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Future projections of precipitation change over tropical land are often enhanced by vegetation responses to CO2 forcing in Earth system models. Projected decreases in rainfall over the Amazon basin and increases over the Maritime Continent are both stronger when plant physiological changes are modeled than if these changes are neglected, but the reasons for this amplification remain unclear. The responses of vegetation to increasing CO2 levels are complex and uncertain, including possible decreases in stomatal conductance and increases in leaf area index due to CO2 fertilization. Our results from an idealized atmospheric general circulation model show that the amplification of rainfall changes occurs even when we use a simplified vegetation parameterization based solely on CO2-driven decreases in stomatal conductance, indicating that this mechanism plays a key role in complex model projections. Based on simulations with rectangular continents we find that reducing terrestrial evaporation to zero with increasing CO2 notably leads to enhanced rainfall over a narrow island. Strong heating and ascent over the island trigger moisture advection from the surrounding ocean. In contrast, over larger continents rainfall depends on continental evaporation. Simulations with two rectangular continents representing South America and Africa reveal that the stronger decrease in rainfall over the Amazon basin seen in Earth system models is due to a combination of local and remote effects, which are fundamentally connected to South America’s size and its location with respect to Africa. The response of tropical rainfall to changes in evapotranspiration is thus connected to size and configuration of the continents.

Using global precipitation datasets (GSMaP, TRMM) and the latest reanalysis data (ERA5), we performed a comprehensive analysis of the tropical rainfall variability that accompanies global-scale, low-frequency normal modes: Rossby, Rossby–gravity, and Kelvin modes. Cross-spectral analysis and lag-regression analysis both showed that coherent rainfall variations accompany not only the wavenumber-1 gravest Rossby mode (“5-day” wave) but other low-frequency modes. The normal mode rainfall variations are enhanced in regions such as the Amazon basin, but also include circumglobally traveling structures with substantial amplitude over the open ocean. These results are remarkably consistent among the three datasets including even ERA5 rainfall data. The circumglobal rainfall signals may be considered primarily as a response to the normal mode dynamical variations. We found that the phase relationship between rainfall and dynamical field variability is strongly dependent on the type of mode and even on the zonal wavenumber. We suggest that this is explained by the difference in relative importance of two underlying processes: 1) moisture-flux convergence and 2) rainfall enhancement associated with adiabatic cooling. Our determined rainfall signals are the response to quasi-monochromatic, periodic waves that have a simple vertical structure and represent one special case of tropospheric wave–rainfall coupling. Implications for the mechanism of 12-h rainfall oscillations believed to be forced by the atmospheric tide are also considered.

Observations have shown that tropical convection is influenced by fluctuations in temperature and moisture in the lower free troposphere (LFT; 600–850 hPa), as well as moist enthalpy (ME) fluctuations beneath the 850 hPa level, referred to as the deep boundary layer (DBL; 850–1000 hPa). A framework is developed that consolidates these three quantities within the context of the buoyancy of an entraining plume. A “plume buoyancy equation” is derived based on a relaxed version of the weak temperature gradient (WTG) approximation. Analysis of this equation using quantities derived from the Dynamics of the Madden–Julian Oscillation (DYNAMO) sounding array data reveals that processes occurring within the DBL and the LFT contribute nearly equally to the evolution of plume buoyancy, indicating that processes that occur in both layers are critical to the evolution of tropical convection. Adiabatic motions play an important role in the evolution of buoyancy both at the daily and longer time scales and are comparable in magnitude to horizontal moisture advection and vertical moist static energy advection by convection. The plume buoyancy equation may explain convective coupling at short time scales in both temperature and moisture fluctuations and can be used to complement the commonly used moist static energy budget, which emphasizes the slower evolution of the convective envelope in tropical motion systems.

Tropical precipitation change under global warming varies with season. The present study investigates the characteristics and cause of the seasonality in rainfall change. Diagnostically, tropical precipitation change is decomposed into thermodynamic and dynamic components. The thermodynamic component represents the wet-get-wetter effect and its seasonality is due mostly to that in the mean vertical velocity, especially in the monsoon regions. The dynamic component includes the warmer-get-wetter effect due to the spatial variations in sea surface temperature (SST) warming, while the seasonality is due to that of the climatological SST and can be largely reproduced by an atmospheric model forced with the monthly climatological SST plus the annual-mean SST warming pattern. In the eastern equatorial Pacific where the SST warming is locally enhanced; for example, rainfall increases only during the March–May season when the climatological SST is high enough for deep convection. To the extent that the seasonality of tropical precipitation change over oceans arises mostly from that of the climatological SST, the results support the notion that reducing model biases in climatology improves regional rainfall projections.

We propose an algorithm to detect and track coastal precipitation systems and we apply it to 18 years of the high-resolution (8 km and 30 min) Climate Prediction Center CMORPH precipitation estimates in the tropics. Coastal precipitation in the Maritime Continent and Central America contributes to up to 80% of the total rainfall. It also contributes strongly to the diurnal cycle over land with the largest contribution from systems lasting between 6 and 12 h and contributions from longer-lived systems peaking later in the day. While the diurnal cycle of coastal precipitation is more intense over land in the summer hemisphere, its timing is independent of seasons over both land and ocean because the relative contributions from systems of different lifespans are insensitive to the seasonal cycle. We investigate the hypothesis that coastal precipitation is enhanced prior to the arrival of the Madden–Julian oscillation (MJO) envelope over the Maritime Continent. Our results support this hypothesis and show that, when considering only coastal precipitation, the diurnal cycle appears reinforced even earlier over islands than previously reported. We discuss the respective roles of coastal and large-scale precipitation in the propagation of the MJO over the Maritime Continent. We also document a shift in diurnal cycle with the phases of the MJO, which results from changes in the relative contributions of short-lived versus long-lived coastal systems.

