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  • View in gallery

    (a) NAME radar composite domain with an inscribed subdomain used for the RDAs and precipitation feature analyses. The origin (0,0) corresponds to the center of the NRC. (b) The RDA subdomain and related terrain variations. Surface elevation is shaded and mean elevation profiles are shown along the sides. The x-axis segments corresponding to the GoC, coastal plain, SMO foothills, and SMO peaks are labeled.

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    Composite equivalent radar reflectivity (dBZ) shaded at 1715 LT 5 Aug 2005. The best-fit ellipse is shown for each feature in the PF database. The rotated domain used for analysis is shown by the dashed line, and terrain is shaded (grayscale) in the background.

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    Reduced-dimension time series of rainfall rate from [(a) 10–26 Jul, (b) 24 Jul–6 Aug, (c) 10–20 Aug 2004]. Local time is shown on the y axis (Julian day and hour). Also shown are (left) the cross-coast dimension and (right) the along-coast dimension. Regimes A (blue) and B (pink) are denoted on the rhs with colored stripes. For reference, the domain-averaged surface elevation is profiled at the top of each column and thin black lines mark the subregions described in Fig. 1 (lhs).

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    (Continued)

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    (Continued)

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    Percentage of time that the rainfall rate meets or exceeds 0.2 mm h−1 as a function of local time (diurnal cycle repeated for clarity): (left) the cross-coast frequency and (right) the along-coast frequency. Mean surface elevation along each dimension is profiled at the top. The vertical black lines on the lhs correspond to the cross-coast zones identified in Fig. 1b. The vertical dashed lines on the rhs are aligned with local peaks in mean elevation and help to identify the close relationship between elevated heat sources and rainfall frequency during the diurnal maximum.

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    Same as in Fig. 4 (left column) except for regime A vs other periods.

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    Same as in Fig. 4 (right column) except for regime B vs other periods.

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    Time series of precipitation feature statistics for the entire NAME IOP. Dark-shaded bars denote time periods for regime A; light-shaded bars denote time periods for regime B. (a) Feature volumetric rainfall. (b) Fraction of volumetric rainfall produced by convective pixels. (c) Mean feature maximum dimension (i.e., ellipse major axis length). (d) Fraction of volumetric rainfall produced by organized features.

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    Diurnal cycle of precipitation feature statistics, broken down by regime AB (intersection of A and B; dashed) and non–regime AB (solid). (a) Total volumetric rainfall. (b) Fraction of volumetric rainfall produced by convective pixels. (c) Mean feature maximum dimension (i.e., ellipse major axis length). (d) Fraction of volumetric rainfall produced by organized features (as defined in text).

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    Diurnal cycle of precipitation feature statistics, broken down by E–W subdomain (Fig. 1b): SMO peaks (gray dashed), SMO foothills (gray solid), coastal plain (black dashed), and GoC (black solid). (a) Feature volumetric rainfall. (b) Fraction of volumetric rainfall produced by convective pixels. (c) Mean feature maximum dimension (i.e., ellipse major axis length). (d) Fraction of volumetric rainfall produced by organized features (as defined in text).

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    Diurnal cycle of precipitation feature statistics, broken down by N–S subdomain: northern half of analysis domain (dashed) and southern half (solid). (a) Feature volumetric rainfall. (b) Fraction of volumetric rainfall produced by convective pixels. (c) Mean feature maximum dimension (i.e., ellipse major axis length). (d) Fraction of volumetric rainfall produced by organized features (as defined in text).

  • View in gallery

    Centroid locations of organized features as a function of regime (see legends) during the following 4-h periods: (a) 1200–1600, (b) 1600–2000, (c) 2000–0000, (d) 0000–0400, (e) 0400–0800, and (f) 0800–1200 LT. Topography is shaded in the background (grayscale).

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    Regime-composited skew T–logp diagrams from Los Mochis during (a) regime A, (b) regime B, (c) regime AB, and (d) no regime. Temperature (solid line), dewpoint (thin dashed line), and surface-based parcel ascent path (thick dashed) are shown.

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    Regime-averaged profiles of cross-coast (thin solid) and along-coast (thin dashed) wind components are plotted from Los Mochis (black) and Mazatlán (blue) for (a) regime A, (b) regime B, (c) regime AB, and (d) no regime. Los Mochis (green solid) and Mazatlán (green dashed) profiles of wind speed are also displayed.

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    Regime-averaged NARR 700-hPa geopotential height contours (dam), relative humidity (%, shaded), and wind vectors (see vector scale at upper right) for (a) regime AB and (b) no regime. The white dashed line in the left panel shows the approximate location of an easterly wave trough. Approximate location of S-Pol is also shown.

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Radar-Observed Characteristics of Precipitating Systems during NAME 2004

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  • 1 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
  • | 3 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
  • | 4 National Center for Atmospheric Research, Boulder, Colorado
  • | 5 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
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Abstract

A multiradar network, operated in the southern Gulf of California (GoC) region during the 2004 North American Monsoon Experiment, is used to analyze the spatial and temporal variabilities of local precipitation. Based on the initial findings of this analysis, it is found that terrain played a key role in this variability, as the diurnal cycle was dominated by convective triggering during the afternoon over the peaks and foothills of the Sierra Madre Occidental (SMO). Precipitating systems grew upscale and moved WNW toward the gulf. Distinct precipitation regimes within the monsoon are identified. The first, regime A, corresponded to enhanced precipitation over the southern portions of the coast and GoC, typically during the overnight and early morning hours. This was due to precipitating systems surviving the westward trip (∼7 m s−1; 3–4 m s−1 in excess of steering winds) from the SMO after sunset, likely because of enhanced environmental wind shear as diagnosed from local soundings. The second, regime B, corresponded to the significant northward/along-coast movement of systems (∼10 m s−1; 4–5 m s−1 in excess of steering winds) and often overlapped with regime A. The weak propagation is explainable by shallow–weak cold pools. Reanalysis data suggest that tropical easterly waves were associated with the occurrence of disturbed regimes. Gulf surges occurred during a small subset of these regimes, so they played a minor role during 2004. Mesoscale convective systems and other organized systems were responsible for most of the rainfall in this region, particularly during the disturbed regimes.

* Current affiliation: Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

Corresponding author address: Timothy J. Lang, Dept. of Atmospheric Science, Colorado State University, Fort Collins, CO 80523. Email: tlang@atmos.colostate.edu

This article included in the North American Monsoon Experiment (NAME) special collection.

