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    (a) Near-surface rain rate (mm h−1) observed with the TRMM PR (shades). Contours indicate values of SST beyond 22°C at 1°C intervals. Vectors indicate horizontal winds at 1000 hPa. (b) Longitudinal distribution of rain rate (mm h−1) averaged from 7.5° to 12.5°N. (c) Vertical cross section of JRA-25/JCDAS vertical pressure velocity (Pa s−1) averaged from 7.5° to 12.5°N. (d) Vertical cross section of convergence (s−1) averaged from 7.5° to 12.5°N. These figures are averaged during 1998–2007 for all seasons.

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    Histograms (%) of rain amounts from PFs binned into areas and maximum heights (a)–(d) over the EP and (e)–(h) over the WP in (a),(e) December–February; (b),(f) March–May; (c),(g) June–August; and (d),(h) September–November during 1998–2007. Abscissa indicates areas in logarithmic scale. Ordinate indicates maximum heights. Black lines indicate the thresholds for the classification of PFs and numbers in (a) indicate PF types.

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    (a) Mean rain rates and contribution from (b) type 1, (c) type 2, (d) type 3, and (e) type 4 in September–November during 1998–2007 (shades; mm h−1). In (a), black contours indicate vertical pressure velocity at 500 hPa (Pa s−1), solid lines indicate 0.02 Pa s−1, and dashed lines indicate −0.08, −0.06, −0.04, and −0.02 Pa s−1. In (b) and (d), black contours indicate mass convergence integrated from 1000 to 925 hPa and solid (dashed) lines indicate values beyond 0.004 kg m−2 s−1 (below −0.004 kg m−2 s−1) at 0.004 kg m−2 s−1 intervals. In (c) and (e), black contours indicate SST beyond 23° at 1°C intervals. In (a)–(e), red thick lines indicate 0 Pa s−1 of vertical pressure velocity at 500 hPa.

  • View in gallery

    Scatter diagrams of rain rates (mm h−1) from each type against mass convergence (×10−3 kg m−2 s−1) integrated from 1000 to 925 hPa in March–May (black), June–August (blue), September–November (red), and December–February (green) during 1998–2007. Plots for types (a) 1, (b) 2, (c) 3, and (d) 4. Longitude range is fixed as 130°E–100°W. Latitude ranges vary with seasons (see text).

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    Scatter diagrams of rain rates (mm h−1) from (a),(c) type 2 and (b),(d) type 4 against (a),(b) SST and (c),(d) deep (1000–400 hPa) convergence (×10−3 kg m−2 s−1) in the four seasons during 1998–2007. Longitude range is fixed as 130°E–100°W. Latitude ranges vary with seasons (see text).

  • View in gallery

    Composites of time series of (a),(b) mass convergence (×10−3 kg m−2 s−1) integrated from 1000 to 925 hPa (black lines) and mass convergence (×10−3 kg m−2 s−1) integrated from 1000 to 400 hPa (gray lines); and (c),(d) relative humidity at 600 hPa during the boreal autumn during 1998–2007. (a),(c) Composites based on the times when type-4 PFs are observed over 6.25°–11.25°N, 146.25°–153.75°E and (b),(d) the times when type-3 PFs are observed over 6.25°–11.25°N, 133.75°–126.25°W. Error bars indicate the 95% confidence intervals.

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Relationships between Rain Characteristics and Environment. Part I: TRMM Precipitation Features and the Large-Scale Environment over the Tropical Pacific

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  • 1 Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Chiba, Japan
  • | 2 Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Chiba, and Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Kanagawa, Japan
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Abstract

Differences in the characteristics of rain systems in the eastern Pacific (EP) intertropical convergence zone (ITCZ) and the western Pacific (WP) warm pool are quantitatively examined in relation to the large-scale environment. This study mainly uses precipitation feature (PF) data observed by the precipitation radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM). The PFs are classified into four types according to their areas and maximum heights. Rain from tall unorganized systems and very tall organized systems tends to be dominant in high-SST regions such as the WP. On the other hand, the EP has more rain from congestus and organized systems with moderate heights than the WP. It is shown that shallow rain from congestus and moderately deep rain from organized systems are highly correlated with shallow (1000–925 hPa) convergence fields with coefficients of 0.75 and 0.66, respectively. These relationships between characteristics of rain systems and the large-scale environment are robust through all seasons.