By comparing a single-column model (SCM) with closely related general circulation models (GCMs), precipitation changes that can be diagnosed from local changes in surface temperature (TS) and relative humidity (RHS) are separated from more complex responses. In the SCM setup, the large-scale tropical circulation is parameterized to respond to the surface temperature departure from a prescribed environment, following the weak temperature gradient (WTG) approximation and using the damped gravity wave (DGW) parameterization. The SCM is also forced with moisture variations. First, it is found that most of the present-day mean tropical rainfall and circulation pattern is associated with TS and RHS patterns. Climate change experiments with the SCM are performed, imposing separately surface warming and CO2 increase. The rainfall responses to future changes in sea surface temperature patterns and plant physiology are successfully reproduced, suggesting that these are direct responses to local changes in convective instability. However, the SCM increases oceanic rainfall too much, and fails to reproduce the land rainfall decrease, both of which are associated with uniform ocean warming. It is argued that remote atmospheric teleconnections play a crucial role in both weakening the atmospheric overturning circulation and constraining precipitation changes. Results suggest that the overturning circulation weakens, both as a direct local response to increased CO2 and in response to energy-imbalance driven exchanges between ascent and descent regions.

A 16-yr Tropical Rainfall Measuring Mission (TRMM) convective feature (CF) dataset and ERA-Interim data are used to understand the favorable thermodynamic and kinematic environments for high-flash-rate thunderstorms globally as well as regionally. We find that intense thunderstorms, defined as having more than 50 lightning flashes within a CF during the ~90-s TRMM overpassing time share a few common thermodynamic features over various regions. These include large convective available potential energy (>1000 J kg−1), small to moderate convection inhibition (CIN), and abundant moisture convergence associated with low-level warm advection. However, each region has its own specific features. Generally, thunderstorms with high lightning flash rates have greater CAPE and wind shear than those with low flash rates, but the differences are much smaller in tropical regions than in subtropical regions. The magnitude of the low- to midtropospheric wind shear is greater over the subtropical regions, including the south-central United States, Argentina, and southwest of the Himalayas, than tropical regions, including central Africa, Colombia, and northwest Mexico, with the exception of Sahel region. Relatively, favorable environments of high-flash-rate thunderstorms in the tropical regions are characterized by higher CAPE, lower CIN, and weaker wind shear compared to the high-flash-rate thunderstorms in the subtropical regions, which have a moderate CAPE and CIN, and stronger low to midtropospheric wind shear.

Precipitation clusters are contiguous raining regions characterized by a precipitation threshold, size, and the total rainfall contained within—termed the cluster power. Tropical observations suggest that the probability distributions of both cluster size and power contain a power-law range (with slope ~ −1.5) bounded by a large-event “cutoff.” Events with values beyond the cutoff signify large, powerful clusters and represent extreme events. A two-dimensional stochastic model is introduced to reproduce the observed cluster distributions, including the slope and the cutoff. The model is equipped with coupled moisture and weak temperature gradient (WTG) energy equations, empirically motivated precipitation parameterization, temporally persistent noise, and lateral mixing processes, all of which collectively shape the model cluster distributions. Moisture–radiative feedbacks aid clustering, but excessively strong feedbacks push the model into a self-aggregating regime. The power-law slope is stable in a realistic parameter range. The cutoff is sensitive to multiple model parameters including the stochastic forcing amplitude, the threshold moisture value that triggers precipitation, and the lateral mixing efficiency. Among the candidates for simple analogs of precipitation clustering, percolation models are ruled out as unsatisfactory, but the stochastic branching process proves useful in formulating a neighbor probability metric. This metric measures the average number of nearest neighbors that a precipitating entity can spawn per time interval and captures the cutoff parameter sensitivity for both cluster size and power. The results here suggest that the clustering tendency and the horizontal scale limiting large tropical precipitating systems arise from aggregate effects of multiple moist processes, which are encapsulated in the neighbor probability metric.

The Lightning Imaging Sensor (LIS) that was on board the Tropical Rainfall Measuring Mission (TRMM) satellite captured optical emissions produced by lightning. In this work, we quantify and evaluate the LIS performance characteristics at both the pixel level of LIS events and contiguous clusters of events known as groups during a recent 2-yr period. Differences in the detection threshold among the four quadrants in the LIS pixel array produce small but meaningful differences in their optical characteristics. In particular, one LIS quadrant (Q1, X ≥ 64; Y ≥ 64) detects 15%–20% more lightning events than the others because of a lower detection threshold. Sensitivity decreases radially from the center of the LIS array to the edges because of sensor optics. The observed falloff behavior is larger on orbit than was measured during the prelaunch laboratory calibration and is likely linked to changes in cloud scattering pathlength with instrument viewing angle. Also, a two-season comparison with the U.S. National Lightning Detection Network (NLDN) has uncovered a 5–7-km north–south LIS location offset that changes sign because of periodic TRMM yaw maneuvers. LIS groups and flashes that had any temporally and spatially corresponding NLDN reports (i.e., NLDN reported the radio signals from the same group and/or from other groups in the same flash) tended to be spatially larger and last longer (only for flashes) than the overall population of groups/flashes.

Three satellite-derived precipitation datasets [the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) dataset, the NOAA Climate Prediction Center morphing technique (CMORPH) dataset, and the newly available Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) dataset] are compared with data obtained from 55 rain gauges mounted on floating buoys in the tropics for the period 1 April 2014–30 April 2017. All three satellite datasets underestimate low rainfall and overestimate high rainfall in the tropical Pacific Ocean, but the TMPA dataset does this the most. In the high-rainfall (higher than 4 mm day−1) Atlantic region, all three satellite datasets overestimate low rainfall and underestimate high rainfall, but the IMERG dataset does this the most. For the Indian Ocean, all three rainfall satellite datasets overestimate rainfall at some gauges and underestimate it at others. Of these three satellite products, IMERG is the most accurate in estimating mean precipitation over the tropical Pacific and Indian Oceans, but it is less accurate over the tropical Atlantic Ocean for regions of high rainfall. The differences between the three satellite datasets vary by region and there is a need to consider uncertainties in the data before using them for research.

We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.

This study aims to characterize the background physical processes in the development of those heavy precipitation clouds that contribute to the Tropical Rainfall Measuring Mission (TRMM) active and passive sensor differences. The combined global observation data from TRMM, CloudSat, and European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) from 2006 to 2014 were utilized to address this issue. Heavy rainfall events were extracted from the top 10% of the rain events from the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) rain-rate climatology. Composite analyses of CloudSat and ERA-Interim were conducted to identify the detailed cloud structures and the background environmental conditions. Over tropical land, TMI tends to preferentially detect deep isolated precipitation clouds for relatively drier and unstable environments, while PR identifies more organized systems. Over the tropical ocean, TMI identifies heavy rainfall events with notable convective organization and clear regional gradients between the western and eastern Pacific Ocean, while PR fails to capture the eastward shallowing of convective systems. The PR–TMI differences for the moist and stable environments are reversed over tropical land.