Abstract

A multiradar network, operated in the southern Gulf of California (GoC) region during the 2004 North American Monsoon Experiment, is used to analyze the spatial and temporal variabilities of local precipitation. Based on the initial findings of this analysis, it is found that terrain played a key role in this variability, as the diurnal cycle was dominated by convective triggering during the afternoon over the peaks and foothills of the Sierra Madre Occidental (SMO). Precipitating systems grew upscale and moved WNW toward the gulf. Distinct precipitation regimes within the monsoon are identified. The first, regime A, corresponded to enhanced precipitation over the southern portions of the coast and GoC, typically during the overnight and early morning hours. This was due to precipitating systems surviving the westward trip (∼7 m s−1; 3–4 m s−1 in excess of steering winds) from the SMO after sunset, likely because of enhanced environmental wind shear as diagnosed from local soundings. The second, regime B, corresponded to the significant northward/along-coast movement of systems (∼10 m s−1; 4–5 m s−1 in excess of steering winds) and often overlapped with regime A. The weak propagation is explainable by shallow–weak cold pools. Reanalysis data suggest that tropical easterly waves were associated with the occurrence of disturbed regimes. Gulf surges occurred during a small subset of these regimes, so they played a minor role during 2004. Mesoscale convective systems and other organized systems were responsible for most of the rainfall in this region, particularly during the disturbed regimes.

* Current affiliation: Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

Corresponding author address: Timothy J. Lang, Dept. of Atmospheric Science, Colorado State University, Fort Collins, CO 80523. Email: tlang@atmos.colostate.edu

This article included in the North American Monsoon Experiment (NAME) special collection.

1. Introduction

The intensive observation period (IOP) of the North American Monsoon Experiment (NAME; Higgins et al. 2006) took place during July and August of 2004. A major component of the IOP was observations from a multiradar network placed in tier I, the core monsoon region consisting of the Gulf of California (GoC) and the Sierra Madre Occidental (SMO) in northwestern Mexico (Higgins et al. 2006). The network, depicted in Fig. 1a, consisted of three radars: the National Center for Atmospheric Research (NCAR) S-band dual-polarization Doppler radar (S-Pol), placed ∼100 km north of Mazatlán, Mexico, on the coast west of the SMO; and two Servício Meteorológico Nacional (SMN) Doppler radars—one at Guasave farther north on the coastal plain and one at Cabo San Lucas at the tip of the Baja California peninsula.

A central goal of NAME is to characterize and understand convective and mesoscale processes in the complex terrain of the core monsoon region and their interaction within the context of the broader monsoon circulation. In particular, precipitation systems at these scales affect the surrounding environment through transports of heat, moisture, and momentum. The response of the large-scale circulation is sensitive to the vertical distribution of latent heating in convective and mesoscale complexes (e.g., Hartmann et al. 1984). Therefore, radar data are needed to characterize and examine the structure, kinematics, morphology, and diurnal cycle of individual precipitating systems (including mesoscale convective systems, or MCSs) within the monsoon. In addition, the data are being used to study the interaction of precipitating systems with the variable surface properties in the region over the SMO, the GoC, and the intervening coastal plain—as well as the influence of transient meteorological events such as tropical easterly waves (e.g., Fuller and Stensrud 2000) and gulf surges (e.g., Hales 1972) on precipitation in the region.

In this paper we will report initial findings on several outstanding research questions and hypotheses relevant to the North American monsoon [NAM; see Higgins et al. (2006) for a complete summary of NAME scientific objectives].

a. Diurnal cycle of precipitation

Ground-based radar assessment of the spatial and temporal variability of precipitation in a tropical region of significant orography (e.g., the SMO) provides insights into the physical characterization of precipitating system life cycles through its ability to constantly monitor storm morphology. Thus, in this paper we will use the NAME radar network to observe and describe statistically the diurnal cycle of precipitating systems in tier I.

Previous investigators (e.g., Negri et al. 1993) have shown that the diurnal cycle of precipitation in the tier I domain has far-reaching implications for the monsoon, but the details of the diurnal cycle are not well understood. Better understanding is required of the location of convective development along the western slopes of the SMO and the evolution and decay of the systems as they move westward over the gulf.

It has been hypothesized that a polarization between late afternoon and evening convection on the SMO and late night/early morning convection over the GoC, seen by previous satellite investigations (e.g., Negri et al. 1993), is caused by either 1) gradual propagation of the developing systems over the SMO to the west due to prevailing winds or 2) westward propagation of gravity waves from the SMO convection, which then force gulf convection later (e.g., Mapes et al. 2003). This study will establish the timing, evolution, and propagation of convective systems, thus testing both of these hypotheses.

b. Intraseasonal variability of precipitation

A major goal of NAME is to better understand regimes associated with intraseasonal variability of convection during July–August in the tier I region and its linkages to precipitation in the southwestern United States, including the influences of surges, jets, easterly waves, surface fluxes, and topographic blocking (Higgins et al. 2006). It has been hypothesized that gulf surges and tropical easterly waves play major roles in organizing convective activity throughout the tier I domain. In this study, the radars will be used to characterize convective activity during gulf surges and during other periods and, thus, statistically determine key convective structure differences (e.g., storm size and organization) between different meteorological regimes. In addition, we will identify changes in the environment (e.g., wind shear) that could be relevant for observed differences in convection.

c. Relative importance of organized systems for precipitation

It has been hypothesized that mesoscale convective systems and other modes of organized convection contribute significantly to total rainfall within the tier I domain (Higgins et al. 2003). The radar data can test this through the characterization of convective organization and total rainfall by different precipitating systems. In particular, we will examine the relative importance of organized systems throughout their characteristic life cycles as a function of both the diurnal cycle and meteorological regime (i.e., intraseasonal variability).

d. Role of terrain in triggering and organizing convection

It has been hypothesized that terrain plays a major role in organizing convection over the tier I domain (Higgins et al. 2003). S-Pol and SMN radar observations can be used to assess the morphology and organization of storms relative to major terrain features. In particular, we will identify preferred locations for convection along the SMO, as well as other locations, and will identify the preferred timing for convection in these regions.

2. Data and methodology

a. Network design, data quality control, and product generation

Figure 1a demonstrates the basic geometry of the NAME radar network. S-Pol was deployed in NAME during 8 July–21 August 2004 to a location 10 km west of La Cruz de Elota, Sinaloa, Mexico. Throughout the IOP, and among other types of scans, S-Pol provided one set of low-angle 360° surveillance scans (0.8°, 1.3°, and 1.8° elevation angles) for rain mapping, usually out to ∼210 km range. Another set of scans extending to higher-elevation angles was used for the analysis of precipitation vertical structure, but these data are not used in this study. Both scan sets were routinely updated every 15 min.

