pandas - 从 ERA5 再分析中提取移动船舶的时间和空间变量

Question

我想从一艘移动的船内的一个站提取测得的风，我有纬度、经度和时间值以及空间中每个时间步长的风值。我可以为所有时间步长提取空间中的一个固定点，但我想提取例如时间步长 x 处的风到船移动时的日期经度和纬度。我如何通过下面的代码执行此操作？

data = xr.open_dataset('C:/Users/William Jacondino/Desktop/Dados/ERA5\\ERA5_2017.nc', decode_times=False)

dir_out = 'C:/Users/William Jacondino/Desktop/MovingShip'
if not os.path.exists(dir_out):
    os.makedirs(dir_out)

print("\nReading the observation station names:\n")

stations = pd.read_csv(r"C:/Users/William Jacondino/Desktop/MovingShip/Date-TIME.csv",index_col=0, sep='\;')

print(stations)

读取观测站名称:

                  Latitude  Longitude
Date-Time                            
16/11/2017 00:00  0.219547 -38.247914
16/11/2017 06:00  0.861717 -38.188858
16/11/2017 12:00  1.529534 -38.131039
16/11/2017 18:00  2.243760 -38.067467
17/11/2017 00:00  2.961202 -38.009050
...                    ...        ...
10/12/2017 00:00 -5.775127 -35.206581
10/12/2017 06:00 -5.775120 -35.206598
10/12/2017 12:00 -5.775119 -35.206583
10/12/2017 18:00 -5.775122 -35.206584
11/12/2017 00:00 -5.775115 -35.206590



# variável tempo e unidade 

times = data.variables['time'][:]
unit =  data.time.units

# variáveis latitude (lat) e longitude (lon)

lon = data.variables['longitude'][:]
lat = data.variables['latitude'][:]

# variável temperatura em 2 metros em celsius

temp = data.variables['t2m'][:]-275.15

# variável temperatura do ponto de orvalho em 2 metros em celsius

tempdw = data.variables['d2m'][:]-275.15

# variável sea surface temperature (sst) em celsius

sst = data.variables['sst'][:]-275.15

# variável Surface sensible heat flux sshf

sshf = data.variables['sshf'][:]
unitsshf = data.sshf.units

# variável Surface latent heat flux

slhf = data.variables['slhf'][:]
unitslhf = data.slhf.units

# variável Mean sea level pressure

msl = data.variables['msl'][:]/100
unitmsl = data.msl.units

# variável Total precipitation em mm/h

tp = data.variables['tp'][:]*1000

# componente zonal do vento em 100 metros

uten100 = data.variables['u100'][:]
unitu100 = data.u100.units

# componente meridional do vento em 100 metros

vten100  = data.variables['v100'][:]
unitv100 = data.v100.units

# componente zonal do vento em 10 metros

uten  = data.variables['u10'][:]
unitu = data.u10.units

# componente meridional do vento em 10 metros

vten  = data.variables['v10'][:]
unitv = data.v10.units

# calculando a velocidade do vento em 10 metros

ws    = (uten**2 + vten**2)**(0.5)

# calculando a velocidade do vento em 100 metros

ws100  = (uten100**2 + vten100**2)**(0.5)

# calculando os ângulos de U e V para obter a direção do vento em 10 metros

wdir = (180 + (np.degrees(np.arctan2(uten, vten)))) % 360

# calculando os ângulos de U e V para obter a direção do vento em 100 metros

wdir100 = (180 + (np.degrees(np.arctan2(uten100, vten100)))) % 360

for key, value in stations.iterrows():
    #print(key,value[0], value[1], value[2])
    station = value[0]
    file_name = "{}{}".format(station+'_1991',".csv")
    #print(file_name)
    lon_point = value[1]
    lat_point = value[2]
    ########################################
    
    # Encontrando o ponto de Latitude e Longitude mais próximo das estações
    
    # Squared difference of lat and lon
    sq_diff_lat = (lat - lat_point)**2
    sq_diff_lon = (lon - lon_point)**2
    
    # Identifying the index of the minimum value for lat and lon
    min_index_lat = sq_diff_lat.argmin()
    min_index_lon = sq_diff_lon.argmin()
    print("Generating time series for station {}".format(station))
    
    ref_date   = datetime.datetime(int(unit[12:16]),int(unit[17:19]),int(unit[20:22]))

    date_range   = list()
    temp_data    = list()
    tempdw_data = list()
    sst_data     = list()
    sshf_data    = list()
    slhf_data    = list()
    msl_data     = list()
    tp_data      = list()
    uten100_data = list()
    vten100_data = list()
    uten_data    = list()
    vten_data    = list()
    ws_data      = list()
    ws100_data   = list()
    wdir_data    = list()
    wdir100_data = list()
    