The driving of tropical precipitation by the variability of the underlying sea surface temperature (SST) plays a critical role in the atmospheric general circulation. To assess the precipitation sensitivity to SST variability, it is necessary to observe and understand the relationship between precipitation and SST. However, the precipitation–SST relationships from any coupled atmosphere–ocean system can be difficult to interpret given the challenge of disentangling the SST-forced atmospheric response and the atmospheric intrinsic variability. This study demonstrates that the two components can be isolated using uncoupled atmosphere-only simulations, which extract the former when driven by time-varying SSTs and the latter when driven by climatological SSTs. With a simple framework that linearly combines the two types of uncoupled simulations, the coupled precipitation–SST relationships are successfully reproduced. Such a framework can be a useful tool for quantitatively diagnosing tropical air–sea interactions. The precipitation sensitivity to SST variability is investigated with the use of uncoupled simulations with prescribed SST anomalies, where the influence of atmospheric intrinsic variability on SST is deactivated. Through a focus on local precipitation–SST relationships, the precipitation sensitivity to local SST variability is determined to be predominantly controlled by the local background SST. In addition, the strength of the precipitation response increases monotonically with the local background SST, with a very sharp growth at high SSTs. These findings are supported by basic principles of moist static stability, from which a simple formula for precipitation sensitivity to local SST variability is derived.

In this study, rainfall data are prepared at a 0.01° scale using 16-yr spaceborne radar data over the area of 36.13°S–36.13°N as provided by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). A spatial resolution that is finer than the field of view is obtained by assuming rainfall uniformity within an instantaneous footprint centered on the PR footprint geolocation. These ultra-high-resolution data reveal local rainfall concentrations over slope areas. A new estimate of the maximum rainfall at Cherrapunji, India, was observed on the valley side, approximately 5 km east of the gauge station, and is approximately 50% higher than the value indicated by the 0.1°-scale data. A case study of Yakushima Island, Japan, indicates that several percent of the sampling error arising from the spatial mismatch may be contained in conventional 0.05°-scale datasets generated without footprint areal information. The differences attributable to the enhancement in the resolution are significant in complex terrain such as the Himalayas. The differences in rainfall averaged for the 0.1° and 0.01° scales exceed 10 mm day−1 over specific slope areas. In the case of New Guinea, the mean rainfall on a mountain ridge can be 30 times smaller than that on an adjacent slope at a distance of 0.25°; this is not well represented by other high-resolution datasets based on gauges and infrared radiometers. The substantial nonuniformity of rainfall climatology highlights the need for a better understanding of kilometer-scale geographic constraints on rainfall and retrieval approaches.

The National Aeronautics and Space Administration (NASA) has always included data reprocessing as a major component of every science mission. A final reprocessing is typically a part of mission closeout (known as phase F). The Tropical Rainfall Measuring Mission (TRMM) is currently in phase F, and NASA is preparing for the last reprocessing of all the TRMM precipitation data as part of the closeout. This reprocessing includes improvements in calibration of both the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). An initial step in the version 8 reprocessing is the improvement of geolocation. The PR calibration is being updated by the Japan Aerospace Exploration Agency (JAXA) using data collected as part of the calibration of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory. JAXA undertook a major effort to ensure TRMM PR and GPM Ku-band calibration is consistent.A major component of the TRMM version 8 reprocessing is to create consistent retrievals with the GPM version 05 (V05) retrievals. To this end, the TRMM version 8 reprocessing uses retrieval algorithms based on the GPM V05 algorithms. This approach ensures consistent retrievals from December 1997 (the beginning of TRMM) through the current ongoing GPM retrievals. An outcome of this reprocessing is the incorporation of TRMM data products into the GPM data suite. Incorporation also means that GPM file naming conventions and reprocessed TRMM data carry the V05 data product version. This paper describes the TRMM version 8 reprocessing, focusing on the improvements in TMI level 1 products.

The common explanation for the progression of the rainy season over Africa is the seasonal excursion of the ITCZ. The ITCZ paradigm stems from a time when tropical rainfall was assumed to be associated mainly with localized convection. Its development was also linked to the emergence of midlatitude frontal concepts. The paradigm has numerous shortcomings, including the diversity of definitions and the large number of parameters used to identify the ITCZ. A historical look at the concept shows that its use over Africa has long been controversial, with many eminent tropical meteorologists harshly criticizing its applicability over this continent. However, the seasonal excursion of the ITCZ remains the classical explanation for African rainy seasons, especially in the equatorial region. This article underscores the shortcomings of the paradigm in equatorial Africa by examining various aspects of the circulation associated with the spatial and temporal patterns of rainfall during the equatorial rainy seasons. The overall conclusion is that a deeper understanding of the seasonal cycle in the equatorial regions of Africa still needs to be developed.

Precipitation observation with the Tropical Rainfall Measuring Mission’s (TRMM’s) precipitation radar (PR) lasted for more than 17 years. To study the changes in the water and energy cycle related to interannual and decadal variabilities of climate, homogeneity of long-term PR data is essential. The aim of the study is to develop a precipitation climate record from the 17-yr PR observation. The focus was on mitigating the discontinuities associated with the switching to redundant electronics in the PR in June 2009. In version 7 of the level-1 PR product, a discontinuity in noise power is found at this timing, indicating a change in the signal-to-noise ratio. To mitigate the effect of this discontinuity on climate studies, the noise power of the B-side PR obtained after June 2009 is artificially increased to match that of the A-side PR. Simulation results show that the storm height and the precipitation frequency detected by the PR relatively decrease by 2.17% and 5.15% in the TRMM coverage area (35°S–35°N), respectively, and that the obvious discontinuity of the time series by the storm height and the precipitation fraction caused by the switching to the redundancy electronics is mitigated. Differences in the statistics of other precipitation parameters caused by the switching are also mitigated. The unconditional precipitation rate derived from the adjusted data obtained over the TRMM coverage area decreases by 0.90% as compared with that determined from the original data. This decrease is mainly caused by reductions in the detection of light precipitation.

