[gradsusr] Problem with GrADS in reading ECMWF synoptic monthly means data
Jennifer Adams
jma at cola.iges.org
Mon Sep 19 11:30:07 EDT 2011
You won't be able to bring this data into GrADS in its current netcdf
or grib formats. The netcdf file does not have a linear time axis, so
that rules it out immediately, and there's no way to map the two time
axes (the 3-hourly one and the monthly one) into the same descriptor
with gribmap. So, I believe your strategy should be to break up your
netcdf file into pieces and bring it into GrADS using the E dimension
for the 3-hourly axis and the T dimension for the monthly axis, or
vice versa. You can map them the other way, but then it will be
awkward to use templating if you should need to add more than one year
to your data set. Break your data into twelve parts, one file for each
month, each file containing the 8 records for that month (00, 03, 06,
09, 12, 15, 18, and 21). The netcdf operators should be able to do
this easily. The output filename should be something like
ei_mnth_fc_sfc_1.5x1.5_198901.nc with a time coordinate that has 8
time steps at 3hr intervals. This coordinate will map to your EDEF.
Then template them together using %m2 in the DSET entry, and put "tp 0
e,y,x total precipitation" in the variable declaration. The EDEF
entry can use the names keyword, and you can name them anything you
want, but 00z 03z, ... 21z would be appropriate. Don't forget to add
"UNPACK scale_factor add_offset".
Good luck!
--Jennifer
On Sep 18, 2011, at 11:44 PM, Mubashar Dogar wrote:
> Dear Jennifer,
>
> Here is the output of ncdump -c as requested. I am also giving the
> output of wgrib -h for the grib format of the same data at the end:
>
> ncdump -c ei_mnth_fc_sfc_1.5x1.5_19890101_19891201.nc
> netcdf ei_mnth_fc_sfc_1.5x1.5_19890101_19891201 {
> dimensions:
> longitude = 240 ;
> latitude = 121 ;
> time = UNLIMITED ; // (96 currently)
> variables:
> float longitude(longitude) ;
> longitude:units = "degrees_east" ;
> longitude:long_name = "longitude" ;
> float latitude(latitude) ;
> latitude:units = "degrees_north" ;
> latitude:long_name = "latitude" ;
> int time(time) ;
> time:units = "hours since 1900-01-01 00:00:0.0" ;
> time:long_name = "time" ;
> short tp(time, latitude, longitude) ;
> tp:scale_factor = 4.25320233951185e-07 ;
> tp:add_offset = 0.0139356174654106 ;
> tp:_FillValue = -32767s ;
> tp:missing_value = -32767s ;
> tp:units = "m" ;
> tp:long_name = "Total precipitation" ;
>
> // global attributes:
> :Conventions = "CF-1.0" ;
> :history = "2011-09-18 14:19:37 GMT by mars2netcdf-0.92" ;
> data:
>
> longitude = 0, 1.5, 3, 4.5, 6, 7.5, 9, 10.5, 12, 13.5, 15, 16.5,
> 18, 19.5,
> 21, 22.5, 24, 25.5, 27, 28.5, 30, 31.5, 33, 34.5, 36, 37.5, 39,
> 40.5, 42,
> 43.5, 45, 46.5, 48, 49.5, 51, 52.5, 54, 55.5, 57, 58.5, 60,
> 61.5, 63,
> 64.5, 66, 67.5, 69, 70.5, 72, 73.5, 75, 76.5, 78, 79.5, 81,
> 82.5, 84,
> 85.5, 87, 88.5, 90, 91.5, 93, 94.5, 96, 97.5, 99, 100.5, 102,
> 103.5, 105,
> 106.5, 108, 109.5, 111, 112.5, 114, 115.5, 117, 118.5, 120,
> 121.5, 123,
> 124.5, 126, 127.5, 129, 130.5, 132, 133.5, 135, 136.5, 138,
> 139.5, 141,
> 142.5, 144, 145.5, 147, 148.5, 150, 151.5, 153, 154.5, 156,
> 157.5, 159,
> 160.5, 162, 163.5, 165, 166.5, 168, 169.5, 171, 172.5, 174,
> 175.5, 177,
> 178.5, 180, 181.5, 183, 184.5, 186, 187.5, 189, 190.5, 192,
> 193.5, 195,
> 196.5, 198, 199.5, 201, 202.5, 204, 205.5, 207, 208.5, 210,
> 211.5, 213,
> 214.5, 216, 217.5, 219, 220.5, 222, 223.5, 225, 226.5, 228,
> 229.5, 231,
> 232.5, 234, 235.5, 237, 238.5, 240, 241.5, 243, 244.5, 246,
> 247.5, 249,
> 250.5, 252, 253.5, 255, 256.5, 258, 259.5, 261, 262.5, 264,
> 265.5, 267,
> 268.5, 270, 271.5, 273, 274.5, 276, 277.5, 279, 280.5, 282,
> 283.5, 285,
> 286.5, 288, 289.5, 291, 292.5, 294, 295.5, 297, 298.5, 300,
> 301.5, 303,
> 304.5, 306, 307.5, 309, 310.5, 312, 313.5, 315, 316.5, 318,
> 319.5, 321,
> 322.5, 324, 325.5, 327, 328.5, 330, 331.5, 333, 334.5, 336,
> 337.5, 339,
> 340.5, 342, 343.5, 345, 346.5, 348, 349.5, 351, 352.5, 354,
