Abstract

To understand how coastal precipitation is controlled by the low-level background wind, we performed comprehensive analysis using the 17-yr observations of the TRMM PR over the entire region of the tropics. We classified the data according to the direction (onshore or offshore) and strength of the cross-shore wind. Under weak winds, the contribution of the diurnal cycle to total precipitation is large, indicating that thermally forced precipitation with a symmetrical propagation pattern with opposite sign across the coastline is dominant. As the background wind strengthens, the contribution of the diurnal cycle reduces owing to the predominance of mechanical forcing; however, the effect of the diurnal cycle remains nonnegligible with an asymmetrical propagation pattern across the coastline. Using the linear theory of the sea–land-breeze circulation, we demonstrated that the difference in propagation is attributable to gravity waves excited by the land–ocean surface heating difference. Under weak winds, symmetrical diurnal phase propagation is caused by the two symmetrical modes of landward and seaward gravity waves. Under stronger background winds, in addition to the Doppler-shifted landward and seaward modes, waves propagating toward the upwind side in the flow-relative frame but with slow group velocity are advected to the downwind near the coastline, forming another mode that moves slowly in the downwind direction. The superposition of the three modes leads to asymmetrical propagation of precipitation with varying phase speed depending on the distance from the coastline.

Abstract

Rainfall over the coastal regions of western India [Western Ghats (WG)] and Myanmar [Arakan Yoma (AY)], two regions experiencing the heaviest rainfall during the Asian summer monsoon, is examined using a Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) dataset spanning 16 years. Rainfall maxima are identified on the upslope of the WG and the coastline of AY, in contrast to the offshore locations observed in previous studies. Continuous rain with slight nocturnal and afternoon–evening maxima occurs over the upslope of the WG, while an afternoon peak over the upslope and a morning peak just off the coast are found in AY, resulting in different locations of the rainfall maxima for the WG (upslope) and AY (coastline). Large rainfall amounts with small diurnal amplitudes are observed over the WG and AY under strong environmental flow perpendicular to the coastal mountains, and vice versa. Composite analysis of the boreal summer intraseasonal oscillation (BSISO) shows that the rain anomaly over the WG slopes lags behind the northward-propagating major rainband. The cyclonic systems associated with the BSISO introduces a southwest wind anomaly behind the major rainband, enhancing the orographic rainfall over the WG, and resulting in the phase lag. This lag is not observed in the AY region where more closed cyclonic circulations occur. Diurnal variations in rainfall over the WG regions are smallest during the strongest BSISO rainfall anomaly phase.

Abstract

Latent heating (LH) from precipitation systems with different sizes, depths, and convective intensities is quantified with 15 years of LH retrievals from version 7 Precipitation Radar (PR) products of the Tropical Rainfall Measuring Mission (TRMM). Organized precipitation systems, such as mesoscale convective systems (MCSs; precipitation area > 2000 km²), contribute to 88% of the LH above 7 km over tropical land and 95% over tropical oceans. LH over tropical land is mainly from convective precipitation, and has one vertical mode with a peak from 4 to 7 km. There are two vertical modes of LH over tropical oceans. The shallow mode from about 1 to 4 km results from small, shallow, and weak precipitation systems, and partially from congestus clouds with radar echo top between 5 and 8 km. The deep mode from 5 to 9 km is mainly from stratiform precipitation in MCSs.

MCSs of different regions and seasons have different LH vertical structure mainly due to the different proportion of stratiform precipitation. MCSs over ocean have a larger fraction of stratiform precipitation and a top-heavy LH structure. MCSs over land have a higher percentage of convective versus stratiform precipitation, which results in a relatively lower-level peak in LH compared to MCSs over the ocean. MCSs during monsoons have properties of LH in between those typical land and oceanic MCSs.

Consistent with the diurnal variation of precipitation, tropical land has a stronger LH diurnal variation than tropical oceans with peak LH in the late afternoon. Over tropical oceans in the early morning, the shallow mode of LH peaks slightly earlier than the deep mode. There are almost no diurnal changes of MCSs LH over oceans. However, the small convective systems over land contribute a significant amount of LH at all vertical levels in the afternoon, when the contribution of MCSs is small.

