NOTICE: This version of the NSF Unidata web site (archive.unidata.ucar.edu) is no longer being updated.
Current content can be found at unidata.ucar.edu.
To learn about what's going on, see About the Archive Site.
hi guys.. I have a problem with the netCDF-3.5.0 installation.. May be that the environment variable are not correctly setted...I don't know.. Anyway, I am trying to install it on a Linux 7.2 Red Hat (Enigma) OS. The processor is a Pentium III. do you have the correct settings? Thanking you in advance... Best, Davide >From owner-netcdfgroup@xxxxxxxxxxxxxxxx Wed Nov 5 0:1:23 2003 +0800 Date: Wed, 5 Nov 2003 0:1:23 +0800 From: "baoqing" <baoqing@xxxxxxxxxxxxxx> To: "netcdfgroup@xxxxxxxxxxxxxxxx" <netcdfgroup@xxxxxxxxxxxxxxxx>, Subject: A question about Time axis for formating a series netCDF file Received: (from majordo@localhost) by unidata.ucar.edu (UCAR/Unidata) id hA4G3Dqp021516 for netcdfgroup-out; Tue, 4 Nov 2003 09:03:13 -0700 (MST) Keywords: 200311041603.hA4G3AOb021459 Message-Id: <200311050328.hA53SCt24517@xxxxxxxxxxxxxxx> "netcdfgroup@xxxxxxxxxxxxxxxx" <netcdfgroup@xxxxxxxxxxxxxxxx> Organization: lasg X-mailer: Foxmail 4.2 [cn] Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="=====000_Dragon466706087443_=====" Sender: owner-netcdfgroup@xxxxxxxxxxxxxxxx Precedence: bulk Reply-To: "baoqing" <baoqing@xxxxxxxxxxxxxx> This is a multi-part message in MIME format. --=====000_Dragon466706087443_==== Content-Type: text/plain; charset="GB2312" Content-Transfer-Encoding: 7bit Hi to everybody! My question is probably simple, but I could not get an answer. I'm making Fortran routines to output model daily and monthly results in netCDF files . The problem is concerning the TIME axis. I define time as follows: name = 'Time' status = nf_put_att_text (ncid,varid_tim,'long_name',4,name) unit = 'days since 1978-01-01 00:00:00' status = nf_put_att_text (ncid,varid_tim,'units' ,30, unit) status = nf_put_att_double (ncid,varid_tim,'varid_range',nf_double & ,2,time_range) ! timetime_range /1d0,365d0/ char = '0000-00-01 00:00:00' status = nf_put_att_text (ncid,varid_tim,'delta_t' ,19, char) char = '0000-00-01 00:00:00' status = nf_put_att_text (ncid,varid_tim,'avg_period' ,19, char) The output files can be recognized by use "ncdump filename1978.01.01.nc |more" However, when I use GrADS to open a series of files by "sdfopen filename1978.01.01.nc filename1978.01.%d2 10", GrADS can not recognized the number of days .I show the information as follows: Data file filename1978.01.%d2.nc is open as file 1 ga> q file Xsize = 128 Ysize = 108 Zsize = 26 Tsize = 10 But, when I "set T 3 ",it told me than "Entire grid contents are set to missing data" I've been confused by this problem for a long time ! Please help me ! Best wish. ************************************** * bao qing * * LASG * * Institute of Atmospheric Physics * * Chinese Academy of Sciences * * P.O.BOX 9804 * * Beijing, 100029 * * P. R. China * * E-mail: baoqing@xxxxxxxxxxxxxx * ************************************** --=====000_Dragon466706087443_==== Content-Type: image/gif; name="fox.gif" Content-Transfer-Encoding: base64 Content-Disposition: FoxmailIcon; filename="fox.gif" R0lGODlhIwAfALMLAP///+Dg4OCgAOCAAOAAAMDA4KCgwKBgQIAAAGBggCAgIAAAAAAAAAAAAAAA AAAAACH/C05FVFNDQVBFMi4wAwHoAwAh+QQJDwALACwAAAAAIwAfAAAE+3DJSem5Na+jux3CxWWi Zx5DIgiD6Lrgan6FoYZDzuborKGDQKEAXBl1Al/lEgwUjcaeTClhBpDQ6ECypYqwzauAuOhSN8BY s6AoDBSjczW9atrcibicmQPZ229yS0AGNUEGCkIKghYiBgGQkZIKi4wHBoWSmgEGjHOXBZucnUov aJeYhUM1pD58fRifCbMJe2Atcy2qBbWlWEdfApmQvD5uMVktFyGhkr0mbllQtCjNxM8dhdJ9CZi8 l5I12BlDyTndw92rNeIUBe6RddyqCQEJfSn1Nu4Tw8TKB9TdQSFAAa19/CRYE0JkRL5aChCAoISg A6cFC4e0qqBgNlhEBLM0bNI4ToJBbwEofURQkcKjSKs2TlhJoGZNhrMQStjVTkNEmzbD8bLxLFXI DiqTKl0aAQAh+QQJDwALACwCAAAAIQAfAAAE+3DJSeW5Na+jux3CxWWi1x1DIgiD6Lrgan6FoYZD zuboXKGDQKEAXBl1At/kEgwUjcaeTMkMIKHRgUTrE12bVgFxwaUCY82CojBQjJSW86ppYyfecGYO RF+3lQY1BoN6gQZBBgpCCjOBQ48GLgYBlJWWCowZBZaUQ5GDm5yWBhqhokIigaeDpaeVqYOOnqQd pqJMcQcJuwkmm4+2UC0oB7K9HUKxjwFYTAKqnccVnryyUCIhtgHSFAE2EwmHMSu8KLYF3BMFGeJG 