This study provides, for the first time, an analysis of the climatological diurnal variations in the lightning flash radiance data product ε from the Tropical Rainfall Measuring Mission Lightning Imaging Sensor (TRMM/LIS). The ε values over 13 years (2002–14), and over a global scale (∼38°S–38°N), reveal novel and remarkably consistent regional and seasonal patterns as a function of the local solar time (LST). In particular, the diurnal variation of ε (over both continental and oceanic regions) is characterized by a monotonic increase from late afternoon (∼2000 LST), attaining a maximum around 0900 LST, followed by a decreasing trend. The continental (oceanic) ε values reach a broader minimum spanning from ∼1500 to 1900 LST (∼1800 to 2000). The relative diurnal amplitude variation in continental ε is about 45%, compared to about 15% for oceanic ε. This study confirms that the results are not affected by diurnal biases associated with instrument detection or other statistical artifacts. Notable agreement is shown between the diurnal variations of ε and the global-scale (∼38°S–38°N) mesoscale convective system areal extent. Comparisons with recently published diurnal variations of cloud-to-ground lightning peak current over the United States also exhibit a marked similarity. Given the novelty of these findings, a few tentative hypotheses about the underlying physical mechanism(s) are discussed.

A compositing scheme that predicts changes in tropical precipitation under climate change from changes in near-surface relative humidity (RH) and temperature is presented. As shown by earlier work, regions of high tropical precipitation in general circulation models (GCMs) are associated with high near-surface RH and temperature. Under climate change, it is found that high precipitation continues to be associated with the highest surface RH and temperatures in most CMIP5 GCMs, meaning that it is the “rank” of a given GCM grid box with respect to others that determines how much precipitation falls rather than the absolute value of surface temperature or RH change, consistent with the weak temperature gradient approximation. Further, it is demonstrated that the majority of CMIP5 GCMs are close to a threshold near which reductions in land RH produce large reductions in the RH ranking of some land regions, causing reductions in precipitation over land, particularly South America, and compensating increases over ocean. Recent work on predicting future changes in specific humidity allows the prediction of the qualitative sense of precipitation change in some GCMs when land surface humidity changes are unknown. However, the magnitudes of predicted changes are too small. Further study, perhaps into the role of radiative and land–atmosphere feedbacks, is necessary.

This study evaluates the performance of the operational and reprocessed Global Lightning Dataset 360 (GLD360) data relative to the Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) during 2012–14. The analysis compares ground- and space-based lightning observations to better characterize the pre- and postupgrade GLD360. The reprocessed, postupgrade data increase the fraction of LIS flashes detected by the GLD360 [i.e., relative detection efficiency (DE)]. The relative DE improves during each year in every region, and year-over-year improvement appears in both datasets. The reprocessed relative DE exceeds 40% throughout large portions of the study domain with relative maxima over the western Atlantic, eastern Pacific, and the Gulf of Mexico. The upgrade results in shorter distances between matched LIS and GLD360 locations, indicating improved location accuracy. On average, the matched LIS flashes last longer (18.6 ms) and are larger (379.3 km2) than the unmatched LIS flashes (6.1 ms, 251.0 km2). For each LIS characteristic examined, the greater the value, the more likely the GLD360 detects the flash. Of the matched LIS flashes, 44.3% have multiple GLD360 strokes, and the mean LIS characteristics increase with increasing stroke count. LIS flashes with four-plus related GLD360 strokes are longest (61.1 ms) and largest (492.7 km2). Of the multistroke flashes, 57.3% contain subsequent strokes that are stronger than the initial stroke. The vast majority of multistroke flashes with a first stroke estimated with a peak current of

The intrinsic atmospheric and ocean-induced tropical precipitation variability is studied using millennial control simulations with various degrees of ocean coupling. A comparison between the coupled simulation and the atmosphere-only simulation with climatological sea surface temperatures (SSTs) shows that a substantial amount of tropical precipitation variability is generated without oceanic influence. This intrinsic atmospheric variability features a red noise spectrum from daily to monthly time scales and a white noise spectrum beyond the monthly time scale. The oceanic impact is inappreciable for submonthly time scales but important at interannual and longer time scales. For time scales longer than a year, it enhances precipitation variability throughout much of the tropical oceans and suppresses it in some subtropical areas, preferentially in the summer hemisphere. The sign of the ocean-induced precipitation variability can be inferred from the local precipitation–SST relationship, which largely reflects the local feedbacks between the two, although nonlocal forcing associated with El Niño–Southern Oscillation also plays a role. The thermodynamic and dynamic nature of the ocean-induced precipitation variability is studied by comparing the fully coupled and slab ocean simulations. For time scales longer than a year, equatorial precipitation variability is almost entirely driven by ocean circulation, except in the Atlantic Ocean. In the rest of the tropics, ocean-induced precipitation variability is dominated by mixed layer thermodynamics. Additional analyses indicate that both dynamic and thermodynamic oceanic processes are important for establishing the leading modes of large-scale tropical precipitation variability. On the other hand, ocean dynamics likely dampens tropical Pacific variability at multidecadal time scales and beyond.

Using 16-yr Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations, rainfall properties in the inner-core region of tropical cyclones (TCs) and the relative importance of stratiform and convective precipitation are examined with respect to the evolution of rapid intensification (RI) events. The onset of RI follows a significant increase in the occurrence and azimuthal coverage of stratiform rainfall in all shear-relative quadrants, especially upshear left. The importance of the increased stratiform occurrence in RI storms is further confirmed by the comparison of two groups of slowly intensifying (SI) storms with one group that underwent RI and the other that did not. Statistically, SI storms that do not undergo RI during their life cycle have a much lower percent occurrence of stratiform rain within the inner core. The relatively greater areal coverage of stratiform rain in RI cases appears to be related to the moistening/humidification of the inner core, particularly in the upshear quadrants. In contrast to rainfall frequency, rainfall intensity and total volumetric rain do not increase much until several hours after RI onset, which is more likely a response or positive feedback rather than the trigger of RI. Despite a low frequency of occurrence, the overall contribution to total volumetric rain by convective precipitation is comparable to that of stratiform rain, owing to its intense rain rate.

