representative studies include the Global Atmosphere Research Program Atlantic Tropical Experiment (GATE) (International and Scientific Management Group of GATE 1974), the Equatorial Mesoscale Experiment (EMEX) (Webster and Houze 1991), the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) (Webster and Lukas 1992), and the South China Sea Monsoon Experiment (SCSMEX) (Lau et al. 2000). Cold-pool dynamics1 is important in the organization of DMMMC (e.g., Zipser 1977; Barnes and Garstang 1982; Barnes and Sieckman 1984; LeMone et al. 1984; Alexander and Young 1992; LeMone et al. 1998; Moncrieff and Liu 1999; Johnson et al. 2005).
Young et al. (2007) raised some important questions regarding the organization of middle- to high-latitude open-cell convection in relation to that of tropical DMMMC. Based on a single case study, they suggested that similarity lies in the feedback between precipitation-driven cold pools and the initiation of arcs of deeper convection (see their Fig. 10). The thermodynamic and kinematic climatology of open-cell convection over the northeast Pacific Ocean described herein lends support to the suspicions of Young et al. (2007) and elaborates on the role of vertical wind shear in open-cell convection cold-pool dynamics. In particular, the thermodynamic profiles typically support convection up to at least 500 hPa, which is deep enough for glaciation and precipitation over the northeast Pacific Ocean. Moreover, the combination of strong low-level vertical wind shear and weaker midlevel vertical wind shear mimics the kinematics for arcs of DMMMC perpendicular to the low-level vertical wind shear vector (LeMone et al. 1998; Johnson et al. 2005). Thus, the thermodynamic and kinematic profiles are appropriate for the organization of open-cell convection via the interaction of precipitation-driven cold pools and the low-level vertical wind shear. The authors stress, however, that they have no information on open-cell orientation relative to vertical wind shear vectors. Thus, conclusions cannot be drawn.
The authors also have no direct evidence documenting the existence or nonexistence of precipitation, and thus precipitation-driven cold pools, in this northeastern Pacific Ocean open-cell climatology. There are, however, numerous references to precipitation associated with open-cell convection spanning a wide range of latitudes. For example, much attention has been given in the recent literature to the relationship between precipitation and the organization of subtropical SMMMC. See the large-eddy simulation research of Xue et al. (2008) and Savic-Jovic and Stevens (2008), the Weather Research and Forecasting model study of Wang and Feingold (2009), and the observational studies of Comstock et al. (2005, 2007), vanZanten and Stevens (2005), and Sharon et al. (2006). A general finding of that body of work is a connection between the local transition from closed-cell convection to open-cell convection and the presence of drizzle. It is believed that the local transition from closed-cell convection to open-cell convection is primarily governed by the precipitation-driven cold-pool genesis and/or precipitation-driven aerosol depletion.
Shallow trade wind cumuli also often take on a mesoscale organization pattern (albeit discrete) and thus the authors include them as SMMMC. For example, Snodgrass et al. (2009) reported on arc-shaped cloud formations, which are reminiscent of individual asymmetric or arc-shaped open cells (see Fig. 16 of Snodgrass et al. 2009). The vast majority of those arc-shaped cloud formations were associated with light to moderate precipitation. This led Snodgrass et al. (2009) to speculate that the arc-shaped features were the result of precipitation-driven cold-pool dynamics and to liken them to arcs of DMMMC.
For higher latitudes, Brümmer (1997) reported precipitation associated with ARKTIS cold-air outbreaks. He documented precipitation intensity increasing in the downwind direction and estimated rates as high as 4–5 mm day−1. Although not directly relating such to organization, Brümmer (1997) noted that the largest precipitation rates corresponded to cases of deepest convective clouds.
Last, NOAA Corps officer M. Glazewski supplied a personal observation that Gulf of Alaska open-cell convection produced frozen precipitation on a day where the surface temperature was 43°F (279.3 K). While serving on a NOAA Corps ship in the Gulf of Alaska, Glazewski observed the passage of an open-cell squall that covered the deck of the ship with opaque ice pellets.