    for index, time in enumerate(times):
        date_time = ref_date+datetime.timedelta(hours=int(time))
        date_range.append(date_time)
        temp_data.append(temp[index, min_index_lat, min_index_lon].values)
        tempdw_data.append(tempdw[index, min_index_lat, min_index_lon].values)
        sst_data.append(sst[index, min_index_lat, min_index_lon].values)
        sshf_data.append(sshf[index, min_index_lat, min_index_lon].values)
        slhf_data.append(slhf[index, min_index_lat, min_index_lon].values)
        msl_data.append(msl[index, min_index_lat, min_index_lon].values)
        tp_data.append(tp[index, min_index_lat, min_index_lon].values)
        uten100_data.append(uten100[index, min_index_lat, min_index_lon].values)
        vten100_data.append(vten100[index, min_index_lat, min_index_lon].values)
        uten_data.append(uten[index, min_index_lat, min_index_lon].values)
        vten_data.append(vten[index, min_index_lat, min_index_lon].values)
        ws_data.append(ws[index,min_index_lat,min_index_lon].values)
        ws100_data.append(ws100[index,min_index_lat,min_index_lon].values)
        wdir_data.append(wdir[index,min_index_lat,min_index_lon].values)
        wdir100_data.append(wdir100[index,min_index_lat,min_index_lon].values)
    ################################################################################    
    
    #print(date_range)
    
    df = pd.DataFrame(date_range, columns = ["Date-Time"])
    df["Date-Time"] = date_range
    df = df.set_index(["Date-Time"])
    df["WS10  ({})".format(unitu)] = ws_data
    df["WDIR10  ({})".format(units.deg)] = wdir_data
    df["WS100  ({})".format(unitu)] = ws100_data
    df["WDIR100  ({})".format(units.deg)] = wdir100_data
    df["Chuva({})".format(units.mm)] = tp_data 
    df["MSLP ({})".format(units.hPa)] = msl_data
    df["T2M ({})".format(units.degC)] = temp_data
    df["Td2M ({})".format(units.degC)] = tempdw_data
    df["Surface Sensible Heat Flux ({})".format(unitsshf)] = sshf_data                      
    df["Surface latent heat flux ({})".format(unitslhf)] = slhf_data 
    df["U10  ({})".format(unitu)] = uten_data
    df["V10  ({})".format(unitv)] = vten_data
    df["U100  ({})".format(unitu100)] = uten100_data
    df["V100  ({})".format(unitv100)] = vten100_data
    df["TSM ({})".format(units.degC)] = sst_data

    print("The following time series is being saved as .csv files")
        
    df.to_csv(os.path.join(dir_out,file_name), sep=';',encoding="utf-8", index=True)
    
print("\n! !Successfuly saved all the Time Series the output Directory!!\n{}".format(dir_out))

我在空间给定点提取固定变量的代码是这样的，但我想在船舶移动期间提取，例如在时间 11/12/2017 00:00，纬度 -5.775115 和经度 - 35.206590 我有一个风的值，在下一个时间步骤中另一个纬度 x 经度我有另一个值。我该如何调整我的代码？

Answer 1

这是 xarray 的 advanced indexing 的另一个完美用例!我觉得这部分用户指南需要披风和主题曲:)

我将使用与您的(我认为)相似的编造数据集和一组站点。第一步是重置日期时间索引，因此您可以使用它从 xarray.Dataset 中提取最近的时间值，因为您需要时间、纬度和经度的公共(public)索引:

In [14]: stations = stations.reset_index(drop=False)
    ...: stations
Out[14]:
            Date-Time   Latitude   Longitude
0 2017-11-16 00:00:00   0.219547  -38.247914
1 2017-11-16 06:00:00   0.861717  -38.188858
2 2017-11-16 12:00:00   1.529534  -38.131039
3 2017-11-16 18:00:00   2.243760  -38.067467
4 2017-11-17 00:00:00   2.961202  -38.009050
5 2017-12-10 00:00:00  -5.775127  -35.206581
6 2017-12-10 06:00:00  -5.775120  -35.206598
7 2017-12-10 12:00:00  -5.775119  -35.206583
8 2017-12-10 18:00:00  -5.775122  -35.206584
9 2017-12-11 00:00:00  -5.775115  -35.206590

In [15]: ds
Out[15]:
<xarray.Dataset>
Dimensions:  (lat: 40, lon: 40, time: 241)
Coordinates:
  * lat      (lat) float64 -9.75 -9.25 -8.75 -8.25 -7.75 ... 8.25 8.75 9.25 9.75
  * lon      (lon) float64 -44.75 -44.25 -43.75 -43.25 ... -26.25 -25.75 -25.25
  * time     (time) datetime64[ns] 2017-11-01 2017-11-01T06:00:00 ... 2017-12-31
Data variables:
    temp     (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
    tempdw   (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
    sst      (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
    ws       (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
    ws100    (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
    wdir     (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236
    wdir100  (lat, lon, time) float64 0.07366 0.3448 0.2456 ... 0.3081 0.4236

使用高级索引规则，如果我们使用 DataArrays 作为索引器从数据集中进行选择，结果将被 reshape 以匹配索引器。这意味着我们可以获取您的站点数据帧，它具有值时间、纬度和经度，并从 xarray 数据集中提取最近的索引:

In [16]: ds_over_observations = ds.sel(
    ...:     time=stations["Date-Time"].to_xarray(),
    ...:     lat=stations["Latitude"].to_xarray(),
    ...:     lon=stations["Longitude"].to_xarray(),
    ...:     method="nearest",
    ...: )

现在，我们的数据与您的数据框具有相同的索引!

