{"id":12,"date":"2025-02-04T17:45:52","date_gmt":"2025-02-04T16:45:52","guid":{"rendered":"https:\/\/sites.lesia.obspm.fr\/filipe-aires\/?page_id=12"},"modified":"2025-02-04T17:45:52","modified_gmt":"2025-02-04T16:45:52","slug":"publications","status":"publish","type":"page","link":"https:\/\/sites.lesia.obspm.fr\/filipe-aires\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>THESES:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>F. Aires<br><em>Resolution des probl\u00e8mes inverses en sciences de l\u2019atmosphere : de l\u2019observation a l\u2019analyse,\u00a0<\/em><br>Th\u00e8se de HDR, 856 pages, 2012.<\/li>\n\n\n\n<li>F. Aires<br><em>Probl\u00e8mes inverses et reseaux de neurones : application a l\u2019interferometre haute resolution IASI et a l\u2019analyse de series temporelles,\u00a0<\/em><br>Th\u00e8se de doctorat, 183 pages, 1999.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>BOOKS:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>145 \u2013 Fassoni-Andrade, Santos Fleischmann, Papa, Paiva, Wongchuig, Melak, Moreira, Paris Ruhoff, Barbosa, Maciel, Novo, Durand, Frappart,\u00a0<strong>Aires<\/strong>, Abrah\u00e3o, Ferreira-Ferreira, Esnipoza, Laipelt, Cost, Espinoza-Villar, Calmant, Pellet,\u00a0<em>Hidrologia da Amaz\u00f4nia vista do espa\u00e7o \u2013 Avan\u00e7os cientificos e desafios futuros<\/em>, ISBN 978-85-88686-48-9, 2024.<\/li>\n\n\n\n<li>144 \u2013\u00a0<strong>Aires<\/strong>,\u00a0<em>Atmospheric water vapour profiling over ocean\/land and for clear\/cloudy situations using microwave observations<\/em><em>, pp. 215-255, in Remote Sensing of Clouds and Precipitation, Ed. C. Andronache, 277 p., ISBN 978-3-319-72582-6, 2018.<\/em><\/li>\n\n\n\n<li>\u00a0143 \u2013 Prigent, Lettenmaier,\u00a0<strong>Aires<\/strong>, Papa,\u00a0<em>Toward a high-resolution monitoring of continental surface water extent and dynamics, at global scale: from GIEMS (Global Inundation Extent from Multi-Satellites) to SWOT (Surface Water Ocean Topography)<\/em>, May 2016, in book \u201cRemote Sensing and Water Resources\u201d.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>ARTICLES:<\/strong><\/h4>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2024:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>142 \u2013\u00a0<strong>Aires<\/strong>, Weston, de Rosnay, Fairbairn,\u00a0<em>Statistical approaches to assimilate ASCAT soil moisture information: Part II Evaluation and potential for assimilation<\/em>, QJRMS, submitted, 2024.<\/li>\n\n\n\n<li>141 \u2013 Boucher,\u00a0<strong>Aires<\/strong>, Doutriaux-Boucher,\u00a0<em>Introducing a New Partial Convolutional Neural Network for IASI Cloud Classification.\u00a0<\/em>RSE, submitted.<\/li>\n\n\n\n<li>140 \u2013 Heberger,\u00a0<strong>Aires<\/strong>, Pellet,\u00a0<em>I<\/em><em>mproving Satellite Remote Sensing Estimates of the Global Terrestrial Hydrologic Cycle via Neural Network Modeling<\/em>, submitted.<\/li>\n\n\n\n<li>139 \u2013 Nguyen,\u00a0<strong>Aires<\/strong>,\u00a0<em>Satellite retrieval and downscaling of the peatlands using machine learning<\/em>, IEE Transactions on Geoscience and Remote Sensing, Submitted.<\/li>\n\n\n\n<li>138 \u2013 Dinh,\u00a0<strong>Aires<\/strong>, Rahn,\u00a0<em>Climate change impact on coffee production over Vietnam and Brazil<\/em>, submitted to Earth\u2019s Future.<\/li>\n\n\n\n<li>137 \u2013 Nguyen,\u00a0<strong>Aires<\/strong>,\u00a0<em>A Downscaling of surface waters at global scale<\/em>, submitted to Remote Sensing.<\/li>\n\n\n\n<li>136 \u2013 Pellet,\u00a0<strong>Aires<\/strong>, Baez Villabuena, Filippucci,\u00a0<em>Estimating the Ebro river discharge at 1~km\/daily resolution using indirect satellite observations<\/em>, Environmental Research Communications, in press, 2024.<\/li>\n\n\n\n<li>135 \u2013 Normandin, Frappart, Bourrel, Blarel,\u00a0Biancamaria, Wigneron, Galenon, Bernard, Coulon, Lubac, Marieu, Vantrepotte, Pham-Duc, Toan Do, Prigent,\u00a0<strong>Aires<\/strong>, Yamazaki, &amp; Ciais,\u00a0<em>Sharp decline in surface water resources for agriculture and fisheries in the Lower Mekong Basin over 2000-2020<\/em>, Science of the Total Environment, 950, 10.1016\/j.scitotenv.2024.175259, 2024.<\/li>\n\n\n\n<li>134 \u2013\u00a0Lehner,\u00a0Anand,\u00a0Fluet-Chouinard,\u00a0Tan,\u00a0<strong>Aires<\/strong>,\u00a0Allen,\u00a0Bousquet,\u00a0Canadell,\u00a0Davidson,\u00a0Finlayson,\u00a0Gumbricht,\u00a0Hilarides,\u00a0Hugelius,\u00a0Jackson,\u00a0Korver,\u00a0McIntyre,\u00a0Nagy,\u00a0Olefeldt,\u00a0Pavelsky,\u00a0Pekel,\u00a0Poulter,\u00a0Prigent,\u00a0Wang,\u00a0Worthington,\u00a0Yamazaki, &amp;\u00a0Thieme,\u00a0<em>Mapping the world\u2019s inland surface waters: an update to the Global Lakes and Wetlands Database (GLWD v2)<\/em>, Earth System Science Data, 10.5194\/essd-2024-204, 2024.<\/li>\n\n\n\n<li>133 \u2013\u00a0<strong>Aires<\/strong>, Pellet,\u00a0<em>Introducing physical expert knowledge into AI models: An hybrid approach to close the water budget from satellite observations<\/em>, J. of Hydrometeorology,\u00a0<a href=\"https:\/\/doi.org\/10.1175\/JHM-D-23-0001.1\" target=\"_blank\" rel=\"noreferrer noopener\">10.1175\/JHM-D-23-0001.1<\/a>, 2024.<\/li>\n\n\n\n<li>132 \u2013 Hascoet, Pellet,\u00a0<strong>Aires<\/strong>,\u00a0<em>Learning global evapotranspiration dataset corrections from a water cycle closure supervision<\/em>,\u00a0 Remote Sensing, 2024,\u00a0<em>16<\/em>(1), 170,\u00a0\u00a0<a href=\"https:\/\/doi.org\/10.3390\/rs16010170\">10.3390\/rs16010170<\/a>, 2024.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2023:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>131 \u2013 Pellet,\u00a0<strong>Aires<\/strong>,\u00a0<em>TA physical\/statistical data-fusion for the dynamical downscaling of GRACE data at daily and 1 km resolution<\/em>, J. of Hydrology, Vol. 628, 2024, 10.1016\/j.jhydrol.2023.130565, 2023.