Global Mesoscale Convective System Latent Heating Characteristics from GPM Retrievals and an MCS Tracking Dataset

Nana Liu aAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by Nana Liu in
Current site
Google Scholar
PubMed
Close
,
L. Ruby Leung aAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by L. Ruby Leung in
Current site
Google Scholar
PubMed
Close
, and
Zhe Feng aAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by Zhe Feng in
Current site
Google Scholar
PubMed
Close
Full access

Abstract

The distribution of latent heating released by mesoscale convective systems (MCSs) plays a crucial role in global energy and water cycles. To investigate the characteristics of MCS latent heating, five years (2014–19) of Global Precipitation Measurement (GPM) Ku-band Precipitation Radar observations and latent heating retrievals are combined with a newly developed global high-resolution (~10 km, hourly) MCS tracking dataset. The results suggest that midlatitude MCSs are shallower and have a lower maximum precipitation rate than tropical MCSs. However, MCSs occurring in the midlatitudes have larger precipitation areas and higher stratiform rain volume fraction, in agreement with previous studies. With substantial spatial and seasonal variability, MCS latent heating profiles are top-heavier in the middle and high latitudes than those in the tropics. Larger magnitudes of latent heating in the stratiform regions are found over the ocean than over land, which is the case for both the tropics and midlatitudes. The larger magnitude is related to a larger precipitating area/volume rather than a higher storm height or more intense convective core typically associated with land systems. A majority of midlatitude MCSs have a relatively high (>70%) stratiform fraction while this is not the case for tropical MCSs, suggesting that midlatitude MCSs tend to produce more stratiform rain while tropical MCSs are more convective. Importantly, the results of this study indicate that storm intensity, latent heating, and rainfall are different metrics of MCSs that can provide multiple constraints to inform development of convection parameterizations in global models.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Nana Liu, nana.liu@pnnl.gov; L. Ruby Leung, ruby.leung@pnnl.gov

Abstract

The distribution of latent heating released by mesoscale convective systems (MCSs) plays a crucial role in global energy and water cycles. To investigate the characteristics of MCS latent heating, five years (2014–19) of Global Precipitation Measurement (GPM) Ku-band Precipitation Radar observations and latent heating retrievals are combined with a newly developed global high-resolution (~10 km, hourly) MCS tracking dataset. The results suggest that midlatitude MCSs are shallower and have a lower maximum precipitation rate than tropical MCSs. However, MCSs occurring in the midlatitudes have larger precipitation areas and higher stratiform rain volume fraction, in agreement with previous studies. With substantial spatial and seasonal variability, MCS latent heating profiles are top-heavier in the middle and high latitudes than those in the tropics. Larger magnitudes of latent heating in the stratiform regions are found over the ocean than over land, which is the case for both the tropics and midlatitudes. The larger magnitude is related to a larger precipitating area/volume rather than a higher storm height or more intense convective core typically associated with land systems. A majority of midlatitude MCSs have a relatively high (>70%) stratiform fraction while this is not the case for tropical MCSs, suggesting that midlatitude MCSs tend to produce more stratiform rain while tropical MCSs are more convective. Importantly, the results of this study indicate that storm intensity, latent heating, and rainfall are different metrics of MCSs that can provide multiple constraints to inform development of convection parameterizations in global models.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Nana Liu, nana.liu@pnnl.gov; L. Ruby Leung, ruby.leung@pnnl.gov

1. Introduction

Latent heating (LH) is a major driver of atmospheric circulation in the tropics and extratropics (Webster 1972; Hartmann et al. 1984; Schumacher et al. 2004; Hagos and Zhang 2010). LH in precipitating systems is also associated with vertical energy transport throughout the troposphere (Houze 1982). A number of studies have shown that the dynamical response of the atmospheric circulation to heating is sensitive to the vertical and horizontal distribution of LH (Anthes and Keyser 1979; Hartmann et al. 1984; Emanuel et al. 1994; Schumacher et al. 2004; Ling et al. 2013). For example, Schumacher et al. (2004) demonstrated that the horizontal variation of the vertical heating distribution plays an important role in shaping the structure of the large-scale circulation response utilizing a simple climate model.

Mesoscale convective systems (MCSs), the largest form of precipitating systems, contribute to over half of the annual rainfall in many regions (Nesbitt et al. 2006; Liu 2011; Rasmussen et al. 2016; Feng et al. 2021a). The release of LH from MCSs is affected by the convective and stratiform regions, each with distinctive vertical heating distribution (Houze 2004; Kodama et al. 2009; Liu et al. 2015). Convective precipitation occurring in the convective core of MCSs is associated with strong upward motion and positive latent heating throughout the entire troposphere, while stratiform precipitation occurring in anvil regions is generally associated with weak mesoscale updrafts, warming in the upper troposphere and cooling at lower levels (Houze 1989; Hagos et al. 2010; Ahmed et al. 2016). The bottom- and middle-heavy profiles in the convective region and the top-heavy profile in the stratiform region have been suggested to be of fundamental importance in atmospheric circulation (Sui and Lau 1989; Puri and Davidson 1992; Chiang et al. 2001; Hagos 2010). Hartmann et al. (1984) demonstrated with a simple global model that the top-heavy heating profiles allow a better representation of a Walker circulation than heat sources that peak in the middle troposphere.

Despite the importance of the structure of heating in governing atmospheric motion, the horizontal and vertical distributions of LH on a global scale remain rather poorly documented and understood. Krishnamurti et al. (2010) reported significant errors from some cumulus parameterization schemes in describing the amplitude and three-dimensional distributions of heating compared to the Tropical Rainfall Measuring Mission (TRMM)-based observed estimates. Such biases in representing the heating fields can produce erroneous circulations, which in turn result in deficiencies in the simulation of the large-scale response to perturbations.

Although the spatial distribution of LH is important to the large-scale circulation and energy transport, there has been no direct measurement to obtain the global or large-scale distribution of LH profiles. Estimates of heating profiles based on in situ sounding networks and field campaigns are only available on regional scales (Reed and Recker 1971; Schumacher et al. 2007; Hagos et al. 2010). LH estimates that rely on reanalysis are not always consistent with those obtained from soundings (Hagos et al. 2010; Ling and Zhang 2011) or satellite retrievals (Krishnamurti et al. 2010; Jiang et al. 2011). Therefore, LH retrievals from the TRMM precipitation radar observations have been widely used to investigate the distribution of LH (Zhang et al. 2010; Schumacher et al. 2004; Barnes et al. 2015; Liu et al. 2015) and considered a reference for model and reanalysis validations across the tropic and subtropics (Chan and Nigam 2009; Krishnamurti et al. 2010). In this study, we use the three-dimensional distribution of LH obtained from the Global Precipitation Measurement Mission (GPM) spectral latent heating (SLH) product, which is modified from the TRMM SLH retrieval algorithm to extend its application to the midlatitudes (Shige et al. 2004, 2007, 2009; Iguchi et al. 2018).

Because the release of LH is dominated by the precipitation process, characterizing MCS LH is important to understanding the spatial distribution of LH and factors that contribute to the variations. Liu et al. (2015) demonstrated that precipitating systems with an area greater than 2000 km2 occurring in the tropical and subtropical regions contribute more than 50% to the total heating in the middle and upper troposphere. Taking advantage of a newly developed global MCS tracking dataset, we combine the tracked MCSs with the GPM precipitation feature (PF) database to estimate the MCS LH. The GPM PF dataset provides the vertical structure of MCSs, as well as the LH retrievals while the new high-resolution MCS tracking database considers spatiotemporal evolution of cloud and precipitation characteristics to improve MCS identifications in the midlatitudes (Feng et al. 2021a). Combining the GPM PF and the MCS tracking database allows for detailed investigations of the global vertical and horizontal distributions of LH. The objective of this study is to present analysis of MCS LH characteristics, with an emphasis on the comparison of land versus ocean and in different geographical regions.

In section 2, we present a description of the data utilized in this study. The details of the characteristic of MCS LH are described in section 3. Finally, section 4 is devoted to a summary and discussion.

