Global and Regional Precipitation Measurement and Applications

Transcription

1 Global and Regional Precipitation Measurement and Applications Yang Hong ( 洪阳 ) 清华大学土木水利学院. 水利水电工程系. 水文水资源所 hongyang@tsinghua.edu.cn 美国国家天气中心 - 遥感水文气象实验室 (

2 Global Energy and Water Exchange Mission: To observe, understand and model the hydrological cycle and energy fluxes in the Earth's atmosphere and at the surface. Grand science questions: 1. How can we better understand and predict precipitation variability and changes? 2. How do changes in land surface and hydrology influence past and future changes in water availability and security? 3. How does a warming world affect climate extremes, especially droughts, floods, and heat waves, and how do land area processes, in particular, contribute? 2

3 Satellite Precipitation estimation technology advances 1970s: Geostationary IR Cloud top data minute temporal resolution 1980s: Passive Microwave (SSM/I) Some characterisation of rainfall 1990s- present TRMM/GPM RADAR 3D imaging of rainfall

4 TRMM (Tropical Rainfall Measuring Mission): 卫星轨道上的天气雷达 (1997~) TRMM: 第一个星载天气雷达 Precipitation Radar (PR). PR: 2-m 相控阵雷达 (128-elements) 13.8GHz Ku-band; PR: 4km 水平精度,250 米垂直精度 at nadir 称为 : 太空飞行的天气雷达, 提供三维降水回波信息 5

5 QPF Ground Satellite Data PDF match PERSIANN-CCS: Multi-Sensor High Quality QPE Merge Satellite, ground (Radar & Gauge), and Model (NWP) NASA funded Project: Synergistic Development of Ground and Spaceborne Precipitation Sensors and Products Quasi-Globe Multi-satellite: IR/VIS/PMW/Space-Radar Blended Merged Global Hourly Rainfall Global IR High Temporal-Spatial Res. Cloud Infrared Images Microwave Satellite TMI/PR, SSMI, AMSU-B, AMSE-R) Sampling Combination 30-min Rain Estimate MW-PR Hourly Rain Rates Quality Contro l Merging Radar Coverage DATA CD-ROM and WEB FTP NWP Model Simulation or Forecasting

6 2005 加入 NASA TRMM Group: Satellite Constellation (9-satellites) With Huffman et al JHM : TRMM-based Multi-satellite Precipitation Analysis 15+ years ( ) of data available; Most requested TRMM product from NASA TMPA: quasi-global real-time update at DMSP Aqua NOAA GOES GMS/MTSAT (Japan) METEOSAT (Europe) Instantaneous SSM/I TRMM AMSR AMSU Calibrate High-Quality (HQ) Estimates to Best Space Radar Merge HQ Estimates 30-day HQ coefficients 3-hourly merged HQ TRMM Hourly IR Tb Match IR and HQ, generate coeffs Apply IR coefficients 30-day IR coefficients Hourly HQ-calib IR precip Merge IR, merged HQ estimates 3-hourly multisatellite (MS) TMPA-RT (mm/h) 18UTC 22 May 2002 TMPA-RT (mm/h) 15UTC 22 May 2002 TMPA-RT (mm/h) 12UTC 22 May 2002 Monthly gauges Compute monthly satellite-gauge combination (SG) Rescale 3-hourly MS to monthly SG Monthly SG Rescaled 3-hourly MS TMPA uses 4 Polar-orbital microwave satellites (NOAA, DoD, NASA) 8and 5 Geo-IR satellites (GOES8-10, GMS, MYSAT, MeteoSAT); all calibrated by TRMM Preci Radar

7 Current Available Multi-satellite Global Precipitation Products Precipitation for Climate Studies (From current ) GPCP: 2.5 o x2.5 o Monthly (Adler et al., 2003) NOAA CMAP: 2.5 o x2.5 o Monthly (Xie and Arkin, 1997) NASA GSFC 1 o Daily 1998~present (Huffman et al., 2001) Near Real-time Satellite Precipitation for mesoscale Study (1998~present) -- NASA TRMM-based MPA, 3-h, 0.25 o, (with Huffman et al. 2007) NOAA CMORPH, 3-h, 0.25 o (Joyce et al., 2004) -- UCI PERSIANN: 6-h, 0.25 o (Hsu et. al. 1997; with Sorooshian et al. 2002) --JAXA GSMaP (0.1 o 1-hour) High-Resolution Satellite Precipitation for hydrology/flash flood NOAA HE and SCaMPR: CONUS, 30-min, 0.04 o (NOAA/NEDIS); UCI PERSIANN-CCS: Global Coverage, 30-min, 0.04 o, (Hong et al. 2004) Oklahoma Ground Radar Development: WSR-88D NEXRAD National Network Q2: 1-km 5 minutes National Mosaic QPE; National Polarimetric NEXRAD Upgrade: 2010~ 9