Corresponding author address: Chie Yokoyama, Department of Atmospheric Sciences, University of Utah, 135 South 1460 East, Salt Lake City, UT 84112-0110. E-mail: chie.yokoyama@utah.edu

Abstract

Differences in the characteristics of rain systems in the eastern Pacific (EP) intertropical convergence zone (ITCZ) and the western Pacific (WP) warm pool are quantitatively examined in relation to the large-scale environment. This study mainly uses precipitation feature (PF) data observed by the precipitation radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM). The PFs are classified into four types according to their areas and maximum heights. Rain from tall unorganized systems and very tall organized systems tends to be dominant in high-SST regions such as the WP. On the other hand, the EP has more rain from congestus and organized systems with moderate heights than the WP. It is shown that shallow rain from congestus and moderately deep rain from organized systems are highly correlated with shallow (1000–925 hPa) convergence fields with coefficients of 0.75 and 0.66, respectively. These relationships between characteristics of rain systems and the large-scale environment are robust through all seasons.

Corresponding author address: Chie Yokoyama, Department of Atmospheric Sciences, University of Utah, 135 South 1460 East, Salt Lake City, UT 84112-0110. E-mail: chie.yokoyama@utah.edu

1. Introduction

The quantification of rain characteristics such as rain top height and stratiform rain ratio (SRR) is essential to understand the effects of tropical rain on large-scale circulations. Differences in the characteristics of rain systems result in different profiles of diabatic heating (Schumacher et al. 2008). Tropical oceanic cumulus convection has a trimodal structure with deep convection sometimes reaching a tropopause height, congestus affected by a melting-level height, and cumuli suppressed below a trade inversion height (Zuidema 1998; Johnson et al. 1999). In terms of the horizontal scale, deep convection is often classified into either large, organized systems such as mesoscale convective systems (MCSs), or smaller systems that may consist of mostly isolated cumulonimbi. Some studies have noted that stratiform rain accounts for 40%–60% of the total rain in organized systems (e.g., Houze 1977; Takayabu 2002; Schumacher and Houze 2003; Yokoyama and Takayabu 2008). In these systems, the large stratiform area is sustained by mesoscale dynamics as well as a supply of ice particles from the convective area (Houze et al. 1989).

In recent years, it has been recognized that shallow rain from congestus plays a considerably important role in the tropics (Petty 1999; Short and Nakamura 2000; Takayabu et al. 2010). Shallow heating may play an important role in convection–circulation coupling because it drives low-level convergence to collect moisture more effectively than deep heating (Wu 2003).

Previous studies consistently showed that there is more shallow rain from congestus as well as more stratiform rain over the eastern Pacific (EP) than the western Pacific (WP) (e.g., Berg et al. 2002; Schumacher and Houze 2003; Nesbitt et al. 2006; Kubar et al. 2007). The existence of shallow meridional return flow was also observed in addition to the deep Hadley circulation over the EP (Trenberth et al. 2000; Zhang et al. 2004). Consistently, there is a large contrast in the vertical pressure velocity ω profile between the EP and the WP (Fig. 1c), while near-surface rain rates over the EP are comparable to those over the WP (Fig. 1b). The ω profile has a double peak around 300 and 850 hPa over the EP, in contrast to a single peak around 300 hPa over the WP. However, it is not well understood what factors of the large-scale environment may explain these differences.

Fig. 1.
Fig. 1.

(a) Near-surface rain rate (mm h−1) observed with the TRMM PR (shades). Contours indicate values of SST beyond 22°C at 1°C intervals. Vectors indicate horizontal winds at 1000 hPa. (b) Longitudinal distribution of rain rate (mm h−1) averaged from 7.5° to 12.5°N. (c) Vertical cross section of JRA-25/JCDAS vertical pressure velocity (Pa s−1) averaged from 7.5° to 12.5°N. (d) Vertical cross section of convergence (s−1) averaged from 7.5° to 12.5°N. These figures are averaged during 1998–2007 for all seasons.

