Algorithm Validation in the Megha-Tropiques Framework: An Attempt to Improve the Microphysic Parameterization in a Radiative Transfer Model

In the framework of the Megha Tropiques Mission, we focus on the rain retrieval from MADRAS and other conical-scanning passive microwave imagers. The retrieval algorithm used here is a Bayesian-based algorithm known as BRAIN (Bayesian Rain retrieval Algorithm Including Neural network), which is described in the paper by Viltard et al. (2006). The retrieved rain rate is obtained through the use of a reference database built from a Radiative Transfer Model (RTM) simulation. In order to improve the simulation of the brightness temperatures, it is necessary to provide the RTM with an accurate parameterization of the ice particles. In this context, a study is in progress to evaluate if polarimetric radar hydrometeor retrievals are useful to distinguish the various ice species in precipitating systems and if it is possible to correlate these with the observed brightness temperatures. The goal of the study is to develop a series of parameterization corresponding to various meteorological situations to be used in the RTM . Datasets from the NCAR dual-wavelength S- and Ka-band radar (S-PolKa) collected during the Dynamics of the MJO (DYNAMO) field campaign that occurred in the Indian Ocean in 2011-2012 were compared with brightness temperatures from TRMM-TMI and MADRAS. Using the SPOLKA data, a PID (Particles IDentificator) classification is built using a fuzzy logic approach. To combine this information with the TMI and MADRAS brightness temperature we co-locate the polarimetric radar data inside the TMI radiometer pixel. A piewedge representation is chosen to show the proportion in the satellite pixel of the various species identified by the SPOLKA radar and associated with the PID

3D wind field retrieval from spaceborne Doppler radar

International audienceBOITATA is a joint effort between Brazil (INPE/AEB) and France (CNES). The goal is to embark a Doppler radar with scanning possibilities onboard a low-orbiting satellite. This instrument should be implemented in addition to a Passive Microwave Radiometer (PMR) between 19 and 183 GHz, an improved ScaraB-like broadband radiometer, a mm/submm PMR and a lightning detection instrument

Use of MADRAS on Megha-Tropiques to study Hurricane Sandy and Typhoon Bophat Evolution

International audienceMegha-Tropiques is a French-Indian satellite dedicated to tropical convection. The low-inclination of (20°) its orbit and the large swath of its main instrument MADRAS allows us to sample the rain systems in series of overpasses separated by 1h30 time lags. Coupled with other similar sensors like TMI on TRMM, AMSR2 on GCOM-W or SSMI and SSMIS on NOAA satellites, we are able to get a good sampling of the temporal evolution of those systems in terms of rain structures and intensities. Hurricanes are particularly interesting because their characteristic life time is a few days and their general motion is relatively slow. Furthermore, the characteristic scale and structure of their precipitation field gives a lot of information about their evolution and those fields are compatible with the spatial resolution of the spaceborne instruments even if the smallest details cannot be captured.MADRAS produced only 15 month of data from October 2011 to January 2013; During its life time it captured a number of hurricanes and typhoons in the various basins. We will focus here on the evolution of two storms that were nicely sampled by MADRAS. First, Hurricane Sandy in October 2011 was observed in its first few days about 16 times from its the tropical storm stage in the Gulf of Mexico and until it moved out of the Caribbean regions as a mature hurricane after crossing through the Island of Cuba. Similarly, typhoon Bophat was sampled while it formed and intensified in the Warm Pool and until it made its landfall in the Philippines.We will show in the presentation how, through examination of the brightness temperatures at 89 and 157 GHz and the rain retrieval algorithm known as BRAIN, we can understand the evolution of each of the storms and how critical satellite with low-inclinations orbits are critical for hurricane forecasts and studies

Characterization of the microphysics of ice in tropical convection

International audienceMegha-Tropiques is a joint satellite between France and India to study the water and energy cycle in the Tropics. The satellite is part of GPM and provides an exceptional sampling of the 23° S-23° N region because of the low inclination of its orbit (20°) combined with the large swath (1700 km) of its main instrument MADRAS. The latter is a 9-channel passive microwave radiometer dedicated mainly to precipitation retrieval. Bauer et al (2005) showed that the most critical source of uncertainties in the precipitation retrieval over land comes from the ice microphysics characteristics. In the framework of the Megha-Tropiques mission we tried to improve the parameterization of precipitating ice in the radiative transfer model.Datasets from two field experiments (Niamey 2010 and Gan 2011) will be used here. These campaigns were specifically designed to improve our knowledge of the ice categories found in the tropical convection. A combination of ground-based (X and S-band) and airborne (W-band) radars was used in conjunction with in-situ probes for microphysics. We will show some comparisons of Particles IDentification (PID) made from the various polarimetric radars in continental Africa and Indian Ocean with the images from these probes. The main idea here is to combine polarimetric radar data and microphysics in-situ measurements, both acquired during the two campaigns to demonstrate the coherence between PID (Particles IDentificator) classification and the microphysical characterization of the ice particles. This analysis will serve as a base to support the construction of a more “climatological” characterization of the ice depending on the convective features: life cycle, environment, season, etc… This climatology will then be compared with the brightness temperature at 89 GHz and 157 GHz of MADRAS

