这里给出一个将GrADS数据文件转为netCDF数据文件的脚本示例程序，其它格式数据转netCDF可以参考：

#-----------------------------------------------------

# Author: Yaqiang Wang

# Date: 2015-3-12

# Purpose: Convert CUACE grads data to netCDF (CUACE/Dust)

# Note: Sample

#-----------------------------------------------------

print 'Loading classes...'

from org.meteoinfo.data import GridData

from org.meteoinfo.data import DataMath

from org.meteoinfo.data.meteodata import MeteoDataInfo

from org.meteoinfo.geoprocess.analysis import ResampleMethods

from org.meteoinfo.data.meteodata.netcdf import NetCDFDataInfo

from org.meteoinfo.projection import ProjectionInfo

from org.meteoinfo.projection import ProjectionNames

from org.meteoinfo.projection import KnownCoordinateSystems

from ucar.nc2 import NetcdfFileWriter

from ucar.nc2 import Attribute

from ucar.ma2 import DataType

from ucar.ma2 import Array

import os.path

import jarray

import datetime

from java.util import Date

from java.text import SimpleDateFormat

#Set date

year = 2014

month = 4

day = 23

hour = 0

sdate = datetime.datetime(year, month, day, hour)

print sdate

#Set directory

dataDir = 'U:/data/cuace_dust/dust_example/2014_case'

outDir = dataDir

infn = os.path.join(dataDir, 'dust_post_'+ sdate.strftime('%Y%m%d%H') + '.ctl')

outfn = os.path.join(dataDir, 'WMO_SDS-WAS_Asian_Center_Model_Forecasting_CUACE-Dust_CMA_' \

    + sdate.strftime('%Y-%m-%d') + '.nc')

#Set output X/Y coordinates and projection

toProjInfo = KnownCoordinateSystems.geographic.world.WGS1984

sx = 70.0

xnum = 161

sy = 20

ynum = 71

delta = 0.5

xlist = []

ylist = []

for i in range(0, xnum):

    xlist.append(sx + delta * i)

for i in range(0, ynum):

    ylist.append(sy + delta * i)

X = jarray.array(xlist, 'd')

Y = jarray.array(ylist, 'd')

#Read GrADS data file

print 'Open GrADS data file...'

mdi = MeteoDataInfo()

mdi.openGrADSData(infn)

dataInfo = mdi.getDataInfo()

dataInfo.setBigEndian(True)

fromProjInfo = mdi.getProjectionInfo()

tnum = dataInfo.getTimeNum()

mvalue = dataInfo.getMissingValue()

#Set output nc data file

print 'Create output NC file: ' + outfn

ncfile = NetcdfFileWriter.createNew(NetcdfFileWriter.Version.netcdf3, outfn)

#Add dimensions

print 'Add dimensions...'

xDim = ncfile.addDimension(None, 'lon', xnum)

yDim = ncfile.addDimension(None, 'lat', ynum)

tDim = ncfile.addDimension(None, 'time', tnum)

#Add global attributes

print 'Add global attributes...'

ncfile.addGroupAttribute(None, Attribute('Conventions', 'CF-1.6'))

ncfile.addGroupAttribute(None, Attribute('Title', 'Sand and dust storm forecasting'))

ncfile.addGroupAttribute(None, Attribute('Model', 'CUACE/Dust'))

ncfile.addGroupAttribute(None, Attribute('Center', 'WMO SDS-WAS Asian Center'))

ncfile.addGroupAttribute(None, Attribute('Agency', 'China Meteorological Administration'))

#Add variables

xvar = ncfile.addVariable(None, 'lon', DataType.FLOAT, [xDim])

xvar.addAttribute(Attribute('units', 'degrees_east'))

xvar.addAttribute(Attribute('long_name', 'Longitude'))

xvar.addAttribute(Attribute('standard_name', 'longitude'))

xvar.addAttribute(Attribute('axis', 'X'))

yvar = ncfile.addVariable(None, 'lat', DataType.FLOAT, [yDim])

yvar.addAttribute(Attribute('units', 'degrees_north'))

yvar.addAttribute(Attribute('long_name', 'Latitude'))

yvar.addAttribute(Attribute('standard_name', 'latitude'))

yvar.addAttribute(Attribute('axis', 'Y'))

tvar = ncfile.addVariable(None, 'time', DataType.INT, [tDim])

tvar.addAttribute(Attribute('units', 'hours since 1900-01-01 00:00:0.0'))

tvar.addAttribute(Attribute('long_name', 'Time'))

tvar.addAttribute(Attribute('standart_name', 'time'))

tvar.addAttribute(Attribute('axis', 'T'))

#Data variables

vnames = ['load','con','dry','wet','aod']

varlist = []

var = ncfile.addVariable(None, 'LOAD_DUST', DataType.FLOAT, [tDim, yDim, xDim])

var.addAttribute(Attribute('long_name', 'Dust load'))

var.addAttribute(Attribute('units', 'kg/m2'))

var.addAttribute(Attribute('missing_value', -9999.0))

varlist.append(var)

var = ncfile.addVariable(None, 'SCONC_DUST', DataType.FLOAT, [tDim, yDim, xDim])

var.addAttribute(Attribute('long_name', 'Surface dust concentration'))

var.addAttribute(Attribute('units', 'ug/m3'))

var.addAttribute(Attribute('missing_value', -9999.0))

varlist.append(var)

var = ncfile.addVariable(None, 'DDEPO_DUST', DataType.FLOAT, [tDim, yDim, xDim])

var.addAttribute(Attribute('long_name', '3-hour accumulated dry deposition'))

var.addAttribute(Attribute('units', 'kg/m2'))

var.addAttribute(Attribute('missing_value', -9999.0))

varlist.append(var)

var = ncfile.addVariable(None, 'WDEPO_DUST', DataType.FLOAT, [tDim, yDim, xDim])

var.addAttribute(Attribute('long_name', '3-hour accumulated wet deposition'))

var.addAttribute(Attribute('units', 'kg/m2'))

var.addAttribute(Attribute('missing_value', -9999.0))

varlist.append(var)

var = ncfile.addVariable(None, 'AOD550_DUST', DataType.FLOAT, [tDim, yDim, xDim])

var.addAttribute(Attribute('long_name', 'Dust optical depth at 550nm'))

var.addAttribute(Attribute('units', '-'))

var.addAttribute(Attribute('missing_value', -9999.0))

varlist.append(var)

#Write nc file

ncfile.create()

#Write x,y,z,t variables

print 'Write x variable...'

shape = jarray.array([xnum], 'i')

data = Array.factory(DataType.FLOAT, shape)

for i in range(0,xnum):

    data.set(i, X[i])

ncfile.write(xvar, data)

print 'Write y variable...'

shape = jarray.array([ynum], 'i')

data = Array.factory(DataType.FLOAT, shape)

for i in range(0,ynum):

    data.set(i, Y[i])

ncfile.write(yvar, data)

print 'Write time variable...'

format = SimpleDateFormat('yyyy-MM-dd')

sdate = format.parse('1900-01-01')

tvalues = dataInfo.getTimeValues(sdate, 'hours')

shape = jarray.array([tnum], 'i')

data = Array.factory(DataType.INT, shape)

for i in range(0,tnum):

    data.set(i, tvalues[i])

ncfile.write(tvar, data)

#Write data variables

print 'Write data variable...'

for vname, var in zip(vnames, varlist):

    for t in range(0, tnum):

        print 'Time: ' + str(t + 1)

        mdi.setTimeIndex(t)

        gData = mdi.getGridData(vname)

        ngData = gData.project(fromProjInfo, toProjInfo, X, Y, ResampleMethods.Bilinear)

        origin = jarray.array([t, 0, 0], 'i')

        ncfile.write(var, origin, NetCDFDataInfo.gridToArray3D(ngData))

#Close nc file

ncfile.flush()

ncfile.close()

print 'Finished'

　　

上面转换的netCDF文件绘制模式结果和地面天气现象观测叠加动画图的示例脚本：

# coding=utf-8

#-----------------------------------------------------

# Author: Yaqiang Wang

# Date: 2015-3-13

# Purpose: Read CUACE/Dust netCDF data and MICAPS observation data to plot figures

# Note: Sample

#-----------------------------------------------------

print 'Loading classes...'

from org.meteoinfo.layout import MapLayout

from org.meteoinfo.data import GridData

from org.meteoinfo.data.meteodata import MeteoDataInfo, DrawMeteoData

from org.meteoinfo.legend import LegendScheme

from org.meteoinfo.shape import ShapeTypes

from org.meteoinfo.global.image import AnimatedGifEncoder

import os.path

import jarray

import datetime

import sys

from java.util import Date, Calendar, Locale

from java.text import SimpleDateFormat

from java.awt import Color

#Set date

year = 2013

month = 3

day = 1

hour = 0

sdate = datetime.datetime(year, month, day, hour)