8 Satellite-retrieved Rain Intensity and Frequency How much it rains? ( ) GPCP Climatology How often it rains? 15-year ( ) TRMM Daily Raining Prob. (%) 0.1mm/h cut-off

9 全球降水及总水量平衡闭合 : 青藏高原是难点 降水通量, 陆表储水量 ( 土壤水 ) 是青藏高原地 - 气耦合系统的关键参数, 研究表明, 高原降水和总水量平衡闭合是全球研究的难点 NASA 全球标准降水产品 TMPA 多年偏差 (Yong and Hong, BAMS, 2014 ) 全球陆地总水量平衡闭合误差图 (Flamig and Hong, JC, 2014) 11

10 地面站点资料稀少 青藏高原降水产品现状 雷达台站少, 而且地形遮挡严重 卫星降水高覆盖率, 但不确定性大 FY 卫星业务化产品夏季严重高估 (FY 静红外误报 虚假水 ) 2010 年 8 月平均小时降水量 ( 国家气象信息中心提供 )

11 我国 FY- 系列卫星现状和规划 : 中国需大力发展主 被动微波卫星降水反演 2011FY-3AM1 2012FY-2G 2012FY-3PM1 2013FY-4A (TEST) 2010FY-2F 2013FY-3RM (TEST) 2009FY-3B (TEST) 2014FY-3AM2 2008FY-2E FY-2C/D 2015FY-4EAST1 2015FY-3PM2 2008FY-3A (TEST) 2016FY-4WEST1 2017FY-4MS (TEST) 2020 FY-4MS 2019FY-3RM FY-3PM3 2016FY-3RM1 2017FY-3AM3 可以借鉴国际经验来协同反演青藏高原降水

12 VPR-IE 星 - 地雷达降水回波识别增强青藏高原产品 中国 印度 mm/hour mm/hour 青藏高原 Cao and Hong et al., 2014 Long and Hong et al., 2014 Shen and Hong et al., D 降水回波信息将非常有利于解决降水随地形坡度变化的现象, 并能提高数值模型的业务预报能力

13 From Tropical (TRMM) to Global Precipitation Measurement (GPM) 1998~~~2016 (TRMM) 2014 ~~2018 GPM GPM, Satellite Constellation, is an expanded mission of the current TRMM, to tackle the Hard Part: Light Rainfall and high latitude snow fall. On Board: Dual-Frequency Precipitation Radar (DPR) (Ku-Band-13.6 GHz; Ka-Band GHz)) DPR will act as benchmark sensor to calibrate GPM and other 8 satellite-sensors NASA TRMM/GPM Science Team Member since 2005 GPM Targets: + Natural Hazards + Hydrology + Weather Forecasting + Water Resources 15

14 全球降水计划 Global Precipitation Courtesy: Mission NASA s GPM ESE 发射 : 双频雷达 Ka/Ku, 13 个通道 (900 公里宽 ) 微波成像仪 GPM: 一个核心卫星与八颗微波小卫星, 穿透云层提供更高精度的 3-D 云微物理结构, 提供全球 Rainfall and Snowfall 产品, 对全球天气水情预报将有划时代的影响 ; 同时提高后续旱涝泥石流灾害预测

15 3-D 降水 from GPM Satellite 2014/3/10, GPM DPR sees 3-D cyclone full range of storm structure

16 与降水密切相关的陆表储水量 ( 土壤水 ) 的变化时间序列较短 地学界首次能从观测与模型相结合手段来探讨流域水热平衡方 程的闭合度 ; 但 GRACE 时间序列短 (2003~) 难以分析其气候效 应, 并造成高原总水量不平衡的争论 ( 到底增加还是减少 )? Water in the Balance: 科学 Science 340, (2013); 美国储水量 ( 土壤水 ) 变化 GRACE team: U. of Texas; JPL; GSFC

17 青藏高原重力卫星总储水量回推初期成果 :GRACE Reconstruction ( ) 本项目团队初步结果表明时空连续长期降水集是重建青藏高原 GRACE 的关键变量 (Long D. and Hong Y. et al. 2014,RSE) 下图是回推重建的雅鲁藏布江上游 35 年 ( ) 的 GRACE 总储水量长时间序列数据集 19