Citation: Monthly Weather Review 140, 9; 10.1175/MWR-D-11-00252.1

In Fig. 1d, weak convergence is found from 1000 to as deep as 400 hPa over the WP warm pool from 120°–160°E. Over the EP, on the other hand, shallow (1000–925 hPa) convergence of easterlies with divergence in the lower to middle troposphere (850–500 hPa) just above the convergence is found. SSTs are 1°–2°C lower over the EP than the WP, but stronger SST gradients exist over the EP (Fig. 1a). Back and Bretherton (2009a) concluded that the SST gradient is the primary driver of strong shallow convergence over the EP.

Back and Bretherton (2009b) simulated a realistic rainfall distribution with an empirical model, which relates a deep mode of convection to SST, and a shallow mode to surface convergence, respectively. However, these relationships have not been observationally confirmed yet. On the other hand, it has been emphasized that dry air in the middle troposphere is one of the main factors to suppress the development of deep cumulus convection (Sherwood 1999; Takayabu et al. 2010). In this study, we analyze observational data to consider differences in convection between the EP and WP.

This study aims to quantitatively and statistically examine mesoscale characteristics of precipitation features (PFs) in relation to the large-scale environment over the tropical Pacific. We compare characteristics of rain systems over the EP with those over the WP to examine which environments are essential for differences in rain characteristics between the two regions. Special emphasis is placed on the depths of the convergence field and their relationship with rain characteristics. In a companion paper (Yokoyama and Takayabu 2012, hereafter Part II), we will compare characteristics of synoptic-scale disturbances over the EP with those over the WP, and discuss the relationship among precipitation systems, synoptic-scale disturbances, and the large-scale environment over the EP.

2. Data and methods

Near-surface rain rates, locations in longitude and latitude, rain flags, and total numbers of observational pixels are obtained from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25 version 6 product. Characteristics of PFs are examined by using the TRMM radar precipitation feature level-2 data from the University of Utah database (Liu et al. 2008), which is defined by grouping contiguous pixels with the PR 2A25 near-surface rain greater than 0 mm h−1. For each PF, a variety of variables including centroid latitude/longitude, numbers of rain pixels, rain volume, convective and stratiform rain volume, and maximum height of the feature from 2A23 storm height (hereafter MAXHT) are provided. The rain volume of each PF is obtained by multiplying the total rain rate by the area. Rain area is derived by assuming that the area per pixel is 17.92 (20.35) km2 before (after) the TRMM boost in August 2001. We also calculate SRR, which is a ratio of stratiform rain volume to total rain volume.

In addition, we utilize the 25-yr Japanese Re-Analysis (JRA-25)/Japan Meteorological Agency Climate Data Assimilation System (JCDAS) data (Onogi et al. 2007) and the National Oceanic and Atmospheric Administration (NOAA) optimum interpolation (OI) SST version 2 (Reynolds et al. 2002) to analyze the large-scale environment. The analysis is performed for the boreal spring (March–May), summer (June–August), autumn (September–November), and winter (December–February) from 1998 to 2007. Longitudes of the analysis regions are fixed as 130°–170°E and 150°–100°W for the WP warm pool and the EP ITCZ, respectively. Latitude ranges are selected as 1.25°–11.25°N, 5°–15°N, 6.25°–16.25°N, and 3.75°–13.75°N for the boreal spring, summer, autumn, and winter, respectively, in order to capture the primary precipitation zone for the analysis regions. In the correlation analysis, all variables (rainfall and environmental variables) are first averaged over the entire 3-month season for 10 yr at each grid box and then the correlation analysis is performed for all parameters already averaged at each box.

