Spaceborne and Ground-based Global and Regional Precipitation Estimation: Multi-Sensor Synergy

Transcription

1 Hydrometeorology and Remote Sensing Lab (hydro.ou.edu) at The University of Oklahoma Spaceborne and Ground-based Global and Regional Precipitation Estimation: Multi-Sensor Synergy Presented by: 温逸馨 (Berry) Advisor: Prof. Yang Hong Hydrometeorology and Remote Sensing Laboratory (HyDROS), University of Oklahoma

2 Multiple Sources for QPE Observations Geosynchronous Satellites VIS, IR, Sounding Near Polar Orbiting Satellite PMW and Space Radar Radar Gauge Precipitation is arguably the most important hydro-meteo-climatic variable; Precipitation LABZ measurement is among the KEY challenges, particularly in high altitude / latitude, over ocean, complex terrains and remote regions.

3 Hydrometeorology and Remote Sensing Lab (hydro.ou.edu) Quantitative Precipitation Estimation (QPE) Research Group Dr. Qing Cao Dr. Pierre-Emmanuel Kirstetter Dr. Sheng Chen Dr. Xinyi Shen Yixin (Berry) Wen Saber Moazami Huan Li

4 Research Topics Remote sensing of cloud, precipitation and soil moisture Instruments: ground radar: (X, C, S-band) Satellites (active/passive microwave): (TRMM, GPM, CoudSat, GOES, SMAP, etc.) In-situ: gauge, disdrometer, etc. Major topics: QPE algorithm development Rainfall estimation Snowfall detection and estimation Ground validation and Error characterization (various ground/spaceborne sensors) Multi-sensor data assimilation Soil moisture retrieval

5 NASA Global Precipitation Measurement mission Project: Using NMQ for Validation & Integration

6 Analysis region: Colorado Basin (area of CBRFC) NASA SPoRT Project: Incorporate Spaceborne Research Products into Ground Radar QPE Objective Enhance NEXRAD-based precipitation estimation by supplementing with spaceborne radar measurements, resulting in seamless regional (and global) precipitation estimates TRMM/PR NMQ/Q2

7 Improved Snowstorm Detection and Estimation through Incorporating NASA Spaceborne Measurements into Polarimetric National Mosaic QPE System (NMQ-Q3)

8 Development and Evaluation of Active and Passive Microwave Combined Global Daily Soil Moisture Retrieval Algorithms

9 Analysis region: Colorado Basin (area of CBRFC) NASA SPoRT Project: Incorporate Spaceborne Research Products into Ground Radar QPE Objective Enhance NEXRAD-based precipitation estimation by supplementing with spaceborne radar measurements, resulting in seamless regional (and global) precipitation estimates TRMM/PR NMQ/Q2

10 Ground Weather Radar Since WWII, Ground Weather Radar has proven its value to capture the heavy precipitation. S-, C-, X-band; NEXRAD, mobile, etc. NMQ (National Mosaic & Multisensor QPE) system - Operational system by NOAA/National Severe Storm Laboratory (NSSL) - 3D radar mosaic - Precipitation classification - High resolution (1x1 km, 5min)

11 Limitations of NEXRAD Problems: Atmosphere surveillance at low level is limited for S-band WSR-88D radar. Radar beam might overshoot the melting layer at the far range. Beam blockage exists in many mountainous regions, resulting in underestimation of precipitation. NEXRAD coverage at a height of 3km AGL (Maddox et al. 2002) (Krajewski et al. 2006)

12 Effect on Precipitation Estimation Radar-based Quantitative Precipitation Estimation (QPE) Underestimation: overshooting the bright band Overestimation: contaminated by bright band. 0 C melting snow ice bright band storm top hail Radar blockage rain Mountain surface Figure (up): Overshooting and BB contamination for Radar beams Figure (left): radar-based QPE (one-hour rain accumulation)

13 Objective Objective Enhance NEXRAD ground radars capability of capturing heavy precipitation by integration of spaceborne precipitation radar. NASA/JAXA TRMM mission Precipitation radar (PR) Vertical profile of reflectivity (VPR), 250m vertical resolution Melting layer detection Precipitation classification update ~90 min

14 Method: VPR-IE VPR Identification and Enhancement (VPR-IE) Determine the representative VPR and apply it to ground radar for near surface QPE TRMM Ku-Band VPR VPR Physical model Empirical model S-Band VPR Range correction Representative AVPR