<\/li>\n\n\n\n<li>130 \u2013 Dinh,\u00a0<strong>Aires<\/strong>,\u00a0<em>Revisiting the bias correction of climate models for impact studies<\/em>, Climate Change, 176, 140 (2023). 10.1007\/s10584-023-03597-y, 2023.<\/li>\n\n\n\n<li>129 \u2013 Nguyen,\u00a0<strong>Aires<\/strong>,\u00a0<em>A global topography-based floodability index for the downscaling, analysis and data-fusion of surface water datasets<\/em>, J. of Hydrology, 10.1016\/j.jhydrol.2023.129406, vol 620, Part A, 129406, 2023.<\/li>\n\n\n\n<li>128 \u2013 Boucher,\u00a0<strong>Aires<\/strong>,\u00a0<em>Towards a new generation of AI-based IASI retrievals of surface temperature: Part II: Assessment<\/em>, QJRMS, 10.1002\/qj.4472, Vol 149, 754, pp 1593-1611,2023.<\/li>\n\n\n\n<li>127 \u2013 Boucher,\u00a0<strong>Aires<\/strong>, Pellet,\u00a0<em>Towards a new generation of AI-based IASI retrievals of surface temperature: Part I: Methodology<\/em>, QJRMS, 10.1002\/qj.4447, Vol 149, 753, pp 1180-1196, 2023.<\/li>\n\n\n\n<li>126 \u2013 Ran,\u00a0<strong>Aires,<\/strong>\u00a0Ciais, Qiu, Wang,\u00a0<em>A Neural Network Classification Framework for Monthly and High Spatial Resolution Surface Water Mapping in the Qinghai\u2013Tibet Plateau from Landsat Observations<\/em>, J. of Hydrometeorology, 24, 10, 10.1175\/JHM-D-22-0211.1, 2023.<\/li>\n\n\n\n<li>125 \u2013 Fluet-Chouinard, Stocker, Zhang, Malhotra,\u00a0 Melton, Poulter, Kaplan, Goldewijk, Siebert, Minayeva, Hugelius, Prigent,<strong>\u00a0Aires<\/strong>, Hoyt, Davidson, Finlayson, Lehner, Jackson, McIntyre,\u00a0<em>Reconstruction of three centuries of wetland loss,\u00a0<\/em>Nature Geoscience, 614, 281-286, 10.1038\/s41586-022-05572-6, 2023.<\/li>\n\n\n\n<li>124 \u2013 Boucher,\u00a0<strong>Aires<\/strong>,\u00a0<em>Improving remote sensing of extreme cases using AI methods \u2013 Application to IASI<\/em>, Environmental Research Letter, 10.1088\/1748-9326\/acb3e3, 18, 024025, 2023.<\/li>\n\n\n\n<li>123 \u2013 Ran,\u00a0<strong>Aires,<\/strong>\u00a0Ciais, Qiu, Hu, Fu, Xue, Wang,\u00a0<em>The Status and Influencing Factors of Surface Water Dynamics on the Qinghai-Tibet Plateau During 2000\u20132020<\/em>,\u00a0<em>IEEE Trans. on Geoscience &amp; Remote Sensing<\/em>, 61, 1-14, 4200114, 10.1109\/TGRS.2022.3231552, 2023.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2022:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>122 \u2013 Dinh,\u00a0<strong>Aires<\/strong>, Rahn,\u00a0<em>Statistical analysis of the weather impact on Robusta coffee yield in Vietnam<\/em>, Frontiers in Environmental Science, Sec. Interdisciplinary Climate Studies, https:\/\/doi.org\/10.3389\/fenvs.2022.820916, 20 June 2022, 2022.<\/li>\n\n\n\n<li>121 \u2013 Pellet,\u00a0<strong>Aires<\/strong>, Yamazaki, Zhou,\u00a0<em>A first continuous and distributed satellite-based mapping of river discharge over the Amazon<\/em>, J. of Hydrology, 614, Par 1, https:\/\/doi.org\/10.1016\/j.jhydrol.2022.128481, 2022.<\/li>\n\n\n\n<li>120 \u2013 Fleischmann, Papa, Fassoni-Andrade, Melack, Wongchuig, Paiva, Hamilton, Fluet-Chouinard,\u00a0<strong>Aires<\/strong>, Al Bitar, Bonnet, Coe, Ferreira-Ferreira, Barbedo, Hess, Jensen, McDonald, Ovando, Park, Parrens, Pinel, Prigent, Resende, Revel, Rosenqvist, Rosenqvist, Rudorff, Silva, Yamazaki, Collischonn,\u00a0<em>How much inundation occurs in the Amazon River basin?<\/em>, RSE, https:\/\/doi.org\/10.1016\/j.rse.2022.113099, 2022.<\/li>\n\n\n\n<li>119 \u2013 Dinh,\u00a0<strong>Aires<\/strong>,\u00a0<em>Nested leave-two-out cross-validation for the optimal crop yield model selection<\/em>, Geoscientific Model Development, 15, 3519-3535, 10.5194\/gmd-2021-218, 2022.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2021:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>118 \u2013 Bouillon, Safieddine, Whitburn, Clarisse,\u00a0<strong>Aires<\/strong>, Pellet, Lezeaux, Scott, Clerbaux,\u00a0<em>Time evolution of the temperature profiles retrieved from 13 years of IASI data using an artificial neural network,\u00a0<\/em>Atmospheric Measurements Techniques, 10.5194\/amt-2021-302, 2021<\/li>\n\n\n\n<li>117 \u2013 Fassoni-Andrade, Fleischmann, Papa, Wongchuig, Paiva, Moreira, Paris, Ruhoff, Barbosa, Maciel, Novo, Durand, Frappart,\u00a0<strong>Aires<\/strong>, Ferreira-Ferreira, Espinoza, Laipelt, Melack, Stephane,\u00a0 Espinoza-Villar, Pellet,<em>Amazon hydrology from Space: Scientific advances and future challenges,\u00a0<\/em>Reviews of Geophysics, 10.1029\/2020FG000728, 2021<\/li>\n\n\n\n<li>116 \u2013\u00a0<strong>Aires<\/strong>, Boucher, Pellet,\u00a0<em>Convolutional Neural Networks for satellite remote sensing at coast resolution. Application for the SST retrieval using IASI,<\/em>\u00a0RSE, 263, 112553,<a href=\"https:\/\/doi.org\/10.1016\/j.rse.2021.112553\" target=\"_blank\" rel=\"noreferrer noopener\">\u00a010.1016\/j.rse.2021.112553, 2021<\/a><\/li>\n\n\n\n<li>115 \u2013 Pellet,\u00a0<strong>Aires<\/strong>, Yamazaki, Papa,\u00a0<em>Satellite monitoring of the water cycle over the Amazon using upstream\/downstream dependency. Part~1: Methodology and initial evaluation<\/em>, Water Resources Research, 57,5, 10.1029\/2020WR028648, 2021.<\/li>\n\n\n\n<li>114 \u2013 Pellet,\u00a0<strong>Aires<\/strong>, Yamazaki, Papa,\u00a0Satellite monitoring of the water cycle over the Amazon using upstream\/downstream dependency. Part~2: Mass-conserved reconstruction of total water storage change and river discharge, Water Resources Research, 57,5, 10.1029\/2020WR028648, 2021.<\/li>\n\n\n\n<li>113 \u2013 Dorigo, Dietrich,\u00a0<strong>Aires<\/strong>, Brocca, Dunkerley, Enomoto, Guntner, Hegglin, Hollmann, Hurst, Johannessen, Kummerow, Lee, Luojus, Looser, Miralles, Pellet, Recknagel, Vargas, Schenider, Schroder, Tapper, Vuglinsky, Wagner, Yu, Zappa, Zemp, Aich,\u00a0<em>Consistent monitoring of global water cycle variability across scales: What are we missing?<\/em>, BAMS,\u00a010.1175\/BAMS-D-19-0316.1, 2021.