3. Results

First, we examine histograms of rain amounts of PFs binned into areas and MAXHTs (Fig. 2). There is a strong relationship between PF MAXHTs and areas, which is consistent with previous studies. Xu et al. (2007) examined various characteristics of tropical cloud object data from the Clouds and the Earth’s Radiant Energy System (CERES) data, and showed that the distribution of cloud-top heights shifts toward higher values when ranging from small (diameter <150 km) to large systems (>300 km). Kubar and Hartmann (2008) also examined the size of systems by looking at contiguous CloudSat pixels. They showed that deep cloud systems have considerably larger sizes (340 km in the WP to 370 km in the EP) than congestus-like systems (~40–90 km). Our study also sheds quantitative insight into the relationship between storm height versus area. The largest contribution to the total rain is from PFs with areas larger than ~103.5 km2 and MAXHTs larger than ~8 km, which is common to the EP and WP in all four seasons. These PFs represent large organized systems such as MCSs. In addition, PFs with smaller areas and smaller MAXHTs have two contribution peaks at the MAXHTs of 2–4 and 5–6 km. These PFs are considered to represent cumulus congestus, although the latter PFs may include some kind of shallow organized systems, because their typical area is ~300 km2.

Fig. 2.
Fig. 2.

Histograms (%) of rain amounts from PFs binned into areas and maximum heights (a)–(d) over the EP and (e)–(h) over the WP in (a),(e) December–February; (b),(f) March–May; (c),(g) June–August; and (d),(h) September–November during 1998–2007. Abscissa indicates areas in logarithmic scale. Ordinate indicates maximum heights. Black lines indicate the thresholds for the classification of PFs and numbers in (a) indicate PF types.

Citation: Monthly Weather Review 140, 9; 10.1175/MWR-D-11-00252.1

Interestingly, the relationship between areas and MAXHTs differs between large-sized PFs with very tall MAXHTs of 14–20 km and those with moderate MAXHTs of 8–14 km. The areas of very tall PFs tend to increase with increasing MAXHTs. On the other hand, moderately tall PFs’ areas are rather fixed and independent of their MAXHTs. While the largest contributions to the total rain are from the very tall PFs over the WP, greater contributions are from the moderately tall PFs over the EP.

There are some seasonal variations in the histograms of PFs with large areas (≥103.5 km2) and large MAXHTs (≥8 km) especially over the EP. In the boreal winter, very tall PFs are less dominant over the EP compared to other seasons, while very tall PFs are dominant over the WP during all seasons. In the boreal spring, rainfall over the EP from very tall PFs is more dominant than that from moderately tall PFs. On the other hand, rainfall from moderately tall PFs in the boreal spring is greater compared to other seasons over the WP. Thus, the distributions in the two regions are relatively similar to each other in the boreal spring. In the boreal summer and autumn, both very tall PFs and moderately tall PFs make large contributions to the total rain over the EP, while very tall PFs dominate over the WP.

Based on these histograms of PFs, we classify PFs into four types. The area of 103.5 km2 and the MAXHTs of 8 and 14 km are chosen as the thresholds for the classifications. We choose the first MAXHT threshold of 8 km as follows. Deep convection over tropical oceans is hypothesized to experience a reboost associated with the release of latent heat of fusion in the midtroposphere (Zipser 2003). Based upon the histogram distributions in Fig. 2, we decided that a MAXHT threshold of 8 km reasonably divides shallower systems for which the reboost is absent or ineffective from deeper systems. As for the second threshold of 14 km, we chose it based upon the change of properties of the histogram.

Roughly speaking, type 1 (area < 103.5 km2, MAXHT < 8 km) represents “congestus” systems, and type 2 (area < 103.5 km2, MAXHT ≥ 8 km) represents small but tall systems. Both type 3 (area ≥ 103.5 km2, 8 ≤ MAXHT < 14 km) and type 4 (area ≥ 103.5 km2, 14 ≤ MAXHT < 20 km) characterize organized systems, but they are different in their MAXHTs. Since the four types are classified in part by MAXHTs, for the PFs with large areas, one random cumulonimbus cloud extending well beyond the others could drastically change which type the PF falls into. To examine whether there is a difference in the distribution of echo-top heights between types 3 and 4, which are classified based on one pixel with particularly tall rain, we calculate the proportion of the area with reflectivity greater than 20 dBZ to the total area of each PF at intervals of 1 km. On average, the areas with 20-dBZ echo-top heights larger than 8 km account for ~5.5% and ~13% of the total area of type-3 PFs and type-4 PFs, respectively. The areas with 20-dBZ echo-top heights larger than 10 km account for ~0.8% and ~3.8% for type 3 and type 4, respectively. And ~0.5% of the total area of type-4 PFs is taller than 14 km in 20-dBZ echo-top heights. Thus, we can confirm that the classification is valid.