> 355.5, 357,
> 358.5 ;
>
> latitude = 90, 88.5, 87, 85.5, 84, 82.5, 81, 79.5, 78, 76.5, 75,
> 73.5, 72,
> 70.5, 69, 67.5, 66, 64.5, 63, 61.5, 60, 58.5, 57, 55.5, 54,
> 52.5, 51,
> 49.5, 48, 46.5, 45, 43.5, 42, 40.5, 39, 37.5, 36, 34.5, 33,
> 31.5, 30,
> 28.5, 27, 25.5, 24, 22.5, 21, 19.5, 18, 16.5, 15, 13.5, 12,
> 10.5, 9, 7.5,
> 6, 4.5, 3, 1.5, 0, -1.5, -3, -4.5, -6, -7.5, -9, -10.5, -12,
> -13.5, -15,
> -16.5, -18, -19.5, -21, -22.5, -24, -25.5, -27, -28.5, -30,
> -31.5, -33,
> -34.5, -36, -37.5, -39, -40.5, -42, -43.5, -45, -46.5, -48,
> -49.5, -51,
> -52.5, -54, -55.5, -57, -58.5, -60, -61.5, -63, -64.5, -66,
> -67.5, -69,
> -70.5, -72, -73.5, -75, -76.5, -78, -79.5, -81, -82.5, -84,
> -85.5, -87,
> -88.5, -90 ;
>
> time = 780171, 780174, 780177, 780180, 780183, 780186, 780189,
> 780192,
> 780915, 780918, 780921, 780924, 780927, 780930, 780933, 780936,
> 781587,
> 781590, 781593, 781596, 781599, 781602, 781605, 781608, 782331,
> 782334,
> 782337, 782340, 782343, 782346, 782349, 782352, 783051, 783054,
> 783057,
> 783060, 783063, 783066, 783069, 783072, 783795, 783798, 783801,
> 783804,
> 783807, 783810, 783813, 783816, 784515, 784518, 784521, 784524,
> 784527,
> 784530, 784533, 784536, 785259, 785262, 785265, 785268, 785271,
> 785274,
> 785277, 785280, 786003, 786006, 786009, 786012, 786015, 786018,
> 786021,
> 786024, 786723, 786726, 786729, 786732, 786735, 786738, 786741,
> 786744,
> 787467, 787470, 787473, 787476, 787479, 787482, 787485, 787488,
> 788187,
> 788190, 788193, 788196, 788199, 788202, 788205, 788208 ;
> }
>
> Here is the output from wgrib for the same data but downloaded in
> grib format:
>
> wgrib -h ei_mnth_fc_sfc_1.5x1.5_19890101_19891201.grib
> 1
> :
> 0
> :d
> =89010100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 2
> :
> 58188
> :d
> =89010100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 3
> :
> 116376
> :d
> =89010100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 4
> :
> 174564
> :d
> =
> 89010100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 5
> :
> 232752
> :d
> =89010112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 6
> :
> 290940
> :d
> =89010112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 7
> :
> 349128
> :d
> =89010112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 8
> :
> 407316
> :d
> =
> 89010112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 9
> :
> 465504
> :d
> =89020100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=28
> 10
> :
> 523692
> :d
> =89020100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=28
> 11
> :
> 581880
> :d
> =89020100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=28
> 12
> :
> 640068
> :d
> =
> 89020100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=28
> 13
> :
> 698256
> :d
> =89020112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=28
> 14
> :
> 756444
> :d
> =89020112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=28
> 15
> :
> 814632
> :d
> =89020112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=28
> 16
> :
> 872820
> :d
> =
> 89020112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=28
> 17
> :
> 931008
> :d
> =89030100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 18
> :
> 989196
> :d
> =89030100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 19
> :
> 1047384
> :d
> =89030100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 20
> :
> 1105572
> :d
> =
> 89030100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 21
> :
> 1163760
> :d
> =89030112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 22
> :
> 1221948
> :d
> =89030112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 23
> :
> 1280136
> :d
> =89030112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 24
> :
> 1338324
> :d
> =
> 89030112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 25
> :
> 1396512
> :d
> =89040100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=30
> 26
> :
> 1454700
> :d
> =89040100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=30
