Quarterly Journal of the Royal Meteorological Society

Kidd, C.; Srinivasan, J.; Roca, R.

doi: 10.1002/qj.2201pmid: N/A

Kidd, C.; Srinivasan, J.; Roca, R.

doi: 10.1002/qj.1869pmid: N/A

The microwave payload of the Megha‐Tropiques mission is explored to quantify the expected improvements in the retrieval of relative humidity profiles. Estimations of the profiles are performed using a generalized additive model that uses cubic smoothing splines to address the nonlinear dependencies between the brightness temperatures (TB) in the 183.31 GHz band and the relative humidity of specified tropospheric layers. Under clear‐sky and oceanic situations, the six‐channel configuration of the SAPHIR radiometer clearly improves the retrieval and reduces by a factor of two the variance of the residuals with respect to the current space‐borne humidity sounders that have three channels in this band (AMSU‐B, MHS). Additional information from the MADRAS radiometer (at 23.8 and 157 GHz) further improves the restitution with correlation coefficient higher than 0.89 throughout the troposphere. Copyright © 2011 Royal Meteorological Society

Aires, Filipe; Bernardo, Frédéric; Prigent, Catherine

doi: 10.1002/qj.1888pmid: N/A

A water‐vapour retrieval algorithm has been developed that uses satellite observations in the microwave region. It is based on neural‐network modelling and includes a dedicated calibration scheme for the satellite observations. The water vapour is retrieved for clear and cloudy scenes, over both ocean and land surfaces. Precipitation cases are excluded. The atmospheric relative humidity profile is retrieved on six atmospheric layers, together with the total column water vapour. By‐products are also retrieved by the algorithm, including surface temperature and microwave emissivities over the continents and surface wind speed over the ocean. A first version of a retrieval chain has been produced for the French–Indian Megha‐Tropiques mission launched on 12 October 2011. The algorithm has been further developed for the instruments AMSR‐E/HSB (resp. AMSU‐A/MHS) on board the AQUA (resp. MetOp) platform, in order to test it on existing satellite observations. In this article, the principles of the inversion method are presented and the theoretical retrieval uncertainties are assessed using direct tests on simulated data as well as estimations using the traditional information‐content analysis. Results of the retrieval algorithm will be evaluated in a companion article for AQUA and MetOp observations using comparisons with European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis and radiosonde measurements. Copyright © 2012 Royal Meteorological Society

Bernardo, Frédéric; Aires, Filipe; Prigent, Catherine

doi: 10.1002/qj.1946pmid: N/A

In a companion article (Paper I), a water‐vapour retrieval algorithm has been developed using microwave sounding observations. An operational chain derived from this algorithm is being used for the Megha‐Tropiques mission, launched in autumn 2011. The water vapour is retrieved for clear and cloudy scenes, excluding precipitation cases, over ocean and land surfaces. By‐products are also calculated by the algorithm, including surface temperature and microwave emissivities over land. Evaluation of the water‐vapour products is the main objective of this article. The algorithm is tested using the HSB/AMSR‐E (resp. MHS/AMSU‐A) instruments on board the AQUA (resp. MetOp) platform, over the Tropics between ±30° in latitude. Results are compared with European Centre for Medium‐Range Weather Forecasts (ECMWF) analyses and data from radiosondes. The root‐mean‐square error for the total column water vapour is ∼5 kg m2 for both clear and cloudy scenes, compared with radiosondes over land. The atmospheric water‐vapour profile is retrieved for six atmospheric layers and the root‐mean‐square error is estimated to be lower than 20% in relative humidity, even for the lowest atmospheric layer over land. A posteriori validation tests on the brightness temperatures indicate an overall positive impact of the retrievals relative to a priori ECMWF analyses. Copyright © 2012 Royal Meteorological Society

Chambon, Philippe; Jobard, Isabelle; Roca, Rémy; Viltard, Nicolas

doi: 10.1002/qj.1907pmid: N/A

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. Copyright © 2012 Royal Meteorological Society

Kirstetter, Pierre‐Emmanuel; Viltard, Nicolas; Gosset, Marielle

doi: 10.1002/qj.1964pmid: N/A

Characterising the error associated with satellite rainfall estimates based on space‐borne 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 ten‐year‐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. Copyright © 2012 Royal Meteorological Society