The SMN radars are C-band Doppler radars. The radars were operational prior to NAME, but did not digitally record their data. Guasave was upgraded to temporarily record data on 10 June 2004. Cabo was similarly upgraded on 15 July. Guasave recorded data into the fall, and Cabo recorded until 14 August. Guasave data have been processed for 8 July–21 August. However, due to a disk failure, Guasave data are mostly missing during 22–31 July. During NAME, the SMN radars ran at a single elevation angle. For Cabo this angle was 0.6°. Guasave varied between 0.5°, 1.0°, and 1.5°. Both radars had data coverage out to at least ∼230 km.

Prior to Cartesian gridding and analysis, data from all three radars were subjected to rigorous quality control efforts, as outlined in the appendix. The version 1 regional composites were created on a 0.02° (∼2 km) latitude–longitude grid. The files, available nearly every 15 min during the NAME IOP, contain near-surface reflectivity and near-surface rain rate. We did not interpolate to a fixed altitude, instead using the lowest-altitude data possible for each grid point.

Sweeps from the same time (within 2 min) and the lowest elevation angle were combined every 15 min to produce network composites. Before converting to a latitude–longitude grid, the data along each ray were smoothed using a 1000-m moving average, and resampled to a more sparse array of 1000-m “gates.” Where radar gates from different radars overlapped, the lowest gate took precedence and higher gates were eliminated. The remaining gates were combined and interpolated to a regular latitude–longitude grid. An inverse-distance weighting method was employed to produce the interpolated values. In the final product, the mean beam height of a grid point is about 3900 m MSL, with a standard deviation of 1900 m.

While the Baja peninsula was very dry, there often was substantial echo south and west of the Cabo radar, over the ocean. After spot intercomparisons with satellite observations [e.g., Geostationary Operational Environmental Satellite (GOES), Moderate Resolution Imaging Spectroradiometer (MODIS)], we suspect that much of this echo is sea clutter. As a result, we limit analyses to the eastern portion of our original domain, that is, the Gulf of California subdomain east to the SMO peaks subdomain (Fig. 1b).

No effort was made to validate rainfall estimates from the radars. This will be addressed in the future. We examine rainfall and not radar reflectivity since the former is of most interest to climate scientists. While there are uncertainties in the rainfall estimates, we feel our quality control efforts (see the appendix) have been extensive and justify the use of rainfall, particularly since we interpret only relative differences and not absolute values of rainfall.

b. Reduced dimension methodology

To examine characteristics of the rainfall climatology, a reduced dimension analysis domain (RDA) was created. The RDA is aligned with the mean orientation of the SMO and the GoC, as illustrated in Fig. 1. The grid is rotated 35° counterclockwise from true north. The data were bilinearly interpolated onto a 2-km Cartesian grid from the NAME radar composite domain (NRC), which is in latitude–longitude coordinates. The orientation of the RDA largely relegates the topographical and related surface variations to the x dimension. However, the coastal plain broadens considerably from the SE to the NW, thus increasing the fraction of x distance over land versus sea from to SE to NW. The lower-right corner of the RDA is outside of the NRC, thus rendering these grid locations vacant.

The analyses from this domain are based on arithmetic averages of the radar-estimated rainfall rate along either the x or y dimension and are subsequently plotted as a function of time (Hovmöller diagrams) at 15-min intervals. Where data are incomplete (meaning no radar was scanning at a particular grid point at a given time), at least 60 km of valid data (contiguous or not) must be present to calculate an average along any given gridline at any given time.

c. Identification of precipitation features within the NAME radar composites

The methodology of identifying precipitation features (PFs) within the NRC is essentially identical to the methodology first outlined in the Tropical Rainfall Measuring Mission (TRMM; Simpson et al. 1988) Precipitation Radar and TRMM Microwave Imager data study of Nesbitt et al. (2000). Here, contiguous areas (including corner pixels) of composite equivalent radar reflectivity ≥15 dBZ (value chosen because of the sensitivities of the SMN radars) are considered as a PF within each composite. Features were identified within each composite for the analysis period. The PFs were identified within the entire composite domain, but only those features with mass-weighted centroid locations within the rotated domain of Fig. 1b were analyzed in this study.

Characteristics of each feature were recorded in a database based upon the reflectivity and rainfall structure, location, and time of occurrence of each PF. A convective–stratiform separation algorithm is applied to the reflectivity field [Yuter and Houze (1998); with tunable parameters a = 8.0 and b = 55.0], and total rainfall volume, convective and stratiform rainfall areas, and rainfall fractions are recorded for each PF, along with its maximum reflectivity value. An ellipse-fitting procedure, developed by Nesbitt et al. (2006), was applied to each PF to objectively estimate the maximum dimension (i.e., twice the major axis length of each ellipse) of each feature. An example of this analysis is shown in Fig. 2, which shows the composite reflectivity from 5 August 2004 at 1715 local time (LT). The best-fit ellipse of each feature is plotted over each identified PF within the composite.

Features were stratified geographically, by regime (defined below), and by their characteristics. The distances of each PF centroid relative to the axes of the rotated coordinate system were calculated in kilometers (using nonspherical geometry as above). The features were assigned to four regions based on their distance from the y axis to examine cross-coast variability (Fig. 1b) and two regions based on their distance from the x axis (to examine along-coast variability). Figure 1b shows the x boundaries of each of the four cross-coast geographic regions in the rotated coordinate. The two along-coast regions result from evenly splitting the Fig. 1b RDA domain into north and south.

To identify features that have become organized on the mesoscale, a subset of PFs has been identified as organized features. These features meet the criteria that their maximum horizontal dimension is ≥ 100 km, and the storm contains at least 16 km2 of convective area. This definition attempts to match the Houze (1993, chapter 9) definition of a mesoscale convective system within the framework of the NAME composites, but could refer to any organized precipitating system.