<\/li>\n\n\n\n<li>112 \u2013<strong>\u00a0Aires<\/strong>, Weston, Fairbairn, De Rosnay,\u00a0<em>Statistical approaches to assimilate ASCAT soil moisture information: Part I Methodolgoies and first assessment,<\/em>\u00a0QJRMS, Vol 147, 746, pages 1823-1852, 10.1002\/qj.3997, 2021.<\/li>\n<\/ul>\n\n\n\n<p><strong>2020:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>111 \u2013 Safieddine, Parracho, George,\u00a0<strong>Aires<\/strong>, Pellet, Clarisse, Whitburn, Lezeaux, Th\u00e9paut, Hersbach, Radnoti, Goettsche, Martin, Doutriaux-Boucher, Coppens, August, Zhou, Clerbeaux,\u00a0<em>Artificial Neural Network to retrieve land and sea skin temperature from IASI<\/em>, Remote Sensing, 12(17),10.3390\/rs12172777, 2020.<\/li>\n\n\n\n<li>110 \u2013 Prigent, Kilic,\u00a0<strong>Aires<\/strong>, Heygter, Pellet, Jimenez,\u00a0<em>Estimation of Sea Ice Concentration from multi-channel passive microwave satellite observations. Part 2: evaluation of a new methodology optimized for the Copernicus Imaging Microwave Radiometer<\/em>, Remote Sensing, 12(10), 1594, 10.3390\/rs12101590, 2020.<\/li>\n\n\n\n<li>109 \u2013 Kilic, Prigent,\u00a0<strong>Aires<\/strong>, Heygster, Pellet, Jimenez,\u00a0<em>Ice concentration retrieval from the analysis of microwaves: a new methodoloy designed for the Copernicus Imaging Microwave Radiometer (CIMR)<\/em>, Remote Sensing,12(7):1060, 10.3390\/rs12071060, 2020.<\/li>\n\n\n\n<li>108 \u2013 Fleischmann, Paiva, Collischonn, Siqueira, Paris, Moreira, Papa, Bitar, Parrens,\u00a0<strong>Aires<\/strong>, Garambois,\u00a0<em>Trade-offs between 1D and 2D regional river hydrodynamic models<\/em>, Water Resources Research, 56(8), 10.1029\/2019WR026812, 2020.<\/li>\n\n\n\n<li>107 \u2013\u00a0<strong>Aires<\/strong>, Venot, Massuel, Gratiot, Pham-Duc, Prigent,\u00a0<em>Surface water evolution (2001-2017) at the Cambodia\/Vietnam border in the Upper Mekong Delta using satellite MODIS observations<\/em>, Remote Sensing, 12(5), 8000,\u00a0<a href=\"https:\/\/doi.org\/10.3390\/rs12050800\">doi.org\/10.3390\/rs12050800, 2020.<\/a><\/li>\n\n\n\n<li>106 \u2013\u00a0<strong>Aires<\/strong>, and Pellet,\u00a0<em>Estimat<\/em><em>ing retrieval errors from neural network inversion schemes \u2013 Application to the retrieval of temperature profiles from IASI<\/em>, IEEE TGRS, p. 1-11,\u00a0<a href=\"https:\/\/doi.org\/10.1109\/TGRS.2020.3026944\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/TGRS.2020.3026944, 2020.<\/a><\/li>\n\n\n\n<li>105 \u2013 Pellet,\u00a0<strong>Aires<\/strong>, Papa, Munier, Decharme,\u00a0<em>Long-term total water storage change from a SAtellite Water Cycle (SAWC) reconstruction over large South Asian basins<\/em>, Hydrol. Earth Syst. Sci., 24, 3033\u20133055, 2020, https:\/\/doi.org\/10.5194\/hess-24-3033-2020.<\/li>\n\n\n\n<li>104 \u2013 Wang, Zhuang,\u00a0<strong>Aires<\/strong>, Prigent, Yu, Keller, Bridgham,\u00a0<em>Simulating Holocen peat soil carbon accumulation in North America<\/em>, JGR-Biosphere, https:\/\/doi.org\/10.1029\/2019JG005230, 2020.<\/li>\n\n\n\n<li>103 \u2013\u00a0<strong>Aires<\/strong>, F.,\u00a0<em>Surface water maps de-noising and missing-data filling using determinist spatial filters based on several a priori information<\/em>, RSE, i237 111481, https:\/\/doi.org\/10.1016\/j.rse.2019.111481, 2020.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2019:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>102 \u2013 Rodriguez-Fernandez, de Rosnay, Albergel, Richaume,\u00a0<strong>Aires<\/strong>, Prigent, Kerr,\u00a0<em>SMOS neural network soil moisture data assimilation in a land surface model and atmospheric impact<\/em>, Rem. Sens., 11, 1334, 10.3390\/rs11111334, 2019.<\/li>\n\n\n\n<li>101 \u2013 Daskin, F.<strong>\u00a0Aires<\/strong>\u00a0, A.C. Staver,\u00a0<em>Determinants of tree cover in tropical floodplains: climate, fire, and hydrology<\/em>, PNASS, 286, 1914, , https:\/\/doi.org\/10.1098\/rspb.2019.1755, 2019.<\/li>\n\n\n\n<li>100 \u2013 Dinh, L-A, and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>River discharge estimation based on satellite water extent and topography at high spatial resolution \u2013 An application over the Amazon<\/em>, J. of Hydrometeorology,\u00a010.1175\/JHM-D-18-0206.1, 2019.<\/li>\n\n\n\n<li>99 \u2013 Favrichon, S., C. Prigent, C. Jimenez, and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Detecting cloud contamination in passive microwave satellite measurements over land<\/em>, Atmos. Meas. Tech., 12, 1531-1543, 10.5194\/amt-12-1531-2019, 2019.<\/li>\n\n\n\n<li>98 \u2013 Kilic, L., P. Catherine, F.\u00a0<strong>Aires<\/strong>, J. Boutin, G. Heygster, R. Tonboe, H. Roquet,\u00a0<em>Expected performances of the Copernicus Imaging Microwave Radiometer (CIMR) for an all-weather and high spatial resolution estimation of ocean and sea ice parameters<\/em>, JGR-Oceans, 123(10), 7564-7580,\u00a010.1029\/2018JC014408, 2019.<\/li>\n\n\n\n<li>97 \u2013 Pellet, V., F.\u00a0<strong>Aires<\/strong>, S. Munier, D. Fern\u00e1ndez Prieto,\u00a0G. Jord\u00e1, W.A. Dorigo,\u00a0J. Polcher,\u00a0and L. Brocca<strong>,\u00a0<\/strong><em>Optimisation of satellite observations to study the water cycle over the Mediterranean region<\/em>, 23(1), Hydrol. Earth Syst. Sci., 465-491, doi: 10.5194\/hess-23-465-2019, 2019.<\/li>\n\n\n\n<li>96 \u2013 Pham-Duc, B., F. Papa, C. Prigent, F. Aires, S. Biancamaria, and F. Frappart<strong>,\u00a0<\/strong><em>Variations of surface and subsurface water storage in the Lower Mekong Basin (Vietnam and Cambodia) from Multisatellite Observations<\/em>, Water, 11, 75, 23(1), doi: 10.3390\/w11010075, 2019.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2018:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>95 \u2013\u00a0<strong>Aires<\/strong>, F., C. Prigent, F. Papa, E. Fluet-Chuinard, B. Lehner, and D. Yamazaki,\u00a0<em>Comparison of visible (G3WBM and GSWO) and multi-satellite (GIEMS-D3) global inundation datasets at high-spatial resolution<\/em><em>,<\/em>\u00a0RSE, 216, 427-441, 10.1016\/j.rse.2018.06.015, 2018.