18 Global Hydrological Prediction System (RT and FCST Modes)

19 CREST: A Distributed Hydrologic Model designed to take advantage of the 4-D remote sensing data, Used at Global or Regional Projects Wang and Hong et al Coupled Routing and Excess STorage (CREST): Three soil layers. Distributed, fully coupled runoff generation and routing model Simulates water and energy fluxes and storage on a regular grid 2 Forcings 17 Parameters 9 Outputs Cell-to-Cell Flow Routing Step I: Rainfall-infiltration Partitioning (distributed and time-variant) Step 2: Flow Routing using Macro-scale Cell-to-Cell Algorithm Step 3: Grid Point Hydrographs--Flood Inundation Mapping 7/13/2014 HyDrometeorology and RemOte Sensing Laboratory (hydro.ou.edu) 21

20 OU-NASA Joint Operational Global Hydrologic Prediction Real-time Virtual Water System: HyDrometeorology and RemOte Sensing Laboratory (hydro.ou.edu) Hong et al WRR; Wang and Hong et al HSJ; Wu et al 2012 JHM

21 中国典型区域多尺度水文暴洪模 拟及预警系统的建立中国自动站与 CMORPH 降水产品融合的逐时降水量网格数据集 (1.0 版 ) (0.1, 1-hour) :00 Precipitation (mm/h) Streamflow (m 3 /s)

22 Our Global Drought map compared to China/CMA Drought tool atestr= #calendar

23 US Flash Flood Database 1. NWS reports from Sept 06 Aug SHAVE reports from summers of 2008, 09, 10 Additional details in Ortega et al. (2009) Bull. Amer. Meteor. Soc. and Gourley et al. (2010) J. Hydrol. 3. USGS stream gauge data Database available for use by research and operational communities

24 美国 NOAA/Multi-Radar Multi-Sensor 多源降水业务化系统 ( 主要开发者之一 ) MRMS:1 公里, 5 分钟,

25 业务化系统 NFL: National Mosaic and Multi-Sensor QPE (NMQ) Flooded Locations And Simulated Hydrographs (FLASH) LAND SLope-Infiltration-Distributed Equilibrium (LANDSLIDE) NMQ Radar Precipitation Observations -250 m/2.5 min FLASH Distributed CREST Hydrologic Models LANDSLIDE Landslide Hotspot Models mm Simulated surface water flow Landslide prediction June 2010, Albert Pike Rec Area, Arkansas 20 fatalities Red: Observations Pink: Predictions

26 NMQ-FLASH: Real-time, direct simulation of flash floods a reality 1-km 2 /5-min resolution of Q2 precipitation forcing and model outputs Addresses service needs in NWS; flash flooding is #1 weatherrelated killer Project will readily incorporate future dual-pol radar products (Q3) and stormscale ensemble precipitation forecasts Will include data assimilation and probabilistic products Photo source: National Geographic

27 Coupled Hydrologic-Landslide Model: CRESLIDE = CREST + SLIDE 30

28 Hurricane Ivan Method & Data Coupled CREST with SLIDE (CRESLIDE) 250 m resolution, hourly simulation Stage IV rainfall STATSGO CONUS soil type (1 km) University of Maryland (1 km) HyDROS Lab, The University of Oklahoma 31 hexg@ou.edu

29 Urban-CREST Flood Model Implemented at Oklahoma City &Dallas Metropolitan Return Period (years) No Flooding Flooding Severe Flooding 101 km 137 km 2010 June 14, OKC Flash Flood

30 Water - Relevant Remote Sensing Missions SMOS ESA's Soil Moisture and Ocean Salinity (2009) TRMM The Tropical Rainfall Measuring Mission GRACE Gravity Recovery and Climate Experiment (2002 GPM Global Precipitation Measurements (2014) SMAP Soil Moisture Active Passive Satellite(2014) MODIS SWOT Moderate Resolution Imaging Spectroradiometer Surface Water and Ocean Topography (2020) (1999), (2002)

31 Seeking Collaboration from following Research Areas Radar/Satellite Remote Sensing Retrieval, Validation, and Applications of Precipitation, Soil Moisture, and Evapotranspiration; Natural Hazard Prediction and Disaster Research (Flood and Landslide etc); Hydrology and Water Resources; Hydrological System Analysis; Distributed Hydrological Modeling and Land Surface Modeling; Data Assimilation Systems for Water, Weather and Climate Prediction; Climate Change Impacts on Water Availability and Hydrologic Extremes; Environmental Resources Planning and Sustainable Development Artificial Intelligence; GIS/Spatial Analysis; Remote Sensing Image Analysis; Hydrometeorology and Remote Sensing Lab (hydro.ou.edu) at The University of Oklahoma