Introduction
The China temperature data product contains near-surface air temperature data (in degrees Celsius) over China from 1979 to 2018, with daily temporal resolution and 0.1º spatial resolution. This product integrates multiple data sources such as reanalysis data (ERA5, CMFD), remote sensing data (MODIS), and in-situ data, and is obtained by combining temperature strategies to distinguish clear-sky and non-clear-sky weather conditions. Preface – Artificial Intelligence Tutorial
Both ERA5 and CMFD are meteorological data.
ERA5 is a global surface and atmospheric hourly reanalysis dataset released by the European Center for Medium-Range Weather Forecasts (ECMWF). This data set spans from 1979 to the present and is updated hourly. ERA5 includes variables such as temperature, humidity, wind, and precipitation, with a resolution of 0.25 degrees. The data accuracy of ERA5 is very high and is an important basic data for studying climate change, meteorological prediction and environmental simulation.
CMFD is a reanalysis data set of meteorological physical elements in China released by the Climate Center of the China Meteorological Administration. This data set covers variables such as temperature, precipitation, air pressure, relative humidity, and surface temperature from 1951 to the present, with a resolution of 0.1 degrees. The release of the CMFD data set provides the Chinese meteorological community and overseas meteorologists with detailed and continuous observation data of meteorological physical elements, helping to better understand and predict climate change in China.
Dataset ID:
CAAS/CDAT
Time range: January 1979-December 2018
Scope: Nationwide
Source: Professor Mao Kebiao’s team
Copy code snippet:
var images = pie.ImageCollection("CAAS/CDAT")
band
| Band name | type | resolution | Invalid value | Description |
|---|---|---|---|---|
| min | Float32 | 0.1° | -9999 | minimum value |
| max | Float32 | 0.1° | -9999 | maximum value |
| avg | Float32 | 0.1° | -9999 | average value |
| date |
string |
Image date |
Code:
/**
* @File : 中国气温数据产品
* @Desc : 加载中国气温数据产品
*/
//加载中国气温数据产品,选择时间和所需波段
var images = pie.ImageCollection("CAAS/CDAT")
.filterDate("1990-03-01","1990-03-31")
.select("avg")
// 输出影像信息
print(images)
// 设置图层显示参数
var visParam = {min: -30, max: 30,
palette: [
"#040274",
"#040281",
"#0502a3",
"#0502b8",
"#0502ce",
"#0502e6",
"#0602ff",
"#235cb1",
"#307ef3",
"#269db1",
"#30c8e2",
"#32d3ef",
"#3be285",
"#3ff38f",
"#86e26f",
"#3ae237",
"#b5e22e",
"#d6e21f",
"#fff705",
"#ffd611",
"#ffb613",
"#ff8b13",
"#ff6e08",
"#ff500d",
"#ff0000",
"#de0101",
"#c21301",
"#a71001",
"#911003"
],
};
//定位地图中心
Map.setCenter(108.25, 33.52, 2);
//加载显示数据
Map.addLayer(images, visParam,"img")
The ownership of this data set belongs to Professor Mao Kebiao’s team and can be used for free for non-commercial scientific research and educational activities on the PIE-Engine platform. <br>The team is mainly engaged in inversion research on key agricultural meteorological parameters such as agricultural big data, agricultural disasters, food security, surface temperature and soil moisture. Through the analysis and prediction of spatial and temporal changes in disasters, the proposal of "hoarding food for the people and food conservation actions" in the new era was adopted by the central and local governments, and further proposed the "Three Tibetan Strategies" to ensure my country's food security, which was recognized by people from all walks of life. In order to respond to extreme events and ensure my country's food security, we have long been committed to promoting the "Three Tibetan Strategies" strategy (stashing food for the people, hiding food in the land, and hiding food in technology).