<\/li>\n\n\n\n<li>94 \u2013 Pellet, V., and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Analyzing the Mediterranean water cycle via satellite data integration<\/em>, Pure Appl. Geophys, 10.1007\/s00024-018-1912-z, 1-29, 2018.<\/li>\n\n\n\n<li>93 \u2013\u00a0<strong>Aires<\/strong>, F., C. Prigent, S. Buehler, M. Milz, P. Eriksson, and S. Crewell,\u00a0<em>Towards more realistic hypotheses for the information content analysis of cloudy\/precipitating situations \u2013 Application to the hyper-spectral instrument in the microwaves<\/em>, QJRMS, 10.1002\/qj.3315, 2018.<\/li>\n\n\n\n<li>92 \u2013 Alemohammad, S.H., Kolassa, J., Prigent, C., Aires, F. and Gentine, P.\u00a0<em>Global Downscaling of Remotely-Sensed Soil Moisture using Neural Networks<\/em>, HESS, 10.5194\/hess-2017-680, 2018.<\/li>\n\n\n\n<li>91 \u2013 Pellet, V., F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Bottleneck channels algorithm for satellite dimension reduction: A case study for IASI<\/em><em>,<\/em>\u00a0IEEE TGRS, 56, 10, 6069-6081, 10.1109\/TGRS.2018.2830123,\u00a02018.<\/li>\n\n\n\n<li>90 \u2013 Mathieu, J., and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>SUsing neural network classifier approach for statistically forecasting extreme corn yield losses in Eastern United States<\/em><em>,<\/em>\u00a0Earth and Space Science, 10.1029\/2017EA000343, 2018.<\/li>\n\n\n\n<li>89 \u2013 Mathieu, J., and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Impact of agro-climatic indices to improve crop yield forecasting<\/em><em>,<\/em>\u00a0Agriculture and Forest Meteorology, 253-254, 15-30, 2018.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2017:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>88 \u2013 Munier, S., F.\u00a0<strong>Aires<\/strong>,\u00a0<em>A new global method of satellite dataset merging and quality characterization constrained by the terrestrial water cycle budget<\/em><em>,\u00a0<\/em>RSE, 205:119-130, 10.1016\/J.rse.2017.11.008, 2017.<\/li>\n\n\n\n<li>87 \u2013 Alemohammad, S.H., B. Fang, A.G. Konings, J.K. Green, J. Kolassa, C. Prigent, F.\u00a0<strong>Aires<\/strong>, D. Miralles, and P. Gentine,\u00a0<em>Water, energy, and carbon with artificial neural networks (WECANN): A statistically-based estimate of global surface turbulent fluxes using solar-induced fluorescence<\/em><em>,<\/em>\u00a0Biogeosciences, 14, 4101-4124, 10.5194\/bg-14-4101-2017, 2017.<\/li>\n\n\n\n<li>86 \u2013 Wang, D., C. Prigent, L. Killic, S. Fox, C. Jimenez, F.\u00a0<strong>Aires<\/strong>, C. Grassoti, and F. Karbou,<em>Surface emissivity at microwaves to millimeter waves\u00a0over polar regions: parameterization and evaluation with aircraft experiments,\u00a0<\/em>J. Atmos. and Ocean. Technology, 34(5), 10.1175\/JTECH-D-16-0188.1, 2017.<\/li>\n\n\n\n<li>85 \u2013 Salameh, E., F. Frappart, F. Papap, A. Guntner, V. Venugopal, A. Getirana, C. Prigent, F.\u00a0<strong>Aires<\/strong>, D. Labat, and B. Laignel,\u00a0<em>Fifteen\u00a0years (1993-2007) of surface freshwater storage variability in the Ganges-Brahmaputra River basin using multi-satellite observations<\/em>, Water,\u00a0<em>9<\/em>(4), 245;\u00a010.3390\/w9040245, 2017.<\/li>\n\n\n\n<li>84 \u2013 Kolassa, J., P. Gentine, F.\u00a0<strong>Aires<\/strong>, and C. Prigent, \u00a0<em>Remote sensing of soil moisture using AMSR-E and ASCAT synergy. Part 2: Product Evaluation<\/em>,\u00a0RSE, 195:202-217,\u00a010.1016\/j.rse.2017.04.020, 2017.<\/li>\n\n\n\n<li>83 \u2013 Pham-Duc, B., C. Prigent, F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Surface water monitoring in the Mekong delta over a year with Sentinel-1 SAR observations<\/em>, Water,\u00a0<em>9,\u00a0<\/em>6, 366,\u00a0<a href=\"http:\/\/dx.doi.org\/10.3390\/w9060366\">1<\/a>0.3390\/w9060366, 2017.<\/li>\n\n\n\n<li>82 \u2013 Prigent, C., F.\u00a0<strong>Aires<\/strong>, D. Wang, S. Fow, S. Harlow,\u00a0<em>Sea-surface emissivity parameterization from microwaves to millimeter waves<\/em>, QJRMS, 143, 702,\u00a0596\u2013605,\u00a010.1002\/qj.2953, 2017.<\/li>\n\n\n\n<li>81 \u2013 Pham-Duc, B., C. Prigent, F.\u00a0<strong>Aires<\/strong>, and F. Papa,\u00a0<em>Comparisons of global terrestrial surface water datasets over 15 years<\/em><em>,<\/em>\u00a0J. of Hydrometeorology,\u00a010.1175\/JHM-D-16-0206.1, 2017.<\/li>\n\n\n\n<li>80 \u2013\u00a0<strong>Aires<\/strong>, F., L. Miolane, C. Prigent, E. Fluet-Chouinard, B. Lehner, and F. Papa,\u00a0<em>A global, long-term and high spatial resolution inundation extent database<\/em><em>,\u00a0<\/em>J. Hydrometeorology, 10.1175\/JHM-D-16-0155.1, 2017.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2016:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>79 \u2013 Wang, D., C. Prigent, F.\u00a0<strong>Aires<\/strong>, and C. Jimenez,\u00a0<em>A<\/em><em>\u00a0statistical retrieval of\u00a0cloud parameters for millimeter wave Ice Cloud Imager on board Metop-SG<\/em>, IEEE TGRS, 99, 10.1109\/ACCESS.2016.2625742, 2016.<\/li>\n\n\n\n<li>77 \u2013 Mathieu, J., and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Statistical impact models for agriculture: an application of mixed-effects and neural networks for corn over USA<\/em>, J. Appl. Meteor. Climatol., 10.1175\/JAMC-D-16-0055.1, 2016.<\/li>\n\n\n\n<li>76 \u2013 Prigent, C., C. Jimenez, F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Towards an \u201call-weather\u201d, long record, and real-time surface temperature retrievals from microwave satellite observations,\u00a0<\/em>JGR,\u00a010.1002\/2015JD024402, 2016.<\/li>\n\n\n\n<li>75 \u2013 Pellet, V., and F.\u00a0<strong>Aires<\/strong>, F.,\u00a0<em>Dimension reduction of satellite observations for remote sensing, Part II: Illustration using hyper-spectral microwave observations,<\/em>\u00a0QJRMS, 10.1002\/qj.2857, 2016.