4YF3k5U16TUaewLvoeHAhty0GfDxQ7dtT4oE3tJ1MMivDgoBCqop9CBw1wIFCEBgQgCHL4LBYQqg YURgseMGFxFBBcA0EgHHjiJaEpg5Uwi6cBNNYKRJ09IQnDk1sBxKtGgEACH5BAkPAAsALAgAAAAb AB8AAAT7cMl56rz44H2GqBpnbVeXCMIArquHckNhnN9gp3ZHUnZQFB0XCvfaVQaBQHDI1BVFyBuz OZBURUsUMukBLq4Lg48SdG0LisJAEcKIfUfpdqZOtN0FZdkzT693EgZ5cYIGSAYKPgqAYYMgb0mR kQqLG4JwBpeSmwYkmnoHn5KZO5qPmYI/Mp2lP3oFEiAJswmMqZKwR6GqtZ6uuEcCnwW9GTcBAkkg H3mSxTxCWrQdzUnEGzbRAgmZxKG4MxhSQ9ya3KoyMs/ZTOfEAQnZA7MB4RPs5IUJQB4KtPb35JGb tUABgn4GAcmTV0GBJoMICJJYCMJftwCUICJAYGQahUYCIEH6IMbtGQmDIUPiIjbD5IWMMGPKjAAA IfkECQ8ACwAsBQAAAB4AHwAABPtwyTmPpbjmvc4Q1rGFXOYlgjCELPulpVcYKDjc6u2VlDcEhYIv RcQJeBLLLzAkEncwnjJgdD4HEmxM+VIFvh/hQiv1vZYFRWGgECGT5tSStk6431zcXM1+LwwGHT4G Mz8GCkAKfhIGLAZfkJEBCopvjYAFkpKBSJghhJoBgDyAoJ+lQUGjJZiZTHchCbIJpISuX5yCK7YF tBu8kblKAqBfvRm2mxUWILdfvhTFwROzMpHHGKA/RSIJmHaPkDPQEzddOd7F3qkz4xjmTjfrvQEJ 5gOyouRj8E7rdB4EKJhFY8O9eLMWKEDwgRICHvcirjigoNhCBLIgRgwxECPTJIcIQpZhcREBgZMn gfTytq/EQpQor/Wi0TIDpZs4c+KMAAAh+QQJDwALACwCAAAAIQAfAAAE+3DJSeW5Na+jux3CxWWi 1x1DIgiD6Lrgan6FoYZDzuboXKGDQKEAXBl1At/kEgwUjcaeTMkMIKHRgUTrE12bVgFxwaUCY82C ojBQjJSW86ppYyfecGYORF+34T9ABjVBBgpCCoAULgYBjo+QCokdBhoHBoOQmgGVGpgnF5mbn54F oJeYg0M1naWgSxcJsgkmql0ol6u0lKYzTAKiQrsVtiYiIQWawxSNvR6zKMmPBcsSos4ZCZjUl5A1 1cHYE9qi2qs138yaQ3s55tQBCe2ynMvBAVgr5nUoAgqzNohJK5BvzqwFChCAkISAl6MYWPZcUCAq IQJZGVRZ2bNDLuKBf9sCSLKIoCEzNu1STiyJgIBLl0KoaVvmpZ0LhC1fwpxGzQbNF3hGCh1KNAIA IfkECQ8ACwAsAAAAACMAHwAABPtwyUnpuTWvo7sdwsVlomceQyIIg+i64Gp+haGGQ87m6Kyhg0Ch AFwZdQJf5RIMFI3GnkwpYQaQ0OhAsqWKsM2rgLjoUjfAWLOgKAwUo3M1vWra3Im4nJkD2dtvcktA BjVBBgpCCoIWIgYBkJGSCouMBwaFkpoBBoxzlwWbnJ1KL2iXmIVDNaQ+fH0YnwmzCXtgLXMtqgW1 pVhHXwKZkLw+bjFZLRchoZK9Jm5ZULQozcTPHYXSfQmYvJeSNdgZQ8k53cPdqzXiFAXukXXcqgkB CX0p9TbuE8PEygfU3UEhQAGtffwkWBNCZES+WgoQgKCEoAOnBQuHtKqgYDZYRASzNGzSOE6CQW8B KH1EUJHCo0irNk5YSaBmTYazEErY1U5DRJs2w/Gy8SxVyA4qkypdGgEAIfkECQ8ACwAsAgAAACEA HwAABPtwyUnluTWvo7sdwsVlotcdQyIIg+i64Gp+haGGQ87m6Fyhg0ChAFwZdQLf5BIMFI3GnkzJ DCCh0YFE6xNdQTkwh0sFxs7hlpLCRKbTo/XGDA4EDIrCQKE01AyAbX4GQXhCfCZ+Q4sGLgZ2kJEB