#sdate = datetime.date.today()

#if len(sys.argv) >= 2:

#    sdate = sdate - datetime.timedelta(days=int(sys.argv[1]))

#    sdate = sdate + datetime.timedelta(days=1)

print sdate

dformat = SimpleDateFormat('HH dd MMM yyy', Locale.ENGLISH)

dformat1 = SimpleDateFormat('yyMMddHH')

cal = Calendar.getInstance()

#Set model

#model = 'CUACE-DUST_CMA'

model = 'ADAM2_KMA'

#Set directory

dataDir = 'D:/Working/2015/International/SDS_Asian_Region_Center/Model_Verification'

obsDir = 'U:/data/micaps/2014/plot'

obsDir = 'E:/MetData/micaps'

runDir = dataDir

outDir = os.path.join(dataDir, 'figure')

if not os.path.exists(outDir):

    os.mkdir(outDir)

#Set input/output file names

infn = os.path.join(dataDir, 'WMO_SDS-WAS_Asian_Center_Model_Forecasting_' + model + '_' \

    + sdate.strftime('%Y-%m-%d') + '.nc')

projfn = os.path.join(runDir, 'sds_asian.mip')

#Plot data

print 'Plot data...'

mapLayout = MapLayout()

mapLayout.loadProjectFile(projfn)

mf = mapLayout.getActiveMapFrame()

title = mapLayout.getTexts().get(2)

legend = mapLayout.getLegends()[0]

#---- Set weather list - sand and dust storm

weathers = [6, 7, 8, 9, 30, 31, 32, 33, 34, 35]

#---- Set weather list - sand and dust storm and haze

#weathers = [5, 6, 7, 8, 9, 30, 31, 32, 33, 34, 35]

#---- Create MeteoDataInfo object

mdi = MeteoDataInfo()

omdi = MeteoDataInfo()

#---- Plot loop

mdi.openNetCDFData(infn)

lsfn = os.path.join(runDir,'dust_conc.lgs')

print 'Read data file: ' + infn

aLS = LegendScheme(ShapeTypes.Polygon)

aLS.importFromXMLFile(lsfn)

tnum = mdi.getDataInfo().getTimeNum()

#tnum = 3

s = 'SCONC_DUST'

giffn = os.path.join(outDir, 'V_' + s + '_' + model + '_' + sdate.strftime('%Y%m%d') + '--loop-.gif')

print giffn

encoder = AnimatedGifEncoder()

encoder.setRepeat(0)

encoder.setDelay(1000)

encoder.start(giffn)

sTime = mdi.getDataInfo().getTimes().get(0)

for t in range(1, tnum):

    mdi.setTimeIndex(t)

    aTime = mdi.getDataInfo().getTimes().get(t)

    cal.setTime(aTime)

    cal.add(Calendar.HOUR, 8)

    bjTime = cal.getTime()

    #---- Open observation weather data

    obsfn = os.path.join(obsDir, dformat1.format(bjTime) + '.000')

    print obsfn

    if not os.path.exists(obsfn):

        continue

    omdi.openMICAPSData(obsfn)

    wData = omdi.getStationData('WeatherNow')

    weatherLayer = DrawMeteoData.createWeatherSymbolLayer(wData, weathers, 'Weather')

    #for lb in weatherLayer.getLegendScheme().getLegendBreaks():

    #    lb.setColor(Color.red)

    weatherLayer.setAvoidCollision(False)

    mf.removeMeteoLayers()

    mf.addLayer(weatherLayer)

    #---- Get grid data and create a shaded layer

    gData = mdi.getGridData(s)

    aLayer = DrawMeteoData.createShadedLayer(gData, aLS, 'Forecasting_' + s, 'Data', True)

    aLayer.setProjInfo(mdi.getProjectionInfo())

    mf.addLayer(aLayer)

    mf.moveLayer(aLayer, 0)

    #---- Set title

    title.setLabelText('Run: ' + dformat.format(sTime) + '    Valid: ' + dformat.format(aTime) \

        + '(H+' + str(t * 3) + ')')

    #---- Set legend

    legend.setLegendLayer(aLayer)

    mapLayout.paintGraphics()

    encoder.addFrame(mapLayout.getViewImage())

    figurefn = os.path.join(outDir, 'V_' + model + '_' + s + '_' + dformat1.format(aTime) + '.png')

    print 'Output figure: ' + figurefn

    mapLayout.exportToPicture(figurefn)

encoder.finish()

print 'Finished'

　　

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