<\/li>\n\n\n\n<li>74 \u2013\u00a0<strong>Aires<\/strong>, F., V. Pellet, C. Prigent, J.-L. Moncet,\u00a0<em>Dimension reduction of satellite observations for remote sensing, Part I: A comparison of compression, channel selection, and bottleneck channel approaches,<\/em>\u00a0QJRMS, 10.1002\/qj.2855, 2016.<\/li>\n\n\n\n<li>73 \u2013 Kolassa, J., P. Gentine, C. Prigent, and F.\u00a0<strong>Aires<\/strong>,\u00a0\u00a0<em>Remote sensing of soil moisture using AMSR-E and ASCAT observations,\u00a0<\/em>RSE, 1-73, 1-14, 2016.<\/li>\n\n\n\n<li>72 \u2013 Prigent, C., Lettenmeyer, F. Aires and F. Papa,\u00a0<em>Challenges in satellite remote sensing of continental hydrology,\u00a0<\/em>Surveys in Geophysics, 10.1007\/s10712-015-9339,\u00a037 (2),\u00a0pp 339\u20133552015, 2016.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2015:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>71 \u2013 Pan, M., C.K. Fisher, N.W. Chaney, W. Zhan, F.\u00a0<strong>Aires<\/strong>, W.T. Crow, D. Entekhavi, and E. Wood,\u00a0<em>Triple collocation: Beyond three estimates and separation of structural\/non-structural errors,\u00a0<\/em>RSE, 171, 299-310, 2015.<\/li>\n\n\n\n<li>70 \u2013 Birman, C., J.-F. Mahfouf, F.\u00a0<strong>Aires<\/strong>, C. Prigent, E. Orlandi, and M. Milz,\u00a0<em>Information content on temperature and water vapour from an hyper-spectral microwave sensor,\u00a0<\/em>QJRMS, 10.1002\/qj.2608, 2015.<\/li>\n\n\n\n<li>69 \u2013 Rodriguez-Fernandez, N.J., F.\u00a0<strong>Aires<\/strong>, P. Richaume, F. Cabot, C. Jimenez, J. Kerr, J. Kolassa, A. Mahmoodi, C. Prigent, and M. Drush,\u00a0<em>Soil moisture retreival from SMOS observations using neural networks,\u00a0<\/em>IEEE TGRS, 53, 11, 10.1109\/TGRS.2015.2430845, 2015.<\/li>\n\n\n\n<li>68 \u2013 Lipton, A.E., P Liang, C. Jimenez, J.-L. Moncet, F.\u00a0<strong>Aires<\/strong>, C. Prigent, R. Lynch, J.F. Galantowicz, R.P. d\u2019Entremont and G. Uymin,\u00a0<em>Sources of discrepancies between satellite-derived and land surface model estimates of latent heat fluxes,\u00a0<\/em>JGR, 120, 2324-2341, 10.1002\/2014JD022641, 2015.<\/li>\n\n\n\n<li>67 \u2013 Prigent, C., P. Liang, Y. Tian, F.\u00a0<strong>Aires<\/strong>, J.-L. Moncet, and S.-A. Boukabara,\u00a0<em>Evaluating modeled microwave emissivity,\u00a0<\/em>JGR, 120, 2706-2718, 10.1002\/2014JD021817, 2015.<\/li>\n\n\n\n<li>66 \u2013\u00a0<strong>Aires<\/strong>, F., C. Prigent, E. Orlandi, M. Miltz, P. Eriksson, and S. Crewell,\u00a0<em>Microwave hyper-spectral measurements for temperature and humidity atmospheric profiling \u2013 Par I: Clear-sky case,\u00a0<\/em>JGR, 120, 21, 11,334-11,351, 10.1002\/2015JD023331, 2015.<\/li>\n\n\n\n<li>65 \u2013 Papa, F., F. Frappart, Y. Malbeteau, M. Shamsudduha, V. Venugopal, M. Sekhar, G. Ramillien, C. Prigent, F.\u00a0<strong>Aires<\/strong>, R.K. Pandey, S. Bala, and S. Calmant,\u00a0<em>Satellite-derived surface and sub-surface water storage in the Ganges-Brahmaputra river basin,\u00a0<\/em>JoH: Regional studies, 10.1016\/j.ejrh.2015.03.004, 2015.<\/li>\n\n\n\n<li>64 \u2013 Munier, S., F.\u00a0<strong>Aires<\/strong>, S. Schlaffer, C. Prigent, F. Papa, P. Maisongrande, and M. Pan,\u00a0<em>Combining datasets of satellite retrieved products. Part II: Evaluation on the Mississippi Basin and closure correction model,\u00a0<\/em>JGR, 10\/2014, 10.1002\/2014JD021953, 2015.<\/li>\n\n\n\n<li>63 \u2013 Prigent, C., F.\u00a0<strong>Aires<\/strong>, C. Jimenez, F. Papa and J. Roger,\u00a0<em>Multi-angle backscattering observations of continental surfaces in Ku band (13 GHz) from satellites: understanding the signals, especially in arid regions,\u00a0<\/em>IEEE Trans. on Geoscience and Rem. Sens., 53, 3, 2015.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2014:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>62 \u2013 Matsui, T., J. Santanello, J. Shi, W-K Tao, D. Wu, C. Peters-Lidard, E. Kemp, M. Chin, D. Starr, M. Sekigushi, and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Introducing multisensor satellite radiance-based evaluation for regional Earth system modeling,\u00a0<\/em>JGR, 119: 10.1002\/jgrd.v119.13, 8450-8475, 2014.<\/li>\n\n\n\n<li>61 \u2013 Paul, M., and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Using Shannon\u2019s entropy to sample heterogeneous and high-dimensional atmospheric datasets,\u00a0<\/em>QJRMS, 10.1002\/qj.2373, 2014.<\/li>\n\n\n\n<li>60 \u2013\u00a0<strong>Aires<\/strong>, F.\u00a0<em>Combining datasets of satellite retrieved products. Part I: Methodology and water budget closure,\u00a0<\/em>J. of Hydrometeor., 10.1175\/JHM-D-13-0148.1, 2014.<\/li>\n\n\n\n<li>59 \u2013\u00a0<strong>Aires<\/strong>, F., F. Papa, C. Prigent, J.-F. Cr\u00e9taux and M. Berge-Nguyen,\u00a0<em>Characterization and downscaling of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data,\u00a0<\/em>J. of Hydrometeoroloy, 27, 1958-1979, 10.1175\/JCLI-D-13-00161.1, 2014.<\/li>\n\n\n\n<li>58 \u2013\u00a0<strong>Aires<\/strong>, F., P. Gentine, K. Findell, B.R. Lintner, and C. Kerr,\u00a0<em>Neural network-based sensitivity analysis of summertime convection over continental US,\u00a0<\/em>J. of Clim., 27, 1958-1979, 10.1175\/JCLI-D-13-00161.1, 2014.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2013<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>57 \u2013 Papa, F., F. Frappart, A. Guntner, C. Prigent, F.\u00a0<strong>Aires<\/strong>, W.B. Rossow, A. Getirana, and R. Maurer,\u00a0<em>Surface freshwater storage and variability in the Amazon basin from multi-satellite observations, 1993-2007,\u00a0<\/em>JGR, 118, 21, 11.951-11.965, 10.1002\/2013JD020500, 2013.<\/li>\n\n\n\n<li>56 \u2013 Foley, E., A. Friend, D. Dalmonech, F.