Satellite estimation of precipitation and satellite-derived statistics of mesoscale convective systems (MCS) are analyzed conjunctively to quantify the contribution of the various types of MCS to the water budget of the tropics. This study focuses on two main mesoscale characteristics of the systems: duration and propagation. Overall, the systems lasting more than 12 h are shown to account for around 75% of the tropical rainfall, and 60% of the rainfall is due to systems traveling more than 250 km, a typical GCM grid. A number of regional features are also revealed by factoring in the convective systems’ morphological parameters in the water budget computation. These findings support the challenging effort to account for such mesoscale features when considering the theory on the future evolution of the water budget as well as the physical parameterizations of climate models. Finally, this analysis provides a simple metric for evaluating high-resolution numerical simulations of the tropical water budget. Furthermore, results are shown to be robust to the selection of the satellite rainfall products.

This study focuses on improving the retrieval of rain from measured microwave brightness temperatures and the capability of the retrieved field to represent the mesoscale structure of a small intense hurricane. For this study, a database is constructed from collocated Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the TRMM Microwave Imager (TMI) data resulting in about 50 000 brightness temperature vectors associated with their corresponding rain-rate profiles. The database is then divided in two: a retrieval database of about 35 000 rain profiles and a test database of about 25 000 rain profiles. Although in principle this approach is used to build a database over both land and ocean, the results presented here are only given for ocean surfaces, for which the conditions for the retrieval are optimal. An algorithm is built using the retrieval database. This algorithm is then used on the test database, and results show that the error can be constrained to reasonable levels for most of the observed rain ranges. The relative error is nonetheless sensitive to the rain rate, with maximum errors at the low and high ends of the rain intensities (+60% and −30%, respectively) and a minimum error between 1 and 7 mm h−1. The retrieval method is optimized to exhibit a low total bias for climatological purposes and thus shows a high standard deviation on point-to-point comparisons. The algorithm is applied to the case of Hurricane Bret (1999). The retrieved rain field is analyzed in terms of structure and intensity and is then compared with the TRMM PR original rain field. The results show that the mesoscale structures are indeed well reproduced even if the retrieved rain misses the highest peaks of precipitation. Nevertheless, the mesoscale asymmetries are well reproduced and the maximum rain is found in the correct quadrant. Once again, the total bias is low, which allows for future calculation of the heat sources/sinks associated with precipitation production and evaporation.

One of the most challenging problems in predicting the Madden–Julian oscillation (MJO) is the initiation of large-scale convective activity associated with the MJO over the tropical Indian Ocean. The lack of observations is a major obstacle. The Dynamics of the MJO (DYNAMO) field campaign collected unprecedented observations from air-, land-, and ship-based platforms from October 2011 to February 2012. Here we provide an overview of the aircraft observations in DYNAMO, which captured an MJO initiation event from November to December 2011. The National Oceanic and Atmospheric Administration (NOAA) WP-3D aircraft was stationed at Diego Garcia and the French Falcon 20 aircraft on Gan Island in the Maldives. Observations from the two aircraft provide a unique dataset of three-dimensional structure of convective cloud systems and their environment from the flight level, airborne Doppler radar, microphysics probes, ocean surface imaging, global positioning system (GPS) dropsonde, and airborne expendable bathythermograph (AXBT) data. The aircraft observations revealed interactions among dry air, the intertropical convergence zone (ITCZ), convective cloud systems, and air–sea interaction induced by convective cold pools, which may play important roles in the multiscale processes of MJO initiation. This overview focuses on some key aspects of the aircraft observations that contribute directly to better understanding of the interactions among convective cloud systems, environmental moisture, and the upper ocean during the MJO initiation over the tropical Indian Ocean. Special emphasis is on the distinct characteristics of convective cloud systems, environmental moisture and winds, air–sea fluxes, and convective cold pools during the convectively suppressed, transition/onset, and active phases of the MJO.