Figures 3a,b–e show the distribution of mean rain rates and the contributions from four rain types in the boreal autumn, respectively. The values are calculated at every 1.25° grid. Note that the color scales vary among PF types. The EP has more rainfall from types 1 and 3 than the WP, while the WP has more rainfall from types 2 and 4 than the EP. These results are consistent with the finding of Kubar and Hartmann (2008) that suggests a higher contribution from shallow and midlevel raining clouds to total rain rates in the EP compared to the WP. To quantify these results, the contributions to total rain rates in the EP and WP from each of the four PF types are calculated (Table 1). It is confirmed that rainfall from type 3 is more dominant over the EP than over the WP, while rainfall from types 2 and 4 is more dominant over the WP than over the EP. The contribution from type 1 over the EP appears to be comparable to that over the WP. This is because rainfall from type 3, which largely contributes to total rainfall, is also large in the region where rainfall from type 1 is large. As shown in Fig. 3b, there is no doubt that type-1 PFs are more frequently found in the EP ITCZ compared to the WP warm pool region.

Fig. 3.
Fig. 3.

(a) Mean rain rates and contribution from (b) type 1, (c) type 2, (d) type 3, and (e) type 4 in September–November during 1998–2007 (shades; mm h−1). In (a), black contours indicate vertical pressure velocity at 500 hPa (Pa s−1), solid lines indicate 0.02 Pa s−1, and dashed lines indicate −0.08, −0.06, −0.04, and −0.02 Pa s−1. In (b) and (d), black contours indicate mass convergence integrated from 1000 to 925 hPa and solid (dashed) lines indicate values beyond 0.004 kg m−2 s−1 (below −0.004 kg m−2 s−1) at 0.004 kg m−2 s−1 intervals. In (c) and (e), black contours indicate SST beyond 23° at 1°C intervals. In (a)–(e), red thick lines indicate 0 Pa s−1 of vertical pressure velocity at 500 hPa.

Citation: Monthly Weather Review 140, 9; 10.1175/MWR-D-11-00252.1

Table 1.

Contributions to the total rainfall over the EP and WP from each of the four PF types during December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON) during 1998–2007.

Table 1.

Next, different rainfall types are examined in relation to large-scale environments. In this study, we emphasize the relationships with SST (contours of Figs. 3c,e) and with shallow mass convergence integrated from 1000 to 925 hPa (contours of Figs. 3b,d). Rainfall from types 2 and 4 is dominant in high-SST regions such as the WP warm pool and the EP warm pool (10°–18°N, 110°–90°W). On the other hand, rainfall from types 1 and 3 is large where strong shallow (1000–925 hPa) convergence appears. Rainfall from type 3 is largest in the maximum shallow convergence region near 140°–120°W. Mean SRRs of types 1, 2, 3, and 4 are 38.0% (42.5%), 50.7% (43.4%), 64.0% (58.4%), and 54.3% (47.8%) over the EP (WP), respectively. It is shown that type 3 corresponds to well-organized systems, which maintain a large amount of stratiform rain compared to type 4.

As described in the introduction, dry air in the middle troposphere is considered to be one of the main factors to suppress the development of deep cumulus convection (Sherwood 1999; Takayabu et al. 2010). Over the EP, shallow convergence is prominent while midtropospheric (600 hPa) relative humidity (RH600) is ~3% lower than that over the WP in the boreal autumn. Cumulus convection can be generated in the shallow convergence field over the EP, but development of deep cumulus convection can easily be discouraged by the entrainment of relatively dry air in the midtroposphere. Table 2 shows correlation coefficients between mean rain rates from each type and the RH600 field over the tropical Pacific in the four seasons. Longitudes of the analysis region are fixed as 130°E–100°W, and latitudes are defined in the same manner as defined in section 2. It is confirmed that type 2, which consists of small tall PFs, is highly correlated with RH600 with a correlation coefficient of 0.77. On the other hand, organized PFs (types 3 and 4) tend to be large in high-RH600 regions, but the correlation coefficients are not very high.