CogVBZJCNQeAl5iQBhqdnkQXfp6AoZ6QIoCcjKAdophMFhcJtwkml4uyUC0om4u5HUKtiwFYTAKm dgXDljW4igVQIiGyAc8Ud88JhDEruCiyzqEZ30befgmbkdGWsBVhAuqd3ryD2vHyOvfO2WlucVMS MB8REAqkaZsxD9cCBQgQQpQz4c0FBcwgIrhFcSWOiIScJlFCQBIBxY8lCahUmemWjY4QV65058zG QhOUcurcqTMCACH5BAkPAAsALAgAAAAbAB8AAAT7cMl56rz44H2GqBpnbVeXCMIArquHckNhnN9g p3ZHUnZQFB0XCvfaVQaBQHDI1BVFyBuzOZBURUuU66ZaXBcGHyW4HdpypLDvKBWeu+mCkuwZ0N47 iUHO3hvsCYEFCXlgfCBqSYqKCgpxawZ7i5MBBo9JiHKUkTuScweRkT8/nJ0/cwUSIIGBhXuaSalH oKOEcbCxRwKePrYZNwECmBUfuAG+PEJarB24gxtnUwmiCaCLMsheygLTntOjMtgX0UPfg8dvgZXI 5CjfM0AeCqwzGG9CrAsKCPL7efdnKijwtA9BKxL3QMwTFaBRQQQIjKx4SKBiRR+DpmXbsM8OosVr g2ZsnOCwpMmTEQAAIfkECQ8ACwAsBQAAAB4AHwAABPtwyTmPpbjmvc4Q1rGFXOYlgjCELPulpVcY KDjc6u2VlDcEhYIvRcQJeBLLLzAkEncwnjJgdD4HEmxM+VLlPquFVup7mW86ZKWcQrvD6k4ZHAgY FIWBIm4wyG8GMz93QHtxCwYsBnWMjQEKhkiJfQWOjn6SgSGBlnaYHH2cm6FBQX08lJVMIkkWCa8J qIGqdZ9KB7MFsRu5jbYeApx1uhmzl2sHILR1uxTCvhOwMo3EGJw/RSIJlAm41DQZaE4328LbpTMz zVlVOee6AQlur3brYuJO5zRCHwqw4OHwtYG1QAGCfgZ5vHljQYEwgwheKWTYsFwlSBARICBDIhkj gY8fgejaZq+EQZAgqemiUTIDxpcwY0YAACH5BAkPAAsALAIAAAAhAB8AAAT7cMlJ5bk1r6O7HcLF ZaLXHUMiCIPouuBqfoWhhkPO5uhcoYNAoQBcGXUC3+QSDBSNxp5MyQwgodGBROsTXZtWAXHBpQJj zYKiMFCMlJbzqmljJ95wZg5EX7fhP0AGNUEGCkIKgBQuBgGOj5AKiR0GGgcGg5CaAZUamCcXmZuf ngWgl5iDQzWdpaBLFwmyCSaqXSiXq7SUpjNMAqJCuxW2JiIhBZrDFI29HrMoyY8FyxKizhkJmNSX kDXVwdgT2qLaqzXfzJpDeznm1AEJ7bKcy8EBWCvmdSgCCrM2iEkrkG/OrAUKEICQhICXoxhY9lxQ ICohAlkZVFnZs0Mu4oF/2wJIsoigITM27VJOLImAgEuXQqhpW+alnQuELV/CnEbNBs0XeEYKHUo0 AgAh+QQJDwALACwAAAAAIwAfAAAE+3DJSee5NVOs9RkD1y1XcYzZZxjh5b6HYaLpUBTrBe7gJ/y0 yqcQMA1+yF8okVgIgpZBIABKWltQ4c56PZ2yEt1xGvhNlV5w+CMtKn4KIlYdbd9AN6qIzp4WFCBx enQbH36AA4IzhBZ5ZAoKfgWMEmSWl2SThAaYAZxkK1kwCwELK52hUGI7XxIrMjcGamxIPWsmTAmL QbRdOitERXsdBVVcLQcCjkW7I8VcP7kJB8GZ0ygyxyAJK9PVfgZNzgVX26cB07A3uuEUmhLVSjvc BQnobNv27RPvMpbFLujZm3ZEQa59/Pj9M/FlgDQFCApC7OBpwbdYHRT4CwARNAETDZhiiatgEBhH SAg8jnS1EEfGlARi+mFScqU6HCspQIwpkxmRBDVHvvqYEZLRo0iNRgAAIfkECQ8ACwAsAgAAACEA HwAABPtwyUnXuTVTrOsZA9dZR3GM22AY4eW+h2GiaVGsF6iDn+DTk08hYBr4jr5QIrEQAC2DQACE