Future projections of the eastern-Pacific (EP) and central-Pacific (CP) types of El Niño in the twenty-first century, as well as their associated tropical circulation and precipitation variability, are investigated using historical runs and representative concentration pathway 8.5 (RCP8.5) simulations from 31 coupled models in phase 5 of the Coupled Model Intercomparison Project (CMIP5). As inferred from CMIP5 models that best capture both El Niño flavors, EP El Niño sea surface temperature (SST) variability will become weaker in the future climate, while no robust change of CP El Niño SST is found. Models also reach no consensus on the future change of relative frequency from CP to EP El Niño. However, there are robust changes in the tropical overturning circulation and precipitation associated with both types of El Niño. Under a warmer climate, magnitudes of precipitation anomalies during EP El Niño are projected to increase, presenting significant enhancement of the dry (wet) signal over the western (central–eastern) Pacific. This is consistent with an accelerated hydrological cycle in the deep tropics; hence, a “wet get wetter” picture appears under global warming, accompanied by a weakened anomalous Walker circulation. For CP El Niño, drier-than-normal conditions will be intensified over the tropical central–eastern Pacific in the future climate, with stronger anomalous sinking related to the strengthened North Pacific local Hadley cell. These results suggest that, besides the enhanced basic-state hydrological cycle over the tropics, other elements, such as the anomalous overturning circulation, might also play a role in determining the ENSO precipitation response to a warmer background climate.

The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.

Previous total lightning climatology studies using Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) observations were reported at coarse resolution (0.5°) and employed significant spatial and temporal smoothing to account for sampling limitations of TRMM’s tropical to subtropical low-Earth-orbit coverage. The analysis reported here uses a 16-yr reprocessed dataset to create a very high-resolution (0.1°) climatology with no further spatial averaging. This analysis reveals that Earth’s principal lightning hotspot occurs over Lake Maracaibo in Venezuela, while the highest flash rate density hotspot previously found at the lower 0.5°-resolution sampling was found in the Congo basin in Africa. Lake Maracaibo’s pattern of convergent windflow (mountain–valley, lake, and sea breezes) occurs over the warm lake waters nearly year-round and contributes to nocturnal thunderstorm development 297 days per year on average. These thunderstorms are very localized, and their persistent development anchored in one location accounts for the high flash rate density. Several other inland lakes with similar conditions, that is, deep nocturnal convection driven by locally forced convergent flow over a warm lake surface, are also revealed. Africa is the continent with the most lightning hotspots, followed by Asia, South America, North America, and Australia. A climatological map of the local hour of maximum flash rate density reveals that most oceanic total lightning maxima are related to nocturnal thunderstorms, while continental lightning tends to occur during the afternoon. Most of the principal continental maxima are located near major mountain ranges, revealing the importance of local topography in thunderstorm development.

This article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Dévényi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main results are 1) the QPF skill was improved substantially with GDM in comparison to GD; 2) the increase in the horizontal resolution without any ad hoc tuning improves the variance of precipitation over continents with complex orography, such as Africa and South America, whereas over oceans there are no significant differences; and 3) the systematic errors (dry or wet biases) remain virtually unchanged for 5-day forecasts. Despite improvements in the tropical precipitation forecasts, especially over southeastern Brazil, dry biases over the Amazon and La Plata remain in BAM. Improving the precipitation forecasts over these regions remains a challenge for the future development of the model to be used not only for numerical weather prediction over South America but also for global climate simulations.

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) algorithms consist of modules. This paper describes version 4 (V4) of GPM DPR level 2 (L2) classification (CSF) modules, which consist of two single-frequency (SF) modules—that is, Ku-only and Ka-only modules—and a dual-frequency (DF) module. Each CSF module detects bright band (BB) and classifies rain into three major types, that is, stratiform, convective, and other. The Ku-only and Ka-only CSF modules use algorithms that are similar to the Tropical Rainfall Measuring Mission (TRMM) rain type classification algorithm 2A23. The DF CSF module uses a new method called the measured dual-frequency ratio (DFRm) method for the rain type classification and the detection of BB. It is shown that the Ku-only CSF module and the DF CSF module produce almost indistinguishable rain type counts in a statistical sense. It is also shown that the DFRm method in the DF CSF module improves the detection of BB.

The authors evaluate the contribution of tropical cyclones (TCs) to daily precipitation extremes over land for TC-active regions around the world. From 1998 to 2012, data from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA 3B42) showed that TCs account for an average of 3.5% ± 1% of the total number of rainy days over land areas experiencing cyclonic activity regardless of the basin considered. TC days represent between 13% and 31% of daily extremes above 4 in. day−1, but can account locally for the large majority (>70%) or almost all (≈100%) of extreme rainfall even over higher-latitude areas marginally affected by cyclonic activity. Moreover, regardless of the TC basin, TC-related extremes occur preferably later in the TC season after the peak of cyclonic activity.

The Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.

The rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land–ocean–coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions.The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land–ocean–coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are applied to the Global Satellite Mapping of Precipitation TMI algorithm. The classification result is validated using the TRMM precipitation radar. The validation shows that these improvements lead to better rain detection in the coastal region.

The authors implemented the Grell–Freitas (GF) parameterization of convection in which the cloud-base mass flux varies quadratically as a function of the convective updraft fraction in the global nonhydrostatic Model for Prediction Across Scales (MPAS). They evaluated the performance of GF using quasi-uniform meshes and a variable-resolution mesh centered over South America, the resolution of which varied between hydrostatic (50 km) and nonhydrostatic (3 km) scales. Four-day forecasts using a 50-km and a 15-km quasi-uniform mesh, initialized with GFS data for 0000 UTC 10 January 2014, reveal that MPAS overestimates precipitation in the tropics relative to the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis data. Results of 4-day forecasts using the variable-resolution mesh reveal that over the refined region of the mesh, GF performs as a precipitating shallow convective scheme, whereas over the coarse region of the mesh, GF acts as a conventional deep convective scheme. As horizontal resolution increases and subgrid-scale motions become increasingly resolved, the contribution of convective and grid-scale precipitation to the total precipitation decreases and increases, respectively. Probability density distributions of precipitation highlight a smooth transition in the partitioning between convective and grid-scale precipitation, including at gray-zone scales across the transition region between the coarsest and finest regions of the global mesh. Variable-resolution meshes spanning between hydrostatic and nonhydrostatic scales are shown to be ideal tools to evaluate the horizontal scale dependence of parameterized convective and grid-scale moist processes.