\u00a0<strong>Aires<\/strong>, A. Archibald, P. Bartlein, L. Bopp, J. Chappellaz, P. Cox, N. Edwards, G. Feulner, P. Friedlingtein, S. P. Harrison, P.O. Hopcroft, C.D. Jones, J. Kolassa, J. Levine, I.C. Prentice, J. Pyle, N. Vazquez Riveiros, E. Wolff, S. Zaehle,\u00a0<em>Improving constraints on biopheric feedbacks in Earth system models,\u00a0<\/em>Biogeosciences Discuss, 10, 10937-10995, 10.5194\/bgd-10-10937-2013, 2013.<\/li>\n\n\n\n<li>55 \u2013 Jimenez, C., D.B. Clark, J. Kolassa, F.\u00a0<strong>Aires<\/strong>, C. Prigent, and E. Blyth,\u00a0<em>A joint analysis of modeled soil moisture fields and satellite observations,\u00a0<\/em>JGR, 118, 12, 6771-6782, 2013.<\/li>\n\n\n\n<li>54 \u2013 Prigent, C., F.\u00a0<strong>Aires<\/strong>, F. Bernardo, J.-C. Orlhac, J.-M. Goutoule, H. Roquet, and C. Donlon,\u00a0<em>Analysis of the potential and limitation of microwave radiometry for the retrieval of Sea Surface Temperature: Definition of new mission concepts,\u00a0<\/em>JGR, 118, 6, 3074-3086, 10.1002\/jgrc.20222, 2013.<\/li>\n\n\n\n<li>53 \u2013 Tian, Y., C.D. Peters-Lidard, K.W. Harrison, C. Prigent, H. Norouzi, F.\u00a0<strong>Aires<\/strong>, S.-A. Boukabara, F.A. Furuzawa, and H. Masunaga,\u00a0<em>Quantifying uncertainties in land surface microwave emissivity retrievals,\u00a0<\/em>IEEE TGRS, 99, 10.1109\/TGRS.2013.2244214, 2013.<\/li>\n\n\n\n<li>52 \u2013\u00a0<strong>Aires<\/strong>, F., F. Papa and C. Prigent,\u00a0<em>A long-term, high-resolution wetland dataset over the Amazon basin, downscaled from a multi-wavelength retrieval using SAR,\u00a0<\/em>J. of Hydrometeorology, 14, 594-6007, 2013.<\/li>\n\n\n\n<li>51 \u2013 Kolassa, J., F.\u00a0<strong>Aires<\/strong>, J. Polcher, C. Prigent, C. Jimenez, and J.M. Pereira,\u00a0<em>Soil moisture retrieval from multi-instrument observations: Part I \u2013 Information content analysis and retrieval methodology,<\/em>\u00a0JGR, 118, 10, 4847-4859, 10.1029\/2012JD018150, 2013.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2012:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>50 \u2013\u00a0<strong>Aires<\/strong>, F., O. Aznay, C. Prigent, M. Paul, F. Bernardo,\u00a0<em>Synergetic multi-wavelegnth remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp measurements,\u00a0<\/em>JGR, 117, D18304, 10.1029\/2011JD017188, 2012.<\/li>\n\n\n\n<li>49 \u2013 Ferraro, R., C. Peters-Lidard, C. Hernandez, F.J. Turk, F.\u00a0<strong>Aires<\/strong>, C. Prigent, W. Lon, S-A. Boukabara, F. Furuzawa, K.Gopalan, K. Harrison, F. Karbou, L. Li, C. Liu, H. Masunaga, L. Moy, S.Ringerud, G. Skofronick-Jackson, Y. Tian, N-Y. Wang,\u00a0<em>An evaluation of microwave land surface emissivities over the continental UnitedStates to benefit GPM-era precipitation algorithms.\u00a0<\/em>IEEE Trans. on Geosci. and Rem. Sens., 99, 1-31, 10.1109\/TGRS.2012.2199121, 2012.<\/li>\n\n\n\n<li>48 \u2013\u00a0<strong>Aires<\/strong>, F.,\u00a0<em>Using random-effect models to build impact indices when the available historical record is short,\u00a0<\/em>J. Appl. Meteorol. and Climat., 51, 1994-2004, 10.1175\/JAMC-D-11-0125.1, 2012.<\/li>\n\n\n\n<li>47 \u2013 Paul, M., F.\u00a0<strong>Aires<\/strong>, and C. Prigent,\u00a0<em>An innovative physical scheme to retrieve simultaneously surface temperature and emissivities based on a high-resolution infrared emissivity interpolator,\u00a0<\/em>JGR, 117, D11302, 10.1029\/2011JD017296, 2012.<\/li>\n\n\n\n<li>46 \u2013 Prigent, C., F. Papa, F.\u00a0<strong>Aires<\/strong>, C. Jimenez, W.B. Rossow, E. Matthews,\u00a0<em>Changes in land surface water dynamics since the 1990s and relation to population pressure,\u00a0<\/em>GRL, 39, L08403, 10.1029\/2012GL051276, 2012.<\/li>\n\n\n\n<li>45 \u2013 Bernardo, F., F.\u00a0<strong>Aires<\/strong>, and C. Prigent,\u00a0<em>Atmospheric water vapour retrieval from microwave instruments \u2013 Part II : Evaluation for the Megha-Tropiques mission,\u00a0<\/em>QJRMS, 10.1002\/qj.1946, 2012.<\/li>\n\n\n\n<li>44 \u2013\u00a0<strong>Aires<\/strong>, F., Bernardo, F., and C. Prigent,\u00a0<em>Atmospheric water vapour retrieval from microwave instruments \u2013 Part I : Methodology,\u00a0<\/em>QJRMS, 10.1002\/qj.1888, 2012.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2011:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>43 \u2013 Prigent, C., J. Catherinot, R. Maurer, F. Papa, C. Jimenez, F.\u00a0<strong>Aires<\/strong>, and W.B. Rossow,\u00a0<em>Evaluation of \u2018all weather\u2019 microwave-derived land surface temperatures with in situ CEOP measurements,\u00a0<\/em>JGR, 116, D23105, 10.1029\/2011JD016439, 2011.<\/li>\n\n\n\n<li>42 \u2013 Prigent, C., N. Rochetin, F.\u00a0<strong>Aires<\/strong>, E. Defer, J-Y Grandpeix, C. Jimenez, F. Papa,\u00a0<em>Impact of the inundated areas on the deep convection at continental scale from satellite observations and modeling experiments,<\/em>\u00a0JGR, 116, D24118, 10.1029\/2011JD16311, 2011.<\/li>\n\n\n\n<li>41 \u2013\u00a0<strong>Aires<\/strong>, F., Marquisseau, F., Prigent, C., and Seze, G.,\u00a0<em>A land and ocean microwave cloud classification derived from AMSU-A and -B, calibrated on MSG-SEVIRI infrared and visible observations,\u00a0<\/em>MWR, 139, 2347-2366, http:\/\/dx\/doi.org\/10.1175\/MWR-D-10-05012.1, 2011.<\/li>\n\n\n\n<li>40 \u2013\u00a0<strong>Aires<\/strong>, F., C. Prigent, F. Bernardo, C. Jimenez, R. Sounders, and P. Brunel,\u00a0<em>A Tool to Estimate Land Surface Emissivities in the Microwaves (TELSEM) for use in numerical weather prediction schemes.\u00a0<\/em>QJRMS, 137: 690-699, 10.1002\/qj.803, 2011.<\/li>\n\n\n\n<li>39 \u2013\u00a0<strong>Aires<\/strong>, F.,\u00a0<em>Measure and exploitation of multi-sensor and multi-wavelength Synergy for remote sensing: Part I \u2013 Theoretical considerations,<\/em>\u00a0JGR, 116, D02301, 10.1029\/2010JD014701, 2011.<\/li>\n\n\n\n<li>38 \u2013\u00a0<strong>Aires<\/strong>, F., M. Paul, C. Prigent, B. Rommen, and M. Bouvet,\u00a0<em>Measure and exploitation of multi-sensor and multi-wavelength Synergy for remote sensing: Part II \u2013 An application for the retrieval of atmospheric temperature and water vapour from METOP,<\/em>\u00a0JGR, 116, D02302, 10.1029\/2010JD014702, 2011.