3. Reduced dimension analysis

The following data are presented in Hovmöller diagrams, one horizontal dimension (x or y) plotted against time, separately, for each horizontal axis. Two types of diagrams are shown: The first depicts the entire time series over 42 days, and the second depicts diurnal variability either for the period of record of portions thereof.

a. Event data

Figure 3 shows the 42-day time series of average rainfall rate in reduced dimension, both transverse (lhs) and parallel (rhs) to the GoC–SMO major axis. Several characteristics are evident including the following:

  • a pronounced diurnal cycle of rainfall over the period of record;

  • horizontal patterns, indicating little movement of precipitation regions;

  • smoothly sloping patterns, indicative of a systematic phase speed;

  • a tendency for major events to originate over SMO peaks and foothills (lhs);

  • phase speeds suggestive of slow movement from the SMO to the GoC (lhs);

  • periods when precipitation progresses well into or across the GoC (lhs);

  • periods when phase speeds exhibit consistent northward along-coast movement (rhs); and

  • periods when precipitation exhibits both along-coast and cross-coast (toward the gulf) components to their movement

The variability in precipitation patterns appears largely systematic. For pragmatic reasons, we identify two regimes to help characterize this variability. We have set an arbitrary threshold of 0.17 mm h−1 for the 24-h average rainfall over the GoC and coastal plain, above which regime A is designated. As a consequence of this threshold, regime A occurs one-third of the time. Regime A is characterized by a coherent progression of enhanced rainfall from the SMO to the GoC. Precipitation over the GoC is mainly nocturnal. A blue stripe along the right-hand side of Fig. 3 marks the periods designated as regime A. It is evident from Fig. 3 that other days exhibit a similar tendency for the coherent progression of precipitation into the GoC under more suppressed conditions.

Regime B is defined when the along-coast progression of precipitation is prominent. The along-coast progression of precipitation manifests itself as sloping rainfall streaks on the rhs of Fig. 3. From previous studies (Carbone et al. 2002) in other regions it has been determined that the sloping rainfall streaks are often associated with organized mesoscale convection while the horizontal structures are characterized as unorganized convection. The pink stripe along the right-hand side of Fig. 3 designates regime B and indicates along-coast motion of 8–13 m s−1. Regime AB refers to the intersection of regime A and regime B, that is, when precipitation moves both along-coast and cross-coast.

Our identification of regimes is phenomenologically based. Further research is required to ascertain whether there is any physical or dynamical basis to the observed precipitation patterns. However, as will be demonstrated below, our initial results suggest that regimes A, B, and AB are correlated with different wind shear environments, as well as with proximity to tropical easterly wave passages.

b. Diurnal cycle

The percentage of time that rainfall meets or exceeds 0.2 mm h−1 is shown in Fig. 4 as a function of x or y distance and time of day (LT) for the entire period of record. Note the maximum frequency of occurrence near 1800 LT over the SMO peaks and foothills (lhs). The persistent triggering of afternoon convection in this region is indicated by the rapid onset of high precipitation frequencies around 1400 LT. While the diurnal maximum is broad, a progression toward the GoC is evident: precipitation moves off the SMO, arrives on the coastal plain (x = ∼100 km) around local midnight, and often persists until sunrise. After 0300 LT, motion away from the SMO and into the GoC decreases significantly as is evidenced by the frequency peak becoming diffuse with little appreciable change in the position of the centroid.

The along-coast diurnal cycle is shown on the right-hand side of Fig. 4. Isolated maxima in precipitation frequency appear to align with local peaks in the mean elevation profile (vertical dashed lines). As in midlatitude North America, elevated heat sources in the SMO play a dominant role in the excitation of deep moist convection (Carbone et al. 2002; Ahijevych et al. 2004). Another feature of significance in Fig. 4 is the along-coast trend of increasing nocturnal precipitation in the southern portion of the domain, where the coastal plain is narrow and a larger fraction of the x dimension resides over the GoC.

Figure 5 is the diurnal cycle for the regime A periods (left panel) versus all other periods (right panel). The most significant differences are in these cross-coast patterns. Rainfall is generally more frequent during regime A; however, the difference in frequency is most prominent at night. On non–regime A days, the precipitation largely remains over the SMO foothills and peaks, and dissipates before local midnight. Regime A exhibits a much higher frequency of occurrence throughout the night along the coastline and into the GoC. This result is not surprising since it is closely related to the metric for stratification of the time series. In Fig. 5, a significant semidiurnal maximum is evident just offshore (∼0800), which is not obvious from the daily time series (Fig. 3). The activity throughout the night and early morning hours is at a location likely to be coincident with the land-breeze front. Doppler radar velocity fields (not shown) exhibit a clear diurnal cycle of nearshore winds and the breeze convergence line over the GoC in the nocturnal hours, in close proximity to the statistical morning rainfall maximum. Surprisingly, there is evidence of convective amplification and the appearance of cross-coast “propagation” from 0800 through 1200 LT, resulting in precipitation across much of the GoC. Phase speeds implied by the patterns in Fig. 5 are approximately 7 m s−1, both for the evening progression of rainfall from the SMO and the nocturnal propagation over the GoC.

Figure 6 shows the diurnal frequency associated with regime B and non–regime B periods. Unlike regime A, the strongest signals appear in the along-coast structures. Regime B exhibits pronounced along-coast movement that is phase locked with the diurnal cycle. The slope of this pattern suggests an along-coast phase speed of 8.5–12.5 m s−1. Other periods exhibit little evidence of systematic movement, reflecting a simple diurnal maximum closely tied to elevated heat sources.

4. Precipitation feature analysis

a. NAME IOP time series

Time series of various precipitation feature statistics, covering the entire domain and NAME IOP, are shown in Fig. 7. A 24-h running mean filter has been applied to all time series, for clarity. Also shown are shaded bars indicating the occurrence of regimes A (dark shading) and B (light shading). Overall, regimes A and B correspond to relative maxima in mean volumetric rainfall per feature (Fig. 7a), mean feature maximum dimension (Fig. 7c), and fraction of rainfall contributed by organized features (Fig. 7d). In fact, larger mean values of these parameters occurred during regimes A, B, and AB relative to nonregime periods, as shown in Table 1. Though not obvious from Fig. 7b, the fraction of rain produced by convective (as opposed to stratiform) pixels actually shows a slight increase during nonregime periods (Table 1). Overall, during regimes A, B, and AB, features tend to be larger and produce more rainfall, and the relative importance of organized features increases.

b. Diurnal cycle

The diurnal cycle for various precipitation feature statistics was determined and broken down by both regime and location. A common theme in all the plots that will be shown here is the occurrence of two relative maxima across various PF statistics: a smaller one in the morning hours, and a larger one in the afternoon/evening. In addition, there is a general tendency for the convective fraction to peak first, followed sequentially by feature rainfall, mean feature maximum dimension, and finally fraction of rain from organized features. This reflects a general tendency for features to grow upscale from small unorganized cumulonimbi to larger and more organized systems.