2. Data and method

The GPM core satellite, which carries a dual-frequency precipitation radar and a multifrequency microwave imager, was launched in February 2014 (Hou et al. 2014; Skofronick-Jackson et al. 2017). As an advanced successor to TRMM, the GPM core satellite currently operates in a non-sun-synchronous orbit within 65° south–north latitudes and thus provides invaluable information of precipitation from the tropics to midlatitudes. The two databases for this analysis are described as follows.

a. Precipitation feature database

The analyses presented in this study are based upon the GPM Ku-band radar PF database. The GPM PFs are defined as contiguous areas with nonzero near-surface precipitation derived from the Ku band radar, which follows the methodology of Nesbitt et al. (2000) and Liu et al. (2008). This database allows a convenient and efficient way to search individual cases and to infer the properties of convective systems for regional and seasonal studies (Xu et al. 2009; Hamada et al. 2014; Liu et al. 2019). Each PF contains a summary of the properties of the individual event observed by the GPM satellite, such as maximum rain rate, echo top height, fraction of convective and stratiform rain, and the sum of latent heating per feature. Also, an ellipse is fitted to each PF to characterize its shape and orientation. The LH profiles for this study are obtained from the 5-yr (April 2014–March 2019) SLH product. These data were obtained from atmos.tamucc.edu/trmm/data.

In the SLH algorithm, a lookup table approach is employed to retrieve LH (Shige et al. 2004, 2007, 2009). The lookup table is generated with a cloud-resolving model that simulates the LH structure based on observations of the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (Webster and Lukas 1992). A further improvement has been made to generalize their applicability for midlatitude systems by using the Japan Meteorological Agency’s high-resolution local forecast model to construct the lookup tables (Iguchi et al. 2018; Takayabu and Tao 2020). Precipitation rate, echo-top height, and precipitation profiles are used as inputs for the lookup tables. In this study, we analyze the profiles of Q1QR to explore the characteristics of LH distribution, where Q1 is apparent heat source (Yanai et al. 1973) and QR is radiative heating. To focus our attention on the dominant latent heating component, we ignore the eddy heat transports since they are relatively minor (Houze 1982). Details of the algorithm are described in Shige et al. (2004, 2007).

b. MCS tracking database

A global MCS database has been developed by using the Flexible Object Tracker algorithm (FLEXTRKR; Feng et al. 2018). Two datasets, including a global merged geostationary satellite 11.5-μm infrared brightness temperature and the Integrated Multisatellite Retrievals for GPM (IMERG) precipitation product, are used in this algorithm. To reduce the computational cost, the tracking algorithm only uses hourly brightness temperature snapshot and hourly mean precipitation data. Before running the tracking algorithm, the infrared data (~4-km spatial resolution) are regridded to match the IMERG resolution (~10 km). Then, several steps are used to identify and track MCSs. First, cold cloud systems (CCSs; defined as contiguous areas with brightness temperature less than 241 K) are identified from geostationary satellite observations. To track a CCS, two CCSs are considered to be continuous if the area overlap is greater than 50% at consecutive time steps. A CCS is terminated if there is no feature at consecutive time steps satisfying the 50% area overlap threshold. Second, a potential MCS is identified if a large CCS (area > 4 × 104 km2) persists continuously for more than 4 h. To better identify robust MCSs from the tracked CCSs, the collocated IMERG precipitation statistics are further used. The precipitation area (contiguous area with hourly rain rate > 2 mm h−1), mean rain rate, rain rate skewness, and heavy-rain volume ratio within the tracked CCSs must all exceed the lifetime-dependent thresholds for the potential MCSs to be identified as final MCSs (Feng et al. 2021a). At middle latitudes, synoptic systems during the cold season can produce large cloud systems with cold cloud top but primarily stratiform precipitation with little convective precipitation. To exclude such systems, an additional criterion of a heavy rain (rain rate > 10 mm h−1) volume ratio is required when identifying final MCSs. Tropical cyclones and orographically enhanced precipitation systems over the U.S. and European west coasts associated with atmospheric rivers (Rutz et al. 2019) are also excluded from the MCS database [see Feng et al. (2021a) for more details]. The high-resolution MCS database is available from 2000 to 2019. In this study, we only use the same time period of the tracked MCSs overlapping with the period of the GPM PF, which is from April 2014 to March 2019. To collocate the MCS tracking database with the GPM DPR PF database, we match the fitted ellipse of the GPM PF with the tracked MCS CCS mask at the nearest hour, as shown in Fig. 1. If the GPM PF overlaps with the MCS CCS mask for more than 30% of their areas, the corresponding GPM PF is marked as MCS PF. An example of the vertical cross section of the GPM Ku-radar reflectivity and SLH for MCS occurring over the eastern China Sea (28.6°N, 129.9°E) is also shown in Figs. 1b and 1c. With a large precipitation area (93 950 km2), the subtropical MCS in the late June is characterized by a large fraction (70%) of volumetric stratiform precipitation.

Fig. 1.
Fig. 1.

(a) Schematic to illustrate the collocation of the MCS and GPM PF databases and (b),(c) an example of MCS occurring in the East China Sea (28.6°N, 129.9°E). In (a) the yellow shading presents an MCS mask identified from IMERG precipitation products while the blue shading presents a GPM PF fitted with an ellipse (orange oval). The red curve indicates the total latent heating profile of each PF. The color fill is the GPM Ku-radar reflectivity in (b) and the spectrum latent heating in (c).

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

3. Results

a. Characteristics of MCSs at different latitudes

We begin by comparing the properties of MCSs at different latitude bands. Figure 2 illustrates the cumulative histogram of several selected characteristics of MCSs at different latitude bands utilizing the GPM PF database. These variables include the maximum 20-dBZ echo-top height, maximum precipitation rate, PF area, and fractions of convective and stratiform volumetric precipitation. In the tropics (20°N–20°S), MCSs are characterized by a higher storm height and a larger maximum rain rate compared to those at middle (20°–40°S and 20°–40°N) and high latitudes (40°–60°N/S; Figs. 2a,b). This is consistent with the well-known fact that deep convection exhibits a preference in some tropical regions, such as the intertropical convergence zone (ITCZ), the Pacific and Indian Oceans, and central Africa (Zipser et al. 2006; Houze et al. 2015; Liu and Liu 2018). It is also important to realize that convection varies in the horizontal as well as the vertical dimension. Comparison of MCS precipitation area indicates that extratropical MCSs contain a larger precipitation area embedded within an MCS and they have a larger fraction of stratiform precipitation compared to those in the tropics. That is, with a much smaller fraction of stratiform precipitation, MCS in the tropics tends to be more convective than those at midlatitudes.

Fig. 2.
Fig. 2.

Cumulative histogram of selected variables for MCSs at different latitudes. (a) Maximum 20-dBZ echo top heights, (b) maximum precipitation rate, (c) precipitation area, and (d) fractions of stratiform rain to the total rain. The red, yellow, and blue lines mark the tropics (20°S–20°N), subtropics (20°–40°N/S), and midlatitudes (40°–60°N/S) respectively.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

Knowledge of the vertical distribution of latent heating is key to understanding the large-scale general circulation. Figure 3 shows the mean volumetric latent heating profiles from stratiform and convective precipitation for different latitudes. Unlike the stratiform regions associated with MCSs in the tropics, stratiform precipitation during the cold season in the midlatitudes associated with frontal systems may be large enough to be detected as MCSs. Several criteria have been applied to exclude the midlatitude stratiform precipitation from the MCS tracking database using the FLEXTRKR algorithm (Feng et al. 2021a). To further minimize the effect of frontal systems on this analysis, we also choose to focus on the LH profiles of MCSs during the summer season of each hemisphere. In the tropics, positive heating throughout the depth of the storm in the convective region and heating in the upper troposphere and cooling at lower levels in the stratiform regions are consistent with previous studies (Houze 1989; Hagos et al. 2010; Barnes et al. 2015; Liu et al. 2015). In addition, tropical oceanic MCSs have larger volumetric heating in both convective and stratiform regions than land MCSs (Fig. 3c). The larger stratiform LH over ocean is also seen in the subtropical and midlatitude MCSs (Figs. 3a,b,d,e). However, the difference in stratiform-heating profile between land and ocean is larger at midlatitudes than the tropics. We note further from Fig. 3 that the stratiform LH profiles exhibit a top-heavier (a larger LH difference between the cooling at low levels and warming at middle and upper troposphere) characteristic for MCSs occurring at midlatitudes of both hemispheres, especially for oceanic MCSs. The different nature of LH profile between tropical and midlatitudes will be related to other MCS properties discussed in the sections below.