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2010:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>37 \u2013\u00a0<strong>Aires<\/strong>, F., F. Bernardo, H. Brogniez, and C. Prigent,\u00a0<em>Calibration for the inversion of satellite observations,\u00a0<\/em>J. of Applied Meteorology and Climatology, 49, 12, 2458-2473, 2010.<\/li>\n\n\n\n<li>36 \u2013 Papa, F., C. Prigent, C. Jimenez, F.\u00a0<strong>Aires<\/strong>, W.B. Rossow, and E. Matthews,\u00a0<em>Interannual variability of surface water extent at global scale, 1993-2004,<\/em>\u00a0JGR, 115, D12111, 10.1029\/2009JD012674., 2010.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2009:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>35 \u2013 Jimenez, C., C. Prigent and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Toward an estimation of global land surface heat fluxes from multisatellite observation,\u00a0<\/em>JGR, 114, D06305, 10.1029\/2008JD011392, 2009.<\/li>\n\n\n\n<li>34 \u2013 Cheruy F., and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Cluster analysis of cloud properties over the Southern Europe Mediterranean area in observations and a model,\u00a0<\/em>MWR, 137, 10, 3161-3176, 2009.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2008:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>33 \u2013 Decharme B., H. Douville, C. Prigent, F. Papa, and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>A new river flooding scheme for global climate applications: Off-line evaluation over South America<\/em>, JGR, 113, D11110, 10.1029\/2007JD009376, 2008.<\/li>\n\n\n\n<li>32 \u2013 Defer, E., C. Prigent, F.\u00a0<strong>Aires<\/strong>, J.R. Pardo, C.J. Walden, O.-Z. Zanife, J.-P. Chaboureau and J.-P Pinty,\u00a0<em>Development of precipitation retrievals at millimeter and submillimeter wavelengths for geostationary satellites.\u00a0<\/em>JGR, 113, D08111, 10.1029\/2007JD008673, 2008.<\/li>\n\n\n\n<li>31 \u2013 Prigent, C., E. Jaumouille, F. Chevallier, and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>A parameterization of the microwave land surface emissivity between 19 and 100 GHz, anchored to satellite-derived estimates,\u00a0<\/em>IEEE Trans. on Geosci. and Rem. Sens., 46, 344-352, 2008.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2007:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>30 \u2013\u00a0<strong>Aires<\/strong>, F., and C. Prigent,\u00a0<em>Sampling Techniques in High-Dimensional Spaces for Satellite Remote Sensing Databases Generation.<\/em>\u00a0JGR, 112, D20301, 10.1029\/2007JD008391, 2007.<\/li>\n\n\n\n<li>29 \u2013 Prigent, C., F. Papa, F.\u00a0<strong>Aires<\/strong>, and W.B. Rossow\u00a0<em>Global inundation dynamics inferred from multiple satellite observations,\u00a0<\/em>JGR, 112, D12107, 10.1029\/2006JD007847, 2007.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2006:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>28 \u2013\u00a0<strong>Aires<\/strong>, F., and C. Prigent, F. Aires\u00a0<em>Toward a new generation of satellite surface products?<\/em>\u00a0JGR, 111, D22S10, 10.1029\/2006JD007362, 2006.<\/li>\n\n\n\n<li>27 \u2013 Prigent, C., F.\u00a0<strong>Aires<\/strong>, and W.B. Rossow\u00a0<em>Land Surface Microwave Emissivities over the Globe for a Decade.\u00a0<\/em>Bulletin of the American Meteorological Society, 10.1175\/BAMS-87-11-1573, 1572-1584, 2006.<\/li>\n\n\n\n<li>26 \u2013 Cordisco, E., C. Prigent, and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Snow characterization at a global scale with passive microwave satellite observations.\u00a0<\/em>JGR, 10.1029\/2005JD006773, 111, D19, D19301, 2006.<\/li>\n\n\n\n<li>25 \u2013 Chen, Y., F.\u00a0<strong>Aires<\/strong>, J.A. Francis, and G.L. Russell,\u00a0<em>Observed relationships between longwave cloud forcing and cloud parameters at SHEBA using a neural network.\u00a0<\/em>J. of Climate, 10.1175\/JCLI3839.1, 19, 16, 4087-4104, 2006.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2005:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>24 \u2013\u00a0<strong>Aires<\/strong>, F., Prigent, C., and Rossow, W.B.,\u00a0<em>Soil moisture at a global scale. II \u2013 Global statistical relationships.<\/em>\u00a0JGR, 110, D11, D11103, 10.1029\/2004JD005094, 2005.<\/li>\n\n\n\n<li>23 \u2013 Prigent, C.,\u00a0<strong>Aires<\/strong>, F., and Rossow, W.B.,\u00a0<em>Soil moisture at a global scale. I \u2013 Presentation of the satellite observations and analysis of their relations with in situ soil moisture measurements.<\/em>\u00a0JGR, 110, D7, D07110,10.1029\/2004JD005087, 2005.<\/li>\n\n\n\n<li>22 \u2013 Prigent, C., Tegen, I.,\u00a0<strong>Aires<\/strong>, F., Marticorena, B., and Zribi, M.,\u00a0<em>Estimation of the aerodynamic roughness length in arid and semi-arid regions over the globe with the ERS scatterometer.\u00a0<\/em>JGR, 110, D9, D09205 10.1029\/2004JD005370, 2005.<\/li>\n\n\n\n<li>21 \u2013 Karbou, F.,\u00a0<strong>Aires<\/strong>, F., Prigent, C.,\u00a0<em>Retrieval of temperature and water vapor atmospheric profiles over Africa using AMSU microwave observations.<\/em>\u00a0JGR, 110, D7, D07109 10.1029\/2004JD005318, 2005.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2004:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>20 \u2013\u00a0<strong>Aires<\/strong>, F.,\u00a0<em>Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 1. Network weights.\u00a0<\/em>JGR, 109, D10303, 10.1029\/2003JD004173, 2004.<\/li>\n\n\n\n<li>19 \u2013\u00a0<strong>Aires<\/strong>, F., C. Prigent, and W.B. Rossow,\u00a0<em>Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 2. Output errors.\u00a0<\/em>JGR, 109, D10304, 10.1029\/2003JD004174, 2004.<\/li>\n\n\n\n<li>18 \u2013\u00a0<strong>Aires<\/strong>, F., C. Prigent, and W.B. Rossow,\u00a0<em>Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 3. Network Jacobians.