rLaeQV3Vejphc0ZpwCdNdrEkkLSg8CmGV7TkEyUWQLapCE1fK0Bvek8yKzgffoBDMygyNo44eWIK CmsFKENilZmbnJYZmJwBBqGjYisaoKGiK6QGI6mhAxKFj5d2kWVHLSZLCYsaRLR5VTkrmCZeFTYG vY0FSC4CuDbJFKJMEgksRj69CSWbBd+fGdtH2szgmTfYEzcaWuhS387i7BSu8FTa4gHfO0uuPQEo I4G/HgqatQMSr5cCBEYmIZAjYUcLBTKkPESwhCMijITGAkza2FEOho0EUq5ZAnLhk4cpVd7y19Il ipE4c+qMAAAh+QQJDwALACwIAAAAGwAfAAAE+3BJeeq8eNp8xtiZdhRH2BnGV63sYZDh0hUFWnm4 1wl8PAekAW/I+yQSC4FpEAh4iFBVjIKDRkslX0fYDPCaRezEABR5mgUFT1Fwgi7k8hZd8NDc2cwL OAcqPGxuUwt7N2h/A4EwU3skd10KCnSDe12Wl10FIZVxl50BKDGVQCiYoAaMj2WEKDQ1lKpyIwdH CYt6sXwnlSR5F1sCXk0rAqo0vlRCRLUJI5cFzRhVyyjNbZY1SBdP1HHNL662BtoS0zwJLwkBzTlH oOQL5gLo0Os7CrXj0jlDzAoIQiIhmJIjRwkFlf4hOBKj4Ap8rQJEUsjQxAoJCgloRHMEEyK8EP80 bgRyJ4HHjxcmqlzJMgIAIfkECQ8ACwAsBQAAAB4AHwAABPtwySmPpTjfPM8YG1cdxSFWg2GAVuse RnmiRaFaX/55Qj8vnkKgNOgZe6BEYiGYeQKBz3HK+qGk0yMIaMUVoYEeFGky/bzQgqKnEFat13Th U4uGup+04tO2w1VAT0N7A30ycAsGCSRCYAoKcogSYJSVYAVWKgaWAZtggCeajZqcoCIqdVAGE6I1 q6ExjaodJAdKCYcZsbKdtCkxaXcSsZWvgQcCqUO5rLyzC7eLzgWLGMADYWTQKtKVNksYOVoDCSpQ i7vU3+FYSOQxCQGLOkqd4BM6U+XU8jwKt4o45DNC7pYCBEUeIZihoyELBcACHESghGHDFv9QSVRI 8R4ehxcSJhIYmUZJRo8zDo4kuUxIgpMoOTyaSbMmzQgAIfkECQ8ACwAsAgAAACEAHwAABPtwyUnX uTVTrOsZA9dZR3GM22AY4eW+h2GiaVGsF6iDn+DTk08hYBr4jr5QIrEQAC2DQACErLaeQV3Vejph c0ZpwCdNdrEkkLSg8CmGV7TkEyUWQLapCE1fK0BvenJBH36AQzODc3liCgprBUAGGWKVlmKRGiua lwEGlZuamZQLK52hok8rMjaTI6xAFyZLCYmpNDkrQ0R7E7AoLgKMdl4Vn6MatAkllgXLxrvIFQkr y7uYBkwUMpjJpgHLrDa12dvNdzvUzuB0IEue2hLcYlsC6gnsRgq05dvR9QIGKFOAQB/BDvOMbNFx QcE8ggiWZGA1RcuOFvt0BXAEUSSiv4sXMUAkQHLNkozxSLTj4WIBQZIl7QxJgDIljD0cc+rcGQEA IfkECQ8ACwAsAAAAACMAHwAABPtwyUnnuadqebdfxyB23lVkn3YNxoi9l4Gm1VoYeCjuQyj8tNqh EAgUBr/kr5dILARBiy6AvCgPgt4sytkJsN8fuAfiSkdJMDaU2UZXPeWVbD7Hfwlk4kuvg3QCTQlF Cgp5bnU2RYuLJ34UQ0SLCJSNjxOMRQSbCI0BlwsGmZsEnUYGZjALATiSAZWsogWDKXA7W61FNwuz AYNOJSJJIxxDB4InCb2+wUhyWiG5RseSsxtHzs8YAgWu3cdGwBpHckmCCUOZJ8fiFQYF5TsJOOiu uhgf3c8i86K+B++6WRtHwdUSee9+6eDnocCEd4yOYJjXCx0SQ+0KTrD3DUfFgHMKEFwM6YEVr4i7 NiiAGCAkgiYbMhm5kVGCoVyFXMKk4E9XN1QqKZHS1eRmu4A/a9pEQIqATyIJjIrDYWCnypxYs2aN