Table 2.

Correlation coefficients between mean rain rates from each type and the large-scale environment fields such as shallow (1000–925 hPa) convergence, SST, and relative humidity at 600 hPa (RH600) during the four seasons during 1998 to 2007. Longitude range is fixed as 130°E–100°W. Latitude ranges vary with seasons (see text).

Table 2.

In addition, lower-tropospheric static stability (LTS; potential temperature at 700 hPa minus that at 1000 hPa), vertical wind shear (horizontal wind at 200 hPa minus that at 850 hPa), and deep (1000–400 hPa) convergence are also considered to be environmental factors that affect cumulus convection. Correlation coefficients between these factors and mean rain rates from each type are shown in Table 3. LTS has a tendency similar to that of SST, which suggests that SST largely controls LTS in this region. Vertical wind shear tends to suppress type 2, but the effects on the other types seem to be relatively small. Deep convergence tends to be favorable for all types.

Table 3.

Correlation coefficients between mean rain rates from each type and the large-scale environment fields such as lower-tropospheric static stability (LTS; potential temperature at 700 hPa minus that at 1000 hPa), vertical wind shear (wind at 200 minus that at 850 hPa), and deep (1000–400 hPa) convergence during the four seasons during 1998–2007. Longitude range is fixed as 130°E–100°W. Latitude ranges vary with seasons (see text).

Table 3.

Here, let us recall the previous discussion that type-3 PFs are dominant in the EP ITCZ together with type 1. Since type 3 is not a shallow PF but a moderately tall PF type, the above argument may seem contradictory. In a companion paper (Part II), we have shown that moderately deep convection is allowed in association with deep convergence of synoptic-scale disturbances over the EP ITCZ, where convergence tends to otherwise be shallow. It is also why the deep precipitation is in the form of more organized with larger stratiform rain ratio in this region compared to the WP.

Figure 3b also shows that rainfall from type 1 is large to the west of Hawaii (15°–20°N, 170°E–170°W) and in the Southern Hemisphere (15°–10°S, 180°–140°W), where shallow convergence is relatively weak. Takayabu et al. (2010) also suggested a dominance of congestus heating in similar regions, where the development of deep cumulus convection is discouraged by dry air in the middle troposphere associated with descending branches of the large-scale circulation. In fact, the distribution of vertical pressure velocity at 500 hPa (contours of Fig. 3a) shows subsidence and weak ascent to the west of Hawaii. Subsidence and weak ascent at 500 hPa are also found in the Southern Hemisphere region with much rainfall from type 1 to the southeast of the core of the South Pacific convergence zone (SPCZ). Thus, type-1 shallow rain over these two regions is related to the coexistence of warm SST and subsidence/weak ascent.

The results in other seasons are consistent with those in the boreal autumn (not shown). In the boreal spring, the SST gradient is relatively weak over the EP, but extends to the central-western Pacific. Therefore, in the boreal spring, the shallow convergence field is also weaker, but extends farther westward. Corresponding to the environment, rainfall from types 1 and 3 is reduced, but rainfall distributions expand farther westward. In addition, rainfall from types 2 and 4 expands to the west of 120°W over the EP, which is related to the westward expansion of the EP warm pool region (≥28°C). As a result, the contrast between the WP and the EP is relatively small in the boreal spring. In the boreal winter, the high-SST region does not expand over the EP, where the contribution of very deep rain from types 2 and 4 decreases. Note that there is little variation in deep rain from types 2 and 4 over the WP, because the pronounced warm pool and the weak but deep convergence are present through all four seasons.