Fig. 3.
Fig. 3.

Mean volumetric latent heating profiles from stratiform and convective precipitation at different latitudes: (a) midlatitudes of the Southern Hemisphere, (b) subtropics of the Southern Hemisphere (20°–40°S), (c) tropics (20°S–20°N), (d) subtropics of the Northern Hemisphere (20°–40°N), and (e) midlatitudes of the Northern Hemisphere (40°–60°N). Solid lines represent latent heating profiles from stratiform precipitations while dashed lines represent profiles from convective precipitation. The mean volumetric latent heating profiles are calculated by summing the MCS latent heating from stratiform (convective) precipitation and dividing it by the total number of MCSs in each latitude band. The red (blue) lines indicate profiles from MCSs over land (ocean).

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

Figure 4 displays the mean profiles of the area of GPM Ku-radar reflectivity greater than 20 and 40 dBZ at the same latitude band as Fig. 3. Here, the area of 20-dBZ radar reflectivity at different levels is considered a proxy of storm size while the area of 40-dBZ radar reflectivity is used as an indicator of storm intensity. Higher 20- and 40-dBZ echo tops over land than ocean indicate that MCSs occurring over land have a higher storm height and a more intense core when comparing to MCSs over oceans. However, a larger area of 20-dBZ radar reflectivity at low levels over ocean than land implies that oceanic MCSs cover larger areas. Similar contrast of the nature of convection between land and ocean has been demonstrated by a number of studies (e.g., Zipser and Gautier 1978; Xu and Emanuel 1989; Nesbitt et al. 2000; Houze et al. 2009). One of the reasons for the contrast is the difference in the shape of lapse rates (Xu and Emanuel 1989). Over land, lapse rates are steep and close to the dry adiabatic lapse rates, which implies that rising parcels of air can achieve large values of buoyancy to support the occurrence of deeper convection. The opposite is true over oceans where lapse rates are close to the moist adiabatic lapse rates which are almost never very steep, so they seldom provide great buoyancy values to support intense deep convection. On the other hand, a comparison of Figs. 3 and 4 shows larger magnitude of MCS LH over oceans, which is associated with the larger area and volumetric precipitation.

Fig. 4.
Fig. 4.

Mean profiles of area of GPM Ku-radar reflectivity greater than 20 and 40 dBZ at different latitudes: (a) midlatitude of the Southern Hemisphere, (b) subtropics of the Southern Hemisphere (20°–40°S), (c) tropics (20°S–20°N), (d) subtropics of the Northern Hemisphere (20°–40°N), and (e) midlatitudes of the Northern Hemisphere (40°–60°N). Solid (dashed) lines represent area of Ku-radar reflectivity greater than 20 (40) dBZ. The red (blue) lines indicate profiles over land (ocean).

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

Figure 5 shows the zonal distribution of several selected variables for MCSs. More properties of MCSs, such as the maximum 20-dBZ echo top heights, precipitation area, and maximum precipitation rate, in different latitude bands are summarized in Table 1. Consistent with Fig. 2, the zonal distribution shows that MCSs in the midlatitudes are characterized by a lower maximum rain rate (Fig. 5b) but a higher fraction of stratiform rain than those in tropical regions (Fig. 5a). The peak altitudes of the release of LH are also lower for MCSs in the midlatitudes than those in tropical regions. Another interesting feature in Fig. 5 is the contrast of MCS properties between land and ocean. In the tropics, oceanic MCSs have a noticeably larger maximum precipitation rate than those over land, consistent with the results of Table 1. Moreover, the maximum 20-dBZ echo heights of land MCSs are generally 1 km higher than that of oceanic MCSs (Table 1). In the midlatitudes (~40°), land MCSs are associated with a smaller area of stratiform precipitation and a larger maximum rain rate than oceanic MCSs (Fig. 5 and Table 1). Oceanic MCSs occurring in the midlatitudes have a larger fraction of volumetric stratiform precipitation than those over land, which was also reported in past research [e.g., Schumacher and Houze (2003) found higher stratiform rain fraction up to 35° latitude using TRMM observations].

Fig. 5.
Fig. 5.

Annual mean zonal distribution of selected variables for MCSs: (a) fraction of stratiform volumetric rain, (b) the maximum precipitation rate, and (c) the peak height of latent heating. Note that hereafter all the figures are created from MCSs for all the seasons unless it is specified.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

Table 1.

Properties of MCSs in different latitude bands including the midlatitudes (Mid), subtropical (Sub), and tropical. N = Northern Hemisphere; S = Southern Hemisphere.

Table 1.

b. MCS LH contributions

Figure 6 illustrates the vertical cross section of the mean LH rate from MCSs, as well as their geographical distributions and contributions to the total LH. The mean LH rate is determined by summing all the LH from MCSs and then dividing that sum by the number of samples (Figs. 6a,b). The zonal distribution of LH clearly shows that the release of LH is dominated by precipitation in the tropical regions, which was also noted in past studies (Malkus 1962; Riehl and Simpson 1979). The upper-level peak of the heating in the tropical regions suggests that the stratiform regions of MCSs are a major consideration in evaluating the interaction of MCSs with large-scale environments. Geisler and Stevens (1982) also pointed out that the vertical gradient of stratiform-type heating is far more effective in elevating the circulation centers and strengthening the upper-tropospheric response of tropical Walker and Hadley circulations to the stratiform anvils than to the deep convective clouds. Besides the tropics, two centers of LH around 6 km are found in the midlatitudes (Fig. 6a), which correspond to regions such as the United States, Argentina, and off the east coast of continents (Fig. 6b). The geographical distribution of MCS LH contribution to the total LH in Fig. 6c is calculated by summing the MCS LH between 4 and 9 km and dividing the sum by the total PF LH in the same layer. We investigate the sum of MCS LH between 4 and 9 km in Figs. 6b and 6c because the majority of heating is from this layer (Fig. 6a). In the tropical regions, larger LH and larger heating contribution to the total LH are found in the well-known favorable regions for MCSs, such as the ITCZ, Bay of Bengal, and the Maritime Continent (Figs. 6b,c; Tao et al. 2006; Liu et al. 2015). It is not surprising that the geographical distribution of MCS LH between 4 and 9 km in Fig. 6c is consistent with the occurrence of MCSs since these large precipitating systems contribute to a significant portion of Earth’s precipitation (Nesbitt et al. 2006; Feng et al. 2021a). Highly variable horizontal distribution of LH, which can also be seen in Figs. 6b and 6c, has been shown to be very important in simulating the large-scale circulation correctly (Hartmann et al. 1984; Emanuel et al. 1994; Chiang et al. 2001; Schumacher et al. 2004).

Fig. 6.
Fig. 6.