This study uses 16-yr Tropical Rainfall Measuring Mission (TRMM) radar precipitation feature (RPF) data to characterize warm rain systems in the tropics with large horizontal extensions, referred to as warm organized rain systems. The systems are selected by specifying the RPFs with minimum infrared brightness temperature warmer than 0°C and rain area greater than 500 km2. ERA-Interim atmospheric fields and SST from NOAA are analyzed to highlight the environmental characteristics of warm organized rain systems. Warm organized systems occur over specific oceanic regions, including the eastern Pacific ITCZ, the eastern part of the SPCZ, and coastal regions. In contrast with ubiquitous warm isolated RPFs, warm organized systems have greater near-surface radar reflectivity. The rainfall amounts generated by warm organized systems are greater in winter than in summer. Composite analyses indicate that warm organized RPFs prefer to coexist with a dry midtroposphere associated with a strong upper-level descent, an enhanced near-surface moisture convergence, and a strong low-level large-scale ascent. The shallow meridional circulation in the eastern Pacific is significantly stronger for warm organized RPFs compared to the circulation for warm isolated RPFs. Warm organized systems over the tropical eastern Pacific occur at warm SSTs with mean value of about 27°C and a strong SST meridional gradient. The warm organized RPFs in the tropical eastern Pacific are found to be at the southern edge of deep ITCZ cores. This is probably related to the meridional asymmetrical thermodynamic structure over the eastern Pacific ITCZ with a higher low-level humidity to the south. Similar favorable large-scale environments for the warm organized RPFs are also found over the SPCZ and other regions.

A great deal of expertise in satellite precipitation estimation has been developed during the Tropical Rainfall Measuring Mission (TRMM) era (1998–2015). The quantification of errors associated with satellite precipitation products (SPPs) is crucial for a correct use of these datasets in hydrological applications, climate studies, and water resources management. This study presents a review of previous work that focused on validating SPPs for liquid precipitation during the TRMM era through comparisons with surface observations, both in terms of mean errors and detection capabilities across different regions of the world. Several SPPs have been considered: TMPA 3B42 (research and real-time products), CPC morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP; both the near-real-time and the Motion Vector Kalman filter products), PERSIANN, and PERSIANN–Cloud Classification System (PERSIANN-CCS). Topography, seasonality, and climatology were shown to play a role in the SPP’s performance, especially in terms of detection probability and bias. Regions with complex terrain exhibited poor rain detection and magnitude-dependent mean errors; low probability of detection was reported in semiarid areas. Winter seasons, usually associated with lighter rain events, snow, and mixed-phase precipitation, showed larger biases.

Motivated by observational evidence of rainfall concentrations near tropical coastlines with a diurnal cycle, the annual mean precipitation amount was quantified in the tropics (latitudes lower than 37°) obtained as a function of coastal distance and compared between land and ocean sides. The data are from the Tropical Rainfall Measuring Mission (TRMM). Precipitation amount peaks at the coastline and decreases rapidly over a distance of 300 km from the coastline on both sides of the coastline. The precipitation inside the “coastal region” (defined by a distance

Using data from the Tropical Rainfall Measuring Mission (TRMM), the modulation of convection by African easterly waves (AEWs) is investigated over regions of the east Atlantic and tropical Africa. To explain the modulation of convection, the large-scale environment (lift, moisture, conditional instability, and shear) is also examined as a function of AEW phase in each region. Over semiarid portions of tropical Africa, unconditional rain rates are greatest in the northerly phase of AEWs due to the strong adiabatic forcing for ascent. Along the Guinea Coast, the western coast of Africa, and over the east Atlantic—where forcing for ascent is weaker—rainfall is shifted toward the trough where the air is moist. Significant contrasts in the characteristics of convection as a function of AEW phase—comparable in magnitude to regional contrasts—are also observed. In all regions, large and high echo-top convective systems are more sensitive to AEW phase than small and low echo-top systems. In semiarid regions, deep convection and large high echo-top convective systems account for a large fraction of the rainfall in the ridge and northerlies. Stratiform and small low echo-top convective systems dominate in the trough and southerlies. Convective system height and conditional rain rates increase with conditional instability and system sizes may increase with shear. Over the east Atlantic, stratiform fractions and convective system sizes and echo-top heights are greatest in the trough while the ridge is dominated by shallow convection. This is primarily related to the presence of moist air in the trough and dry air in the ridge.

Shear-relative distributions of four types of precipitation/convection in tropical cyclones (TCs) are statistically analyzed using 14 years of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. The dataset of 1139 TRMM PR overpasses of tropical storms through category-2 hurricanes over global TC-prone basins is divided by future 24-h intensity change. It is found that increased and widespread shallow precipitation (defined as where the 20-dBZ radar echo height

The hot tower (HT) in the inner core plays an important role in tropical cyclone (TC) rapid intensification (RI). With the help of Tropical Rainfall Measurement Mission (TRMM) data and the Statistical Hurricane Intensity Prediction Scheme dataset, the potential of HTs in operational RI prediction is reassessed in this study. The stand-alone HT-based RI prediction scheme showed little skill in the northern Atlantic (NA) and eastern and central Pacific (ECP), but yielded skill scores of >0.3 in the southern Indian Ocean (SI) and western North Pacific (WNP) basins. The inaccurate predictions are due to four scenarios: 1) RI events may have already begun prior to the TRMM overpass. 2) RI events are driven by non-HT factors. 3) The HT has already dissipated or has not occurred at the TRMM overpass time. 4) Large false alarms result from the unfavorable environment. When the HT was used in conjunction with the TC’s previous 12-h intensity change, the potential intensity, the percentage area from 50 to 200 km of cloud-top brightness temperatures lower than −10°C, and the 850–200-hPa vertical shear magnitude with the vortex removed, the predictive skill score in the SI was 0.56. This score was comparable to that of the RI index scheme, which is considered the most advanced RI prediction method. When the HT information was combined with the aforementioned four environmental factors in the NA, ECP, South Pacific, and WNP, the skill scores were 0.23, 0.32, 0.42, and 0.42, respectively.

