GPM卫星数据下载、读取、绘图，一步到位——GPM-API库

用户11172986

发布于 2024-09-24 18:38:20

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发布于 2024-09-24 18:38:20

文章被收录于专栏：气python风雨

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由于可视化代码过长隐藏，可点击运行Fork查看若没有成功加载可视化图，点击运行可以查看 ps:隐藏代码在【代码已被隐藏】所在行，点击所在行，可以看到该行的最右角，会出现个三角形，点击查看即可

前言

项目目标

通过Python完成gpm卫星数据下载、读取与绘图

项目方法

在以下内容中，将详细介绍GPM-api库的使用

安装与导入库

!pip install gpm-api -i https://pypi.mirrors.ustc.edu.cn/simple/

import gpm

gpm.available_products(satellites="GPM")

['1A-GMI',
 '1B-GMI',
 '1B-Ka',
 '1B-Ku',
 '1C-GMI',
 '1C-GMI-R',
 '2A-DPR',
 '2A-ENV-DPR',
 '2A-ENV-Ka',
 '2A-ENV-Ku',
 '2A-GMI',
 '2A-GMI-CLIM',
 '2A-GPM-SLH',
 '2A-Ka',
 '2A-Ku',
 '2B-GPM-CORRA',
 '2B-GPM-CSAT',
 '2B-GPM-CSH']

前期准备

在开始使用GPM-API之前，我们需要将以下信息保存到配置文件中：

1. 注册两个账户第一个参考https://www.heywhale.com/mw/project/646b52629f707085adc80af3 第二个是PPS，地址为https://registration.pps.eosdis.nasa.gov/registration/
2. 配置目录

为了便于创建配置文件，您可以运行以下脚本：

import gpm

username_pps = "<your PPS username>"  # 小写的邮箱
password_pps = "<your PPS password>"  # 小写的邮箱
username_earthdata = "<your EarthData username>" #邮箱
password_earthdata = "<your EarthData password>" #密码
base_dir = "<path/to/a/local/directory/>"  # where to download all GPM data
gpm.define_configs(
    username_pps=username_pps,
    password_pps=password_pps,
    username_earthdata=username_earthdata,
    password_earthdata=password_earthdata,
    base_dir=base_dir,
)

Saved .netrc, .urs_cookies, and .dodsrc to: /home/mw/
The GPM-API config file has been updated successfully!

成功了会有如下提示 Saved .netrc, .urs_cookies, and .dodsrc to: /home/mw/ The GPM-API config file has been updated successfully!

下载

import gpm
import datetime

product = "2A-DPR"
product_type = 'RS'
version = 7

start_time = datetime.datetime(2020, 7, 22, 0, 1, 11)
end_time = datetime.datetime(2020, 7, 22, 0, 23, 5)

gpm.download(
    product=product,
    product_type=product_type,
    version=version,
    n_threads=2,
    start_time=start_time,
    end_time=end_time,
)

100%|██████████| 1/1 [01:12<00:00, 72.94s/it]
/opt/conda/lib/python3.9/site-packages/gpm/io/download.py:996: GPMDownloadWarning: 'No data found on PPS on date 2020-07-22 for product 2A-DPR'
  status, available_version = _download_daily_data(


1 files has been download.
All the available GPM 2A-DPR product files are now on disk.
Integrity checking of GPM files has completed.





[]

或者像如下示例下载某一天或者某一个月的数据

# Download data over a specific day
gpm.download_daily_data(
    year=2022,
    month=1,
    day=1,
    product=product,
    product_type=product_type,
    version=version,
    storage=storage,
)

# Download data over a specific month
download_monthly_data(
    year=2022,
    month=1,
    product=product,
    product_type=product_type,
    version=version,
    storage=storage,
)

转为xarray格式

像这样打开单个数据

import gpm
ds = gpm.open_granule('/home/mw/project/GPM/RS/V07/RADAR/2A-DPR/2020/07/21/2A.GPM.DPR.V9-20211125.20200721-S232107-E005341.036345.V07A.HDF5')
print(ds)