The authors’ planned future work is to further investigate the connection between middle- to high-latitude open-cell convection and tropical DMMMC. One possibility is to identify robust events of squall–lull patterns from the SDWS image archive. Then, those events would be cross-checked with MODIS true and false color images to further test the anticipated temporal and spatial correspondence between the MODIS and SDWS signatures of open-cell convection. For each event, several of the best-defined cases of open cells from MODIS would be selected for quantitative analysis. MODIS and archived mesoscale numerical weather prediction model output would be employed to document cloud properties (e.g., type, height, microphysics), the synoptic setting, environmental thermodynamics, and, especially, the relationship between open-cell orientation and the environmental vertical wind shear. Last, an attempt would be made to discover if precipitation was occurring at the time of the cases. This could be accomplished, for example, via a passive microwave spaceborne data archive.
6. Summary
SDWS images and NCEP–NCAR reanalysis data were used to produce an 8-yr (1999–2006) climatology of the frequency of open-cell convection over the northeastern Pacific Ocean and of the thermodynamic and kinematic environments of its formation. This approach yielded a more comprehensive view of open-cell convection than was possible from the motivating study of Young et al. (2007).
Following the methodology of Young et al. (2007), 616 events of open-cell convection were observed in the JHUAPL SDWS image archive. Open-cell convection is a cold-season phenomenon, a result linked to the preferred synoptic setting, cold-air outbreak. As is typical of cold-air outbreaks, the temperature difference between the near-surface air and the sea surface was negative and the surface sensible and latent heat fluxes were positive. The 700–850-hPa-layer median static stability was nearly moist adiabatic while that for the 925-hPa to near-surface, 850–925-hPa, and the 500–700-hPa layers was conditionally unstable. The median magnitude of the vertical wind shear was largest in the 925-hPa to near-surface and 500–700-hPa layers while that at midlevels was relatively weak. Those thermodynamic and kinematic results bolster the suggestion of Young et al. (2007) that the organization of open-cell convection over the northeastern Pacific Ocean is controlled by cold-pool dynamics in a manner similar to that producing radar-detected arcs of tropical deep moist maritime convection (see Johnson et al. 2005).
Planned future work includes the employment of MODIS, passive microwave, and high-resolution numerical model data to further investigate the relationship between open-cell convection over the northeastern Pacific Ocean and tropical DMMMC. Particular attention would be devoted to the relationship between open-cell orientation and the environmental vertical wind shear and the existence or nonexistence of precipitation.
Acknowledgments
This work was supported by the Office of Naval Research through Grants N00014-04-1-0539, N00014-06-1-0046, N00014-07-1-0934, and N00014-10-1-0569; Dr. Nathaniel Winstead supplied the SAR-derived wind speed images and insights into their creation. Scientific inspiration for the present research was drawn from the work of Dr. Garpee Barleszi.
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The SDWS image from 0301 UTC 8 Nov 2006. The pixel size is 600 m × 600 m. The pale blue arrows indicate the NOGAPS wind vectors. The thin white lines form a latitude–longitude grid. The squall appears as an area of stronger wind (1), with a sharp gradient along its leading edge. The trailing lull appears as an area of weaker wind upwind of the squall (2), with its sides and upwind edge also marked by a sharp gradient of wind speed (3).
Citation: Journal of Applied Meteorology and Climatology 50, 3; 10.1175/2010JAMC2624.1
The MODIS true color image from the Aqua satellite at 2310 UTC 7 Nov 2006. (The image was gathered from http://rapidfire.sci.gsfc.nasa.gov/.) The pixel size is 500 m × 500 m. The image is 100 km × 100 km. The image is centered at approximately at 42°N, 146°W. The image is rotated approximately 15° counterclockwise from true north. The arc clouds are found along the downwind edge (1) of the nearly cloud-free center (2) while ring clouds are found along the cell’s sides and rear (3).
Citation: Journal of Applied Meteorology and Climatology 50, 3; 10.1175/2010JAMC2624.1
Event locations plotted to the nearest 0.25° of open-cell convection from the 8-yr SDWS image collection.
Citation: Journal of Applied Meteorology and Climatology 50, 3; 10.1175/2010JAMC2624.1
Year, month, day, and hour distributions of events from the 8-yr SDWS image collection.
Citation: Journal of Applied Meteorology and Climatology 50, 3; 10.1175/2010JAMC2624.1
First quartiles, medians, and third quartiles for sensible heat flux (H), latent heat flux (HL), near-surface air temperature–sea surface temperature difference (ΔT), layer lapse rates (Γ), and layer magnitudes of the vector wind difference (ΔV). Numeric subscripts are pressure levels in hectopascals defining the upper and lower bounds of the layer. SFC is surface.
That is, the interaction of the precipitation-driven cold pool and the vertical wind shear of the environment.