Abstract

The relationship among surface rainfall, its intensity, and its associated stratiform amount is established by examining observed precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The results show that for moderate–high stratiform fractions, rain probabilities are strongly skewed toward light rain intensities. For convective-type rain, the peak probability of occurrence shifts to higher intensities but is still significantly skewed toward weaker rain rates. The main differences between the distributions for oceanic and continental rain are for heavily convective rain. The peak occurrence, as well as the tail of the distribution containing the extreme events, is shifted to higher intensities for continental rain. For rainy areas sampled at 0.5° horizontal resolution, the occurrence of conditional rain rates over 100 mm day⁻¹ is significantly higher over land. Distributions of rain intensity versus stratiform fraction for simulated precipitation data obtained from cloud-resolving model (CRM) simulations are quite similar to those from the satellite, providing a basis for mapping simulated cloud quantities to the satellite observations.

An improved convective–stratiform heating (CSH) algorithm is developed based on two sources of information: gridded rainfall quantities (i.e., the conditional intensity and the stratiform fraction) observed from the TRMM PR and synthetic cloud process data (i.e., latent heating, eddy heat flux convergence, and radiative heating/cooling) obtained from CRM simulations of convective cloud systems. The new CSH algorithm-derived heating has a noticeably different heating structure over both ocean and land regions compared to the previous CSH algorithm. Major differences between the new and old algorithms include a significant increase in the amount of low- and midlevel heating, a downward emphasis in the level of maximum cloud heating by about 1 km, and a larger variance between land and ocean in the new CSH algorithm.

Abstract

Three-dimensional distributions of the apparent heat source (Q ₁) − radiative heating (Q_R ) estimated from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) utilizing the spectral latent heating (SLH) algorithm are analyzed. Mass-weighted and vertically integrated Q ₁ − Q_R averaged over the tropical oceans is estimated as ∼72.6 J s⁻¹ (∼2.51 mm day⁻¹) and that over tropical land is ∼73.7 J s⁻¹ (∼2.55 mm day⁻¹) for 30°N–30°S. It is shown that nondrizzle precipitation over tropical and subtropical oceans consists of two dominant modes of rainfall systems: deep systems and congestus. A rough estimate of the shallow-heating contribution against the total heating is about 46.7% for the average tropical oceans, which is substantially larger than the 23.7% over tropical land.

Although cumulus congestus heating linearly correlates with SST, deep-mode heating is dynamically bounded by large-scale subsidence. It is notable that a substantial amount of rain, as large as 2.38 mm day⁻¹ on average, is brought from congestus clouds under the large-scale subsiding circulation. It is also notable that, even in the region with SSTs warmer than 28°C, large-scale subsidence effectively suppresses the deep convection, with the remaining heating by congestus clouds.

The results support that the entrainment of mid–lower-tropospheric dry air, which accompanies the large-scale subsidence, is the major factor suppressing the deep convection. Therefore, a representation of the realistic entrainment is very important for proper reproduction of precipitation distribution and the resultant large-scale circulation.

Abstract

A K-means clustering algorithm was used to classify Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) scenes within 1° square patches over the tropical (15°S–15°N) oceans. Three cluster centroids or “regimes” that minimize the Euclidean distance metric in a five-dimensional space of standardized variables were sought [convective surface rainfall rate; ratio of convective rain to total rain; and fractions of convective echo profiles with tops in three fixed height ranges (<5, 5–9, and >9 km)]. Independent cluster computations in adjacent ocean basins return very similar clusters in terms of PR echo-top distributions, rainfall, and diabatic heating profiles. The clusters consist of shallow convection (SHAL cluster), with a unimodal distribution of PR echo tops and composite diabatic heating rates of ∼2 K day⁻¹ below 3 km; midlevel convection (MID-LEV cluster), with a bimodal distribution of PR echo tops and ∼5 K day⁻¹ heating up to about 7 km; and deeper convection (DEEP cluster), with a multimodal distribution of PR echo tops and >20 K day⁻¹ heating from 5 to 10 km. Each contributes roughly 20%–40% in terms of total tropical rainfall, but with MID-LEV clusters especially enhanced in the Indian and Atlantic sectors, SHAL relatively enhanced in the central and east Pacific, and DEEP most prominent in the western Pacific. While the clusters themselves are quite similar in rainfall and heating, specific cloud types defined according to the PR echo top and surface rainfall rate are less similar and exhibit systematic differences from one cluster to another, implying that the degree to which precipitation structures are similar decreases when one considers individual precipitating clouds as repeating tropical structures instead of larger-scale cluster ensembles themselves.