Figure 4 shows the scatter diagrams of mean rain rates from each type against shallow convergence fields over the tropical Pacific in the four seasons. Longitudes of the analysis region are fixed as 130°E–100°W, and latitudes are defined in the same manner as defined in section 2. Correlation coefficients between mean rain rates from each type and the large-scale environment fields are shown in Table 2. Rainfall from type 1 is highly correlated with shallow convergence fields with a correlation coefficient of 0.75. Rainfall from type 3 also correlates with shallow convergence fields with a correlation coefficient of 0.66. The above relationships are robust regardless of season.

Fig. 4.
Fig. 4.

Scatter diagrams of rain rates (mm h−1) from each type against mass convergence (×10−3 kg m−2 s−1) integrated from 1000 to 925 hPa in March–May (black), June–August (blue), September–November (red), and December–February (green) during 1998–2007. Plots for types (a) 1, (b) 2, (c) 3, and (d) 4. Longitude range is fixed as 130°E–100°W. Latitude ranges vary with seasons (see text).

Citation: Monthly Weather Review 140, 9; 10.1175/MWR-D-11-00252.1

In contrast, rainfall from types 2 and 4 is less correlated with shallow convergence fields with correlation coefficients of 0.41 and 0.30, respectively. These tall types of PFs have the largest rainfall in the moderate range of the shallow convergence (~2.5–5 × 10−3 kg m−2 s−1). Types 2 and 4 are also examined in relation to SST and deep (1000–400 hPa) convergence fields (Fig. 5). Type 2 is highly correlated with SST (0.77). Rain rates from type 2 seem to “turn on” around 27°C, which is often cited as the convective threshold in the current climate (e.g., Waliser and Graham 1993; Kubar et al. 2011). Rainfall from type 2 also has a high correlation with the RH600 field, as already shown. The correlation of type 4 with SST is relatively low (0.45), but rain rates of type 4 tend to be large in high-SST regions. Rainfall from types 2 and 4 tends to increase with increase in deep convergence at a given SST (not shown). Deep rainfall from types 2 and 4 is dominant in high-SST regions over the WP, in association with deep convergence.

Fig. 5.
Fig. 5.

Scatter diagrams of rain rates (mm h−1) from (a),(c) type 2 and (b),(d) type 4 against (a),(b) SST and (c),(d) deep (1000–400 hPa) convergence (×10−3 kg m−2 s−1) in the four seasons during 1998–2007. Longitude range is fixed as 130°E–100°W. Latitude ranges vary with seasons (see text).

Citation: Monthly Weather Review 140, 9; 10.1175/MWR-D-11-00252.1

The above results suggest that both SST and shallow convergence driven by strong SST gradients are important factors to determine the characteristics of rain systems over the tropical Pacific. However, no single environmental variable explains more than 44% and 27% of variance of rainfall from types 3 and 4, respectively. To examine whether more variance is explained by combining multiple variables, we calculate multiple correlation coefficients for rainfall from types 3 and 4, using SST and shallow convergence as explanatory variables. As a result, the multiple correlation coefficient for type 3 is 0.67, indicating a contribution ratio of 45%. The multiple correlation coefficient for type 4 is 0.51, indicating a contribution ratio of 26%. These values are comparable to the simple correlation coefficients. Other factors should be considered in addition to the seasonally averaged environmental variables that we consider here, in order to explain more variance of rainfall from these organized rain systems. Organized rain systems are often coupled with synoptic-scale disturbances, and maintained through interaction between them. It may be important to consider where and when synoptic-scale disturbances are dominant.

There is still the question as to whether convergence is cause or effect of cumulus convection. Finally, a composite analysis is performed for the EP and WP during the boreal autumn to examine the temporal relationship among organized rain systems and the environmental variables (Fig. 6). The reference times for the EP are defined as the times when type-3 PFs are observed over 6.25°–11.25°N, 133.75°–126.25°W, while those for the WP are defined as the times when type-4 PFs are observed over 6.25°–11.25°N, 146.25°–153.75°E. We first divide each region into six 2.5° grids, and consider the center of PFs observed in each grid as the center of the grid. Then, we make composites of the environmental variables, which are averaged over the 5° grid around the PF center, every 6 h. The composites for the EP and WP consist of 411 cases and 125 cases, respectively.