AAAh+QQJDwALACwCAAAAIQAfAAAE+3DJSde5p2p59z5D2HlXkXnVNRgi5l7GiVJqYdxgqA+g4M+0 QyEQKAx8SB8vkVgIgJxc4HhJHgQ8GdSiE1y9vi/Pso2KkN8rKKMFqnhJ67hshvsSx4R3Tn8LmAlE Cgp4bR4GNjcxIENEjkQmQIgFlJQxlI8Imo4FM42PmI8BBKQInAEen6IBBqKkpJAGqauiN5+brAaB qBWqtAMStrELgcUUQ5WqSVkmgAUJz0RNE0WKlQFxKsJF0I3TC5aAkwVWVaFFz97UuhMJLEd3gEKi zUzHGu9I7rrzoBjHsjR02SftwLhnGwIKDPEHUbEcIb6V2eEuWoIehCROZAgoNIECBEcGIaAzYUcW BYgEabJH8gUhYYM+ImDZJ4PMV5CYvNQI5eMrApAweXRHs2fMo0iRRgAAIfkECQ8ACwAsCAAAABsA HwAABPtwSXnqmTivqun4XLcdxdVVgwFabGWY5zEURi1/+CALvFgVgUBhwCvydInEQhD7BIgV40Gg g51wgmmWp9VtRCNQUTuVXWAG4QSlM0q9mrSQrRUkiIksPA5k25NBCgp3VnwBKEBBikElYAsGfQWJ QQiVigWOkIuKBJ0IlwEimgFynJ0EjAZgo6Sslq0JQXyTmI+jNAuxuhiQk2o/B0kJBcOxAUoSvZsl MjWJxJJByBsfAgHWh1HRjNDSHkRGwgkky8FJE1jhNeO+cyES1epy472S0xjpPAmQujcf9/ji6eN3 bMcggOhyFBGnAAERQQjA5MhxQcGohgjOdZhoYSKQswCCMGrUwEICxlOMknhEmKHhKVRCoiVYydJk yJs4cUYAACH5BAkPAAsALAUAAAAeAB8AAAT7cMkpjz00U6vzGSDXLVaBjdVnhFdrGSdKfoVhf2A+ fEIvz4VAoDDoGXu7RGIhkFlAgaLleBDsYs6coLrtcXek3yxk5FY/GOzouTtSweJxu5coJrZwMVug TAgVCnVqIzZjQUKIQiZxEjAFh0IIkogFjBKJQgSaCJQBcTYGmJqaigY/oIc2kJMBNn6eGqqQpo0G hzULfroUsom0QAd9BQnDQkuNj5CtE0+qisSHx7bKyylVj5TD0ciY1X0JB9QmfRO2UQFGYAmu4Zgm IhNa6QProQHg04/HGvJI9La6cIDYx68fHYD3eAQiWBDEEXp9FCAoAgiBDB0YrygwF0AnIgIlFzFe COQMkEeQKFxI8DjqWQKSDFFIHEVAEbaX61DKMMmzp88IACH5BAkPAAsALAIAAAAhAB8AAAT7cMlJ 17mnann3PkPYeVeRedU1GCLmXsaJUmph3GCoD6Dgz7RDIRAoDHxIHy+RWAiAnFzgeEkeBDwZ1KIT XL2+L8+yjYqQ3ysoowWqeEnruGyG+xLHhHdOfwuYCUQKCnhtZTVEiYkmdDQFQ4kIkotQBhqKRASa CIsBKDcbBpiaBJxFlh4GBR4BN5ABk62iBYEVqluuRDYLtAGBTRKqq25CB4AmCb2+E7fEK6q6xpC0 wY/EV4+LyEXAC7MzgAlCmCbG3dBFKAk34q/RI9WUGuuivgfCj90U9do7A+u9xO3Qt89dnDuqfvUg RJDZtINKwilAcGQQglSQjhzUcUE0AbqJCJhoEDali7+O9IYMAimyoL+TGUCS0sWEkAF9GGB2mEiK gK5sCWy2jPKCwsqjSJNGAAAh+QQJDwALACwAAAAAIwAfAAAE+3DJSee5NVOs9RkD1y1XcYzZZxjh 5b6HYaLpUBTrBe7gJ/y0yqcQMA1+yF8okVgIgpZBIABKWltQ4c56PZ2yEt1xGvhNlV5w+CMtKn4K IlYdbd9AN6qIzp4WFCBxenQbH36AA4IzhBZ5ZAoKfgWMEmSWl2SThAaYAZxkK1kwCwELK52hUGI7 XxIrMjcGamxIPWsmTAmLQbRdOitERXsdBVVcLQcCjkW7I8VcP7kJB8GZ0ygyxyAJK9PVfgZNzgVX 