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International audienceThe Megha-Tropiques mission is operating a suite of payloads dedicated to the documentation of the water and energy cycles in the intertropical region in a low inclination orbit. The satellite was launched in October, 2011 and we here review the scientific activity after the first three years of the mission. The microwave sounder (SAPHIR) and the broad band radiometer (SCARAB) are functioning nominally and exhibit instrumental performances well within the original specifications. The microwave imager, MADRAS, stopped acquisition of scientific data on January 26th, 2013 due to a mechanical failure. During its 16 months of operation, this radiometer experienced electrical issues making its usage difficult and delayed its validation. A suite of geophysical products has been retrieved from the Megha-Tropiques payloads, ranging from TOA radiative flux to water vapor profiles and instantaneous rain rates. Some of these geophysical products have been merged with geostationary data to provide, for instance, daily accumulation of rainfall all over the intertropical region. These products compare favorably with references from ground based or space-borne observation systems. The contribution of the mission unique orbit to its scientific objectives is investigated. Preliminary studies indicate a positive impact on both, humidity Numerical Weather Prediction forecasts thanks to the assimilation of SAPHIR Level 1 data, and on the rainfall estimation derived from the Global Precipitation Mission constellation. After a long commissioning phase, most of the data and the geophysical products suite are validated and readily available for further scientific investigation by the international community

Characterising the error associated with satellite rainfall estimates based on spaceborne passive and active microwave measurements is a major issue for many applications, such as water budget studies or assessment of natural hazards caused by extreme rainfall events. We focus here on the error structure of the Bayesian Rain retrieval Algorithm Including Neural Network (BRAIN), the algorithm that provides instantaneous quantitative precipitation estimates at the surface based on the MADRAS radiometer on board the Megha-Tropiques satellite. A version of BRAIN using data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has been compared to reference values derived either from TRMM Precipitation Radar (PR) or from a ground validation (GV) dataset. The ground-based measurements were provided by two densified rain-gauge networks in West Africa, using a geostatistical framework. The comparisons were carried out at the BRAIN retrieval scale for TMI (instantaneous and 12.5 km) and over a tenyear-long period. The primary contribution of this study is to provide some insight into the most significant error sources of satellite rainfall retrieval. This involves comparisons of rainfall detectability, distributions and spatial representativeness, as well as separation of systematic biases and random errors using Generalized Additive Models for Location, Scale and Shape. In spite of their different sampling properties, the three rain estimates were found to detect rainfall consistently. The most important BRAIN-TMI error is due to the rain/no-rain delimitation which causes about 20% of volume rainfall loss relative to PR and GV. BRAIN-TMI presents a narrow PDF relative to GV and catches the spatial structure of the most active part of rain fields. The conditional bias is significant (e.g. +2 mm h −1 for light-moderate rain rates, −2 mm h −1 for rain rates greater than 8 mm h −1 ) and the overall bias is within 10%. The PR shows a significant underestimation for high rain rates with respect to GV. The proposed framework could be applied to the evaluation of other passive microwave sensors (SSMI, AMSR-E or MADRAS) or rainfall satellite products.

A combination of passive microwave and radar observations from the Tropical Rainfall Measuring Mission (TRMM) satellite is used to investigate the consistency between the two sensors. Rather than relying on some absolute ''truth'' to verify retrievals, this paper focuses on one assumption-namely, the drop size distribution (DSD)-and how different DSDs lead to improved or reduced consistency. Results from a case in the central Pacific suggest that a crude consistency may be achieved if a different drop size is used for the radiometer and the radar. In this particular case, a Marshall-Palmer or a gamma distribution with the shape parameters properly set leads to similar results. Although this study offers no independent validation of its conclusions, it does demonstrate that rainfall validation need not be confined to surface rainfall measurements, which are only loosely related to the volumetric observations made by most sensors. As mean size distributions of raindrops are measured in the TRMM field experiments by disdrometers, profilers, multiparameter radars, and direct aircraft observations, the technique presented in this paper can be applied on a storm-by-storm basis, and conclusions can be verified directly.

An ongoing change in the theoretical framework from deterministic to probabilistic satellite rainfall estimations emerges from applications that require an error associated with rain estimates. The error budget for accumulated rainfall consists of several terms; these terms are related to sampling, algorithmic and calibration errors. From a number of studies, various errors were derived which have improved our understanding of the different terms in this error budget. In this paper, a methodological effort leading to the evaluation of a Tropical Amount of Precipitation with an Estimation of ERrors (TAPEER) is presented. It involves first merging passive microwave instantaneous rain rates from the BRAIN algorithm together with infrared imagery to build rain accumulations, and then evaluating the different terms of the error budget using two techniques. A dedicated error model is used to evaluate sampling errors and a forward error propagation approach is used for the estimation of algorithmic and calibration errors. One of the main findings in this study is the large contribution of the sampling errors and the algorithmic errors of BRAIN on medium rain rates (2-10 mm h −1 ) in the total error budget. This methodology leads to the formulation of a satellite rainfall product called TAPEER-BRAIN. This product will be one of the operational rainfall products for the Megha-Tropiques mission and will provide 1 • /1-day accumulated rainfall estimations and associated error over the whole tropical belt.