数值天气预报对卫星大数据的需求分析

doi:10.11959/j.issn.2096-0271.2022017

Abstract

Abstract:

Multi cooperative satellites can provide multi spectral, multi temporal, multi factor, multi scale and multi-level remote sensing data, which is rich in valuable information for numerical weather prediction (NWP).In order to support earth system seamless fine gridded forecasting service in the future, the application status of satellite observation big data was discussed for numerical weather prediction from the aspects of detection variables, time density, spatial coverage, horizontal and vertical resolution, as well as accuracy and timeliness.At the same time, in order to make satellite big data be highly tolerant with NWP, the challenges and prospects were summarized, such as multi-satellite integrated and consistent processing, all-weather, coupled data assimilation methods, deep integration with artificial intelligence, and interaction between satellite observation and prediction.

Key words: numerical weather prediction, satellite, big data, feature variable, spatiotemporal resolution, accuracy

CLC Number:

P237，P456

Hequn YANG, Xiaofeng WANG, Yanqing GAO, Yiwen LU, Bingxin MA, Xinyao WANG. Analysis of satellite big data requirements in numerical weather prediction[J]. Big Data Research, 2022, 8(2): 89-102.

Figures/Tables 4

探测领域	变量	高度层次	精度	水平分辨率	垂直分辨率	观测周期	及时性	覆盖范围
基本	大气温度	边界层	0.5 K¹⁾	0.5 km	0.1 km	15 min	10 min	全球
大气			1 K²⁾	2 km	0.25 km	60 min	30 min
要素			3 K³⁾	10 km	1 km	3h	2h
			（其他单元格上标同，故略）
	湿度	自由对流层	2%	2 km	0.3 km	15 min	15 min	全球
			5%	10 km	0.4 km	60 min	30 min
			10 %	30 km	1 km	6h	2h
	风速（水平）	边界层	1 m/s	0.5 km	0.1 km	15 min	15 min	全球
			2 m/s	2 km	0.2 km	60 min	30 min
			5 m/s	10 km	0.4 km	12 h	2h
	水汽含量	整层	1 kg/m²	0.5 km	-	15 min	15 min	全球
			2 kg/m²	5 km		60 min	30 min
			5 kg/m²	20 km		6h	2h
云和降水	云水云冰	边界层、自	5%	0.5 km	0.1 km	15 min	15 min	全球
		由对流层	8%	2 km	0.17 km	60 min	30 min
			20%	10 km	0.5 km	3h	2h
	地表雨强	近地层	0.1 mm/h	0.5 km	-	15 min	15 min	全球
			0.2 mm/h	2 km		30 min	30 min
			1 mm/h	10 km		2h	2h
气溶胶和辐射	射出长波辐射	大气层顶	5 W/m²	2 km	-	30 min	60 min	全球
			10 W/m²	10 km		60 min	3h
			20 W/m²	50 km		6h	12 h
海洋	有效波高	海洋表面	0.1 m	5 km	-	30 min	10 min	全球海洋
			0.3 m	10 km		3h	30 min
			0.5 m	40 km		6h	3h
	海冰厚度	海洋表面	20 cm	2 km	-	12 h	12 h	全球海洋
			50 cm	10 km		24 h	24 h
			100 cm	40 km		2d	3d
陆地	地表温度	陆地表面	0.5 K	1 km	-	15 min	15 min	全球陆地
			1K	5 km		30 min	30 min
			3K	20 km		2h	2h
	叶面积指数	陆地表面	5%	1 km	-	12 h	24 h	全球陆地
			10%	5 km		24 h	3d
			20%	40 km		2d	7d
	土壤水含量	陆地表面	0.02 m³/m³	1 km	-	60 min	30 min	全球陆地
			0.04 m³/m³	5 km		3h	60 min
			0.08 m³/m³	40 km		6h	6h
大气化学	臭氧含量	整层	5 mol/mol	5 km	1 km	15 min	15 min	全球
			10 mol/mol	20 km	1.4 km	60 min	30 min
			20 mol/mol	100 km	3 km	6h	2h
注：上标1）表示最优目标，2）表示技术突破值，3）表示门槛阈值。来源于WMOOSCAR网站。

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主要领域/属性	项目	期望目标
临近和短期预报	龙卷风	提前60 min
	雷暴、对流扰动	提前6~12 h
	火灾、溢油、化学或放射性污染等局地灾害性事故	实时发展趋势预测
中期预报	7~10天预报	准确率达90%以上
	台风（飓风）	3天路径预报精度达到75海里，2天强度预报精度达到9海里/h
	洪水	提前4天预报
	空气质量	可以开展7~10天预报
气候预测	ENSO（厄尔尼诺和南方涛动）、MJO（热带大气季节内振荡）等	可以常规预报15~20个月的厄尔尼诺事件，实现较高精度的季节到次季节尺度延伸期预报
水平分辨率	全球	1~5 km
	区域/城市	100 m
垂直分辨率	上层对流层	500 m
	边界层	30~50 m
诊断变量/要素	风压、温湿度等	100个

高度范围	垂直分辨率
500 hPa~地表	0.3~0.5 km/层
300~500 hPa	0.5~1 km/层
100~300hPa	1~2 km/层
100 hPa以下	2~3 km/层

Analysis of satellite big data requirements in numerical weather prediction

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