DYCECT-WIVERN: mesurer les vitesses Doppler pour restituer le vent 3D dans la convection

International audienceLa mission DYCECT a été proposée à la prospective du CNES en 2014. Elle a pour ambition d’effectuer une mesure de la vitesse Doppler associée au déplacement des hydrométéores dans la convection avec un balayage permettant d’accéder au champ 3D de vent. Ce champ ouvrirait ensuite la porte à la réalisation de bilan d’eau et d’énergie en prenant en compte les termes de transports horizontaux et verticaux. La présentation fera un état des lieux des activités en question dans la communauté française et au niveau de l’Europe

Statistical database from existing data sets to assess the performances of a dynamics and microphysics studies-oriented mission: DYCECT

International audienceA breakthrough for energy and water budgets would be to retrieve the horizontal and vertical transport terms. We propose here to study the feasibility of a mission that would carry a scanning polarimetric Doppler radar: DYCECT (DYnamique et Cycle de l'Eau dans la Convection Tropicale). The purpose of the mission would not only to be able to measure rain but also to retrieve instantaneous wind fields and statistics of dynamical properties in convection and to retrieve the microphysical properties of precipitating ice. In this study we used the existing data sets (TRMM-PR, CloudSat, GPM-DPR) to build a statistical database allowing us to characterize the cloud types and properties as seen by the future instrument. We can thus define more precisely the effective added value of the latter. We evaluated the occurrence and statistical properties of the rain systems that will be observed, depending on the radar characteristics. The poor temporal sampling of DYCECT (1 to 2 overpasses per day) will force us to work on averaged dynamical properties. It is thus necessary to check how long it takes to converge towards such values because this might impact the minimum required duration of the mission. Datasets already available (TRMM, GPM-Core, Cloudsat) will allow us to characterize the occurrence of the systems according to size, height of the detected clouds, average ice/water content, etc., which will impact the detection capabilities of the future instrument

Evolution temporelle de variables du bilan d'eau dans la convection tropicale à partir des données de Megha-Tropiques

International audienc

Rain Retrieval from TMI Brightness Temperature Measurements using a PR-based Database.

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 (ϩ60% and Ϫ30%, respectively) and a minimum error between 1 and 7 mm h Ϫ1. 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

Rain retrieval using the SAPHIR water vapor sounder on Megha-Tropiques.

International audienceMegha-Tropiques is an Indo-French satellite launched in 2011 to study the water and energy cycle in the tropical belt. The satellite carries on board three passive instruments: MADRAS, an microwave imager, SAPHIR a microwave water vapor sounder, and ScaraB a broadband VIRS to compute TOA radiative budget. Unfortunately, MADRAS worked nominally only for about 14 month before failing. This was a dramatic loss for the rain retrieval objectives of the Megha-Tropiques mission. As an alternative solution an algorithm was developed to retrieve rain from SAPHIR using a combination of the 183 GHz channels. The latter are nominally designed to retrieve water vapor profiles but are also sensitive to scattering by ice. Bennartz and Bauer (2005) showed some preliminary results on the scattering regimes of such sounding instruments. We pushed further on and showed that the sounding properties remain true even in the scattering regime. By co-locating SAPHIR and three space-borne radars: CPR on CloudSat, PR on TRMM and DPR on GPM, we were able to test extensively the information content of the microwave brightness temperatures in scattering regime using the RTTOV-scatt radiative transfer model. This allows us to gather information on the vertical structure of the precipitating ice on the upper part of the cloud. The vertical structure of ice is in turn related to the properties of the convection: deep or shallow and intense or weak. Using these last properties, a rain retrieval algorithm was designed. The presentation will detail how the algorithm works, evaluate its performances and compare the results with the retrieval from MADRAS over the fourteen-month when the two instruments were functioning together