Based upon on the findings of Y. Li and R. E. Carbone, the association of tropical rainfall with SST structure is further explored, with emphasis on the MJO passband. Analyses include the tropical Indian Ocean, Maritime Continent, and tropical western Pacific regions. The authors examine the anomalies of and correlations between SST structure, the frequency of rainfall events, and rainfall amount. Based on detailed examination of a 49-month time series, all findings are statistical inferences and interpretations consistent with established theory. The statistical inferences are broadly consistent with a pivotal role played by the convergent Laplacian of SST together with an expected, but somewhat indirect, role of SST itself. The main role of SST in the MJO passband appears limited to production of moist static energy, which is highly correlated with cumulative precipitation, yet bears a decidedly conditional relationship to the occurrence of rainfall. If rain occurs, then more rain is likely over warmer SST. The convergent Laplacian of SST is strongly associated with the onset of rainfall, apparently through its capacity to induce vertical air motion with sufficient kinetic energy to overcome convective inhibition in a conditionally unstable troposphere. The convergent Laplacian of SST is directly associated with the location and the variability of rainfall event frequency while having a less direct relationship to cumulative rainfall. These nuanced interpretations of rainfall forcing by the Laplacian of SST, and conditional modulation of cumulative rainfall by SST, may underlie systematic errors in highly parameterized models as a consequence of variable asymmetry in the field of Laplacian anomalies.

Accurate estimation of high-resolution precipitation on the global scale is extremely challenging. The operational Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has created over 16 years of high-resolution quantitative precipitation estimation (QPE), and has built the foundation for improved measurements in the upcoming Global Precipitation Measurement (GPM) mission. TMPA is intended to produce the “best effort” estimates of quasi-global precipitation from almost all available satelliteborne precipitation-related sensors by consistently calibrating them with the high-quality measurements from the core instrument platform aboard TRMM. Recently, the TMPA system has been upgraded to version 7 to take advantage of newer and better sources of satellite inputs than version 6, and has attracted a large user base. A key product from TMPA is the near-real-time product (TMPA-RT), as its timeliness is particularly appealing for time-sensitive applications such as flood and landslide monitoring. TMPA-RT’s error characteristics on a global scale have yet to be extensively quantified and understood. In this study, efforts are focused on a systematic evaluation of four sets of mainstream TMPA-RT estimates on the global scale. The analysis herein indicates that the latest version 7 TMPA-RT with the monthly climatological calibration had the lowest daily systematic biases of approximately 9% over land and –11% over ocean (relative to the gauge-adjusted research product). However, there still exist some unresolved issues in mountainous areas (especially the Tibetan Plateau) and high-latitude belts, and for estimating extreme rainfall rates with high variability at small scales. These global error characteristics and their regional and seasonal variations revealed in this paper are expected to serve as the benchmark for the upcoming GPM mission.

A preliminary study toward consistent soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is presented. Its predecessor, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), has provided Earth scientists with a consistent and continuous global soil moisture dataset. A major challenge remains to achieve synergy between these soil moisture datasets, which is hampered by the lack of an overlapping observation period of the sensors. Here, observations of the multifrequency microwave radiometer on board the Tropical Rainfall Measuring Mission (TRMM) satellite were used to improve consistency between AMSR-E and AMSR2. Several scenarios to achieve synergy between the AMSR-E and AMSR2 soil moisture products were evaluated. The novel soil moisture retrievals from C-band observations, a frequency band that is lacking on board the TRMM satellite, are also presented. A global comparison of soil moisture retrievals against ERA-Interim soil moisture demonstrates the need for an intercalibration procedure. Several different scenarios based on filtering were tested, and the impact on the soil moisture retrievals was evaluated against two independent reference soil moisture datasets (reanalysis and in situ soil moisture) that cover both individual observation periods of the AMSR-E and AMSR2 sensors. Results show a high degree of consistency between both satellite products and two independent reference products for the soil moisture products retrieved from X-band observations. Care should be taken in the interpretation of the presented soil moisture products, and future research is needed to further align the AMSR2 and AMSR-E sensor calibrations.

A total of 10 years (2002–11) of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) reflectivities, signaling heavy rainfall (>10 mm h−1), were objectively classified by applying the K-means clustering method in order to obtain typical reflectivity profiles associated with heavy rainfall over East Asia. Two types of heavy rainfall emerged as the most important rain processes over East Asia: type 1 (cold type) characterized by high storm height and abundant ice water under convectively unstable conditions, developing mostly over inland China; and type 2 (warm type) associated with a lower storm height and lower ice water content, developing mostly over the ocean. These two types also show sharp contrasts in relation to their seasonal changes and in the diurnal variation of frequency maxima, in addition to other contrasting meteorological parameters. The PR-derived heavy rain events were observed over the Korean peninsula and their spatiotemporal evolution was examined using 10-yr composites of 11-μm brightness temperature from geostationary satellites and Interim ECMWF Re-Analysis (ERA-Interim) data. Cold-type heavy rainfall over Korea is characterized by an eastward moving cloud system with an oval shape while the warm type shows a comparatively wide spatial distribution over an area extending from the southwest to northeast. Overall the warm-type process appears to link the low-level moisture convergence area to the vertically aligned divergence area formed over the jet stream level. This setup continuously pushes air upward under moist-adiabatically near-neutral conditions and thus yields heavy rainfall. As warm-type heavy rainfall persists longer, it is considered to be more responsible for flood events occurring over the Korean peninsula.

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.

With 15 yr of the Tropical Rainfall Measuring Mission (TRMM) observations, the passive microwave radiometers [TRMM Microwave Imager (TMI)] and the precipitation radar (PR) report a close geographical distribution of annual precipitation between 36°S and 36°N. However, large discrepancies between PR and TMI precipitation retrievals are also found over several specific regions, such as central Africa, the Amazon, the tropical east Pacific, and north Indian Ocean. To understand these discrepancies, the PR near-surface and the TMI surface precipitation retrievals are compared at both pixel and precipitation system levels using collocated pixels and a precipitation feature database from 1998 to 2012. Over land, the TMI overestimates precipitation in deep and intense convective systems, but misses significant amounts of warm rainfall in shallow systems. Over the ocean, because of the partial beam filling of large footprints of the lower-frequency sensors, the TMI reports a larger precipitation area than the PR and underestimates the precipitation rate in the convective precipitation region. The TMI tends to overestimate precipitation compared to the PR in a large proportion of shallow systems over the tropical east Pacific and trade wind regions with large-scale descent. The PR tends to overestimate precipitation compared to the TMI in a large proportion of shallow systems over rainy oceans, such as the west Pacific and the Atlantic ITCZ. All these findings imply that there are still large uncertainties in the precipitation climatology over some regions. Further ground validation campaigns are still needed, especially over the ocean.

Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

To improve the accuracy of quantitative precipitation estimation (QPE) in complex terrain, a new rainfall rate estimation algorithm has been developed and applied on two C-band dual-polarization radars in Taiwan. In this algorithm, the specific attenuation A is utilized in the rainfall rate R estimation, and the parameters used in the R(A) method were estimated using the local drop size distribution (DSD) and drop shape relation (DSR) observations. In areas of complex terrain where the lowest antenna tilt is completely blocked, observations from higher tilts are used in radar QPE. Correction of the vertical profile of rain rate estimated by the R(A) algorithm (VPRA) is applied to account for the vertical variability of rain. It has been found that the VPRA correction improved the accuracy of estimated rainfall in severely blocked areas. The R(A)–VPRA scheme was tested for different precipitation cases including typhoon, stratiform, and convective rain. Compared to existing rainfall estimation algorithms such as rainfall–reflectivity (R–Z) and rainfall–specific differential phase (R–KDP), the new method is able to provide accurate and robust rainfall estimates when the radar reflectivity is miscalibrated or significantly biased by attenuation or when the lower tilt of the radar beam is significantly blocked.

The midlatitude response to localized equatorial heating events that last 2 days is examined through experimentation with an atmospheric general circulation model. Such responses are argued to be important because many tropical rainfall events only last a short time and because the responses to such pulses serve as building blocks with which to study the impacts of more general heating fluctuations. The experiments indicate that short-lived heating produces responses in midlatitudes at locations far removed from the source and these responses persist much longer than the pulses themselves. Indeed pulse forcing, which is essentially white in time, produces upper-tropospheric responses that have an e-damping time of almost a week and that are detectable for more than two weeks in the experiments. Moreover the upper-tropospheric structure of the reaction to short pulses is remarkably similar to the reaction to steady tropical heating, including having a preference for occurring at special geographical locations and being composed of recurring patterns that resemble the leading patterns of responses to steady heating. This similarity is argued to be a consequence of the responses to pulses having little or no phase propagation in the extratropics. The impact of short-lived tropical heating also produces a persistent response in midlatitude surface fields and in the statistics of synoptic eddies. The implications these results have for subseasonal variability are discussed. These include 1) the potential for improving subseasonal prediction through improved assimilation and short-range forecasts of tropical precipitation and 2) the difficulties involved in attributing subseasonal midlatitude events to tropical heating.

The daily cycle of precipitation (DCP) in the austral autumn on the northern coast of Brazil (NCB) is examined in detail. The Tropical Rainfall Measuring Mission 3B42 dataset was used to obtain the DCP, and the intradaily variability was measured using the coefficient of variation (CV). The DCP data of the NCB were grouped into five regimes. A new regime was found, called the shore regime. It has a minimum CV, and its cycle shows both continental (late afternoon peak) and oceanic features (morning peak). The landside coastal regime was divided into two areas: a continental coast regime, with very high CV, and an inland coast regime, with clear inland phase propagation. The continental regime was divided into two categories: an inland regime with low and high variability. The Forecast and Tracking of the Evolution of Cloud Clusters (ForTraCC) data were used to relate convective systems (CS) and their processes to the DCP. The following processes are studied for the CS: initiation/dissipation, merge/split, area increase/reduction, and advection. Initiation is more concentrated in time, while dissipation is more distributed. Physical mechanisms that generate initiation can promote area expansion and hence CS merge. By considering a simple parameterization, the time scale of the CS area reduction under environmental conditions that are unfavorable to initiation ranges from 6 to 12 h. Therefore, there is upscaling of the CS in the afternoon and slow decay during the night and morning, which leads to a more uniform cycle inland.

Central equatorial Africa is deficient in long-term, ground-based measurements of rainfall; therefore, the aim of this study is to assess the accuracy of three high-resolution, satellite-based rainfall products in western Uganda for the 2001–10 period. The three products are African Rainfall Climatology, version 2 (ARC2); African Rainfall Estimation Algorithm, version 2 (RFE2); and 3B42 from the Tropical Rainfall Measuring Mission, version 7 (i.e., 3B42v7). Daily rainfall totals from six gauges were used to assess the accuracy of satellite-based rainfall estimates of rainfall days, daily rainfall totals, 10-day rainfall totals, monthly rainfall totals, and seasonal rainfall totals. The northern stations had a mean annual rainfall total of 1390 mm, while the southern stations had a mean annual rainfall total of 900 mm. 3B42v7 was the only product that did not underestimate boreal-summer rainfall at the northern stations, which had ~3 times as much rainfall during boreal summer than did the southern stations. The three products tended to overestimate rainfall days at all stations and were borderline satisfactory at identifying rainfall days at the northern stations; the products did not perform satisfactorily at the southern stations. At the northern stations, 3B42v7 performed satisfactorily at estimating monthly and seasonal rainfall totals, ARC2 was only satisfactory at estimating seasonal rainfall totals, and RFE2 did not perform satisfactorily at any time step. The satellite products performed worst at the two stations located in rain shadows, and 3B42v7 had substantial overestimates at those stations.

Some previous studies were unable to detect seasonal organization to the rainfall in the Sahara Desert, while others reported seasonal patterns only in the less-arid periphery of the Sahara. In contrast, the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) satellite detects four rainy seasons in the part of the Sahara where the TRMM radar saw the least rainfall during a 15-yr period (1998–2012). According to the TRMM radar, approximately 20°–27°N, 22°–32°E is the portion of the Sahara that has the lowest average annual rain accumulation (1–5 mm yr−1). Winter (January and February) has light rain throughout this region but more rain to the north over the Mediterranean Sea. Spring (April and May) has heavier rain and has lightning observed by the TRMM Lightning Imaging Sensor (LIS). Summer rain and lightning (July and August) occur primarily south of 23°N. At a maximum over the Red Sea, autumn rain and lightning (October and November) can be heavy in the northeastern portion of the study area, but these storms are unreliable: that is, the TRMM radar detects such storms in only 6 of the 15 years. These four rainy seasons are each separated by a comparatively drier month in the monthly rainfall climatology. The few rain gauges in this arid region broadly agree with the TRMM radar on the seasonal organization of rainfall. This seasonality is reason to reevaluate the idea that Saharan rainfall is highly irregular and unpredictable.