> 27
> :
> 1512888
> :d
> =89040100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=30
> 28
> :
> 1571076
> :d
> =
> 89040100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=30
> 29
> :
> 1629264
> :d
> =89040112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=30
> 30
> :
> 1687452
> :d
> =89040112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=30
> 31
> :
> 1745640
> :d
> =89040112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=30
> 32
> :
> 1803828
> :d
> =
> 89040112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=30
> 33
> :
> 1862016
> :d
> =89050100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 34
> :
> 1920204
> :d
> =89050100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 35
> :
> 1978392
> :d
> =89050100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 36
> :
> 2036580
> :d
> =
> 89050100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 37
> :
> 2094768
> :d
> =89050112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 38
> :
> 2152956
> :d
> =89050112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 39
> :
> 2211144
> :d
> =89050112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 40
> :
> 2269332
> :d
> =
> 89050112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 41
> :
> 2327520
> :d
> =89060100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=30
> 42
> :
> 2385708
> :d
> =89060100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=30
> 43
> :
> 2443896
> :d
> =89060100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=30
> 44
> :
> 2502084
> :d
> =
> 89060100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=30
> 45
> :
> 2560272
> :d
> =89060112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=30
> 46
> :
> 2618460
> :d
> =89060112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=30
> 47
> :
> 2676648
> :d
> =89060112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=30
> 48
> :
> 2734836
> :d
> =
> 89060112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=30
> 49
> :
> 2793024
> :d
> =89070100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 50
> :
> 2851212
> :d
> =89070100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 51
> :
> 2909400
> :d
> =89070100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 52
> :
> 2967588
> :d
> =
> 89070100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 53
> :
> 3025776
> :d
> =89070112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 54
> :
> 3083964
> :d
> =89070112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 55
> :
> 3142152
> :d
> =89070112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 56
> :
> 3200340
> :d
> =
> 89070112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 57
> :
> 3258528
> :d
> =89080100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 58
> :
> 3316716
> :d
> =89080100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 59
> :
> 3374904
> :d
> =89080100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 60
> :
> 3433092
> :d
> =
> 89080100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 61
> :
> 3491280
> :d
> =89080112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 62
> :
> 3549468
> :d
> =89080112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 63
> :
> 3607656
> :d
> =89080112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 64
> :
> 3665844
> :d
> =
> 89080112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 65
> :
> 3724032
> :d
> =89090100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=30
> 66
> :
> 3782220
> :d
> =89090100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=30
> 67
> :
> 3840408
> :d
> =89090100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=30
> 68
> :
> 3898596
> :d
> =
> 89090100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=30
> 69
> :
> 3956784
> :d
> =89090112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=30
> 70
> :
> 4014972
> :d
> =89090112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=30
> 71
> :
> 4073160
> :d
