GOES-16 MCMIPC 系列 ABI 2 级云层和水汽成像 CONUS数据

简介: GOES-16 MCMIPC 系列 ABI 2 级云层和水汽成像产品提供 CONUS 地区的高分辨率(2公里)云、植被、雪/冰及气溶胶特征。反射波段(1-6)用于白天观测,发射波段(7-16)测量大气顶部亮度温度。数据由 NOAA 提供,适用于气象分析、环境监测等领域。该产品包含多个波段,支持对地表、云层、水蒸气等进行定性分析,并附有详细的数据质量标志。

GOES-16 MCMIPC 系列 ABI 2 级云层和水汽成像 CONUS

简介

云层和水分成像产品的分辨率均为 2 公里。 1-6 波段为反射波段。 无量纲的 "反射系数 "以太阳天顶角为标准。 这些波段有助于确定云、植被、雪/冰和气溶胶的特征。 第 7-16 波段为发射波段。 大气顶部 (TOA) 的亮度温度以开尔文为单位测量。 这些波段支持根据发射特性对地表、云层、水蒸气、臭氧、火山灰和尘埃进行定性。 README NOAA 的卫星和产品运行办公室有一个提供状态更新的 "一般卫星信息 "频道。

Resolution
2000 meters

Bands

Name Units Min Max Wavelength Description
CMI_C01 Reflectance factor 0 1.3 0.45-0.49µm

Visible - Blue

Daytime aerosol over land, coastal water mapping.

DQF_C01 0 4

Data quality flags

CMI_C02 Reflectance factor 0 1.3 0.59-0.69µm

Visible - Red

Daytime clouds, fog, insolation, winds

DQF_C02 0 4

Data quality flags

CMI_C03 Reflectance factor 0 1.3 0.846-0.885µm

Near-IR - Veggie

Daytime vegetation, burn scar, aerosol over water, winds

DQF_C03 0 4

Data quality flags

CMI_C04 Reflectance factor 0 1.3 1.371-1.386µm

Near-IR - Cirrus

Daytime cirrus cloud

DQF_C04 0 4

Data quality flags

CMI_C05 Reflectance factor 0 1.3 1.58-1.64µm

Near-IR - Snow/Ice

Daytime cloud-top phase and particle size, snow

DQF_C05 0 4

Data quality flags

CMI_C06 Reflectance factor 0 1.3 2.225-2.275µm

Near IR - Cloud Particle Size

Daytime land, cloud properties, particle size, vegetation, snow

DQF_C06 0 4

Data quality flags

CMI_C07 K 197.31 411.86 3.80-4.00µm

Infrared - Shortwave Window

Brightness

DQF_C07 0 4

Data quality flags

CMI_C08 K 138.05 311.06 5.77-6.6µm

Infrared - Upper-level water vapor

High-level atmospheric water vapor, winds, rainfall

Brightness

DQF_C08 0 4

Data quality flags

CMI_C09 K 137.7 311.08 6.75-7.15µm

Infrared - Mid-level water vapor

Mid-level atmospheric water vapor, winds, rainfall

Brightness

DQF_C09 0 4

Data quality flags

CMI_C10 K 126.91 331.2 7.24-7.44µm

Infrared - Lower-level water vapor

Lower-level water vapor, winds, and sulfur dioxide

Brightness

DQF_C10 0 4

Data quality flags

CMI_C11 K 127.69 341.3 8.3-8.7µm

Infrared - Cloud-top phase

Total water for stability, cloud phase, dust, sulfur dioxide, rainfall

Brightness

DQF_C11 0 4

Data quality flags

CMI_C12 K 117.49 311.06 9.42-9.8µm

Infrared - Ozone

Total ozone, turbulence, winds

DQF_C12 0 4

Data quality flags

CMI_C13 K 89.62 341.27 10.1-10.6µm

Infrared - "Clean" longwave window

Surface and clouds

Brightness

DQF_C13 0 4

Data quality flags

CMI_C14 K 96.19 341.28 10.8-11.6µm

Infrared - Longwave window

Imagery, sea surface temperature, clouds, rainfall

Brightness

DQF_C14 0 4

Data quality flags

CMI_C15 K 97.38 341.28 11.8-12.8µm

Infrared "Dirty" longwave

Total water, volcanic ash, sea surface temperature

Brightness

DQF_C15 0 4

Data quality flags

CMI_C16 K 92.7 318.26 13.0-13.6µm

Infrared - CO_2 longwave

Air temperature, cloud heights

Brightness

DQF_C16 0 4

Data quality flags

DQF_C01 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C02 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C03 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C04 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C05 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C06 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C07 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C08 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C09 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C10 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C11 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C12 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C13 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C14 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C15 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded

DQF_C16 Class Table

Value Color Description
0 #ffffff Good pixels
1 #ff00ff Conditionally usable pixels
2 #0000ff Out of range pixels
3 #00ffff No value pixels
4 #ffff00 Focal plane temperature threshold exceeded
摘要

Dataset Availability

2017-07-10T00:00:00Z–2025-01-15T20:56:17Z

Dataset Provider

NOAA

Earth Engine Snippet

ee.ImageCollection("NOAA/GOES/16/MCMIPC")

影像属性

Image Properties

Name Type Description
CMI_C01_offset DOUBLE

Offset to add to scaled CMI_C01 values

CMI_C01_scale DOUBLE

Scale to multiply with raw CMI_C01 values

CMI_C02_offset DOUBLE

Offset to add to scaled CMI_C02 values

CMI_C02_scale DOUBLE

Scale to multiply with raw CMI_C02 values

CMI_C03_offset DOUBLE

Offset to add to scaled CMI_C03 values

CMI_C03_scale DOUBLE

Scale to multiply with raw CMI_C03 values

CMI_C04_offset DOUBLE

Offset to add to scaled CMI_C04 values

CMI_C04_scale DOUBLE

Scale to multiply with raw CMI_C04 values

CMI_C05_offset DOUBLE

Offset to add to scaled CMI_C05 values

CMI_C05_scale DOUBLE

Scale to multiply with raw CMI_C05 values

CMI_C06_offset DOUBLE

Offset to add to scaled CMI_C06 values

CMI_C06_scale DOUBLE

Scale to multiply with raw CMI_C06 values

CMI_C07_offset DOUBLE

Offset to add to scaled CMI_C07 values

CMI_C07_scale DOUBLE

Scale to multiply with raw CMI_C07 values

CMI_C08_offset DOUBLE

Offset to add to scaled CMI_C08 values

CMI_C08_scale DOUBLE

Scale to multiply with raw CMI_C08 values

CMI_C09_offset DOUBLE

Offset to add to scaled CMI_C09 values

CMI_C09_scale DOUBLE

Scale to multiply with raw CMI_C09 values

CMI_C10_offset DOUBLE

Offset to add to scaled CMI_C10 values

CMI_C10_scale DOUBLE

Scale to multiply with raw CMI_C10 values

CMI_C11_offset DOUBLE

Offset to add to scaled CMI_C11 values

CMI_C11_scale DOUBLE

Scale to multiply with raw CMI_C11 values

CMI_C12_offset DOUBLE

Offset to add to scaled CMI_C12 values

CMI_C12_scale DOUBLE

Scale to multiply with raw CMI_C12 values

CMI_C13_offset DOUBLE

Offset to add to scaled CMI_C13 values

CMI_C13_scale DOUBLE

Scale to multiply with raw CMI_C13 values

CMI_C14_offset DOUBLE

Offset to add to scaled CMI_C14 values

CMI_C14_scale DOUBLE

Scale to multiply with raw CMI_C14 values

CMI_C15_offset DOUBLE

Offset to add to scaled CMI_C15 values

CMI_C15_scale DOUBLE

Scale to multiply with raw CMI_C15 values

CMI_C16_offset DOUBLE

Offset to add to scaled CMI_C16 values

CMI_C16_scale DOUBLE

Scale to multiply with raw CMI_C16 values

代码
// Band aliases.
var BLUE = 'CMI_C01';
var RED = 'CMI_C02';
var VEGGIE = 'CMI_C03';
var GREEN = 'GREEN';
// 16 pairs of CMI and DQF followed by Bah 2018 synthetic green.
// Band numbers in the EE asset, 0-based.
var NUM_BANDS = 33;
// Skipping the interleaved DQF bands.
var BLUE_BAND_INDEX = (1 - 1) 2;
var RED_BAND_INDEX = (2 - 1)
2;
var VEGGIE_BAND_INDEX = (3 - 1) * 2;
var GREEN_BAND_INDEX = NUM_BANDS - 1;

// Visualization range for GOES RGB.
var GOES_MIN = 0.0;
var GOES_MAX = 0.7; // Alternatively 1.0 or 1.3.
var GAMMA = 1.3;

var goesRgbViz = {
bands: [RED, GREEN, BLUE],
min: GOES_MIN,
max: GOES_MAX,
gamma: GAMMA
};

var applyScaleAndOffset = function(image) {
image = ee.Image(image);
var bands = new Array(NUM_BANDS);
for (var i = 1; i < 17; i++) {
var bandName = 'CMI_C' + (100 + i + '').slice(-2);
var offset = ee.Number(image.get(bandName + '_offset'));
var scale = ee.Number(image.get(bandName + '_scale'));
bands[(i-1) * 2] = image.select(bandName).multiply(scale).add(offset);

var dqfName = 'DQF_C' + (100 + i + '').slice(-2);
bands[(i-1) * 2 + 1] = image.select(dqfName);