Features were assigned as members of the A, B, and AB regimes based upon their time of occurrence. Figure 8 shows the diurnal cycle of precipitation feature statistics for regime AB. Given the time overlap between A and B (e.g., Fig. 7), plots for regime A only and regime B only give very similar results, so we consider only AB here. Non-AB periods show a maximum in mean volumetric rainfall per feature near 1900 LT (Fig. 8a). The regime AB peak comes later in time, near 2100 LT. There is more rainfall per feature throughout the day during regime AB.

As expected from Table 1, the diurnal cycle of the convective area fraction tends to overlap between AB and non-AB periods (Fig. 8b). They both show two diurnal peaks, occurring during a late afternoon peak and a morning peak. However, the non-AB morning peak is a couple of hours earlier than the AB peak. During the AB period, the mean feature maximum dimension (Fig. 8c) is larger than that for the non-AB period, and the shapes of the diurnal cycles are about the same. The diurnal cycle of the organized feature rainfall fraction (Fig. 8d) shows that in both regime AB and the non-AB regime, there is a relative maximum near midnight, and another morning peak. However, as in the convective area fraction, the morning AB peak comes 2 h later than for non-AB. Also, the organized feature rainfall fraction during AB is larger than non-AB throughout the day.

Figure 9 shows the diurnal cycle of PF statistics by gulf-normal location, that is, mean behavior for each subdomain in Fig. 1b (SMO peaks, SMO foothills, coastal plain, GoC), regardless of regime. Most rain per feature falls over the SMO foothills (Fig. 9a), but generally only during the afternoon and evening hours. The foothills also receive the greatest rainfall 2 h later than the SMO peaks. The coastal plain and GoC tend to have broader peaks in their feature rainfall diurnal cycle. Coastal plain rainfall peaks at the same time as that for the foothills, but extends much further into the overnight and morning hours than the SMO. The peak time for the coastal plain probably reflects the combination of afternoon convection triggered along the coastline (e.g., sea-breeze front) and late evening convection moving off the SMO. The GoC diurnal cycle is roughly 12 h out of phase with the SMO, with rainfall mainly occurring over a broad time period including overnight and in the morning. The diurnal cycles in the rest of Fig. 9 mostly conform to expectations derived from Fig. 9a.

Splitting the entire domain (Fig. 1b) equally into a northern half and southern half (i.e., gulf-parallel subdomains) also reveals interesting characteristics of the diurnal cycle, as seen in Fig. 10. The main result is that the morning secondary peak in the statistics is more significant in the south than the north. In addition, the amplitude of the afternoon/evening peak in the PF statistics often is smaller in the south. As will be shown in the next section, this is due to a greater number of early morning organized systems in the south when compared to the north. The environmental reasons for this phenomenon will be explored in section 5.

c. Organized features and the diurnal cycle

To highlight the spatial coherence of organized modes of convection within the diurnal cycle, Fig. 11 shows the location of all organized features during the day. Each panel in the figure shows the centroid location of each organized feature, and the color of the symbol indicates the regime assigned to each PF.

Considering early afternoon (1200–1600 LT; Fig. 11a), organized features mainly appear over land; however, there is a spatial separation in the locations where features form during this time period based on the regime(s) under consideration. These features are likely triggered by solar insolation over the elevated terrain of the SMO. During “no regime” periods, organized features were mainly confined to the high terrain. During disturbed periods, particularly regime AB, organized features tended to be located in the foothills. During the late afternoon (1600–2000 LT; Fig. 11b), there is evidence that the bulk of the organized features have advanced toward the coast, with organized features during disturbed periods having locations closer to the coast than during nondisturbed times. In Fig. 11c (2000–0000 LT), there is generally more organized feature activity in the northern portion of the domain during the evening hours than to the south, similar to the bulk results of Fig. 10. These organized features in the northern part of the domain either dissipate as they reach the coast by midnight (Fig. 11d), or exit the domain.

The few features in the southern foothills of the domain in Fig. 11c, along with features that likely propagate in from outside the southern edge of the domain, appear to have long lifetimes as they propagate into the GoC during the early morning hours (0000–0800 LT; Figs. 11d and 11e). The majority of these overgulf early morning organized features occur during regime AB. During the late morning (Fig. 11f), there is some evidence of organized features forming again along the SMO high terrain as the daily cycle of solar insolation leads to convective development.

5. Environmental influences

The sounding data collected during the NAME IOP at Los Mochis and Mazatlán (see Fig. 1) allow an examination of the environmental influences (thermodynamic and shear profiles) associated with the precipitation regimes identified above. Sounding data from the Los Mochis Integrated Sounding System (ISS) site and Mazatlán International Airport are composited during the same time periods as the radar data analysis. While thermodynamic data and winds are available at Los Mochis, currently only wind data are available from Mazatlán due to a dry bias that has been identified in the operational soundings at Mazatlán (P. Cieselski 2005, personal communication).

To examine thermodynamic profiles as a function of regime, Fig. 12 shows regime-composite skew T–logp diagrams at Los Mochis, showing temperature, dewpoint, and pseudoadiabatic surface-based parcel ascent path. Regime AB soundings are the moistest on average, especially above the level of free convection (LFC, which is around 700 hPa in all regimes). Relatively small differences in the soundings are present below the lifting condensation level (LCL, which was located at roughly 900 hPa in all cases), and all soundings display a weak conditionally unstable layer just above the LFC to near 550 hPa, with a pseudoadiabatic layer above until nearly the tropopause. This unstable layer (possibly due to elevated sensible and latent heating over the nearby SMO) contributes to significant overall conditional instability; all regime-averaged soundings contain at least 1500 J kg−1 of CAPE [CAPE and convective inhibition (CIN) were calculated using the average temperature and dewpoint of the lowest 50 hPa in the sounding], with slightly higher CAPE values during the no-regime periods, and the least during regime A (see Table 2). However, there is a significant cap in the averaged soundings, with 40–70 J kg−1 of CIN present in the averaged soundings, with the largest (smallest) cap present in the AB (no regime) composite. In addition, the no-regime periods are drier near midlevels (i.e., 450 hPa).

The significant amount of CAPE available above the LFC (at around 750 hPa in each regime; Fig. 12) suggests that, provided the necessary lifting mechanisms exist (e.g., sea-breeze convergence zone, or long-lived convective systems with established cold-pool or gravity wave dynamics), systems could potentially tap the positive energy aloft if they are able to lift parcels above the LFC. It is apparent that thermodynamic profiles are sufficient for deep convective triggering if the cap could be broken by appropriate forcing mechanisms.