(a) Vertical cross section of mean MCS latent heating, (b) geographical distribution of MCS latent heating between 4 and 9 km, and (c) the percentage of MCSs latent heating between 4 and 9 km to the total latent heating in the same layer (Fig. 5b divided the total latent heating of all PFs between 4 and 9 km). The two dashed lines in (a) indicate the altitudes of 4 and 9 km. The zonal distribution of LH in (a) is calculated by the total MCS LH divided by the GPM sample area in each 5° latitude bin. Black boxes in (b) indicate selected regions for Figs. 810.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

c. Seasonal and regional variability of MCS LH

As Fig. 6 indicates, the majority of MCS heating is centered in the layer of ~4–9 km because of the top-heavy structure of stratiform LH profiles. Thus, we present the seasonal variation of the mean LH rate between 4 and 9 km in Fig. 7. Large value of LH all-year round in the west Pacific warm pool implies copious amounts of MCS rainfall over this region year-round. The largest MCSs in the warm pool regions are not only notable for their great size but also their especially large area of stratiform precipitation (Nakazawa 1988; Houze et al. 2000). During boreal summer [June–August (JJA)], the maximum LH is found over the tropical eastern Pacific, the Atlantic Ocean off the western coast of Africa, and the Bay of Bengal, as these regions feature the heaviest rainfall produced by MCSs. For example, the Bay of Bengal has the largest release of LH during the South Asian monsoon season when the occurrence of systems with wide intense radar echo maximizes (Houze et al. 2007, 2015). Such wide intense echoes are elements of MCSs and favored in more oceanic conditions (Schumacher and Houze 2006; Wang et al. 2019). During the Southern Hemisphere summer [December–February (DJF)], larger LH values are located in Argentina, the Amazon basin, and the Maritime Continent. An interesting feature in Fig. 7 is the large LH in the western Maritime Continent during the September–November (SON) season. Although both the western Maritime Continent and Bay of Bengal are located in the Indian Ocean, the release of LH from MCSs peaks in different seasons for these two regions. The peak LH west of the Maritime Continent during SON is consistent with the meridional moisture flux convergence associated with the Australian monsoon providing favorable environment for convection and the Madden–Julian oscillation (MJO) in that area between October and February (Barnes et al. 2015; Hagos et al. 2019).

Fig. 7.
Fig. 7.

Seasonal variation of total MCS latent heating between 4 and 9 km: (a) DJF, (b) MAM, (c) JJA, and (d) SON.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

The rectangles in Fig. 5b show the locations we select to investigate the regional variation of the LH profiles. Figure 8 illustrates the mean volumetric LH profiles from MCSs occurring in selected tropical regions. Two regions, including the Bay of Bengal (BB) and the Atlantic Ocean off the west coast of Africa (TATLANTIC), have the largest LH release in both stratiform and convective regions compared to other tropical regions. As seen in Fig. 9, a relatively larger area of precipitation at lower levels (below ~5 km) is also found in those two regions. MCSs occurring in tropical land regions, such as the Sahel (SAHEL) and Congo (CONGO), have less heating in the stratiform regions than those over tropical oceans. In contrast, MCSs in those two land regions have higher storm heights and more intense cores, compared to tropical ocean MCSs (Fig. 9). Note that the latent heating in the convective regions is not the largest over SAHEL and CONGO (Fig. 9). This is because the convective rain volume over SAHEL (23 490 km2 mm h−1) and CONGO (19 595 km2 mm h−1) is smaller than other selected regions, such as BB (33 323 km2 mm h−1) and TATLANTIC (34 494 km2 mm h−1). Consistent with the larger area of 20-dBZ radar reflectivity (Fig. 9), the eastern Maritime Continent (EMT) has a larger value of LH in both stratiform and convective regions above the melting layer than that in the western Maritime Continent (WMT). Additionally, MCSs over EMT are characterized by higher storm heights and more intense core than those over WMT (Fig. 9). The comparison of the LH and radar reflectivity profiles over selected tropical regions suggests that the release of LH is sensitive to the precipitation area/volume, which is consistent with previous studies (Schumacher et al. 2004).

Fig. 8.
Fig. 8.

Mean volumetric latent heating profiles from MCSs over selected tropical regions: (a) stratiform and (b) convective.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

Fig. 9.
Fig. 9.

Mean profiles of area of Ku-radar reflectivity equal to or greater than (a) 20 and (b) 40 dBZ from MCSs occurring over selected tropical regions.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

Figure 10 shows the mean LH volumes from all-season MCSs occurring at midlatitudes. The magnitude of LH from the midlatitude oceanic [e.g., the northwest Atlantic (NWATLANTIC) and East China Sea (ECS)] MCSs is also greater than those over land [e.g., the southern United States (SUS) and Argentina (ARGEN)], similar to that from the tropics. Comparison of Figs. 8 and 10 suggests that the stratiform LH profile at midlatitudes is top-heavier than those in tropical regions. More importantly, the results of Figs. 810 indicate that storm intensity, in terms of storm heights and strong (>40 dBZ) radar echoes, is a distinct metric different from the amount of LH release/rainfall. The combined use of these metrics can provide important insight into how convection is represented in global models to guide model development.

Fig. 10.
Fig. 10.

As in Fig. 8, but for selected extratropical regions.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

Table 2 quantitatively summarizes the properties of MCSs over selected regions. MCSs contribute more than 50% to the total rainfall over selected tropical regions, which is consistent with previous studies (Nesbitt et al. 2000, 2006; Liu 2011; Feng et al. 2021a). This is also the case for subtropical land, such as ARGEN and SUS. Similar to their rain contribution, MCS LH between 4 and 9 km accounts for more than 50% of the total heating in this layer over these regions. In the subtropical oceanic regions, including NWATLANTIC and ECS, MCS contributes less than 50% to the total rainfall/latent heating in the layer of 4–9 km. The largest volumetric rain and precipitation area over BB and TATLANTIC confirm their largest LH release in both stratiform and convective regions among the selected tropical regions (Figs. 8 and 9). Both tropical and subtropical oceanic MCSs are characterized by larger volumetric rain, larger area, but lower storm heights than continental MCSs.

Table 2.

Properties of MCSs over selected tropical and subtropical regions, including the number of MCSs, volumetric rain (VolRain), rain contribution, area of 20-dBZ area, latent heating contribution in the layer of 4–9 km, and the maximum 20-dBZ echo top heights (MAXHT20).

Table 2.

d. MCS LH with low and high stratiform fraction

Schumacher et al. (2004) demonstrated that the circulation centers move upward with the increasing stratiform rain fraction. They also showed that the horizontal variability of the stratiform rain fraction creates a more vertically tilted wind field. To examine the LH characteristics with different volumetric stratiform rain fraction, we classify MCSs into two categories based on their fraction of stratiform/convective precipitation. The two categories include MCSs with 30%–70% and greater than 70% volumetric stratiform rain for all seasons. Although the classification is arbitrary, it allows us to contrast the shape of the LH profiles between the two extremes. Figure 11 shows the mean volumetric LH profiles for each category in the tropics and in the midlatitudes. The peak magnitude of stratiform LH of MCSs with greater than 70% stratiform precipitation is 3 times more than that with 30%–70% stratiform rain. This is the case for MCSs occurring both in the tropics and in the midlatitudes. On the other hand, stratiform LH profiles from MCSs with more than 70% volumetric stratiform rain is top-heavier in midlatitudes than in tropical regions. In tropics, larger release of the convective LH is found for MCSs with more than 70% volumetric stratiform rain than that with lower stratiform rain, although the convective LH profiles of the two categories in midlatitudes are similar.

Fig. 11.
Fig. 11.

Mean volumetric latent heating profiles from MCSs with different stratiform fraction in the (a) tropics and (b) subtropics and shallow precipitation features. Solid lines represent latent heating profiles from stratiform precipitations while dashed lines represent profiles from convective precipitation.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

Figure 12 illustrates the geographical distribution of MCS population and the percentage of MCSs with 30%–70% and greater than 70% stratiform rain. More MCSs are found in the tropical regions than in the midlatitudes. In the tropics, MCSs exhibit a strong preference over certain regions, such as the ITCZ, the Maritime Continent, and the Amazon, which is consistent with past studies (Nesbitt et al. 2006; Yuan and Houze 2010; Liu and Zipser 2013). At midlatitudes, Argentina and off the east coast of North America are two regions that are favorable for MCSs. Comparison of Figs. 12b and 12c shows that a majority of MCSs occurring in the tropics has a relatively low fraction (30%–70%) of stratiform rain, which is not the case for the midlatitude MCSs. This is consistent with the results shown in Fig. 2 that MCSs in midlatitudes are associated with a larger fraction of stratiform precipitation than those in the tropics.

Fig. 12.
Fig. 12.