> =89090112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=30
> 72
> :
> 4131348
> :d
> =
> 89090112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=30
> 73
> :
> 4189536
> :d
> =89100100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 74
> :
> 4247724
> :d
> =89100100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 75
> :
> 4305912
> :d
> =89100100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 76
> :
> 4364100
> :d
> =
> 89100100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 77
> :
> 4422288
> :d
> =89100112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 78
> :
> 4480476
> :d
> =89100112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 79
> :
> 4538664
> :d
> =89100112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 80
> :
> 4596852
> :d
> =
> 89100112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 81
> :
> 4655040
> :d
> =89110100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=30
> 82
> :
> 4713228
> :d
> =89110100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=30
> 83
> :
> 4771416
> :d
> =89110100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=30
> 84
> :
> 4829604
> :d
> =
> 89110100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=30
> 85
> :
> 4887792
> :d
> =89110112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=30
> 86
> :
> 4945980
> :d
> =89110112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=30
> 87
> :
> 5004168
> :d
> =89110112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=30
> 88
> :
> 5062356
> :d
> =
> 89110112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=30
> 89
> :
> 5120544
> :d
> =89120100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 90
> :
> 5178732
> :d
> =89120100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 91
> :
> 5236920
> :d
> =89120100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 92
> :
> 5295108
> :d
> =
> 89120100:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
> 93
> :
> 5353296
> :d
> =89120112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=3:P2=24:TimeU=1:sfc:
> 3hr fcst:ave at 24hr:mon mean:NAve=31
> 94
> :
> 5411484
> :d
> =89120112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=6:P2=24:TimeU=1:sfc:
> 6hr fcst:ave at 24hr:mon mean:NAve=31
> 95
> :
> 5469672
> :d
> =89120112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=9:P2=24:TimeU=1:sfc:
> 9hr fcst:ave at 24hr:mon mean:NAve=31
> 96
> :
> 5527860
> :d
> =
> 89120112:TP:kpds5=228:kpds6=1:kpds7=0:TR=113:P1=12:P2=24:TimeU=1:sfc:
> 12hr fcst:ave at 24hr:mon mean:NAve=31
>
>
>
> Today's Topics:
>
> 1. Re: Box Plot in GrADS? (Jayakrishnan PR)
> 2. Re: Convert monthly to yearly accumulated rainfall
> (Rafanoharana Serge Claudio)
> 3. Invitation to connect on LinkedIn (Rajeevan K via LinkedIn)
> 4. Re: lats4d and re (Arlindo da Silva)
> 5. Re: Moisture flux convergence/divergence (Arlindo da Silva)
> 6. Problem with GrADS in reading ECMWF synoptic monthly means
> data (Mubashar Dogar)
> 7. Re: Problem with GrADS in reading ECMWF synoptic monthly
> means data (Jennifer Adams)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sat, 17 Sep 2011 10:24:57 +0530
> From: Jayakrishnan PR <prjayakrishnan at gmail.com>
> Subject: Re: [gradsusr] Box Plot in GrADS?
> To: GrADS Users Forum <gradsusr at gradsusr.org>
> Message-ID:
> <CA+P5ZEE1-peEObQ1nMB_c1qFa_PNXNOJZ6mq03Eqi=DiJzbB6A at mail.gmail.com
> >
> Content-Type: text/plain; charset="iso-8859-1"
>
> Dear Matei,
> I am wondering why you are sticking to Grads itself.
> Grads is
> a software designed to plot griided data and analysis of that data.
> it is
> not suitable for plotting these kind of graphs. You can choose
> softwares
> such as NCL or origin for plotting these kind of graphs. No need for
> sticking into Grads.
> >
> >
> >
>
>
> --
> Sincerely
> ***********************************************
> Jayakrishnan.P.R
> CSIR-Senior Research Fellow
> Department of Atmospheric Sciences
> Cochin University of Science and Technology (CUSAT), Cochin-682 016
> Kerala, India.
