Analytical/conceptual models for the satelite products : an example from the MT Water Vapor product

The uncertainty model developed for the Megha-Tropiques Water Vapor profiles will be presented

Exploitation des mesures "vapeur d'eau" du satellite Megha-Tropiques pour l'élaboration d'un algorithme de restitution de profils associés aux fonctions de densité de probabilité de l'erreur conditionnelle

Water vapor has a central role in climatic systems: in a global scope, water vapor is important to energy distribution from tropical zones to polar regions via circulation cells; at mesoscale it participates to cloud systems development, precipitating or not, and in the lowest scale wet thermodynamic laws are the kernel of the clouds microphysics. Finally, water vapor is the most abundant greenhouse gas which is the key in the positive feedback phenomenon. The Megha-Tropiques mission was conceived to ameliorate the tropical water vapor cycle documentation and also the energy budget, through its three instruments: two microwave radiometers (MADRAS, an imager and SAPHIR, a sounder) dedicated to rain (liquid and iced ones) and atmospheric water vapor observations respectively; and a multispectral radiometer (ScaRaB)dedicated to radiative flux measurements at the top of the atmosphere with the aim to tropical water vapor and energy budget to describe this tropical systems evolution, it is composed by two microwaves radiometers. The payload characteristics allow, theoretically, an enhanced resolution around 183 GHz of microwave spectra, and soundings in presence of convective clouds. With the aim to build a learning database with correlated and also representative to problem data, an important tropical clear sky radiosoundings database was built for the 1990-2008 period to be coupled to a radiative transfer model to obtain synthetic brightness temperatures of two radiometers. We designed a methodology that allows us to develop a water vapor profile restitution algorithm from SAPHIR and MADRAS observations, and specially to quantify the restitution of conditional uncertainties. The approach was oriented to purely statistic restitution methods with the aim to extract the maximum information, without complementary information of the atmosphere thermodynamic structure or a priori profiles, to focus on inverse problem restrictions. Three statistical models were optimized using this learning database to estimate seven layers tropospheric water vapor profiles, a neural network (MLP), the generalized additive model and the support vector machines, and two conditional error pdf modeling hypothesis were tested, a Gaussian hypothesis (HG) and a two mixed Gaussian model (M2G). The optimized models are shown similar behaviors, which lead us to conclude that we obtain a model-independency restitution accuracy and this accuracy is directly related to physical constraints. Also, maximal precision was achieved in mid-tropospheric layers (maximal bias: 2.2% and maximal correlation coefficient: 0.87 in errors restitutions) while extreme layers show degraded precision values (at surface and the top of the troposphere, maximal bias: 6.92 associated to a fort dispersion with correlation coefficient: 0.58), this behavior could be explained by instrumental information contents. From conditional error probability functions, knowing observed brightness temperatures, humidity confidence intervals were estimated by each layer. The two hypotheses were tested and we obtained better results from the Gaussian Hypothesis. This methodology was tested using real data from Megha-Tropiques "water vapor" validation campaign in summer 2012 at Ouagadougou, which gave us radiosoundings measurements colocalized with satellite observations. Taking into account the incidence angle, SAPHIR calibration uncertainties and in-situ associated errors from measurement, results are consistent with the learning database with better accuracy (bias: 4.55% and correlation coefficient: 0.874 for error estimations) at mid-tropospheric layers, degrading it to extreme layers (bias: -4.81% and correlation coefficient: 0.491). Systematic application to SAPHIR observations could lead to tropical water vapor variability studies using theirs associated intervals confidence.La place de la vapeur d'eau est centrale dans le système climatique : à l'échelle globale, elle participe à la redistribution de l'excédent d'énergie des régions tropicales vers les régions polaires via les grandes cellules de circulation, à méso-échelle elle participe au développement (maturation, dissipation) des systèmes nuageux, précipitants ou non, et à plus petite échelle, ce sont les lois de la