Geographical distribution of (a) MCS population, (b) MCS fraction with volumetric stratiform fraction 30%–70%, and (c) MCS fraction with volumetric stratiform fraction greater than 70%. The distribution is created on a 5° × 5° grid.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-20-0997.1

4. Summary

This study analyzed the vertical and horizontal distributions of latent heating (LH) derived from the latest versions of the GPM spectrum LH (SLH) products. To investigate the LH characteristics associated with mesoscale convective systems (MCSs), we combine a 5-yr GPM precipitation feature dataset and a newly developed global MCS tracking dataset. The GPM precipitation features and the tracked MCS masks are collocated in space and time to determine the LH associated with MCSs. This study is unique in examining the vertical and horizontal distribution of LH on global scales. With the help of satellite retrievals and a tracked MCS dataset, we are able to document and compare the characteristics of MCSs, as well as their heating profiles, across the entire world including the tropics and the midlatitudes.

Tropical MCSs have a higher echo-top height and a larger maximum rain rate when compared to MCSs occurring in the midlatitudes. However, midlatitude MCSs exhibit a higher fraction of stratiform rain, which has also been reported in previous studies [e.g., Schumacher and Houze (2003) found higher stratiform rain fraction up to 35° latitude from TRMM observations]. Also, midlatitude oceanic MCSs are characterized by a top-heavier latent heating profile, though the peak heights are lower than those in the tropical regions. More importantly, a majority of MCSs in the tropics are found with a relatively lower (30%–70%) volumetric stratiform fraction, while this is not the case for MCSs at midlatitudes. The results of this study suggest that precipitation from tropical MCSs is more convective while precipitation from midlatitude MCSs tends to have a higher fraction of stratiform rain. As described by Houze (1989), a large convective fraction indicates stronger upward motion and positive LH throughout the entire troposphere, while a large fraction of stratiform rain is generally associated with weak mesoscale ascent and heating in upper levels with descent and cooling at lower levels. This implies that the occurrence of stratiform regions in MCSs can be a major consideration in evaluating the interaction of MCSs with the larger-scale environments.

A large fraction of the total latent heating is produced by MCSs over various regions (Fig. 7), such as the ITCZ, Bay of Bengal, the Maritime Continent, Argentina, and the central United States, similar to their number and rainfall contribution to the total rain (Nesbitt et al. 2000, 2006; Liu 2011; Feng et al. 2021a). The release of MCS LH also exhibits distinct seasonal and regional variations. A large amount of LH is found year-round in the west Pacific warm pool because of the high sea surface temperature and the copious amounts of rainfall over this region. The maximum heating from MCSs is found during boreal summer (JJA) over the heaviest rainfall regions, such as the eastern tropical Pacific, the Atlantic Ocean off the western coast of Africa, and the Bay of Bengal (Fig. 7). Also, the Bay of Bengal and the Atlantic Ocean off the west coast of Africa have a higher amount of LH release in both the stratiform and convective regions of MCSs, compared to other selected regions (Fig. 8). The larger magnitude of LH is related to a larger precipitating area/volume rather than a higher storm height or more intense convective core that is usually associated with land systems (Figs. 8 and 9).

There are some limitations inherent in the data and methods used in this study, which warrant consideration when interpreting the results here. First, the GPM SLH product used for the current analysis relies on cloud-resolving modeling through a lookup table approach. This lookup table approach can lead to systematic errors since the LH profiles vary with different types of convective systems occurring at a variety of geographic locations. Further studies and comparison using different LH products, such as the convective–stratiform (Tao et al. 2006), hydrometeor (Yang and Smith 2000), and precipitation radar heating (Satoh and Noda 2001), are still needed to quantify the release of MCS LH characteristics globally and regionally. However, lacking direct measurements of LH, a better understanding of global LH can be best accomplished utilizing satellite-based LH retrievals in the current stage. Second, with an emphasis on MCSs, the uncertainties of the analysis can be related to the MCS database derived from the IMERG products. The IMERG precipitation product is obtained from different passive microwave measurements and relies on geostationary satellite infrared retrievals for gap filling, which introduces unrealistic temporal fluctuations in the precipitation estimates that may affect MCS identification. Moreover, the definition of MCSs in the midlatitudes, particularly over ocean, is ambiguous. During cold seasons, synoptic systems at midlatitudes often produce cloud systems large enough with ample precipitation to be detected as MCSs based on the definition. The nature of these synoptically forced cloud systems, including whether they should be defined as MCSs, requires further research. Even though efforts have been made to minimize the effect of such systems by using multiple criteria when tracking MCSs and leveraging other feature-tracking datasets, potential biases, as well as the inconsistency of the IMERG precipitation product, can still impact the results of this study. Future research is needed to account for the effects of such biases when improved LH and precipitation products become available.

The global vertical and horizontal distributions of MCS latent heating derived from spaceborne radar observations can be used to understand the role of MCS in the global energy and water cycles. Variations of the LH profile and its spatial distribution have important implications for atmospheric circulation and energy budget. Given the significant role of MCSs in precipitation and atmospheric circulation, the horizontal and vertical distribution of LH presented in this study, along with the 3D characteristics of convective/stratiform echoes and their associated precipitation, can provide important benchmarks for numerical weather prediction models and climate models. MCSs are generally poorly simulated in models that rely on convection parameterizations (e.g., Van Weverberg et al. 2018; Feng et al. 2021b). This study highlights that storm intensity, LH release, and rainfall are distinct metrics of MCSs. Combining these metrics with other metrics such as MCS number, MCS precipitation, and their large-scale environment (Feng et al. 2021b) may provide multiple constraints critically needed to inform development of convection parameterizations in global models.

Acknowledgments

This research is supported by the U.S. Department of Energy Office of Science Biological and Environmental Research as part of the Regional and Global Model Analysis program area. PNNL is operated for the Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830. The analysis of this study is performed using computational resources provided by the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. The Global Precipitation Measurement (GPM) precipitation feature dataset is obtained at http://atmos.tamucc.edu/trmm/data/. The global MCS dataset is archived at the NERSC High Performance Storage System. It is derived from the Global Merged IR dataset (https://doi.org/10.5067/P4HZB9N27EKU) and the GPM IMERG precipitation data V06 (https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_06/summary).

REFERENCES

  • Ahmed, F., C. Schumacher, Z. Feng, and S. Hagos, 2016: A retrieval of tropical latent heating using the 3D structure of precipitation features. J. Appl. Meteor. Climatol., 55, 19651982, https://doi.org/10.1175/JAMC-D-15-0038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anthes, R. A., and D. Keyser, 1979: Tests of a fine-mesh model over Europe and the United States. Mon. Wea. Rev., 107, 963984, https://doi.org/10.1175/1520-0493(1979)107<0963:TOAFMM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, H. C., M. D. Zuluaga, and R. A. Houze, 2015: Latent heating characteristics of the MJO computed from TRMM observations. J. Geophys. Res. Atmos., 120, 13221334, https://doi.org/10.1002/2014JD022530.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, S. C., and S. Nigam, 2009: Residual diagnosis of diabatic heating from ERA-40 and NCEP reanalyses: Intercomparisons with TRMM. J. Climate, 22, 414428, https://doi.org/10.1175/2008JCLI2417.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiang, J. C. H., S. E. Zebiak, and M. A. Cane, 2001: Relative roles of elevated heating and surface temperature gradients in driving anomalous surface winds over tropical oceans. J. Atmos. Sci., 58, 13711394, https://doi.org/10.1175/1520-0469(2001)058<1371:RROEHA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., J. D. Neelin, and C. Bretherton, 1994: On large-scale circulations in convecting atmospheres. Quart. J. Roy. Meteor. Soc., 120, 11111143, https://doi.org/10.1002/qj.49712051902.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., L. R. Leung, R. A. Houze, S. Hagos, J. Hardin, Q. Yang, B. Han, and J. Fan, 2018: Structure and evolution of mesoscale convective systems: Sensitivity to cloud microphysics in convection-permitting simulations over the United States. J. Adv. Model. Earth Syst., 10, 14701494, https://doi.org/10.1029/2018MS001305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., and Coauthors, 2021a: A global high-resolution mesoscale convective system database using satellite-derived cloud tops, surface precipitation, and tracking. J. Geophys. Res. Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., F. Song, K. Sakaguchi, and L. R. Leung, 2021b: Evaluation of mesoscale convective systems in climate simulations: Methodological development and results from MPAS-CAM over the United States. J. Climate, 34, 26112633, https://doi.org/10.1175/JCLI-D-20-0136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geisler, J. E., and D. E. Stevens, 1982: On the vertical structure of damped steady circulation in the tropics. Quart. J. Roy. Meteor. Soc., 108, 8793, https://doi.org/10.1002/qj.49710845505.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S., 2010: Building blocks of tropical diabatic heating. J. Atmos. Sci., 67, 23412354, https://doi.org/10.1175/2010JAS3252.1.