15 Example: Height of Radar Beam Terrain: Mean Sea Level (MSL) HSRH: Above ground level (AGL) KESX KFSX KIWA KYUX KEMX

16 Example: Height of Radar Beam HSRH: Mean Sea Level (MSL) KESX KFSX KEMX Terrain: Mean Sea Level (MSL) Colorado River Grand Canyon Flagstaff Gila Mountains Sonoran Desert

17 Example: Hybrid Scan Reflectivity HSR: dbz HSR: dbz (At MSL)

18 Example: Vertical Structure Freezing level close to ground Overshooting BB contamination KFSX Vertical structure of storm UTC Temporal variation of mean VPR

19 Method: VPR-IE VPR Identification and Enhancement (VPR-IE) Determine the representative VPR and apply it to ground radar for near surface QPE TRMM Ku-Band VPR VPR Physical model Empirical model S-Band VPR Range correction Representative AVPR

20 VPR Conversion: Physics-Based Model h T : the top of precipitating h M : the interface between solid and melting layers; Physics-based VPR model h S band VPR Ku band VPR Δh E : the melting layer s thickness; Dg: density factor, varying between 0 (light snow) to 1 (hail); G: the slope of VPR in the liquid layer. Nonlinear regression Normalized Z ht hm he Dg G

21 VPR Conversion: Empirical Model Z S =Z Ku +DFR(Z Ku ) Q. Cao, Y. Hong, Y. Qi, Y. Wen, J. Zhang, J. Gourley, L. Liao, 2012: Empirical Conversion of Vertical Profile of Reflectivity (VPR) from Ku-band to S-band Frequency, Journal of Geophysical Research, in press.

22 Range Effect: apparent VPR from true representative VPR Normalized VPR 10 km 80 km 160 km 240 km Illustration of a VPR model (black line) and an apparent (red line) VPR. The VPR corrected reflectivity, Zc, is obtained by: Zc(x,y) = Zobs(x, y)/ρ(x,y) ρ(x,y): Apparent normalized VPR at each pixel.

23 Rainfall, mm/h Rainfall, mm/hr VPR Correction: Direct PR-based Q2 only Q2- TRMM 12/08/2009 Figure: Comparison of 1-hr radar rainfall accumulations ending at 02Z on 8 Dec We use HADS 1-hr rain gauge measurements as a reference to 2 Fig. QPE 6. QPE error error [ (radar (multiplicative gauge)/gauge bias ((radar-raing ] evaluate the VPR correction performance. 3 December terms of 2009 range case for (left) the 8 and Dec for 2009 the case. 22 Januar Y. Wen, Q. Cao, P. Kirstetter, Y. Hong, J.J. Gourley, J. Zhang, G. Zhang, 7 B. Yong, 2012: Incorporating NASA spaceborne radar data into NOAA National Mosaic QPE system for improved 8 precipitation measurement: a physically based VPR identification and enhancement method. Journal of Hydrometeorology, 9 in press.

24 5.3 Real-Time Issues One-time correction Continuous correction Variation of melting layer Real-time (5-min) t 1 t 2 t 3 t One-time correction is not sufficient Correction for high temporal resolution (ground-based radar) Climatological VPR need event-based adjustment o Height of melting layer (terrain effect) o BB width: little effect on near-surface rain estimation o BB peak: less effect, hard to quantify

25 Climatology-based VPR correction (left) Ground radar KFSX 1 hr rainfall accumulation Bias=-77.5% RMSE=2.36 Bias=-16.4% RMSE=2.15 (right) 1-hr rainfall accumulation after climatological VPR- IE (Unit in mm) Bias ratio between radar and gauge 1-hr rainfall accumulation blue dots: radar overestimation red dots: radar underestimation bubble size: rainfall amount measured by rain gauges.

26 Conclusions VPR Identification and Enhancement (VPR-IE) approach is effective to improve QPE by integrating TRMM PR into NEXRAD measurements, especially for the mountainous west region of U.S. A real-time VPR-IE system will be built at the University of Oklahoma as a demo of potential multi-sensor QPE system, which is based on NSSL s NMQ system (Q2) and NASA s TRMM mission. Future development will be extended to CONUS wide region and accommodate the polarimetric NMQ system (Q3) and NASA s Global Precipitation Measurement (GPM) mission.

27 Questions?

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