In [17]: ds_over_observations
Out[17]:
<xarray.Dataset>
Dimensions:  (index: 10)
Coordinates:
    lat      (index) float64 0.25 0.75 1.75 2.25 ... -5.75 -5.75 -5.75 -5.75
    lon      (index) float64 -38.25 -38.25 -38.25 ... -35.25 -35.25 -35.25
    time     (index) datetime64[ns] 2017-11-16 ... 2017-12-11
  * index    (index) int64 0 1 2 3 4 5 6 7 8 9
Data variables:
    temp     (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
    tempdw   (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
    sst      (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
    ws       (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
    ws100    (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
    wdir     (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095
    wdir100  (index) float64 0.1887 0.222 0.6754 0.919 ... 0.1134 0.9231 0.6095

您可以使用 .to_dataframe 将其转储到 pandas 中:

In [18]: df = ds_over_observations.to_dataframe()

In [19]: df
Out[19]:
        lat    lon                time      temp    tempdw       sst        ws     ws100      wdir   wdir100
index
0      0.25 -38.25 2017-11-16 00:00:00  0.188724  0.188724  0.188724  0.188724  0.188724  0.188724  0.188724
1      0.75 -38.25 2017-11-16 06:00:00  0.222025  0.222025  0.222025  0.222025  0.222025  0.222025  0.222025
2      1.75 -38.25 2017-11-16 12:00:00  0.675417  0.675417  0.675417  0.675417  0.675417  0.675417  0.675417
3      2.25 -38.25 2017-11-16 18:00:00  0.919019  0.919019  0.919019  0.919019  0.919019  0.919019  0.919019
4      2.75 -38.25 2017-11-17 00:00:00  0.566266  0.566266  0.566266  0.566266  0.566266  0.566266  0.566266
5     -5.75 -35.25 2017-12-10 00:00:00  0.652490  0.652490  0.652490  0.652490  0.652490  0.652490  0.652490
6     -5.75 -35.25 2017-12-10 06:00:00  0.429541  0.429541  0.429541  0.429541  0.429541  0.429541  0.429541
7     -5.75 -35.25 2017-12-10 12:00:00  0.113352  0.113352  0.113352  0.113352  0.113352  0.113352  0.113352
8     -5.75 -35.25 2017-12-10 18:00:00  0.923058  0.923058  0.923058  0.923058  0.923058  0.923058  0.923058
9     -5.75 -35.25 2017-12-11 00:00:00  0.609493  0.609493  0.609493  0.609493  0.609493  0.609493  0.609493

结果中的索引与站点数据相同。如果愿意，您可以使用 pd.concat([stations, df], axis=1).set_index("Date-Time") 合并原始值以恢复原始索引，以及所有天气数据:

In [20]: pd.concat([stations, df], axis=1).set_index("Date-Time")
Out[20]:
                     Latitude  Longitude   lat    lon                time      temp    tempdw       sst        ws     ws100      wdir   wdir100
Date-Time
2017-11-16 00:00:00  0.219547 -38.247914  0.25 -38.25 2017-11-16 00:00:00  0.188724  0.188724  0.188724  0.188724  0.188724  0.188724  0.188724
2017-11-16 06:00:00  0.861717 -38.188858  0.75 -38.25 2017-11-16 06:00:00  0.222025  0.222025  0.222025  0.222025  0.222025  0.222025  0.222025
2017-11-16 12:00:00  1.529534 -38.131039  1.75 -38.25 2017-11-16 12:00:00  0.675417  0.675417  0.675417  0.675417  0.675417  0.675417  0.675417
2017-11-16 18:00:00  2.243760 -38.067467  2.25 -38.25 2017-11-16 18:00:00  0.919019  0.919019  0.919019  0.919019  0.919019  0.919019  0.919019
2017-11-17 00:00:00  2.961202 -38.009050  2.75 -38.25 2017-11-17 00:00:00  0.566266  0.566266  0.566266  0.566266  0.566266  0.566266  0.566266
2017-12-10 00:00:00 -5.775127 -35.206581 -5.75 -35.25 2017-12-10 00:00:00  0.652490  0.652490  0.652490  0.652490  0.652490  0.652490  0.652490
2017-12-10 06:00:00 -5.775120 -35.206598 -5.75 -35.25 2017-12-10 06:00:00  0.429541  0.429541  0.429541  0.429541  0.429541  0.429541  0.429541
2017-12-10 12:00:00 -5.775119 -35.206583 -5.75 -35.25 2017-12-10 12:00:00  0.113352  0.113352  0.113352  0.113352  0.113352  0.113352  0.113352
2017-12-10 18:00:00 -5.775122 -35.206584 -5.75 -35.25 2017-12-10 18:00:00  0.923058  0.923058  0.923058  0.923058  0.923058  0.923058  0.923058
2017-12-11 00:00:00 -5.775115 -35.206590 -5.75 -35.25 2017-12-11 00:00:00  0.609493  0.609493  0.609493  0.609493  0.609493  0.609493  0.609493