thermodynamique humide qui régissent la microphysique de ces nuages. Finalement c'est le plus abondant des gaz à effet de serre qui est au centre d'une boucle de rétroaction fortement positive. La mission satellite Megha-Tropiques a été conçue pour améliorer la documentation du cycle de l'eau et de l'énergie des régions tropicales, via notamment trois instruments : deux radiomètres microondes MADRAS (un imageur) et SAPHIR (un sondeur) respectivement dédiés à l'observation des précipitations (liquides et glacées) et de l'humidité relative atmosphérique, et un radiomètre multi-spectral ScaRaB pour la mesure des flux radiatifs au sommet de l'atmosphère dans le bilan de l'eau et l'énergie de l'atmosphère tropicale et décrire l'évolution de ces systèmes. Les caractéristiques des instruments embarqués permettraient une résolution étendue autours de la raie à 183 GHz du spectre microonde, qui permet de sonder le contenu en vapeur d'eau même en présence des nuages convectifs. Afin de construire une base d'apprentissage où les valeurs d'entrée et sortie soient parfaitement colocalisées et qui, en même temps, soit représentative du problème à modéliser, une large base de radiosondages obtenus par ciel claire et couvrant la bande tropicale (±30° en latitude) sur la période 1990-2008 a été exploitée en parallèle à un modèle de transfert radiatif pour l'obtention des températures de brillance simulées des deux radiomètres. Nous avons mis au point une méthodologie qui nous a permis de développer un algorithme de restitution des profils de vapeur d'eau à partir des observations SAPHIR et MADRAS, et surtout de quantifier l'incertitude conditionnelle d'estimation. L'approche s'est orientée vers l'exploitation des méthodes purement statistiques de restitution des profils afin d'extraire le maximum d'information issues des observations, sans utiliser d'information complémentaire sur la structure thermodynamique de l'atmosphère ou des profils a priori, pour se concentrer sur les diverses restrictions du problème inverse. Trois modèles statistiques ont été optimisés sur ces données d'apprentissage pour l'estimation des profils sur 7 couches de la troposphère, un réseaux de neurones (modèle perceptron multicouches), le modèle additif généralisé et le modèle de machines à vecteur de support (Least Square-Support Vector Machines), et deux hypothèses de modélisation de la fonction de distribution de la probabilité (pdf) de l'erreur conditionnelle sur chacune des couches ont été testées, l'hypothèse Gaussienne (HG) et le mélange de deux distributions Gaussiennes (M2G). L'effort porté sur l'optimisation des modèles statistiques a permis de démontrer que les comportements des trois modèles d'estimation sont semblables, ce qui nous permet de dire que la restitution est indépendante de l'approche utilisée et qu'elle est directement reliée aux contraintes physiques du problème posé. Ainsi, le maximum de précision pour la restitution des profils verticaux d'humidité relative est obtenu aux couches situées dans la moyenne troposphère (biais maximum de 2,2% et coefficient de corrélation minimum de 0,87 pour l'erreur d'estimation) tandis que la précision se dégrade aux extrêmes de la troposphère (à la surface et proche de la tropopause, avec toutefois un biais maximale de 6,92% associé à une forte dispersion pour un coefficient de corrélation maximum de 0,58 pour l'erreur d'estimation), ce qui est expliqué par le contenu en information des mesures simulées utilisées. A partir de la densité de probabilité de l'erreur, connaissant les températures de brillance observées, des intervalles de confiance conditionnels de l'humidité de chacune de couches de l'atmosphère ont été estimés. Les algorithmes d'inversion développés ont été appliqués sur des données réelles issues de la campagne "vapeur d'eau" de validation Megha-Tropiques de l'été 2012 à Ouagadougou qui a permis d'obtenir des mesures par radiosondages coïncidentes avec les passages du satellite. Après prise en compte de l'angle de visée, des incertitudes liées à l'étalonnage de SAPHIR et des erreurs associées à la mesure in situ, l'exploitation de ces données a révélé un comportement semblable aux données de l'apprentissage, avec une bonne performance (biais de 4,55% et coefficient de corrélation de 0,874 sur l'erreur d'estimation) en moyenne troposphère et une dégradation aux extrêmes de la colonne atmosphérique (biais de -4,81% et coefficient de corrélation de 0,419). L'application systématique sur l'ensemble des mesures réalisées par SAPHIR permettra donc mener des études de la variabilité de la vapeur d'eau tropicale en tenant compte des intervalles de confiance associés à la restitution