}

// Bah, Gunshor, Schmit, Generation of GOES-16 True Color Imagery without a
// Green Band, 2018. https://doi.org/10.1029/2018EA000379
// Green = 0.45 Red + 0.10 NIR + 0.45 * Blue
var green1 = bands[RED_BAND_INDEX].multiply(0.45);
var green2 = bands[VEGGIE_BAND_INDEX].multiply(0.10);
var green3 = bands[BLUE_BAND_INDEX].multiply(0.45);
var green = green1.add(green2).add(green3);
bands[GREEN_BAND_INDEX] = green.rename(GREEN);

return ee.Image(ee.Image(bands).copyProperties(image, image.propertyNames()));
};

var collection = 'NOAA/GOES/16/MCMIPC/';
var imageName = '2020211203115800000';
var assetId = collection + imageName;
var image = applyScaleAndOffset(assetId);
Map.setCenter(-75, 37, 5);
Map.addLayer(image, goesRgbViz);

引用

海洋大气局的数据、信息和产品,无论以何种方式提供,均不受版权保护,公众在随后的使用中也不受任何限制。 一旦获得,可用于任何合法用途。

Bah, Gunshor, Schmit, Generation of GOES-16 True Color Imagery without a Green Band, 2018. doi:10.1029/2018EA000379

Product User Guide (PUG) Volume 5, L2+ Products.

Schmit, T., Griffith, P., et al, (2016), A closer look at the ABI on the GOES-R series, Bull. Amer. Meteor. Soc., 98(4), 681-698. doi:10.1175/BAMS-D-15-00230.1

网址推荐
知识星球

知识星球 | 深度连接铁杆粉丝,运营高品质社群,知识变现的工具 (zsxq.com)https://wx.zsxq.com/group/48888525452428

机器学习

https://www.cbedai.net/xg

干旱监测平台

慧天干旱监测与预警-首页https://www.htdrought.com/

相关文章
GEE——利用Landsat系列数据集进行1984-2023EVI指数趋势分析
GEE——利用Landsat系列数据集进行1984-2023EVI指数趋势分析
1007 0
|
Dart Android开发
鸿蒙Flutter实战:05-使用第三方插件
在鸿蒙Flutter开发中,使用原生功能需借助插件。可自编原生ArkTS代码或采用第三方插件。自编代码通过PlatformView或MethodChannel实现;第三方插件需确保适配鸿蒙,否则须配置替代插件或自行开发。
532 1
鸿蒙Flutter实战:05-使用第三方插件
|
存储 运维 算法
GFS分布式文件系统
GFS分布式文件系统
396 0
|
JavaScript
Vue使用Element的表格Table显示树形数据,多选框全选无法选中全部节点
Vue使用Element的表格Table显示树形数据,多选框全选无法选中全部节点
1757 0
|
机器学习/深度学习 编解码 计算机视觉
RT-DETR改进策略【Backbone/主干网络】| 替换骨干网络为:Swin Transformer,提高多尺度特征提取能力
RT-DETR改进策略【Backbone/主干网络】| 替换骨干网络为:Swin Transformer,提高多尺度特征提取能力
561 12
RT-DETR改进策略【Backbone/主干网络】| 替换骨干网络为:Swin Transformer,提高多尺度特征提取能力
|
12月前
|
存储 Shell 对象存储
对象存储使用ossutil进行删除存储桶操作
本教程介绍如何使用阿里云控制台的Cloud shell执行操作,通过wget下载ossutil工具,解压并进入工具目录,使用通用命令进行OSS存储桶删除操作。具体步骤包括下载、解压、进入目录和执行删除命令。参考文档:&lt;https://help.aliyun.com/zh/oss/developer-reference/rm&gt;。
483 1
|
安全 Java Linux
(七)Java网络编程-IO模型篇之从BIO、NIO、AIO到内核select、epoll剖析!
IO(Input/Output)方面的基本知识,相信大家都不陌生,毕竟这也是在学习编程基础时就已经接触过的内容,但最初的IO教学大多数是停留在最基本的BIO,而并未对于NIO、AIO、多路复用等的高级内容进行详细讲述,但这些却是大部分高性能技术的底层核心,因此本文则准备围绕着IO知识进行展开。
606 1
|
机器学习/深度学习 数据采集 算法
Python实现Catboost回归模型(CatBoostRegressor算法)项目实战
Python实现Catboost回归模型(CatBoostRegressor算法)项目实战
|
数据采集 存储 关系型数据库
基于python的中国气象局气象数据采集,可以作为数据集使用
本文介绍了一个基于Python的中国气象局气象数据采集项目,使用requests、lxml等技术将天气数据爬取并存储至MySQL数据库,可用于数据集创建。
1442 0
|
搜索推荐 安全 API
Matomo用户行为分析 - 安装篇
Matomo用户行为分析 - 安装篇
465 0