26cB07A3uuEUmhLVSjvcBQnobNv27RPvMpbFLujZm3ZEQa59/Pj9M/FlgDQFCApC7OBpwbdYHRT4 CwARNAETDZhiiatgEBhHSAg8jnS1EEfGlARi+mFScqU6HCspQIwpkxmRBDVHvvqYEZLRo0iNRgAA IfkECQ8ACwAsAgAAACEAHwAABPtwyUnXuTVTrOsZA9dZR3GM22AY4eW+h2GiaVGsF6iDn+DTk08h YBr4jr5QIrEQAC2DQACErLaeQV3Vejphc0ZpwCdNdrEkkLSg8CmGV7TkEyUWQLapCE1fK0Bvek8y KzgffoBDMygyNo44eWIKCmsFKENilZmbnJYZmJwBBqGjYisaoKGiK6QGI6mhAxKFj5d2kWVHLSZL CYsaRLR5VTkrmCZeFTYGvY0FSC4CuDbJFKJMEgksRj69CSWbBd+fGdtH2szgmTfYEzcaWuhS387i 7BSu8FTa4gHfO0uuPQEoI4G/HgqatQMSr5cCBEYmIZAjYUcLBTKkPESwhCMijITGAkza2FEOho0E Uq5ZAnLhk4cpVd7y19IlipE4c+qMAAAh+QQJDwALACwIAAAAGwAfAAAE+3BJeeq8eNp8xtiZdhRH 2BnGV63sYZDh0hUFWnm41wl8PAekAW/I+yQSC4FpEAh4iFBVjIKDRkslX0fYDPCaRezEABR5mgUF T1Fwgi7k8hZd8NDc2cwLOAcqPGxuUwt7N2h/A4EwU3skd10KCnSDe12Wl10FIZVxl50BKDGVQCiY oAaMj2WEKDQ1lKpyIwdHCYt6sXwnlSR5F1sCXk0rAqo0vlRCRLUJI5cFzRhVyyjNbZY1SBdP1HHN L662BtoS0zwJLwkBzTlHoOQL5gLo0Os7CrXj0jlDzAoIQiIhmJIjRwkFlf4hOBKj4Ap8rQJEUsjQ xAoJCgloRHMEEyK8EP80bgRyJ4HHjxcmqlzJMgIAIfkECQ8ACwAsBQAAAB4AHwAABPtwySmPpTjf PM8YG1cdxSFWg2GAVuseRnmiRaFaX/55Qj8vnkKgNOgZe6BEYiGYeQKBz3HK+qGk0yMIaMUVoYEe FGky/bzQgqKnEFat13ThU4uGup+04tO2w1VAT0N7A30ycAsGCSRCYAoKcogSYJSVYAVWKgaWAZtg gCeajZqcoCIqdVAGE6I1q6ExjaodJAdKCYcZsbKdtCkxaXcSsZWvgQcCqUO5rLyzC7eLzgWLGMAD YWTQKtKVNksYOVoDCSpQi7vU3+FYSOQxCQGLOkqd4BM6U+XU8jwKt4o45DNC7pYCBEUeIZihoyEL BcACHESghGHDFv9QSVRI8R4ehxcSJhIYmUZJRo8zDo4kuUxIgpMoOTyaSbMmzQgAIfkECQ8ACwAs AgAAACEAHwAABPtwyUnXuTVTrOsZA9dZR3GM22AY4eW+h2GiaVGsF6iDn+DTk08hYBr4jr5QIrEQ AC2DQACErLaeQV3Vejphc0ZpwCdNdrEkkLSg8CmGV7TkEyUWQLapCE1fK0BvenJBH36AQzODc3li CgprBUAGGWKVlmKRGiualwEGlZuamZQLK52hok8rMjaTI6xAFyZLCYmpNDkrQ0R7E7AoLgKMdl4V n6MatAkllgXLxrvIFQkry7uYBkwUMpjJpgHLrDa12dvNdzvUzuB0IEue2hLcYlsC6gnsRgq05dvR 9QIGKFOAQB/BDvOMbNFxQcE8ggiWZGA1RcuOFvt0BXAEUSSiv4sXMUAkQHLNkozxSLTj4WIBQZIl 7QxJgDIljD0cc+rcGQEAIfkECQ8ACwAsAAAAACMAHwAABPtwyUnpuTWvo7sdwsVlomceQyIIg+i6 4Gp+haGGQ87m6Kyhg0ChAFwZdQJf5RIMFI3GnkwpYQaQ0OhAsqWKsM2rgLjoUjfAWLOgKAwUo3M1 vWra3Im4nJkD2dtvcktABjVBBgpCCoIWIgYBkJGSCouMBwaFkpoBBoxzlwWbnJ1KL2iXmIVDNaQ+ fH0YnwmzCXtgLXMtqgW1pVhHXwKZkLw+bjFZLRchoZK9Jm5ZULQozcTPHYXSfQmYvJeSNdgZQ8k5 