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

Retrieval of Relative Humidity Profiles and its Associated Error from Megha-Tropiques Measurements

Water vapor has a great role in the atmosphere dynamics and thermodynamics processes and it is the main greenhouse gas regulating the Earth's climate. Measuring water vapor present important problems which hinder detailed and intensive studies. The water vapor's principal aspect is its strong variability spatio-temporal and passive remote sensing helps measuring vast areas per day; but passive remote sensing obtains indirect measures, yielding to use restitution methods. Regarding detection problems is important to keep in mind the water vapor vertical behavior, near surface the absolute humidity is bigger than in higher altitudes; in consequence, instruments must have an expanded work scale to obtain acceptable precision values for all cases. The SAPHIR microwave radiometer onboard the recent Megha-Tropiques plateform observes the tropospheric relative humidity with six channels in the strong water vapor absorption band (near183.31, ranging from 0.2GHz to 11GHz). With respect to MHS and AMSU-B radiometers, this configuration is aimed at providing an improved retrieval of the tropospheric relative humidity. The Megha-Tropiques' tropical orbit is an important advantage with an enhanced sample rate, it allows 3 to 6 observations each day for any point between 23°S and 23°N. In this work we focus on the retrieval of relative humidity profiles distribution given a set of 22 levels of relative humidity obtained by tropical radiosoundings in clear sky scenes and the associated set of simulated satellite brightness temperatures using the RTTOV model. Retrieval of the relative humidity profiles from satellite measurements are commonly based on neural network algorithms (ex: [Aires and Pringent, 2001]). Alternative statistical models exist such as support vector machines (SVM) and additive models (ex: Generalized Additive Model). A comparison of three models was performed, in equal conditions of input and output data sets, through their statistical values (error variance, correlation coefficient and error mean) obtaining a seven layers profile of relative humidity. The three models show the same behavior with respect to layers, mid-tropospheric layers reach the best statistical values suggesting a model-independent problem. The smallest relative humidity error standard deviation (2.45% from 4st layer) is obtained thanks to an improved version of the SVM while the neural network reveals higher values for almost all layers. GAM model has better results than the neural network for high layers. In a general way, the improved SVM method obtains better results respect to other models. Finally, the associated error of retrieval is studied through the characterization of the probability density function of the relative humidity at given atmospheric pressure

Tropospheric Relative Humidity Profile Statistical Retrievals and their Confidence Interval from Megha-Tropiques Measurements

International audienceThe combination of the two microwave radiometers, SAPHIR and MADRAS, on board the Megha-Tropiques platform is explored to define a retrieval method that estimates not only the relative humidity profile but also the associated confidence intervals. A comparison of three retrievals models was performed, in equal conditions of input and output data sets, through their statistical values (error variance, correlation coefficient and error mean) obtaining a profile of seven layers of relative humidity. The three models show the same behavior with respect to layers, mid-tropospheric layers reaching the best statistical values suggesting a modelindependent problem. Finally, the study of the probability density function of the relative humidity at a given atmospheric pressure further gives insight of the confidence intervals

Evaluation of the atmospheric humidity as seen by SAPHIR/Megha-Tropiques: accounting for uncertainties

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A layer-averaged relative humidity profile retrieval for microwave observations: design and results for the Megha-Tropiques payload

A statistical method trained and optimized to retrieve relative humidity (RH) profiles is presented and evaluated with measurements from radiosoundings. The method makes use of the microwave payload of the Megha-Tropiques plateform, namely the SAPHIR sounder and the MADRAS imager. The approach, based on a Generalized Additive Model (GAM), embeds both the physical and statistical characteritics of the inverse problem in the training phase and no explicit thermodynamical constraint, such as a temperature profile or an integrated water vapor content, is provided to the model at the stage of retrieval. The model is built for cloud-free conditions in order to avoid the cases of scattering of the microwave radiation in the 18.7-183.31 GHz range covered by the payload. Two instrumental configurations are tested: a SAPHIR-MADRAS scheme and a SAPHIR-only scheme, to deal with the stop of data acquisition of MADRAS in January 2013 for technical reasons. A comparison to retrievals based on the Multi-Layer Perceptron (MLP) technique and on the Least Square-Support Vector Machines (LS-SVM) shows equivalent performance over a large realistic set, promising low errors (bias 0.8) throughout the troposphere (150-900 hPa). A comparison to radiosounding measurements performed during the international field experiment CINDY/DYNAMO/AMIE of winter 2011-2012 confirms these results for the mid-tropospheric layers (correlation between 0.6 and 0.92), with an expected degradation of the quality of the estimates at the surface and top layers. Finally a rapid insight of the large-scale RH field from Megha-Tropiques is discussed and compared to ERA-Interim

Estimating confidence intervals around relative humidity profiles from satellite observations: Application to the SAPHIR sounder

International audienceA novel scheme for the estimation of layer-averaged relative humidity (RH) profiles from space-borne observations in the 183.31GHz line is presented. Named ARPIA for Atmospheric Relative humidity Profiles Including Analysis of confidence intervals, it provides for each vector of observations the parameters of the distribution of the RH instead of its expectation as usually done by the current methods. The profiles are composed of 6 layers distributed between 100 and 950hPa. The approach combines the 6 channels of the SAPHIR instrument onboard the Megha-Tropiques satellite and the Generalized Additive Model for Location, Scale and Shape (GAMLSS) to infer the parametric distributions, assuming that they follow a Gaussian law. The knowledge of the conditional uncertainty is an asset in the evaluation using radiosounding profiles of RH with a dedicated bayesian method. Taking the uncertainties into account in both the ARPIA estimates and the in situ measurements yields to have biases, root-mean-square and correlation coefficients in the range -0.56% - 9.79%, 1.58% - 13.32% and 0.55 - 0.98 respectively, the largest biases being obtained over the continent, in the mid-tropospheric layers

Water vapor of the tropical atmosphere from the SAPHIR microwave sounder

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