> Mob: 09895417565
>
> ***********************************************
> -------------- next part --------------
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>
> ------------------------------
>
> Message: 2
> Date: Sat, 17 Sep 2011 15:11:20 +0700
> From: Rafanoharana Serge Claudio <rafanoharana at gmail.com>
> Subject: Re: [gradsusr] Convert monthly to yearly accumulated rainfall
> To: GrADS Users Forum <gradsusr at gradsusr.org>
> Message-ID:
> <CAPFxStgNLcjM=4girADmnhMXVH+S-bo6=hzzRQxHJrrN6-+CTw at mail.gmail.com
> >
> Content-Type: text/plain; charset="iso-8859-1"
>
> Dear Chris,
>
> I do not know if one can do it directly while having the 50 time
> steps but
> what I can say is that you can process for every year (then you'll
> have 50
> outputs), then assign time for every year, then merge using cdo all
> 50 years
> into one single file.
>
> Hope this can help,
>
> Serge
>
> On Sat, Sep 17, 2011 at 3:34 AM, Alauda arvensis
> <a.arvensis at gmail.com>wrote:
>
> > Dear GrADS users,
> >
> > My data consists of 50 years of monthly rainfall. I want to
> convert this
> > data into yearly accumulated rainfall for every year. So I am
> looking for
> > something like:
> >
> > 'pyearly = sum(pmonthly, t=1, t=12)' ... but I need it for every
> year, so
> > that in the end I have 50 time steps.
> >
> > Any hints welcome! Thanks!
> >
> > Kind regards,
> > Chris
> >
> > PS: I already tried "mon2yr.gs" from Bin Guan's page, but
> something does
> > not appear to work.
> >
> > _______________________________________________
> > gradsusr mailing list
> > gradsusr at gradsusr.org
> > http://gradsusr.org/mailman/listinfo/gradsusr
> >
> >
>
>
> --
> Serge
> -------------- next part --------------
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>
> ------------------------------
>
> Message: 3
> Date: Sat, 17 Sep 2011 12:23:40 +0000 (UTC)
> From: Rajeevan K via LinkedIn <member at linkedin.com>
> Subject: [gradsusr] Invitation to connect on LinkedIn
> To: Ning Cao <gradsusr at gradsusr.org>
> Message-ID:
> <786703422.8558443.1316262220088.JavaMail.app at ela4-
> app0131.prod>
> Content-Type: text/plain; charset="utf-8"
>
> LinkedIn
> ------------
>
>
>
>
> Rajeevan K requested to add you as a connection on LinkedIn:
>
> ------------------------------------------
>
> Ning,
>
> I'd like to add you to my professional network on LinkedIn.
>
> - Rajeevan
>
> Accept invitation from Rajeevan K
> http://www.linkedin.com/e/ofpc7q-gsokjh2t-51/X_QvJ7UefcfFkLAYwdQqLHU00gfkGZ-5LN/blk/I161349014_13/1BpC5vrmRLoRZcjkkZt5YCpnlOt3RApnhMpmdzgmhxrSNBszYPclYQcj0Vd3cNdz59bT1imBhDqDB3bP8Sd3sRdPoQczgLrCBxbOYWrSlI/EML_comm_afe/?hs=false&tok=3cpy5iTqgDLAU1
>
> View invitation from Rajeevan K
> http://www.linkedin.com/e/ofpc7q-gsokjh2t-51/X_QvJ7UefcfFkLAYwdQqLHU00gfkGZ-5LN/blk/I161349014_13/3cNnPgNc3AQcP4SckALqnpPbOYWrSlI/svi/?hs=false&tok=27OH6j0pIDLAU1
>
>
> --
> (c) 2011, LinkedIn Corporation
> -------------- next part --------------
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> ------------------------------
>
> Message: 4
> Date: Sat, 17 Sep 2011 09:54:51 -0400
> From: Arlindo da Silva <dasilva at alum.mit.edu>
> Subject: Re: [gradsusr] lats4d and re
> To: GrADS Users Forum <gradsusr at gradsusr.org>
> Message-ID:
> <CALz7xZfF+=6FEJbFc_zi1t4WAPHUXwRe0RUYwVPc=qPiVnbuUw at mail.gmail.com
> >
> Content-Type: text/plain; charset="iso-8859-1"
>
> On Fri, Sep 16, 2011 at 5:20 PM, Coly SAR <colysar at gmail.com> wrote:
>
> > I have a 3D nc file with 6 variables and I want to regrid it to
> 0.5? using
> > lats4d.
