使用metpy cape_cin函数时获取“ValueError：零大小数组到没有身份的减少操作最小值”

Question

我正在尝试使用metpy 的 cape_cin 和 get_layer 函数计算指定层中的 cape 和 cin。我为从 NCDC 服务器访问的 RAP 垂直配置文件执行此操作，用于 3 种不同的包裹类型（ML、MU 和 SB）。当我尝试计算 mu 和 sb 配置文件时出现此错误ValueError: zero-size array to reduction operation minimum which has no identity，但不适用于 ml 配置文件......即使我以完全相同的方式计算层：

首先，我必须从 RAP 垂直剖面创建压力、温度、露点和高度的垂直剖面：

from datetime import datetime, timedelta   
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS
import numpy as np
from metpy.units import units
import cartopy.crs as ccrs
from metpy.calc import (dewpoint_from_relative_humidity, mixed_parcel, most_unstable_parcel, parcel_profile, pressure_to_height_std, lcl, height_to_pressure_std, get_layer, cape_cin)

year=2019
month=5
day=23
hour=0

cenlat = 34.91269
cenlon = -98.21048

time_start = datetime(year, month, day, hour, 0) #specified time
hour = time_start.hour
if hour < 10:
    hour = '0'+str(hour)
day = time_start.day
if day < 10:
    day = '0'+str(day)
month = time_start.month
if month < 10:
    month = '0'+str(month)

cat = TDSCatalog('https://www.ncdc.noaa.gov/thredds/catalog/model-rap130-old/'+str(time_start.year)+str(month)+'/'+str(time_start.year)+str(month)+str(day)+'/catalog.html?dataset=rap130-old/'+str(time_start.year)+str(month)+'/'+str(time_start.year)+str(month)+str(day)+'/rap_130_'+str(time_start.year)+str(month)+str(day)+'_'+str(hour)+'00_000.grb2')
latest_ds = list(cat.datasets.values())[0]
print(latest_ds.access_urls)
ncss = NCSS(latest_ds.access_urls['NetcdfSubset'])

query = ncss.query()
query.variables('Pressure_surface').variables('Geopotential_height_isobaric').variables('Geopotential_height_surface').variables('Relative_humidity_isobaric').variables('Temperature_isobaric').variables('Dewpoint_temperature_height_above_ground').variables('Temperature_height_above_ground').variables
query.add_lonlat().lonlat_box(cenlon-2.1, cenlon +2.1, cenlat-2.1, cenlat+2.1)
data1 = ncss.get_data(query)
dlev = data1.variables['Geopotential_height_isobaric'].dimensions[1]
dlat = data1.variables['Geopotential_height_isobaric'].dimensions[2]
dlon = data1.variables['Geopotential_height_isobaric'].dimensions[3]

SFCP = (np.asarray(data1.variables['Pressure_surface'][:])/100.) * units('hPa')
hgt = np.asarray(data1.variables['Geopotential_height_isobaric'][:]) * units('meter')
sfc_hgt = np.asarray(data1.variables['Geopotential_height_surface'][:]) * units('meter')
Temp_up = np.asarray(data1.variables['Temperature_isobaric'][:]) * units('kelvin')
RH_up = np.asarray(data1.variables['Relative_humidity_isobaric'][:])
Td = (np.asarray(data1.variables['Dewpoint_temperature_height_above_ground'][:]) * units('kelvin')).to('degC')
T = np.asarray(data1.variables['Temperature_height_above_ground'][:]) * units('kelvin')

# Get the dimension data
lats_r = data1.variables[dlat][:]
lons_r= data1.variables[dlon][:]
lev = (np.asarray(data1.variables[dlev][:])/100.) * units('hPa')

# Set up our array of latitude and longitude values and transform to the desired projection.
flon = float(cenlon)
flat = float(cenlat)
crs = ccrs.PlateCarree()
crlons, crlats = np.meshgrid(lons_r[:]*1000, lats_r[:]*1000)
trlatlons = crs.transform_points(ccrs.LambertConformal(central_longitude=265, central_latitude=25, standard_parallels=(25.,25.)),crlons,crlats)
trlons = trlatlons[:,:,0]
trlats = trlatlons[:,:,1]
dlon = np.abs(trlons - cenlon)
dlat = np.abs(trlats - cenlat)
ilon = np.where(dlon == np.min(dlon)) #position in the dlon array with minimal difference between gridpoint lon and input lon
ilat = np.where(dlat == np.min(dlat)) #position in the dlat array with minimal difference between gridpoint lat and input lat

Tdc_up = dewpoint_from_relative_humidity(Temp_up[0,:,ilat[0][0], ilon[1][0]],RH_up[0,:,ilat[0][0], ilon[1][0]]/100)

p_sounding = np.sort(np.append(lev, SFCP[0,ilat[0][0], ilon[1][0]]))
ind = np.where(p_sounding >= SFCP[0,ilat[0][0], ilon[1][0]])[0][0]
hgt_sounding = np.insert(hgt[0,:,ilat[0][0], ilon[1][0]].magnitude, ind, sfc_hgt[0,ilat[0][0], ilon[1][0]].magnitude) * hgt.units
T_sounding = (np.insert(Temp_up[0,:,ilat[0][0], ilon[1][0]].magnitude, ind, T[0,0,ilat[0][0], ilon[1][0]].magnitude) * T.units).to(Tdc_up.units)
Td_sounding = np.insert(Tdc_up.magnitude, ind, Td[0,0,ilat[0][0], ilon[1][0]].magnitude) * Tdc_up.units
p_skewt = p_sounding[p_sounding <= SFCP[0,ilat[0][0], ilon[1][0]]]
hgt_skewt = hgt_sounding[p_sounding <= SFCP[0,ilat[0][0], ilon[1][0]]]
T_skewt = T_sounding[p_sounding <= SFCP[0,ilat[0][0], ilon[1][0]]]
Td_skewt = Td_sounding[p_sounding <= SFCP[0,ilat[0][0], ilon[1][0]]]