  • Hagos, S., and C. Zhang, 2010: Diabatic heating, divergent circulation and moisture transport in the African monsoon system. Quart. J. Roy. Meteor. Soc., 136 (Suppl. 1), 411425, https://doi.org/10.1002/qj.538.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S., and Coauthors, 2010: Estimates of tropical diabatic heating profiles: Commonalities and uncertainties. J. Climate, 23, 542558, https://doi.org/10.1175/2009JCLI3025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S., C. Zhang, L. R. Leung, C. D. Burleyson, and K. Balaguru, 2019: Zonal migration of monsoon moisture flux convergence and the strength of Madden–Julian oscillation events. Geophys. Res. Lett., 46, 85548562, https://doi.org/10.1029/2019GL083468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamada, A., Y. Murayama, and Y. N. Takayabu, 2014: Regional characteristics of extreme rainfall extracted from TRMM PR measurements. J. Climate, 27, 81518169, https://doi.org/10.1175/JCLI-D-14-00107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., H. H. Hendon, and R. A. Houze, 1984: Some implications of the mesoscale circulations in tropical cloud clusters for large-scale dynamics and climate. J. Atmos. Sci., 41, 113121, https://doi.org/10.1175/1520-0469(1984)041<0113:SIOTMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 1982: Cloud clusters and large-scale vertical motions in the tropics. J. Meteor. Soc. Japan, 60, 396410, https://doi.org/10.2151/jmsj1965.60.1_396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 1989: Observed structure of mesoscale convective systems and implications for large-scale heating. Quart. J. Roy. Meteor. Soc., 115, 425461, https://doi.org/10.1002/qj.49711548702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 2004: Mesoscale convective systems. Rev. Geophys., 42, RG4003, https://doi.org/10.1029/2004RG000150.

  • Houze, R. A., S. S. Chen, D. E. Kingsmill, Y. Serra, and S. E. Yuter, 2000: Convection over the Pacific warm pool in relation to the atmospheric Kelvin–Rossby wave. J. Atmos. Sci., 57, 30583089, https://doi.org/10.1175/1520-0469(2000)057<3058:COTPWP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., D. C. Wilton, and B. F. Smull, 2007: Monsoon convection in the Himalayan region as seen by the TRMM precipitation radar. Quart. J. Roy. Meteor. Soc., 133, 13891411, https://doi.org/10.1002/qj.106.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., W. Lee, and M. M. Bell, 2009: Convective contribution to the genesis of Hurricane Ophelia (2005). Mon. Wea. Rev., 137, 27782800, https://doi.org/10.1175/2009MWR2727.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., K. L. Rasmussen, M. D. Zuluaga, and S. R. Brodzik, 2015: The variable nature of convection in the tropics and subtropics: A legacy of 16 years of the Tropical Rainfall Measuring Mission satellite. Rev. Geophys., 53, 9941021, https://doi.org/10.1002/2015RG000488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iguchi, T., and Coauthors, 2018: GPM/DPR level-2 Algorithm Theoretical Basis Doc., 127 pp., https://www.eorc.jaxa.jp/GPM/doc/algorithm/ATBD_DPR_201811_with_Appendix3b.pdf.

  • Jiang, X., and Coauthors, 2011: Vertical diabatic heating structure of the MJO: Intercomparison between recent reanalyses and TRMM estimates. Mon. Wea. Rev., 139, 32083223, https://doi.org/10.1175/2011MWR3636.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kodama, Y., M. Katsumata, S. Mori, S. Satoh, Y. Hirose, and H. Ueda, 2009: Climatology of warm rain and associated latent heating derived from TRMM PR observations. J. Climate, 22, 49084929, https://doi.org/10.1175/2009JCLI2575.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., A. Chakraborty, and A. K. Mishra, 2010: Improving multimodel forecasts of the vertical distribution of heating using the TRMM profiles. J. Climate, 23, 10791094, https://doi.org/10.1175/2009JCLI2878.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, J., and C. Zhang, 2011: Structural evolution in heating profiles of the MJO in global reanalyses and TRMM retrievals. J. Climate, 24, 825842, https://doi.org/10.1175/2010JCLI3826.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, J., C. Li, W. Zhou, X. Jia, and C. Zhang, 2013: Effect of boundary layer latent heating on MJO simulations. Adv. Atmos. Sci., 30, 101115, https://doi.org/10.1007/s00376-012-2031-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., 2011: Rainfall contributions from precipitation systems with different sizes, convective intensities, and durations over the tropics and subtropics. J. Hydrometeor., 12, 394412, https://doi.org/10.1175/2010JHM1320.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., and E. Zipser, 2013: Regional variation of morphology of organized convection in the tropics and subtropics. J. Geophys. Res. Atmos., 118, 453466, https://doi.org/10.1029/2012JD018409.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., E. Zipser, D. J. Cecil, S. W. Nesbitt, and S. Sherwood, 2008: A cloud and precipitation feature database from nine years of TRMM observations. J. Appl. Meteor. Climatol., 47, 27122728, https://doi.org/10.1175/2008JAMC1890.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., S. Shige, Y. N. Takayabu, and E. Zipser, 2015: Latent heating contribution from precipitation systems with different sizes, depths, and intensities in the tropics. J. Climate, 28, 186203, https://doi.org/10.1175/JCLI-D-14-00370.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, N., and C. Liu, 2018: Synoptic environments and characteristics of convection reaching the tropopause over Northeast China. Mon. Wea. Rev., 146, 745759, https://doi.org/10.1175/MWR-D-17-0245.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, N., C. Liu, and T. Lavigne, 2019: The variation of the intensity, height, and size of precipitation systems with El Niño–Southern Oscillation in the tropics and subtropics. J. Climate, 32, 42814297, https://doi.org/10.1175/JCLI-D-18-0766.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malkus, J. S., 1962: Large-scale interactions. Physical Oceanography, M. N. Hill, Ed., Vol. 1, The Sea—Ideas and Observations in Progress in the Study of the Seas, John Wiley and Sons, 88–294.

  • Nakazawa, T., 1988: Tropical super clusters within intraseasonal variations over the western Pacific. J. Meteor. Soc. Japan, 66, 823839, https://doi.org/10.2151/jmsj1965.66.6_823.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nesbitt, S. W., E. J. Zipser, and D. J. Cecil, 2000: A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations. J. Climate, 13, 40874106, https://doi.org/10.1175/1520-0442(2000)013<4087:ACOPFI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nesbitt, S. W., R. Cifelli, and S. A. Rutledge, 2006: Storm morphology and rainfall characteristics of TRMM precipitation features. Mon. Wea. Rev., 134, 27022721, https://doi.org/10.1175/MWR3200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Puri, K., and N. E. Davidson, 1992: The use of infrared satellite cloud imagery data as proxy data for moisture and diabatic heating in data assimilation. Mon. Wea. Rev., 120, 23292341, https://doi.org/10.1175/1520-0493(1992)120<2329:TUOISC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, K. L., M. M. Chaplin, M. D. Zuluaga, and R. A. Houze, 2016: Contribution of extreme convective storms to rainfall in South America. J. Hydrometeor., 17, 353367, https://doi.org/10.1175/JHM-D-15-0067.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reed, R. J., and E. E. Recker, 1971: Structure and properties of synoptic-scale wave disturbances in the equatorial western Pacific. J. Atmos. Sci., 28, 11171133, https://doi.org/10.1175/1520-0469(1971)028<1117:SAPOSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riehl, H., and J. Simpson, 1979: The heat balance of the equatorial trough zone, revisited. Contrib. Atmos. Phys., 52, 287305.