Figure 13 shows regime-averaged wind profiles composited as above, and rotated 35° in a mathematical sense as was done for the radar composites to look at cross-coast and along-coast shear profiles. Profiles of regime-composited cross-coast and along-coast wind directions, as well as wind speeds, are shown for Los Mochis and Mazatlán. Note that positive cross-coast wind is away from the gulf toward the SMO, and positive along-coast wind means away from the mouth of the gulf toward, say, Arizona.

Below 850 hPa, very different characteristic wind directions and speeds exist between the two sites during the disturbed regimes (A, B, AB). At Los Mochis, there is a low-level wind maximum on average of about 3–4 m s−1 in these regimes, which has a large southerly component (in unrotated space). This is likely associated with the GoC low-level jet, which is a climatological feature of the NAM (Douglas 1995; Stensrud et al. 1997). This feature is not present in terms of southerly flow at Mazatlán (which is south and east of the climatological position of the jet), where light northwesterly winds are present at low levels (in unrotated space). The difference between the Mazatlán and Los Mochis low-level flows is consistent with mean wind patterns observed during the Southwest Area Monsoon Project (SWAMP) and reflect contrasts in thermal structure across the northern and southern portions of the GoC region (Douglas 1995).

Above 850 hPa, the wind directions become more similar between the two sites (southeasterly); however, along-coast component speeds are generally stronger at Mazatlán than at Los Mochis. This is especially true at around 650 hPa during the disturbed regimes, where low-level shear (which is largely unidirectional) between the surface and roughly 4 km is more pronounced at Mazatlán. Table 3 shows that values of 0–4-km shear are larger at Mazatlán, nearly by a factor of 2 during disturbed regimes. This increased shear may explain the prevalence of the longer-lived convective systems over the southern portion of the radar domain, which were observed to last into the early morning hours (e.g., Rotunno et al. 1988).

To examine cell propagation modes between the regimes (recall the definitional differences in terms of cell propagation in regimes A, B, and their intersection AB), cross-coast and along-coast winds at 700 hPa are displayed in Table 3 as an indicator of prevalent steering flows. Recall that the cross-coast precipitating system movement was ∼7 m s−1 during regime A, and the along-coast movement was ∼10 m s−1 during regime B. At both Los Mochis and Mazatlán, slightly stronger regime-composited positive along-coast winds are observed in regime B compared with regimes A and AB. Note that precipitating system phase speeds slightly exceed the cross-coast and along-coast winds, in regimes A and B, respectively, suggesting weak propagation.

Given the large time overlap with the A and B regimes, periods where the regime was classifed as B but not A were also examined to highlight B periods exclusively. This yielded average along-coast winds at 700 hPa of 6.8 (2.7) m s−1 at Mazatlán (Los Mochis) that differ by a greater margin than the differences between B and A regimes alone, which largely overlap in time. This indicates that southerly flow is 2 m s−1 stronger, on average, when convective systems propagate with a coast-parallel component relative to times when the steering flow is more coast-normal.

North American Regional Reanalysis data (NARR; Mesinger et al. 2006) are used to examine large-scale influences on the aforementioned precipitation regimes identified during NAME. The NARR reanalyses are a subset of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis and, since 2003, have been run in near–real time as a forecasting tool. For this analysis, 3-hourly data were averaged at 700 hPa during the regime periods in order to ascertain correlations between radar-observed precipitation features and the large-scale circulation.

Figure 14 shows the composite 700-hPa wind field and relative humidity fields for regime AB (Fig. 14a) and no regime (Fig. 14b). During regime AB (the 700-hPa characteristics of regime A—not shown—are similar to regime AB), the radar domain is located in close proximity to a tropical easterly wave (EW) trough (in a composite sense) with a predominant southeasterly flow component and high relative humidity advecting into the tier I domain. In contrast, the composite EW position during no regime is farther east and has a weaker intensity (in terms of the 700-hPa height gradient). During these periods, the 700-hPa flow over the radar domain is easterly, and is more heavily influenced by the subtropical ridge centered over northern Mexico. The composite pattern during regime B (not shown) is similar to the no-regime pattern except that there is slightly more moisture over the radar domain.

The increase in relative moistness and strength of southeasterly flow in the NARR composite at 700 hPa during regime AB is consistent with the analysis of the field campaign soundings presented herein. The increased shear and moisture associated with an EW passage preferentially influences the southern portion of the radar domain, and is linked with the radar observations of more numerous longer-lived organized convective features to the south. In addition, this suggests that the juxtaposition of the flow patterns associated with EWs in relation to the southern SMO is an important factor in determining convective system lifetimes and propagation modes in these regimes. However, it is clear that the signals are relatively subtle in the observed sounding data, and more detailed analyses are necessary to quantify the role of EW forcing on the precipitation regimes in the region.

6. Discussion and conclusions

The initial findings presented in this study demonstrate that the SMO plays an important role in the triggering of precipitating systems in the core NAM region. In the along-coast reduced-dimension analysis, isolated maxima in precipitation frequency were found to align roughly with local peaks in the mean along-coast elevation profile. Precipitation begins along the peaks and foothills of the SMO in the late afternoon and moves WNW toward the GoC. During undisturbed periods, SMO convection does not survive the trip to the gulf after sunset.

Intraseasonal variability in the study region is considerable. We identified two major disturbed regimes: A and B. During regime A there is enhanced precipitation over the coast and GoC, especially overnight and in the early morning. During regime B there is significant along-coast movement of precipitating systems. There is considerable—though not complete—overlap between these two regimes, such that when A was occurring, often so was B (and vice versa). This led to the recognition of a third disturbed regime: AB. The occurrence of these regimes appears to be related to the enhancement of low-level shear in the environment, particularly near the southern gulf, allowing precipitating systems to scale upward and have longer life cycles (e.g., Rotunno et al. 1988).

During regime AB, organized convection seems to be weakly propagating in excess of steering winds: 3–4 m s−1 from the SMO to the sea and 4–5 m s−1 NW along the SMO–GoC major axis. The cold pool required to effect this implied propagation is 500–1000 m deep and has 1–2°C negative buoyancy (Keenan and Carbone 1992), so either very shallow or extremely weak cold pools are all that is needed to explain this behavior. More elaborate mechanisms, such the Mapes et al. (2003) gravity wave hypothesis originally used to explain observations of convection moving at 15 m s−1 up to several hundred kilometers offshore of Colombia, are not needed.

