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A Combined IR and Lightning Rainfall Algorithm for Application to GOES-R Robert Adler, Weixin Xu and Nai-Yu Wang University of Maryland Goal: Develop and.

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Presentation on theme: "A Combined IR and Lightning Rainfall Algorithm for Application to GOES-R Robert Adler, Weixin Xu and Nai-Yu Wang University of Maryland Goal: Develop and."— Presentation transcript:

1 A Combined IR and Lightning Rainfall Algorithm for Application to GOES-R Robert Adler, Weixin Xu and Nai-Yu Wang University of Maryland Goal: Develop and test a combined geo-IR and lightning rain algorithm for use with GOES-R [and also applicable with other types of lightning information] Xu, Weixin, R. Adler, Nai-Yu Wang, 2014: Combining Satellite Infrared and Lightning Information to Estimate Warm ‐ Season Convective and Stratiform Rainfall. J. Appl. Meteor. Climatol., 53, 180–199. Xu, Weixin, R. Adler, Nai-Yu Wang, 2013: Improving Geostationary Satellite Rainfall Estimates Using Lightning Observations: Underlying Lightning–Rainfall–Cloud Relationships. J. Appl. Meteor. Climatol., 52, 213–229.

2 Approach 1.Utilize Tropical Rainfall Measuring Mission (TRMM) data (IR, Lightning, Passive Microwave and Radar) to develop and test an instantaneous rain estimation technique for use in deep convective situations. 2.Apply IR-based Convective-Stratiform Technique (CST; Adler and Negri, 1988). CST defines convective cores by T b minima and adds stratiform rain through T b threshold. 3.Use Lightning flash rate as additional information to CST to detect new convective cores, eliminate incorrect IR- defined cores, and estimate convective core rainfall rates. 3.Compare CST and CSTL against TRMM PMW and Radar rainrates to understand impact of Lightning information.

3 How Is Lightning Observed by TRMM LIS? Lightning events are in color While GOES-R GLM continuously monitors lightning, TRMM LIS monitors a region (600 km) every 80-90s.

4 IR-lightning Combined Algorithm IR-based Conv./ Strat. Technique (CST) Convective Area Stratiform Area Convective Rain Rates Stratiform Rain Rates Base Algorithm (Adler and Negri, 1988) Lightning Information

5 Development and Evaluation Datasets (TRMM) Dependent set: (1427 cases); Independent set: (2009 cases); Warm season (May-Aug), Southern CONUS; Case criteria: > 5 flashes, every 800 by 800 km 2. Lightning events are in color

6 Convective/Stratiform Technique (CST) IR Tb & local Minima (+) CST: 1. Find local Tb minima; Do slope (gradient) test; 2. Assign conv. area as a function of min Tb; 3. Assign rain rates for C/S. Rain Rate Convective Area Stratiform Area black outlines = convective areas from TRMM radar

7 Slope test for T b minima Convective cores or not? Conv. Area as a function of Tmin Functions for Convective Core and Area Red: Conv. Blue: Non-Conv. Conv. cores and area defined by TRMM radar observations Slope TbTb TbTb Area

8 1. Remove convective cores not associated with lightning. 2. Define additional convective areas by lightning area. Use of Lightning (CST+Lightning) IR Tb CST + Lightning (Conv/Strat) CST (Conv/Strat) PMW (Passive MicroWave) (Conv/Strat 10 mm/hr) Convective Area Stratiform Area

9 Lightning info. consistently improves the convective detection (POD) by 8%, lowers the false alarm (FAR) by 30%. Statistics ( ) on Convective Rain Area PODFARCSI

10 Functions for Rainrate Assignment Convective RR Stratiform RR RR = 2.5 mm hr -1 RR as a function of Lightning Density Based on: Xu, Adler, and Wang, JAMC(2013)

11 Instantaneous Rain Estimates (10 km res.) PMW RR PR RR CST RRCST+L RR

12 Instantaneous Rain Estimates PMW RR IR CST RR CST+L RR

13 PMW RR IR CST RRCST+L RR Instantaneous Rain Estimates

14 PMW RRIR CST RRCST+L RR NOAA Operational GOES IR Product Instantaneous Rain Estimates

15 Instantaneous Rainrate (20km res.) CST vs. PMWCSTL vs. PMW May 2007—Similar in other months

16 Rainfall Statistics (20km res.) BIAS and RMSE vs. PMWCorr. Coeff. vs. PMW May-August Lightning information clearly improves rainfall statistics

17 Estimation of Rainfall Total (over 800x800 km 2 ) CST vs. PMWCSTL vs. PMW Rain Volume over TRMM Scene

18 Summary and Suggested Next Steps Results indicate that satellite lightning information from GLM will be very valuable in improving GOES-based rain estimation. This comes from the use of lightning information to establish location of convective cores “unseen” by IR and eliminate incorrect cores defined by IR, and by flash rate-rain rate relations. A GOES-R IR/Lightning algorithm should be fully developed and tested, building on the convective-stratiform separation concept, which takes advantage of strength of the GLM lightning data. Geostationary-based rain estimates should be a part of an overall integrated precipitation analysis system using ground-based (radar, raingauge) and low-orbit merged microwave estimates to provide users with an integrated space-time best estimate. Xu, W., R. F. Adler, and N.-Y. Wang, 2014: Combining Satellite Infrared and Lightning Information to Estimate Warm Season Convective and Stratiform Rainfall. J. Appl. Meteor. Climatol.

19 Example 1: Early Stage of Convective Systems PMW RRIR CST RRCST+L RR

20 Estimates of Convective Precipitation PR C/S IR Tb CST C/S CST+L C/S Convective-Stratiform Separation


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