> > I issued this command below but the file created remains the same
> as infile
> > and no regrid done.
> >
> >
> >
> > >lats4d -i runet.nc -o test_05 -lon -38.5 29 -lat -10.5 25 -func
> re(@,0.5)
> >
> > I am using opengrad v 2.9 and need any suggestion on how to do
> this regrid.
> >
> >
> Type "lats4d -h" and read the section on regridding. Unless you use
> one of
> the predefined resolutions you will need to create (or point to one)
> dimension environment file (option -de).
>
> Arlindo
>
>
>
> --
> Arlindo da Silva
> dasilva at alum.mit.edu
> -------------- next part --------------
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> ------------------------------
>
> Message: 5
> Date: Sat, 17 Sep 2011 10:12:57 -0400
> From: Arlindo da Silva <dasilva at alum.mit.edu>
> Subject: Re: [gradsusr] Moisture flux convergence/divergence
> To: GrADS Users Forum <gradsusr at gradsusr.org>
> Message-ID:
> <CALz7xZf5MaHEvP1bX1Z93ysYNg5xVCGOvBdBaM5aHTkEs6+HSw at mail.gmail.com
> >
> Content-Type: text/plain; charset="iso-8859-1"
>
> 2011/9/16 Hyacinth Nnamchi <hyacinth.1 at hotmail.com>
>
> > Jeff,
> >
> > I seems my question wasn't well framed, and so you dealt with the
> typo. I
> > have attached two Figs showing what the calculations below will
> give and
> > what exactly I'll like to do.
> >
> >
> >From the attached plot it appears that you want to plot the divergent
> component of the mass flux, as given by the Helmholtz decomposition:
>
> http://en.wikipedia.org/wiki/Helmholtz_decomposition
>
> The first step is to compute the velocity potential
>
> ga-> chi = fish_chi(zon,mer)
>
> (As Roger mentions, make sure to average in the vertical first if
> this is
> what you want.) Next, the divergent component of the mass flux is
> related to
> the gradient of chi; see this for more information:
>
> Calculating the Divergent
> Wind<http://opengrads.org/doc/udxt/fish/fish.html#calculating_the_divergent_wind
> >
>
> Notice that you will need an opengrads build in order to use the fish
> extension; more info here:
>
> http://opengrads.org/wiki/index.php?title=Installing_the_OpenGrADS_Bundle
>
> Good luck,
>
> Arlindo
>
>
>
>
>
>
> > Thanks,
> >
> > Hyacinth
> >
> >
> > > Date: Thu, 15 Sep 2011 17:34:50 -0500>
> > From: Jeffrey Duda <jdduda at iastate.edu>>
> > Subject: Re: [gradsusr] Moisture flux convergence/divergence
> > > To: GrADS Users Forum <gradsusr at gradsusr.org>
> > > Message-ID:
> > > <CAP9LQ-VNkxcu-+ey_QkZU7f4MHf+VwJDvav=Dci3ns+x+pjG8Q at mail.gmail.com
> >
> > > Content-Type: text/plain; charset="iso-8859-1"
> >
> > >
> > > Set the graphics output to vector ('set gxout vector'), then 'd
> zon;mer'
> > > (note the semicolon instead of a comma).
> > >
> > > Jeff Duda
> > >
> > > 2011/9/15 Hyacinth Nnamchi <hyacinth.1 at hotmail.com>
> > >
> > > > Hello,
> > > >
> > > > This is not directly about GrADS but since most of address
> similar!
> > > > problems, I guess someone on this forum will be able to answer
> my
> > question.
> > > >
> > > > I understand that to calculate moisture transport, given zonal,
> > meridional
> > > > winds and specific humidity, can simply do:
> > > >
> > > > 'define zon=uwnd.1*shum.3'
> > > > 'define mer=vwnd.2*shum.3'
> > > > 'd zon,mer'
> > > >
> > > > But, I want to plot vectors indicating field of net moisture
> flux (all
> > arrows converging/diverging) instead of just transport.