During the NAME IOP, there were at least two significant gulf surges (Higgins et al. 2006). These surges tended to overlap with disturbed precipitation regimes (both A and B). For example, the first identified gulf surge overlapped with the first appearances of regimes A and B, around 12–13 July 2004. Overall, time periods associated with gulf surges form a small subset of regime A and B periods. However, there were many disturbed regime periods not associated with a gulf surge, particularly during August. Thus, during 2004 gulf surges could have played only a small role in tier I precipitation. When they occurred, they appeared to be important (i.e., were associated with disturbed regimes), but they did not occur often enough to be a major factor.

This study also suggests a possible link between tropical EWs and precipitation regimes, which could have important implications for the initiation of moisture surges in the GoC. Although a number of previous investigators have identified relationships between tropical EW and moisture surges in the GoC (Hales 1972; Brenner 1974; Stensrud et al. 1997; Fuller and Stensrud 2000; Anderson et al. 2000), the understanding of mechanism(s) whereby EWs trigger surges remains elusive. It may be that the precipitation features identified herein, through the action of cold pool and/or gravity wave dynamics, play a role in surge initiation. This is an important topic for future research.

Our hypothesis of the importance of the rainfall contribution of organized rainfall modes is supported by the evidence. The development and propagation of organized systems (e.g., MCSs and other large precipitation features) are key components of the diurnal cycle of precipitation in this region, particularly during disturbed regimes. During disturbed periods, organized features account for ∼75% of all feature-produced rainfall (Table 1). This fraction is still large during undisturbed periods, ∼60%. Organized systems are particularly important for rainfall in the evening along the SMO, and during the early morning along the southern portions of the coast and GoC.

Acknowledgments

The lead S-Pol engineer was Don Ferraro of NCAR’s Earth Observing Laboratory (EOL). Jon Lutz, of NCAR/EOL, provided engineering oversight for the S-Pol operation and led the SMN radar upgrade and data collection efforts. He was assisted in the latter by Arturo Valdez-Manzanilla of Juarez University and by Armando Rodriguez Davila of SMN. We thank all the other NCAR engineers, technicians, and field scientists, as well as Robert Bowie of the CSU-CHILL radar staff, for contributing to the successful S-Pol operation. Bob Rilling of NCAR/EOL led the initial quality control and distribution of the radar data. Further data quality control efforts, as outlined in the appendix, were assisted by Chad Chriestenson, Lee Nelson, and Gustavo Pereira of Colorado State University (CSU). Sounding data were obtained from Richard Johnson and Paul Ciesielski of CSU. We thank SMN for their cooperation in making the S-Pol deployment and the SMN upgrades possible. Radar observations and analyses were funded by the National Oceanic and Atmospheric Administration (NOAA; SMN radars) and the National Science Foundation (NSF; S-Pol). NCAR is funded by NSF.

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APPENDIX

Data Quality Control

Quality control of S-Pol radar data

S-Pol data were corrected for attenuation as well as clutter, insect, and second-trip contamination. Nonmeteorological echo was removed via thresholds on various polarimetric fields. Any remaining spurious echo was removed by hand using NCAR’s soloii software. Differential phase (ΦDP) was filtered using a 21-gate (3.15 km based on 150-m gate spacing) finite-impulse response filter (developed by J. Hubbert of NCAR and V. N. Bringi of Colorado State University). Specific differential phase (KDP) was calculated from the slope of a line fitted to the filtered ΦDP field. The window of the fitted line varied from 31 to 11 gates (4.65–1.65 km) as reflectivity (ZH) increased. The rainfall attenuation correction methodology was based on Carey et al. (2000b). The value of ZH for all radars (including SMN) was further corrected for gaseous attenuation following Battan (1973, chapter 6).

Significant amounts of beam blockage due to terrain occurred in S-Pol’s northeast sector (351°–105° azimuth). Correction of this blockage followed the basic methodology of Carey et al. (2000a). The locations of the blocks were determined to the nearest degree in azimuth and nearest kilometer in range by visual inspection of clear-air radar sweeps. Then, in the blocked regions, we examined the behavior of ZH as a function of azimuth for a given range of KDP. Due to the self-consistency between polarimetric variables in rain (Scarchilli et al. 1996), for a given range of KDP, ZH should vary only over a small range as well. The difference in the median ZH values in unblocked regions, and median ZH values in a blocked ray, is the positive dBZ correction that needs to be applied to ZH. We only used this methodology to correct small blocks (down to −5 dBZ). For larger blocks, we used the following methodology. If 0.8° had a severe block in a particular ray (reduction >5 dBZ), then we used information from 1.3° at all ranges greater than that of the block. If the 1.3° ray itself was severely blocked, then we resorted to 1.8° (which was never blocked more than 5 dBZ). In addition, we filled in low-level gaps caused by clutter removal (0.8° and 1.3°) using information from higher sweeps (1.3° and 1.8°). As this correction technique is experimental, S-Pol data within the 351°–105° azimuths are expected to be of inferior quality to the data outside these boundaries. If we corrected blocked ZH by applying a positive dBZ offset, then we set the differential reflectivity to a missing data value.

Rain rates were calculated using the Colorado State University blended rainfall algorithm (Cifelli et al. 2002). This algorithm varies between various polarimetric rainfall estimators depending on the values of the polarimetric variables and the presence of mixed-phase precipitation. The ZR relationship used as part of the algorithm was Z=221R1.25. This ZR, used mainly in light rain, was determined via intercomparisons of reflectivity with gauge rain rates at the NOAA profiler site ∼45 km northwest of S-Pol.

Quality control of SMN radar data

We used the most complete Cabo and Guasave sweeps closest in time to each 15-min mark. We applied automated filters on ZH, noise-corrected power, and on total power. Due to antenna backlash (a lag between radar gears, the servo mechanism, and encoders that manifests itself as an offset between azimuths obtained during clockwise and counterclockwise motions of the antenna) when Guasave changed spin direction every few days, that radar required a small correction to the measured azimuths. We then applied an automated clutter filter to the Guasave data. This clutter filter queried a clutter map created from clear-air Guasave sweeps taken over several days. Clutter-affected gates were removed. Cabo data did not have many storms overrun its clutter, so its clutter was hand edited only. We hand edited the filtered datasets for any remaining clutter, noise, second-trip contamination, and insects using soloii.

A reflectivity offset was then applied to the data based on visual and statistical intercomparisons with S-Pol reflectivities. The statistical evaluation compared the closest gates within 500 m in the horizontal and 200 m in the vertical distance. Histograms of reflectivity differences were obtained from this statistical intercomparison. In addition, visual intercomparison of well-placed echoes was done. Based on these methods, a reflectivity correction was applied to the SMN radar data.