Kacimi, Sahra; Viltard, Nicolas; Kirstetter, Pierre‐Emmanuel

doi: 10.1002/qj.2114pmid: N/A

The detection of rainfall remains a challenge for the monitoring of precipitation from space. A methodology is presented to identify rain events from spaceborne passive microwave data using neural networks. We focus on BRAIN, the algorithm that provides instantaneous quantitative precipitation estimates at the surface, based on the MADRAS radiometer onboard the Megha‐Tropiques satellite. A version of BRAIN using data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has been used to compare several multilayer perceptrons (MLP) trained on different combinations of TMI brightness temperatures with the conventional GSCAT‐2 algorithm approach used for rainfall detection. These classifiers were compared at a global scale to reference values from the TRMM Precipitation Radar (PR). They were also compared to ground measurements using two 1° × 1° dense rain‐gauge networks from different climatic zones in West Africa to assess the influence of rainfall types. At the global scale the MLPs provide better Probability of Detection than the GSCAT‐2 decision tree but tend to have a higher False Alarm Rate. While no unique solution exists given the strong regional dependence of the classifiers' performances, the screen based on the 19, 21 and 85 GHz channels provides the best detection results at the instantaneous scales. As to accumulated rainfall, the screen that exhibits the lower bias relative to the PR makes use of the 37 and 85 GHz channels. The evaluation over West Africa using 10 years of TRMM overpasses shows that MLPs are in better agreement with both the PR and the gauges than GSCAT‐2. The MLP trained on the 37 and 85 GHz channels increases the Probability of Detection by nearly 35% compared to the former screening over the two studied regions. Better results are obtained in the case of organized systems. Copyright © 2013 Royal Meteorological Society

Gosset, Marielle; Viarre, Julien; Quantin, Guillaume; Alcoba, Matias

doi: 10.1002/qj.2130pmid: N/A

The evaluation of rainfall products over the West African region will be an important component of the Megha‐Tropiques (MT) Ground Validation (GV) plan. In this paper, two dense research gauge networks from Benin and Niger, integrated in the MT GV plan, are presented and are used to evaluate several currently available global or regional satellite‐based rainfall products. Eight products—the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction Center Morphing method (CMORPH), Tropical Rainfall Measuring Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) 3B42 real‐time and gauge‐adjusted version, Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center (CPC) African Rainfall Estimate (RFE), Estimation des Precipitation par SATellite (EPSAT), and Global Precipitation Climatology Project One Degree Daily estimate (GPCP‐1DD)—are compared to the ground reference. The comparisons are carried out at daily, 1° resolution, over the rainy season (June–September), between the years 2003 and 2010. The work focuses on the ability of the various products to reproduce salient features of the rainfall regime that impact the hydrological response. The products are analysed on a multi‐criteria basis, focusing in particular on the way they distribute the rainfall within the season and by rain rate class. Standard statistical diagnoses such as the correlation coefficient, bias, root mean square error and Nash skill score are computed and the inter‐annual variability is documented. Two simplified hydrological models are used to illustrate how the nature and structure of the product error impact the model output in terms of runoff (calculated using the Soil Conservation Service method, SCS, in Niger) or outflow (calculated with the ‘modèle du Génie Rural à 4 paramètres Journalier’, GR4J model, in Benin). Copyright © 2013 Royal Meteorological Society

Fiolleau, Thomas; Roca, Rémy

doi: 10.1002/qj.2174pmid: N/A

The ability of the current and upcoming space‐borne microwave observing systems to document precipitation processes during the life cycle of tropical convective systems is investigated with emphasis on sampling considerations. A composite technique is introduced that will serve as a Day 1 algorithm for the Megha‐Tropiques mission. It is exemplified using the Tropical Rainfall Measurement Mission (TRMM) satellite observations from the TRMM Microwave Imager (TMI) instrument and the fleet of operational geostationary infrared images for the boreal summer 2009 over the whole intertropical belt. At the system scale, over both land and oceanic regions, rainfall is overall strong at the beginning (the first third) of the life cycle and then smoothly decreases as the system shrinks and dissipates. Larger rain yields are observed for the land systems (∼6 mm h−1 maximum) compared to the systems over ocean (∼4 mm h−1 maximum). An in‐depth analysis of the sensitivity of the results to various aspects of the sampling is performed using simulated observations. The benefit of using various platforms is discussed, including considerations of constellation configuration. The entire Tropics as well as regional scales are explored, revealing the expected improvements from the inclusion of the Megha‐Tropiques observations. The sampling results are also strongly supportive of the use of multiple‐platform microwave observations from the upcoming Global Precipitation Mission constellation to build a mesoscale convective system precipitation composite life cycle, although the merging of the parameters derived from various resolution radiometers would deserve further investigations. Copyright © 2013 Royal Meteorological Society

Kidd, C.; Srinivasan, J.; Roca, R.

doi: 10.1002/qj.2041pmid: N/A

The major scientific objective of the Megha‐Tropiques (MT) satellite, an Indian Space Research Organisation (ISRO)–Centre National d'Études Spatiales (CNES) collaborative project, is to understand the energy and water cycles in the global tropical region. With its 20° inclined orbit, it will frequently measure radiation emitted by the Earth‐Atmosphere System in the visible, infrared and microwave spectrum through its four sensors on board. Various geophysical parameters, namely water vapour, cloud liquid water and surface winds over oceanic regions, and the rainfall, humidity profile and top‐of‐atmosphere radiative fluxes over land as well as over oceanic regions will be derived from the measurements made by these instruments. This article deals with the efforts made by ISRO to develop algorithms for deriving these geophysical parameters from the microwave imager and sounder, mentioning the pre‐launch specifications with prelude examples from existing space‐borne sensors of similar types. The sensor‐specific algorithms are presented in different sections. Copyright © 2012 Royal Meteorological Society