  • Rutz, J. J., and Coauthors, 2019: The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying uncertainties in atmospheric river climatology. J. Geophys. Res. Atmos., 124, 13 77713 802, https://doi.org/10.1029/2019JD030936.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Satoh, S., and A. Noda, 2001: Retrieval of latent heating profiles from TRMM radar data. Proc. 30th Int. Conf. on Radar Meteorology, Munich, Germany, Amer. Meteor. Soc., 340–342.

  • Schumacher, C., and R. A. Houze Jr., 2003: Stratiform rain in the tropics as seen by the TRMM precipitation radar. J. Climate, 16, 17391756, https://doi.org/10.1175/1520-0442(2003)016<1739:SRITTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, C., and R. A. Houze Jr., 2006: Stratiform precipitation production over sub-Saharan Africa and the tropical east Atlantic as observed by TRMM. Quart. J. Roy. Meteor. Soc., 132, 22352255, https://doi.org/10.1256/qj.05.121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, C., R. A. Houze Jr., and I. Kraucunas, 2004: The tropical dynamical response to latent heating estimates derived from the TRMM precipitation radar. J. Atmos. Sci., 61, 13411358, https://doi.org/10.1175/1520-0469(2004)061<1341:TTDRTL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, C., M. H. Zhang, and P. E. Ciesielski, 2007: Heating structures of the TRMM field campaigns. J. Atmos. Sci., 64, 25932610, https://doi.org/10.1175/JAS3938.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shige, S., Y. N. Takayabu, W. Tao, and D. E. Johnson, 2004: Spectral retrieval of latent heating profiles from TRMM PR data. Part I: Development of a model-based algorithm. J. Appl. Meteor., 43, 10951113, https://doi.org/10.1175/1520-0450(2004)043<1095:SROLHP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shige, S., Y. N. Takayabu, W.-K. Tao, and C.-L. Shie, 2007: Spectral retrieval of latent heating profiles from TRMM PR data. Part II: Algorithm improvement and heating estimates over tropical ocean regions. J. Appl. Meteor. Climatol., 46, 10981124, https://doi.org/10.1175/JAM2510.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shige, S., Y. N. Takayabu, S. Kida, W. Tao, X. Zeng, C. Yokoyama, and T. L’Ecuyer, 2009: Spectral retrieval of latent heating profiles from TRMM PR data. Part IV: Comparisons of lookup tables from two- and three-dimensional cloud-resolving model simulations. J. Climate, 22, 55775594, https://doi.org/10.1175/2009JCLI2919.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G., and Coauthors, 2017: The Global Precipitation Measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc., 98, 16791695, https://doi.org/10.1175/BAMS-D-15-00306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sui, C., and K. Lau, 1989: Origin of low-frequency (intraseasonal) oscillations in the tropical atmosphere. Part II: Structure and propagation of mobile wave-CISK modes and their modification by lower boundary forcings. J. Atmos. Sci., 46, 3756, https://doi.org/10.1175/1520-0469(1989)046<0037:OOLFOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takayabu, Y. N., and W.-K. Tao, 2020: Latent heating retrievals from satellite observations. Satellite Precipitation Measurement, V. Levizzani et al., Eds., Advances in Global Change Research Series, Vol. 69, Springer Nature, 897–915.

    • Crossref
    • Export Citation
  • Tao, W., and Coauthors, 2006: Retrieval of latent heating from TRMM measurements. Bull. Amer. Meteor. Soc., 87, 15551572, https://doi.org/10.1175/BAMS-87-11-1555.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Weverberg, K., and Coauthors, 2018: CAUSES: Attribution of surface radiation biases in NWP and climate models near the U.S. Southern Great Plains. J. Geophys. Res. Atmos., 123, 36123644, https://doi.org/10.1002/2017JD027188

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J. Y., R. A. Houze, J. W. Fan, S. R. Brodzik, Z. Feng, and J. C. Hardin, 2019: The detection of mesoscale convective systems by the GPM Ku-band spaceborne radar. J. Meteor. Soc. Japan, 97, 10591073, https://doi.org/10.2151/jmsj.2019-058.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, P. J., 1972: Response of the tropical atmosphere to local steady forcing. Mon. Wea. Rev., 100, 518541, https://doi.org/10.1175/1520-0493(1972)100<0518:ROTTAT>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, P. J., and R. Lukas, 1992: TOGA COARE: The Coupled Ocean–Atmosphere Response Experiment. Bull. Amer. Meteor. Soc., 73, 13771416, https://doi.org/10.1175/1520-0477(1992)073<1377:TCTCOR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, K., and K. A. Emanuel, 1989: Is the tropical atmosphere conditionally unstable? Mon. Wea. Rev., 117, 14711479, https://doi.org/10.1175/1520-0493(1989)117<1471:ITTACU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, W., E. J. Zipser, and C. Liu, 2009: Rainfall characteristics and convective properties of mei-yu precipitation systems over South China, Taiwan, and the South China Sea. Part I: TRMM observations. Mon. Wea. Rev., 137, 42614275, https://doi.org/10.1175/2009MWR2982.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yanai, M., S. Esbensen, and J. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci., 30, 611627, https://doi.org/10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, S., and E. A. Smith, 2000: Vertical structure and transient behavior of convective–stratiform heating in TOGA COARE from combined satellite–sounding analysis. J. Appl. Meteor., 39, 14911513, https://doi.org/10.1175/1520-0450(2000)039<1491:VSATBO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, J., and R. A. Houze, 2010: Global variability of mesoscale convective system anvil structure from A-Train satellite data. J. Climate, 23, 58645888, https://doi.org/10.1175/2010JCLI3671.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and Coauthors, 2010: MJO signals in latent heating: Results from TRMM retrievals. J. Atmos. Sci., 67, 34883508, https://doi.org/10.1175/2010JAS3398.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zipser, E. J., and C. Gautier, 1978: Mesoscale events within a GATE tropical depression. Mon. Wea. Rev., 106, 789805, https://doi.org/10.1175/1520-0493(1978)106<0789:MEWAGT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zipser, E. J., D. J. Cecil, C. Liu, S. W. Nesbitt, and D. P. Yorty, 2006: Where are the most intense thunderstorms on Earth? Bull. Amer. Meteor. Soc., 87, 10571072, https://doi.org/10.1175/BAMS-87-8-1057.

    • Crossref
    • Search Google Scholar
    • Export Citation
Save
  • Ahmed, F., C. Schumacher, Z. Feng, and S. Hagos, 2016: A retrieval of tropical latent heating using the 3D structure of precipitation features. J. Appl. Meteor. Climatol., 55, 19651982, https://doi.org/10.1175/JAMC-D-15-0038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anthes, R. A., and D. Keyser, 1979: Tests of a fine-mesh model over Europe and the United States. Mon. Wea. Rev., 107, 963984, https://doi.org/10.1175/1520-0493(1979)107<0963:TOAFMM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, H. C., M. D. Zuluaga, and R. A. Houze, 2015: Latent heating characteristics of the MJO computed from TRMM observations. J. Geophys. Res. Atmos., 120, 13221334, https://doi.org/10.1002/2014JD022530.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, S. C., and S. Nigam, 2009: Residual diagnosis of diabatic heating from ERA-40 and NCEP reanalyses: Intercomparisons with TRMM. J. Climate, 22, 414428, https://doi.org/10.1175/2008JCLI2417.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiang, J. C. H., S. E. Zebiak, and M. A. Cane, 2001: Relative roles of elevated heating and surface temperature gradients in driving anomalous surface winds over tropical oceans. J. Atmos. Sci., 58, 13711394, https://doi.org/10.1175/1520-0469(2001)058<1371:RROEHA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., J. D. Neelin, and C. Bretherton, 1994: On large-scale circulations in convecting atmospheres. Quart. J. Roy. Meteor. Soc., 120, 11111143, https://doi.org/10.1002/qj.49712051902.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., L. R. Leung, R. A. Houze, S. Hagos, J. Hardin, Q. Yang, B. Han, and J. Fan, 2018: Structure and evolution of mesoscale convective systems: Sensitivity to cloud microphysics in convection-permitting simulations over the United States. J. Adv. Model. Earth Syst., 10, 14701494, https://doi.org/10.1029/2018MS001305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., and Coauthors, 2021a: A global high-resolution mesoscale convective system database using satellite-derived cloud tops, surface precipitation, and tracking. J. Geophys. Res. Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., F. Song, K. Sakaguchi, and L. R. Leung, 2021b: Evaluation of mesoscale convective systems in climate simulations: Methodological development and results from MPAS-CAM over the United States. J. Climate, 34, 26112633, https://doi.org/10.1175/JCLI-D-20-0136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geisler, J. E., and D. E. Stevens, 1982: On the vertical structure of damped steady circulation in the tropics. Quart. J. Roy. Meteor. Soc., 108, 8793, https://doi.org/10.1002/qj.49710845505.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S., 2010: Building blocks of tropical diabatic heating. J. Atmos. Sci., 67, 23412354, https://doi.org/10.1175/2010JAS3252.1.