3cPdqzXiFAXukXXcqgkBCX0p9TbuE8PEygfU3UEhQAGtffwkWBNCZES+WgoQgKCEoAOnBQuHtKqg YDZYRASzNGzSOE6CQW8BKH1EUJHCo0irNk5YSaBmTYazEErY1U5DRJs2w/Gy8SxVyA4qkypdGgEA IfkECQ8ACwAsAgAAACEAHwAABPtwyUnluTWvo7sdwsVlotcdQyIIg+i64Gp+haGGQ87m6Fyhg0Ch AFwZdQLf5BIMFI3GnkzJDCCh0YFE6xNdm1YBccGlAmPNgqIwUIyUlvOqaWMn3nBmDkRft5UGNQaD eoEGQQYKQgozgUOPBi4GAZSVlgqMGQWWlEORg5uclgYaoaJCIoGng6WnlamDjp6kHaaiTHEHCbsJ JpuPtlAtKAeyvR1CsY8BWEwCqp3HFZ68slAiIbYB0hQBNhMJhzErvCi2BdwTBRniRuGBd5OVNek1 GnsC76HhwIbctBnw8UO3bU+KBN7SdTDIrw4KAQqqKfQgcNcCBQhAYEIAhy+CwWEKoGFEYLHjBhcR QQXANBIBx44iWhKYOVMIunATTWCkSdPSEJw5NbAcSrRoBAAh+QQJDwALACwIAAAAGwAfAAAE+3DJ eeq8+OB9hqgaZ21XlwjCAK6rh3JDYZzfYKd2R1J2UBQdFwr32lUGgUBwyNQVRcgbszmQVEVLFDLp AS6uC4OPEnRtC4rCQBHCiH1H6XamTrTdBWXZM0+vdxIGeXGCBkgGCj4KgGGDIG9JkZEKixuCcAaX kpsGJJp6B5+SmTuaj5mCPzKdpT96BRIgCbMJjKmSsEehqrWerrhHAp8FvRk3AQJJIB95ksU8Qlq0 Hc1JxBs20QIJmcShuDMYUkPcmtyqMjLP2UznxAEJ2QOzAeET7OSFCUAeCrT29+SRm7VAAYJ+BgHJ k1dBgSaDCAiSWAjCX7cAlCAiQGBkGoVGAiBB+iDG7RkJgyFD4iI2w+SFjDBjyowAACH5BAkPAAsA LAUAAAAeAB8AAAT7cMk5j6W45r3OENaxhVzmJYIwhCz7paVXGCg43OrtlZQ3BIWCL0XECXgSyy8w JBJ3MJ4yYHQ+BxJsTPlSBb4f4UIr9b2WBUVhoBAhk+bUkrZOuN9c3FzNfi8MBh0+BjM/BgpACn4S BiwGX5CRAQqKb42ABZKSgUiYIYSaAYA8gKCfpUFBoyWYmUx3IQmyCaSErl+cgiu2BbQbvJG5SgKg X70ZtpsVFiC3X74UxcETszKRxxigP0UiCZh2j5Az0BM3XTnexd6pM+MY5k43670BCeYDsqLkY/BO 63QeBCiYRWPDvXizFihA8IESAh73Iq44oKDYQgSyIEYMMRAj0ySHCEKWYXERAYGTJ4H08ravxEKU KK/1otEyA6WbOHPijAAAIfkECQ8ACwAsAgAAACEAHwAABPtwyUnluTWvo7sdwsVlotcdQyIIg+i6 4Gp+haGGQ87m6Fyhg0ChAFwZdQLf5BIMFI3GnkzJDCCh0YFE6xNdm1YBccGlAmPNgqIwUIyUlvOq aWMn3nBmDkRft+E/QAY1QQYKQgqAFC4GAY6PkAqJHQYaBwaDkJoBlRqYJxeZm5+eBaCXmINDNZ2l oEsXCbIJJqpdKJertJSmM0wCokK7FbYmIiEFmsMUjb0esyjJjwXLEqLOGQmY1JeQNdXB2BPaotqr Nd/MmkN7OebUAQntspzLwQFYK+Z1KAIKszaISSuQb86sBQoQgJCEgJejGFj2XFAgKiECWRlUWdmz Qy7igX/bAkiyiKAhMzbtUk4siYCAS5dCqGlb5qWdC4QtX8KcRs0GzRd4RgodSjQCACH+PEZJTEUg SURFTlRJVFkNCkNyZWF0ZWQgb3IgbW9kaWZpZWQgYnkNCkVEIVNPTiAnOTYgW1hmL1VDZl0NCgA7 --=====000_Dragon466706087443_=====--
netcdfgroup
archives: