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Change retrieve_micaps_server.get_model_grid's ensemble number dimension position.

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NMC-DAVE 2021-04-21 14:16:57 +08:00
parent 851b350989
commit 9c7fe4a22e
12 changed files with 195 additions and 23 deletions
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cache/57c3914d41ebc94531a782f7016b88d67d9f63e2.json vendored
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cache/c17ad8d303536745d4004e8b587ba39427593e08.json vendored
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examples/retrieve_cmadaas_model.ipynb
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@ -43,7 +43,7 @@
{
"data": {
"text/plain": [
"<xarray.core.options.set_options at 0x7f76030646d0>"
"<xarray.core.options.set_options at 0x7fc33c112590>"
]
},
"execution_count": 2,
@ -126,6 +126,77 @@
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 读取CRA40再分析数据\n",
"\n",
"CRA40为中国研制的第一套长时间序列全球大气再分析数据集-逐6小时产品，覆盖全球范围，时间跨度40年(197901-201812)并准实时更新，水平分辨率34公里、0.25°、0.5°、1.0°、2.5°。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<pre>&lt;xarray.Dataset&gt;\n",
"Dimensions: (lat: 361, level: 1, lon: 720, time: 1)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2020-04-19\n",
" * level (level) int64 500\n",
" * lat (lat) float64 90.0 89.5 89.0 ... -89.0 -89.5 -90.0\n",
" * lon (lon) float64 0.0 0.5 1.0 1.5 ... 358.5 359.0 359.5\n",
" forecast_reference_time datetime64[ns] 2020-04-19\n",
" forecast_period (time) float64 0.0\n",
"Data variables:\n",
" geopotential_height (time, level, lat, lon) float32 5.14e+03 ... 4.8...\n",
"Attributes:\n",
" Conventions: CF-1.6\n",
" Origin: CIMISS Server by MUSIC API</pre>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 361, level: 1, lon: 720, time: 1)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2020-04-19\n",
" * level (level) int64 500\n",
" * lat (lat) float64 90.0 89.5 89.0 ... -89.0 -89.5 -90.0\n",
" * lon (lon) float64 0.0 0.5 1.0 1.5 ... 358.5 359.0 359.5\n",
" forecast_reference_time datetime64[ns] 2020-04-19\n",
" forecast_period (time) float64 0.0\n",
"Data variables:\n",
" geopotential_height (time, level, lat, lon) float32 5.14e+03 ... 4.8...\n",
"Attributes:\n",
" Conventions: CF-1.6\n",
" Origin: CIMISS Server by MUSIC API"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# set retrieve parameters\n",
"data_code = \"NAFP_CRA40_FTM_6HOR_ANA\"\n",
"init_time = \"2020041900\"\n",
"valid_time = 0\n",
"fcst_ele = \"GPH\" # 位势高度场\n",
"fcst_level = 500 # 500hPa层次\n",
"level_type = \"-\"\n",
"\n",
"# retrieve data from CMADaSS\n",
"data = cmadaas_model_grid(data_code, init_time, valid_time, fcst_ele, fcst_level, level_type,\n",
" varname='geopotential_height', units='gpm', \n",
" levattrs={'long_name':'isobaric', 'units':'hPa', '_CoordinateAxisType':'isobaric'},\n",
" cache=False)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": null,

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examples/retrieve_mdfs_model.ipynb
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nmc_met_io/retrieve_cmadaas.py
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@ -1423,7 +1423,7 @@ def cmadaas_model_grid(data_code, init_time, valid_time, fcst_ele, fcst_level, l
:param fcst_ele: forecast element, like 2m temperature "TEM"
:param fcst_level: vertical level, like 0
:param level_type: forecast level type, 表示Grib数据中的层次类型, 可在云平台上查询.
:param limit: [min_lat, min_lon, max_lat, max_lon],
:param limit: [min_lat, min_lon, max_lat, max_lon]
:param varname: set variable name, default is 'data'
:param units: forecast element's units, defaults to retrieved units.
:param scale_off: [scale, offset], return values = values*scale + offset.

34
nmc_met_io/retrieve_micaps_server.py
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@ -272,9 +272,9 @@ def get_model_grid(directory, filename=None, suffix="*.024",
if data_type == 4:
data = np.full((nmem, nlat, nlon), np.nan)
else:
data = np.full((2, nmem, nlat, nlon), np.nan)
data = np.full((nmem, 2, nlat, nlon), np.nan)
ind = 0
for imem in range(nmem):
for _ in range(nmem):
head_info_mem = np.frombuffer(
byteArray[ind:(ind+278)], dtype=head_dtype)
ind += 278
@ -285,7 +285,7 @@ def get_model_grid(directory, filename=None, suffix="*.024",
if data_type == 4:
data[number, :, :] = np.squeeze(data_mem['data'])
else:
data[:, number, :, :] = np.squeeze(data_mem['data'])
data[number, :, :, :] = np.squeeze(data_mem['data'])
# scale and offset the data, if necessary.
if scale_off is not None:
@ -340,18 +340,18 @@ def get_model_grid(directory, filename=None, suffix="*.024",
'lat':lat_coord, 'lon':lon_coord})
else:
if level[0] == 0:
data = data[np.newaxis, ...]
data = data[:, np.newaxis, ...]
data = xr.Dataset({
varname:(['time', 'number', 'lat', 'lon'], data, varattrs)},
varname:(['number', 'time', 'lat', 'lon'], data, varattrs)},
coords={
'time':time_coord, 'number':number_coord,
'number':number_coord, 'time':time_coord,
'lat':lat_coord, 'lon':lon_coord})
else:
data = data[np.newaxis, :, np.newaxis, ...]
data = data[:, np.newaxis, np.newaxis, ...]
data = xr.Dataset({
varname:(['time', 'number', 'level', 'lat', 'lon'], data, varattrs)},
varname:(['number', 'time', 'level', 'lat', 'lon'], data, varattrs)},
coords={
'time':time_coord, 'number':number_coord, 'level':level_coord,
'number':number_coord, 'time':time_coord, 'level':level_coord,
'lat':lat_coord, 'lon':lon_coord})
elif data_type == 11:
speedattrs = {'long_name':'wind speed', 'units':'m/s'}
@ -386,25 +386,25 @@ def get_model_grid(directory, filename=None, suffix="*.024",
angle = 270. - angle
angle[angle<0] = angle[angle<0] + 360.
if level[0] == 0:
speed = speed[np.newaxis, ...]
angle = angle[np.newaxis, ...]
speed = speed[:, np.newaxis, ...]
angle = angle[:, np.newaxis, ...]
data = xr.Dataset({
'speed': (
['time', 'number', 'lat', 'lon'], speed, speedattrs),
['number', 'time', 'lat', 'lon'], speed, speedattrs),
'angle': (
['time', 'number', 'lat', 'lon'], angle, angleattrs)},
['number', 'time', 'lat', 'lon'], angle, angleattrs)},
coords={
'lon': lon_coord, 'lat': lat_coord,
'number': number_coord, 'time': time_coord})
else:
speed = speed[np.newaxis, :, np.newaxis, ...]
angle = angle[np.newaxis, :, np.newaxis, ...]
speed = speed[:, np.newaxis, np.newaxis, ...]
angle = angle[:, np.newaxis, np.newaxis, ...]
data = xr.Dataset({
'speed': (
['time', 'number', 'level', 'lat', 'lon'],
['number', 'time', 'level', 'lat', 'lon'],
speed, speedattrs),
'angle': (
['time', 'number', 'level', 'lat', 'lon'],
['number', 'time', 'level', 'lat', 'lon'],
angle, angleattrs)},
coords={
'lon': lon_coord, 'lat': lat_coord, 'level': level_coord,