Fig. 6.
Fig. 6.

Composites of time series of (a),(b) mass convergence (×10−3 kg m−2 s−1) integrated from 1000 to 925 hPa (black lines) and mass convergence (×10−3 kg m−2 s−1) integrated from 1000 to 400 hPa (gray lines); and (c),(d) relative humidity at 600 hPa during the boreal autumn during 1998–2007. (a),(c) Composites based on the times when type-4 PFs are observed over 6.25°–11.25°N, 146.25°–153.75°E and (b),(d) the times when type-3 PFs are observed over 6.25°–11.25°N, 133.75°–126.25°W. Error bars indicate the 95% confidence intervals.

Citation: Monthly Weather Review 140, 9; 10.1175/MWR-D-11-00252.1

Figure 6 shows that shallow convergence over the EP is always larger than ~0.01 kg m−2 s−1 with its peak 6 h before the reference time. Shallow convergence over the EP has a smaller amplitude than deep convergence over the WP, which has its peak 6 h after the reference time. It is suggested that the shallow convergence field over the EP is more driven by large SST gradients than by type-3 PFs. Note that deep convergence over the EP also has larger amplitude than shallow convergence, and its peak is found at the reference time. On the other hand, over the WP, deep convergence driven by type-4 PFs themselves is considered to make a relatively large contribution to the convergence field, because of weak SST gradients. Over the EP and WP, RH600 increases toward the reference time, and gradually decreases after that time. It is shown that RH600 is associated with organized PFs.

4. Summary and discussion

Mesoscale characteristics of PFs over the EP are compared with those over the WP, and examined in relation to the large-scale environment. Over the WP warm pool, where deep (1000–400 hPa) convergence exists, rainfall from tall but less-organized systems and very tall organized systems is large. On the other hand, in the region with very shallow (1000–925 hPa) convergence over the EP, rainfall from congestus and organized systems with moderate heights is large. The distributions of SST and a shallow convergence field vary seasonally, which results in changes of the distributions of dominant rain systems. The above relationships between rain systems and the large-scale environment are robust through all seasons.

Shallow rain from congestus is highly correlated with the shallow convergence field. This result observationally supports the assumption of Back and Bretherton (2009b). Over the EP, strong SST gradients drive strong shallow convergence (Back and Bretherton 2009a). Therefore, the shallow convergence can primarily be an external forcing to generate shallow rain from congestus. Overall, the environment over the EP such as lower SST, the existence of divergence in the lower–middle troposphere, and drier air in the midtroposphere is less favorable for development of deep convection, compared to that over the WP. In such an environment, it appears that the shallow convergence field over the EP affects the very low part of the troposphere and generates shallow rain.

Moreover, our results indicate that moderately tall organized systems with large SRRs coexist in the region with much rainfall from congestus. It is suggested that moderately deep rain can be maintained by the organization of rain systems even in shallow convergence. In Part II, we will explore the role played by synoptic-scale disturbances in the EP and WP in determining the properties of precipitation systems. In short, the EP is less favorable for development of deep convection than the WP, and has strong shallow convergence. It is suggested that these differences in the environment between the EP and WP cause the differences in dominant characteristics of rain systems, resulting in the large-scale circulation differences.

Acknowledgments

This work is based on a part of the first author’s doctoral dissertation. The first author expresses appreciation to M. Kimoto, M. Satoh, H. Hasumi, H. Nakamura, K. Iga, and A. Sumi for their helpful comments and discussions. The authors thank E. J. Zipser, C. Liu, and S. W. Nesbitt for providing the Radar Precipitation Feature Level-2 data from the University of Utah TRMM database. They would also like to express their gratitude to two anonymous reviewers for their very helpful comments. They want to acknowledge the Japan Meteorological Agency and NOAA for providing valuable data. This work is supported by Precipitation Measuring Mission project of the Japan Aerospace Exploration Agency (JAXA).

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