<\/em>\u00a0JGR, 109, D10305, 10.1029\/2003JD004175, 2004.<\/li>\n\n\n\n<li>17 \u2013\u00a0<strong>Aires<\/strong>, F., C. Prigent, and W.B. Rossow,\u00a0<em>Temporal interpolation of global surface skin temperature diurnal cycle over land under clear and cloudy conditions.<\/em>\u00a0JGR, 109, D04313, 10.1029\/2003JD003527, 2004.<\/li>\n\n\n\n<li>16 \u2013\u00a0<strong>Aires<\/strong>, F., C. Prigent, and W.B. Rossow,\u00a0<em>Neural network uncertainty assessment using Bayesian statistics: A remote sensing application.<\/em>\u00a0Neural Computation, 16, 2415-2458, 2004.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2003:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>15 \u2013\u00a0<strong>Aires<\/strong>, F., and W.B. Rossow,\u00a0<em>Inferring instantaneous, multivariate and nonlinear sensitivities for the analysis of feedback processes in a dynamical system: The Lorenz model case study.\u00a0<\/em>QJRMS, 129, 239-275, 2003.<\/li>\n\n\n\n<li>14 \u2013 Chen, Y., J.R. Miller, J.A. Francis, G.L. Russell, and F.\u00a0<strong>Aires<\/strong>,\u00a0<em>Observed and modeled relationships among Arctic climate variables.\u00a0<\/em>JGR, 108, D24, 4799, 10.1029\/2003JD003824, 2003.<\/li>\n\n\n\n<li>13 \u2013 Prigent, C., F.\u00a0<strong>Aires<\/strong>, and W.B. Rossow,\u00a0<em>Retrieval of surface and atmospheric geophysical variables over snow from microwave satellite observations.\u00a0<\/em>JAM, 42, 368-380, 10.1175\/1520-0450(2003) 042&lt;0368:ROSAAG&gt;2.0.CO;2, 2003.<\/li>\n\n\n\n<li>12 \u2013 Prigent, C., F.\u00a0<strong>Aires<\/strong>, and W.B. Rossow,\u00a0<em>Land surface skin temperatures from a combined analysis of microwave and infrared satellite observations for an all-weather evaluation of the differences between air and skin temperatures.\u00a0<\/em>JGR, 108, D10, 4310, 10.1029\/2002JD002301, 2003.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2002:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>11 \u2013\u00a0<strong>Aires<\/strong>, F., W.B. Rossow, and A. Ch\u00e9din,\u00a0<em>Rotation of EOFs by the Independent Component Analysis: Towards a Solution of the Mixing Problem in the Decomposition of Geophysical Time Series,<\/em>\u00a0JAS, 59, 1, 111-123, 2002.<\/li>\n\n\n\n<li>10 \u2013\u00a0<strong>Aires<\/strong>, F., W.B. Rossow, N. Scott, and A. Ch\u00e9din,\u00a0<em>Remote sensing from the IASI instrument. 1 Compression, de-noising, and first-guess retrieval algorithms,\u00a0<\/em>JGR, 107, no. D22, 4619, 10.1029\/2001JD000955, 2002.<\/li>\n\n\n\n<li>9 \u2013\u00a0<strong>Aires<\/strong>, F., W.B. Rossow, N. Scott, and A. Ch\u00e9din,\u00a0<em>Remote sensing from the IASI instrument. 2 Simultaneous retrieval of temperature, water vapor and ozone atmospheric profiles,\u00a0<\/em>JGR, 107, D22, 4620, 10.1029\/2001JD001591, 2002.<\/li>\n\n\n\n<li>8 \u2013\u00a0<strong>Aires<\/strong>, F., A. Chedin, N. Scott, and W.B. Rossow,<br><em>A regularized neural network approach for retrieval of atmospheric and surface temperatures with the IASI instru<\/em><em>ment,\u00a0<\/em>JAM, 41, 2, 144-159, 2002.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2001:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>7 \u2013 Prigent, C., E. Matthews, F.\u00a0<strong>Aires<\/strong>, and W. B. Rossow,\u00a0<em>Remote sensing of global wetland dynamics with multiple satellite data sets,\u00a0<\/em>GRL, 28 , 24 , 4,631-4,634, 2001.<\/li>\n\n\n\n<li>6 \u2013 Prigent, C., F.\u00a0<strong>Aires<\/strong>, W. B. Rossow, and E. Matthews,\u00a0<em>Joint characterization of the vegetation by satellite observations from visible to microwavelengths: a sensitivity analysis,<\/em>\u00a0JGR, 106, D18, 20,665-20,685, 2001.<\/li>\n\n\n\n<li>5 \u2013\u00a0<strong>Aires<\/strong>, F. C. Prigent, W.B. Rossow, and M. Rothstein,\u00a0<em>A new neural network approach including first-guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature and emissivities over land from satellite microwave observations,<\/em>\u00a0JGR, 106, D14, 14,887-14,907, 2001.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2000:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>4 \u2013\u00a0<strong>Aires<\/strong>\u00a0F., Ch\u00e9din A. and Nadal J.-P.\u00a0<em>Independent component analysis of multivariate time series. Application to the tropical SST variability.\u00a0<\/em>JGR, 105 , D13, 17,437-17,455, 2000.<\/li>\n\n\n\n<li>3 \u2013 Nadal J.-P., Korutcheva E. and\u00a0<strong>Aires<\/strong>\u00a0F.,\u00a0<em>Blind source separation in the presence of weak sources,\u00a0<\/em>Neural Networks, 13, 6, 589-596, 2000.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1999:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>2 \u2013\u00a0<strong>Aires<\/strong>\u00a0F., Ch\u00e9din A. and Nadal J.-P.\u00a0<em>Analysis of geophysical time series and information theory: Independent Component Analysis,<\/em>\u00a0CRAS IIa, 328, 569-575, 1999.<\/li>\n\n\n\n<li>1 \u2013\u00a0<strong>Aires<\/strong>, F., M. Schmitt, N. Scott and A. Ch\u00e9din,\u00a0<em>The Weight Smoothing regularisation for MLP for resolving the input contribution\u2019s errors in functional interpolations.\u00a0<\/em>IEEE Trans. on Neural networks, 10, 6, 1502-1510, 1999.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>THESES: BOOKS: ARTICLES: 2024: 2023: 2022: 2021: 2020: 2019: 2018: 2017: 2016: 2015: 2014: 2013 2012: 2011: 2010: 2009: 2008: 2007: 2006: 2005: 2004: 2003: 2002: 2001: 2000: 1999:<\/p>\n","protected":false},"author":101,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-12","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Publications - Filipe Aires<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sites.lesia.obspm.fr\/filipe-aires\/publications\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Publications - 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