An attenuation correction by rain was based on the Patterson et al. (1979) algorithm, which uses a ZR relationship to estimate rainfall, then iteratively corrects ZH at a gate based on the theoretical treatment of attenuation by all the rainfall up to the given gate. The ZR scheme used was the same as the S-Pol ZR scheme. Based on intercomparions with S-Pol and TRMM precipitation radar data, we believe that the corrected SMN ZH measurements are accurate to within 1–2 dBZ. No blockage correction was attempted at Cabo or Guasave, but blockage was only a minor problem for these radars, either due to a lack of blocks (Guasave) or a lack of storms in blocked areas (Cabo). The SMN radar rainfall rates were determined from the aforementioned ZR relationship, with capping at 53 dBZ (231.5 mm h−1) to reduce ice contamination. More information on NAME radar quality control can be found in Lang et al. (2005) and online (http://radarmet.atmos.colostate.edu/~tlang/readme_NAME_regional_ radar_composites_v2.pdf).

Fig. 1.
Fig. 1.

(a) NAME radar composite domain with an inscribed subdomain used for the RDAs and precipitation feature analyses. The origin (0,0) corresponds to the center of the NRC. (b) The RDA subdomain and related terrain variations. Surface elevation is shaded and mean elevation profiles are shown along the sides. The x-axis segments corresponding to the GoC, coastal plain, SMO foothills, and SMO peaks are labeled.

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 2.
Fig. 2.

Composite equivalent radar reflectivity (dBZ) shaded at 1715 LT 5 Aug 2005. The best-fit ellipse is shown for each feature in the PF database. The rotated domain used for analysis is shown by the dashed line, and terrain is shaded (grayscale) in the background.

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 3.
Fig. 3.

Reduced-dimension time series of rainfall rate from [(a) 10–26 Jul, (b) 24 Jul–6 Aug, (c) 10–20 Aug 2004]. Local time is shown on the y axis (Julian day and hour). Also shown are (left) the cross-coast dimension and (right) the along-coast dimension. Regimes A (blue) and B (pink) are denoted on the rhs with colored stripes. For reference, the domain-averaged surface elevation is profiled at the top of each column and thin black lines mark the subregions described in Fig. 1 (lhs).

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 3.
Fig. 3.

(Continued)

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 3.
Fig. 3.

(Continued)

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 4.
Fig. 4.

Percentage of time that the rainfall rate meets or exceeds 0.2 mm h−1 as a function of local time (diurnal cycle repeated for clarity): (left) the cross-coast frequency and (right) the along-coast frequency. Mean surface elevation along each dimension is profiled at the top. The vertical black lines on the lhs correspond to the cross-coast zones identified in Fig. 1b. The vertical dashed lines on the rhs are aligned with local peaks in mean elevation and help to identify the close relationship between elevated heat sources and rainfall frequency during the diurnal maximum.

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 5.
Fig. 5.

Same as in Fig. 4 (left column) except for regime A vs other periods.

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 6.
Fig. 6.

Same as in Fig. 4 (right column) except for regime B vs other periods.

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 7.
Fig. 7.

Time series of precipitation feature statistics for the entire NAME IOP. Dark-shaded bars denote time periods for regime A; light-shaded bars denote time periods for regime B. (a) Feature volumetric rainfall. (b) Fraction of volumetric rainfall produced by convective pixels. (c) Mean feature maximum dimension (i.e., ellipse major axis length). (d) Fraction of volumetric rainfall produced by organized features.

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 8.
Fig. 8.

Diurnal cycle of precipitation feature statistics, broken down by regime AB (intersection of A and B; dashed) and non–regime AB (solid). (a) Total volumetric rainfall. (b) Fraction of volumetric rainfall produced by convective pixels. (c) Mean feature maximum dimension (i.e., ellipse major axis length). (d) Fraction of volumetric rainfall produced by organized features (as defined in text).

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 9.
Fig. 9.

Diurnal cycle of precipitation feature statistics, broken down by E–W subdomain (Fig. 1b): SMO peaks (gray dashed), SMO foothills (gray solid), coastal plain (black dashed), and GoC (black solid). (a) Feature volumetric rainfall. (b) Fraction of volumetric rainfall produced by convective pixels. (c) Mean feature maximum dimension (i.e., ellipse major axis length). (d) Fraction of volumetric rainfall produced by organized features (as defined in text).

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 10.
Fig. 10.

Diurnal cycle of precipitation feature statistics, broken down by N–S subdomain: northern half of analysis domain (dashed) and southern half (solid). (a) Feature volumetric rainfall. (b) Fraction of volumetric rainfall produced by convective pixels. (c) Mean feature maximum dimension (i.e., ellipse major axis length). (d) Fraction of volumetric rainfall produced by organized features (as defined in text).

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 11.
Fig. 11.

Centroid locations of organized features as a function of regime (see legends) during the following 4-h periods: (a) 1200–1600, (b) 1600–2000, (c) 2000–0000, (d) 0000–0400, (e) 0400–0800, and (f) 0800–1200 LT. Topography is shaded in the background (grayscale).

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 12.
Fig. 12.

Regime-composited skew T–logp diagrams from Los Mochis during (a) regime A, (b) regime B, (c) regime AB, and (d) no regime. Temperature (solid line), dewpoint (thin dashed line), and surface-based parcel ascent path (thick dashed) are shown.

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 13.
Fig. 13.

Regime-averaged profiles of cross-coast (thin solid) and along-coast (thin dashed) wind components are plotted from Los Mochis (black) and Mazatlán (blue) for (a) regime A, (b) regime B, (c) regime AB, and (d) no regime. Los Mochis (green solid) and Mazatlán (green dashed) profiles of wind speed are also displayed.

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Fig. 14.
Fig. 14.

Regime-averaged NARR 700-hPa geopotential height contours (dam), relative humidity (%, shaded), and wind vectors (see vector scale at upper right) for (a) regime AB and (b) no regime. The white dashed line in the left panel shows the approximate location of an easterly wave trough. Approximate location of S-Pol is also shown.

Citation: Journal of Climate 20, 9; 10.1175/JCLI4082.1

Table 1.

Mean values of precipitation feature statistics during and outside of disturbed regimes. All differences in means are significant at the 95% confidence level (Student’s t test).

Table 1.
Table 2.

Regime-averaged CAPE and CIN values at Los Mochis.

Table 2.
Table 3.

Regime-averaged 0–4-km wind shear and 700-hPa rotated wind components at Los Mochis and Mazatlán.

Table 3.
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