'scan_mode' has not been specified. Default to FS.
<xarray.Dataset> Size: 5GB
Dimensions:                       (cross_track: 49, along_track: 7936,
                                   nfreqHI: 3, range: 176, nNode: 5,
                                   nbinSZP: 7, radar_frequency: 2, nNUBF: 3,
                                   method: 6, three: 3, foreBack: 2,
                                   nearFar: 2, nsdew: 3, four: 4, nNP: 4,
                                   XYZ: 3, DSD_params: 2)
Coordinates: (12/16)
    sunLocalTime                  (cross_track, along_track) float32 2MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    height                        (cross_track, along_track, range) float32 274MB dask.array<chunksize=(49, 30, 176), meta=np.ndarray>
    dataQuality                   (along_track, radar_frequency) float32 63kB dask.array<chunksize=(32, 2), meta=np.ndarray>
    SCorientation                 (along_track) float32 32kB dask.array<chunksize=(32,), meta=np.ndarray>
    lon                           (cross_track, along_track) float32 2MB 13.8...
    lat                           (cross_track, along_track) float32 2MB -66....
    ...                            ...
    gpm_along_track_id            (along_track) int64 63kB ...
  * range                         (range) int64 1kB 1 2 3 4 ... 173 174 175 176
    gpm_range_id                  (range) int64 1kB ...
  * radar_frequency               (radar_frequency) <U2 16B 'Ku' 'Ka'
  * DSD_params                    (DSD_params) <U2 16B 'Nw' 'Dm'
    crsWGS84                      int64 8B 0
Dimensions without coordinates: cross_track, along_track, nfreqHI, nNode,
                                nbinSZP, nNUBF, method, three, foreBack,
                                nearFar, nsdew, four, nNP, XYZ
Data variables: (12/139)
    flagBB                        (cross_track, along_track) float64 3MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    binBBPeak                     (cross_track, along_track) float32 2MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    binBBTop                      (cross_track, along_track) float32 2MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    binDFRmMLBottom               (cross_track, along_track) float32 2MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    binDFRmMLTop                  (cross_track, along_track) float32 2MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    binBBBottom                   (cross_track, along_track) float32 2MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    ...                            ...
    precipWaterIntegrated_Liquid  (cross_track, along_track) float32 2MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    precipWaterIntegrated_Solid   (cross_track, along_track) float32 2MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    precipWaterIntegrated         (cross_track, along_track) float32 2MB dask.array<chunksize=(49, 32), meta=np.ndarray>
    dBNw                          (cross_track, along_track, range) float32 274MB dask.array<chunksize=(49, 15, 176), meta=np.ndarray>
    Dm                            (cross_track, along_track, range) float32 274MB dask.array<chunksize=(49, 15, 176), meta=np.ndarray>
    Nw                            (cross_track, along_track, range) float32 274MB dask.array<chunksize=(49, 15, 176), meta=np.ndarray>
Attributes: (12/23)
    FileName:              2A.GPM.DPR.V9-20211125.20200721-S232107-E005341.03...
    EphemerisFileName:     
    AttitudeFileName:      
    TotalQualityCode:      Good
    DielectricFactorKa:    0.8989
    DielectricFactorKu:    0.9255
    ...                    ...
    DataFormatVersion:     7h
    MetadataVersion:       7h
    ProcessingMode:        STD
    ScanMode:              FS
    history:               Created by ghiggi/gpm_api software on 2024-09-24 0...
    gpm_api_product:       2A-DPR

或者打开数据集

import gpm
import datetime

product = "2A-DPR"
product_type = "RS"
version = 7
start_time = datetime.datetime(2020,7, 22, 0, 1, 11)
end_time = datetime.datetime(2020,7, 22, 0, 23, 5)
ds = gpm.open_dataset(product=product,
                      product_type=product_type,
                      version=version,
                      start_time=start_time,
                      end_time=end_time)
print(ds)

简单可视化

# Plot a specific variable of the dataset
ds["precipRateNearSurface"].gpm.plot_map()

<cartopy.mpl.geocollection.GeoQuadMesh at 0x7fcb025d0ee0>

from gpm.utils.geospatial import get_country_extent

title = ds.gpm.title(add_timestep=False)
extent = get_country_extent("United States")
print("Extent: ", extent)
da = ds["precipRateNearSurface"]
p = da.gpm.plot_map()
p.axes.set_extent(extent)
p.axes.set_title(label=title)

Extent:  Extent(xmin=-171.99111060299998, xmax=-66.76465999999999, ymin=18.71619, ymax=71.5577635769)





Text(0.5, 1.0, '2A-DPR')

import cartopy.crs as ccrs
import matplotlib.pyplot as plt

from gpm.visualization.plot import plot_cartopy_background

# Define some figure options
dpi = 100
figsize = (12, 10)

# Examples of Cartopy projections
crs_proj = ccrs.InterruptedGoodeHomolosine()
crs_proj = ccrs.Mollweide()
crs_proj = ccrs.Robinson()

# Select a single timestep
da = ds["precipRateNearSurface"]

# Create the map
fig, ax = plt.subplots(subplot_kw={"projection": crs_proj}, figsize=figsize, dpi=dpi)
plot_cartopy_background(ax)
da.gpm.plot_map(ax=ax)

<cartopy.mpl.geocollection.GeoQuadMesh at 0x7fcaebfeb5e0>

小结

利用该库可以便捷地进行GPM卫星数据的下载与简单可视化，难得的是能够转为xarray格式，这大大方便我们进行数据处理参考链接： https://gpm-api.readthedocs.io/en/latest/03_quickstart.html https://github.com/ghiggi/gpm_api/

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原始发表：2024-09-24，如有侵权请联系 cloudcommunity@tencent.com 删除