> > > >
> > > > Thanks for any ideas.
> > > >
> > > > Hyacinth
> > > >
> > > >
> > > > __
> >
> > _______________________________________________
> > gradsusr mailing list
> > gradsusr at gradsusr.org
> > http://gradsusr.org/mailman/listinfo/gradsusr
> >
> >
>
>
> --
> Arlindo da Silva
> dasilva at alum.mit.edu
> -------------- next part --------------
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> ------------------------------
>
> Message: 6
> Date: Sun, 18 Sep 2011 21:39:04 +0300
> From: Mubashar Dogar <mubashardogar at gmail.com>
> Subject: [gradsusr] Problem with GrADS in reading ECMWF synoptic
> monthly means data
> To: gradsusr at gradsusr.org
> Message-ID:
> <CAHBht237iOnGUtkYuZL6+rXEtm-KSgsti2W1anm6-BY7SkAU2Q at mail.gmail.com
> >
> Content-Type: text/plain; charset="iso-8859-1"
>
> Dear GrADS users,
>
> I am facing a problem with time steps of a netcdf file downloaded for
> synoptic monthly means of ECMWF ERA-INTERIM forecast product for the
> year
> 1989 (downloaded for surface total precipitation for all time steps
> available from: http://data-portal.ecmwf.int/data/d/interim_mnth/).
> This
> file is basically synoptic monthly means generated by long term
> average of
> corresponding 3 hour in all the days of a month, giving 8 time steps
> in a
> month. So instead of reading 96 values that represents to 12 months (8
> values in each months) GrADS read all these values by considering 8
> time
> steps in a day instead in a month. I mean GrADS reads these hours in
> a daily
> format instead of monthly. Does GrADS not deal with synoptic monthly
> means?
> I have grib file as well for the same data, what should be time
> increment in
> a ctl file to open the same grib file?
>
>
> Mubashar Dogar
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> ------------------------------
>
> Message: 7
> Date: Sun, 18 Sep 2011 15:47:24 -0400
> From: Jennifer Adams <jma at cola.iges.org>
> Subject: Re: [gradsusr] Problem with GrADS in reading ECMWF synoptic
> monthly means data
> To: GrADS Users Forum <gradsusr at gradsusr.org>
> Message-ID: <93628EC9-0E8F-4EE1-9D80-0147B93D7F6A at cola.iges.org>
> Content-Type: text/plain; charset="us-ascii"
>
> Please send the output from ncdump -c on your netcdf file. --Jennifer
>
> On Sep 18, 2011, at 2:39 PM, Mubashar Dogar wrote:
>
> >
> > Dear GrADS users,
> >
> > I am facing a problem with time steps of a netcdf file downloaded
> > for synoptic monthly means of ECMWF ERA-INTERIM forecast product for
> > the year 1989 (downloaded for surface total precipitation for all
> > time steps available from: http://data-portal.ecmwf.int/data/d/interim_mnth/)
> > . This file is basically synoptic monthly means generated by long
> > term average of corresponding 3 hour in all the days of a month,
> > giving 8 time steps in a month. So instead of reading 96 values that
> > represents to 12 months (8 values in each months) GrADS read all
> > these values by considering 8 time steps in a day instead in a
> > month. I mean GrADS reads these hours in a daily format instead of
> > monthly. Does GrADS not deal with synoptic monthly means? I have
> > grib file as well for the same data, what should be time increment
> > in a ctl file to open the same grib file?
> >
> >
> > Mubashar Dogar
> >
> >
> > _______________________________________________
> > gradsusr mailing list
> > gradsusr at gradsusr.org
> > http://gradsusr.org/mailman/listinfo/gradsusr
>
> --
> Jennifer M. Adams
> IGES/COLA
> 4041 Powder Mill Road, Suite 302
> Calverton, MD 20705
> jma at cola.iges.org
>
>
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--
Jennifer M. Adams
IGES/COLA
4041 Powder Mill Road, Suite 302
Calverton, MD 20705
jma at cola.iges.org
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