AGLhgts = hgt_skewt[::-1]-hgt_skewt[-1]

接下来，我使用 mixed_pa​​rcel、most_unstable 和 parcel_profile 函数为每种宗地类型创建垂直剖面，并计算我要计算 cape_cin 的层的 etop 和底部的压力值（LCL 到 LCL+2km）：

ml_p, ml_T, ml_Td = mixed_parcel(np.flip(p_skewt), np.flip(T_skewt), np.flip(Td_skewt))
ml_profile = parcel_profile(p_skewt[::-1], ml_T, ml_Td)
ml_profile = (ml_profile - 273.15*units('kelvin')).magnitude*units('degC')

mu_p, mu_T, mu_Td, mu_index = most_unstable_parcel(np.flip(p_skewt), np.flip(T_skewt), np.flip(Td_skewt))
mu_profile = parcel_profile(p_skewt[::-1], mu_T, mu_Td)
mu_profile = (mu_profile - 273.15*units('kelvin')).magnitude*units('degC')

#Note: sbpcl_profile will have the exact same values of p_skewt, T_skewt, and Td_skewt in pprof below:
pprof = parcel_profile(p_skewt[::-1], T_skewt[-1], Td_skewt[-1])
pprof = (pprof - 273.15*units('kelvin')).magnitude*units('degC')

mllcl = lcl(ml_p, ml_T, ml_Td)
mllcl_h = pressure_to_height_std(mllcl[0]) - hgt_skewt[-1]

mulcl = lcl(mu_p, mu_T, mu_Td)
mulcl_h = pressure_to_height_std(mulcl[0]) - hgt_skewt[-1]

sblcl = lcl(p_skewt[-1], T_skewt[-1], Td_skewt[-1])
sblcl_h = pressure_to_height_std(sblcl[0]) - hgt_skewt[-1]

mllcl2000 = mllcl_h + 2*units('kilometer')
mulcl2000 = mulcl_h + 2*units('kilometer')
sblcl2000 = sblcl_h + 2*units('kilometer')
mllcl2000_p = height_to_pressure_std(mllcl2000)
mulcl2000_p = height_to_pressure_std(mulcl2000)
sblcl2000_p = height_to_pressure_std(sblcl2000)

计算完所有这些后，我使用 get_layer 函数创建压力、温度、露点和地块温度的数组，我需要计算 cape_cin，然后去计算感兴趣层中的实际 cape_cin 值：

ml_LCL_CAPE_layer = get_layer(p_skewt, T_skewt, Td_skewt, ml_profile[::-1], bottom = mllcl[0], depth = mllcl[0] - mllcl2000_p)
mu_LCL_CAPE_layer = get_layer(p_skewt, T_skewt, Td_skewt, mu_profile[::-1], bottom = mulcl[0], depth = mulcl[0] - mulcl2000_p)
sb_LCL_CAPE_layer = get_layer(p_skewt, T_skewt, Td_skewt, pprof[::-1], bottom = sblcl[0], depth = sblcl[0] - sblcl2000_p)

mlLCLCAPE = cape_cin(ml_LCL_CAPE_layer[0], ml_LCL_CAPE_layer[1], ml_LCL_CAPE_layer[2], ml_LCL_CAPE_layer[3])
muLCLCAPE = cape_cin(mu_LCL_CAPE_layer[0], mu_LCL_CAPE_layer[1], mu_LCL_CAPE_layer[2], mu_LCL_CAPE_layer[3])
sbLCLCAPE = cape_cin(sb_LCL_CAPE_layer[0], sb_LCL_CAPE_layer[1], sb_LCL_CAPE_layer[2], sb_LCL_CAPE_layer[3])

mlLCLCAPEcin = mlLCLCAPE[0] + mlLCLCAPE[1]
muLCLCAPEcin = muLCLCAPE[0] + muLCLCAPE[1]
sbLCLCAPEcin = sbLCLCAPE[0] + sbLCLCAPE[1]

3 个 get_layer 函数中每一个的压力、温度、露点和地块温度的数组似乎都填充了正确的值，并且每种地块类型的这 4 个数组都是相同的形状。上面的 mlLCLCAPEcin 计算给出了正确的输出（99.26 j/kg - 当我在 SkewT 上绘制它时验证），但 MU 和 SB 配置文件的完全相同的计算给出了上面提到的错误。我正在使用 Metpy v 1.1，并尝试使用不同的位置和来自不同预测时间的输出，但仍然遇到相同的问题。

Answer 1

如果我修复上面的示例代码（存在一些名称问题和一些索引问题）：

T_sounding = (np.insert(Temp_up[0,:,ilat[0][0], ilon[1][0]].magnitude, ind, Temp_up[0,0,ilat[0][0], ilon[1][0]].magnitude) * Temp_up.units).to(Tdc_up.units)
Td_sounding = np.insert(Tdc_up.magnitude, ind, Tdc_up[0].magnitude) * Tdc_up.units

我现在没有遇到任何错误。如果我上面的修复无法为您解决，那么了解您是否正在运行最新的 MetPy 1.1 会很有帮助。