  • Hagos, S., and C. Zhang, 2010: Diabatic heating, divergent circulation and moisture transport in the African monsoon system. Quart. J. Roy. Meteor. Soc., 136 (Suppl. 1), 411425, https://doi.org/10.1002/qj.538.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S., and Coauthors, 2010: Estimates of tropical diabatic heating profiles: Commonalities and uncertainties. J. Climate, 23, 542558, https://doi.org/10.1175/2009JCLI3025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S., C. Zhang, L. R. Leung, C. D. Burleyson, and K. Balaguru, 2019: Zonal migration of monsoon moisture flux convergence and the strength of Madden–Julian oscillation events. Geophys. Res. Lett., 46, 85548562, https://doi.org/10.1029/2019GL083468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamada, A., Y. Murayama, and Y. N. Takayabu, 2014: Regional characteristics of extreme rainfall extracted from TRMM PR measurements. J. Climate, 27, 81518169, https://doi.org/10.1175/JCLI-D-14-00107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., H. H. Hendon, and R. A. Houze, 1984: Some implications of the mesoscale circulations in tropical cloud clusters for large-scale dynamics and climate. J. Atmos. Sci., 41, 113121, https://doi.org/10.1175/1520-0469(1984)041<0113:SIOTMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 1982: Cloud clusters and large-scale vertical motions in the tropics. J. Meteor. Soc. Japan, 60, 396410, https://doi.org/10.2151/jmsj1965.60.1_396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 1989: Observed structure of mesoscale convective systems and implications for large-scale heating. Quart. J. Roy. Meteor. Soc., 115, 425461, https://doi.org/10.1002/qj.49711548702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 2004: Mesoscale convective systems. Rev. Geophys., 42, RG4003, https://doi.org/10.1029/2004RG000150.

  • Houze, R. A., S. S. Chen, D. E. Kingsmill, Y. Serra, and S. E. Yuter, 2000: Convection over the Pacific warm pool in relation to the atmospheric Kelvin–Rossby wave. J. Atmos. Sci., 57, 30583089, https://doi.org/10.1175/1520-0469(2000)057<3058:COTPWP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., D. C. Wilton, and B. F. Smull, 2007: Monsoon convection in the Himalayan region as seen by the TRMM precipitation radar. Quart. J. Roy. Meteor. Soc., 133, 13891411, https://doi.org/10.1002/qj.106.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., W. Lee, and M. M. Bell, 2009: Convective contribution to the genesis of Hurricane Ophelia (2005). Mon. Wea. Rev., 137, 27782800, https://doi.org/10.1175/2009MWR2727.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., K. L. Rasmussen, M. D. Zuluaga, and S. R. Brodzik, 2015: The variable nature of convection in the tropics and subtropics: A legacy of 16 years of the Tropical Rainfall Measuring Mission satellite. Rev. Geophys., 53, 9941021, https://doi.org/10.1002/2015RG000488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iguchi, T., and Coauthors, 2018: GPM/DPR level-2 Algorithm Theoretical Basis Doc., 127 pp., https://www.eorc.jaxa.jp/GPM/doc/algorithm/ATBD_DPR_201811_with_Appendix3b.pdf.

  • Jiang, X., and Coauthors, 2011: Vertical diabatic heating structure of the MJO: Intercomparison between recent reanalyses and TRMM estimates. Mon. Wea. Rev., 139, 32083223, https://doi.org/10.1175/2011MWR3636.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kodama, Y., M. Katsumata, S. Mori, S. Satoh, Y. Hirose, and H. Ueda, 2009: Climatology of warm rain and associated latent heating derived from TRMM PR observations. J. Climate, 22, 49084929, https://doi.org/10.1175/2009JCLI2575.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., A. Chakraborty, and A. K. Mishra, 2010: Improving multimodel forecasts of the vertical distribution of heating using the TRMM profiles. J. Climate, 23, 10791094, https://doi.org/10.1175/2009JCLI2878.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, J., and C. Zhang, 2011: Structural evolution in heating profiles of the MJO in global reanalyses and TRMM retrievals. J. Climate, 24, 825842, https://doi.org/10.1175/2010JCLI3826.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, J., C. Li, W. Zhou, X. Jia, and C. Zhang, 2013: Effect of boundary layer latent heating on MJO simulations. Adv. Atmos. Sci., 30, 101115, https://doi.org/10.1007/s00376-012-2031-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., 2011: Rainfall contributions from precipitation systems with different sizes, convective intensities, and durations over the tropics and subtropics. J. Hydrometeor., 12, 394412, https://doi.org/10.1175/2010JHM1320.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., and E. Zipser, 2013: Regional variation of morphology of organized convection in the tropics and subtropics. J. Geophys. Res. Atmos., 118, 453466, https://doi.org/10.1029/2012JD018409.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., E. Zipser, D. J. Cecil, S. W. Nesbitt, and S. Sherwood, 2008: A cloud and precipitation feature database from nine years of TRMM observations. J. Appl. Meteor. Climatol., 47, 27122728, https://doi.org/10.1175/2008JAMC1890.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., S. Shige, Y. N. Takayabu, and E. Zipser, 2015: Latent heating contribution from precipitation systems with different sizes, depths, and intensities in the tropics. J. Climate, 28, 186203, https://doi.org/10.1175/JCLI-D-14-00370.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, N., and C. Liu, 2018: Synoptic environments and characteristics of convection reaching the tropopause over Northeast China. Mon. Wea. Rev., 146, 745759, https://doi.org/10.1175/MWR-D-17-0245.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, N., C. Liu, and T. Lavigne, 2019: The variation of the intensity, height, and size of precipitation systems with El Niño–Southern Oscillation in the tropics and subtropics. J. Climate, 32, 42814297, https://doi.org/10.1175/JCLI-D-18-0766.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malkus, J. S., 1962: Large-scale interactions. Physical Oceanography, M. N. Hill, Ed., Vol. 1, The Sea—Ideas and Observations in Progress in the Study of the Seas, John Wiley and Sons, 88–294.

  • Nakazawa, T., 1988: Tropical super clusters within intraseasonal variations over the western Pacific. J. Meteor. Soc. Japan, 66, 823839, https://doi.org/10.2151/jmsj1965.66.6_823.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nesbitt, S. W., E. J. Zipser, and D. J. Cecil, 2000: A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations. J. Climate, 13, 40874106, https://doi.org/10.1175/1520-0442(2000)013<4087:ACOPFI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nesbitt, S. W., R. Cifelli, and S. A. Rutledge, 2006: Storm morphology and rainfall characteristics of TRMM precipitation features. Mon. Wea. Rev., 134, 27022721, https://doi.org/10.1175/MWR3200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Puri, K., and N. E. Davidson, 1992: The use of infrared satellite cloud imagery data as proxy data for moisture and diabatic heating in data assimilation. Mon. Wea. Rev., 120, 23292341, https://doi.org/10.1175/1520-0493(1992)120<2329:TUOISC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, K. L., M. M. Chaplin, M. D. Zuluaga, and R. A. Houze, 2016: Contribution of extreme convective storms to rainfall in South America. J. Hydrometeor., 17, 353367, https://doi.org/10.1175/JHM-D-15-0067.1.

    • Crossref