Abstract

In this study, the spatial variability in precipitation at a 0.1° scale is investigated using long-term data from the Tropical Rainfall Measuring Mission Precipitation Radar. Marked regional heterogeneities emerged for orographic rainfall on characteristic scales of tens of kilometers, high concentrations of small-scale systems (<10 km) over alpine areas, and sharp declines around mountain summits. In detecting microclimates, an additional concern is suspicious echoes observed around certain geographical areas with relatively low rainfall. A finescale land–river contrast can be extracted in the diurnal behavior of rainfall in medium-scale systems (10–100 km), corresponding to the course of the Amazon River. In addition, rainfall enhancement over small islands (0.1°–1°) was identified in terms of the storm scale. Even 0.1°-scale flat islands experience more rainfall than the adjacent ocean, primarily as a result of localized small or moderate systems. By contrast, compared with small islands, high-impact large-scale systems (>100 km) result in more rainfall over the adjacent ocean. Finescale hourly data represented the abrupt asymmetric fluctuation in rainfall across the coastline in the tropics and subtropics (30°S–30°N). Significant diurnal modulations in the rainfall due to large-scale systems are found over tropical offshore regions of vast landmasses but not over small islands or in the midlatitudes between 30° and 36°. Rainfall enhancement over small tropical islands is generated by abundant afternoon rainfall, which results from medium-scale storms that are regulated by the island size and inactivity of rainfall over coastal waters.

Abstract

This study aims to evaluate the consistency and discrepancies in estimates of diabatic heating profiles associated with precipitation based on satellite observations and microphysics and those derived from the thermodynamics of the large-scale environment. It presents a survey of diabatic heating profile estimates from four Tropical Rainfall Measuring Mission (TRMM) products, four global reanalyses, and in situ sounding measurements from eight field campaigns at various tropical locations. Common in most of the estimates are the following: (i) bottom-heavy profiles, ubiquitous over the oceans, are associated with relatively low rain rates, while top-heavy profiles are generally associated with high rain rates; (ii) temporal variability of latent heating profiles is dominated by two modes, a deep mode with a peak in the upper troposphere and a shallow mode with a low-level peak; and (iii) the structure of the deep modes is almost the same in different estimates and different regions in the tropics. The primary uncertainty is in the amount of shallow heating over the tropical oceans, which differs substantially among the estimates.

Abstract

The spectral latent heating (SLH) algorithm was developed to estimate latent heating profiles for the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR). The method uses TRMM PR information (precipitation-top height, precipitation rates at the surface and melting level, and rain type) to select heating profiles from lookup tables (LUTs). LUTs for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) using a cloud-resolving model (CRM).

The two-dimensional (2D) CRM was used in previous studies. The availability of exponentially increasing computer capabilities has resulted in three-dimensional (3D) CRM simulations for multiday periods becoming increasingly prevalent. In this study, LUTs from the 2D and 3D simulations are compared. Using the LUTs from 3D simulations results in less agreement between the SLH-retrieved heating and sounding-based heating for the South China Sea Monsoon Experiment (SCSMEX). The level of SLH-estimated maximum heating is lower than that of the sounding-derived maximum heating. This is explained by the fact that using the 3D LUTs results in stronger convective heating and weaker stratiform heating above the melting level than is the case if using the 2D LUTs. More condensate is generated in and carried from the convective region in the 3D model than in the 2D model, and less condensate is produced by the stratiform region’s own upward motion.