1. Assimilation of GPS Radio Occultation Data for an Intense Atmospheric River with the NCEP GSI System Zaizhong Ma and Ying-Hwa Kuo MMM/NCAR Marty Ralph, Ellen Sukovich, and Paul Neiman NOAA/ESRL/PSD

2. Atmospheric River case: Nov 6-8, 2006 From Neiman et al. (2008)

3. Observed Daily Precipitation 24-h precipitation ending at 1200 UTC 7 November 2006 Flooding and debris flow on White River, Oregon

4. Experiment Setup  CTRL: operational observation data;  LOC: CTRL+GPS with Local operator  NON: CTRL+GPS with Non-local operator Three runs: 18 UTC 0200 UTC 03 First-guess is AVN analysis Cycling experiments with 6-hr assimilation window from 3 to 9 Nov. 2006 CTCL LOC NON 12 UTC 0918 UTC 09 First-guess is 6h WRF forecast …………… First-guess is 6h WRF forecast  System: NCEP Gridpoint Statistical Interpolation (GSI) + WRF ARW  Case: AR took place in the early of Nov.2006  Setup:  Cycling Assimilation: 36km38L; Ptop: 50hPa  24h Forecast: triple nested domain, 36x12x4km

5. GPS RO soundings for one week (Nov. 3-9, 2006) The distribution of GPS RO soundings with the time in each 3h cycling assimilation window.

6. PWV of analysis at 0600 UTC 07 Nov. 2006 SSM/I observation Non-Local analysisLocal minus Non-Local

7. The 3-h WRF forecasts fit to GPS refractivity with time. The value is cost function for CTRL (blue), LOC (red) and NON-LOC (green) runs, respectively. 3h Forecast Verification in the Cycling Assimilation

8. The statistics of difference for the assimilation domain from 0000 UTC 03 to 1800 UTC 09 November 2006. Bias (left panel) and Standard Deviation (middle panel) errors of 3-h WRF forecasts verified against GPS RO refractivity for CTRL (dashed curve), LOC (thin curve) and NON-LOC (thick curve). The right panel shows the total number of verifying GPS soundings at each level during one-week cycling period. Standard Deviation and Bias of 3h forecast fit to GPS Refractivity

9. GPS Impact on 24h WRF forecast D1 D2 D3 24h forecast starting from 1200 UTC 6, 3 domains nested. Assimilation on D1. D3 only covers Washington and Oregon states.

10. 24-hr accumulative precipitation ending at 1200 UTC 7 Nov. 2006 OBS LOC CTRL NON-LOC

11. 24h PWV Difference between LOC (or NON) and CTRL experiments LOC - CTRL NONLOC - CTRL

12. Bias and Standard Deviation of 24h forecast fit to GPS Refractivity

13. QPF and evaluation data SITES 50 sites in WA, OR, & CA (117” precip. total) 22 sites in “wet” region (107” precip. total) 28 sites in “dry” region (10” precip. total) WA OR CA DATA 1200 UTC 6 Nov. to 1200 UTC 7 Nov. 2006 Model quantitative precipitation forecast (QPF) –Forecasts made from 12 Z to 12 Z –Resolution of 4 km Quantitative precipitation estimates (QPE) –From NWRFC –Gauge-based –12 Z to 12 Z –Resolution of 4 km Verification Region

14. All 50 sites (wet area and dry area) 24 h COSMIC QPF (in)NWRFC (in) CTRLLOCALNONLOCALObserved Avg Precipitation1.72 1.862.33 Avg Bias0.74 0.80 24 h COSMIC QPF (in) NWRFC (in) Site IDCTRLLOCALNONLOCALObserved Astoria, ORAST2.072.444.873.03 Frances, WAFRAW14.404.602.693.00 Cinebar, WACINW13.694.204.844.80 Cougar, WACUGW15.426.598.526.97 Packwood, WAOHAW14.524.886.025.70 Aberdeen, WAABEW14.314.173.775.34 Enumclaw, WAENUW13.182.903.167.16 Glacier, WAGLAW13.423.783.364.60 Leavenworth, WALWNW13.233.033.164.30 Marblemount, WAMARW15.976.265.403.90 Seattle, WASEA1.741.401.703.06 Skykomish, WASKYW13.763.974.208.60 Stampede Pass, WASMP2.853.053.787.47 Quillayute, WAUIL2.281.923.372.35 Verlot, WAVERW17.547.618.513.40 Bonneville Dam, ORBONO32.962.462.245.24 Detroit Dam, ORDETO32.242.361.752.33 Lees Camp, ORLEEO33.103.093.6913.60 Portland, ORPDX1.070.790.942.57 Three Lynx, ORTLYO31.841.861.263.70 Salem, ORSLE1.150.790.522.16 Summit, ORSMIO32.031.341.153.50 Avg ppt3.313.343.594.85 Avg Bias0.680.690.74 24 h COSMIC QPF (in) NWRFC (in) Site IDCTRLLOCALNONLOCALObserved Brookings, OR4BK0.33 1.080.48 Burns Airport, ORBNO0.430.350.220.00 Cougar Dam, ORCGRO31.241.131.420.85 Colville, WACQV0.170.120.260.44 Crater Lake, ORCRLO31.581.541.820.20 The Dalles, ORDLS0.070.020.000.52 Eugene, OREUG1.060.68 1.25 Spokane, WAGEG0.640.490.760.22 Agness, ORILHO30.960.600.780.10 Klamath Falls, ORLMT0.140.080.020.00 Meacham, ORMEH0.550.680.260.98 Rogue Valley, ORMFR0.090.150.300.00 Mazama, WAMZAW11.441.991.161.55 Enterprise, ORNTPO30.200.220.300.00 Oak Knoll, CAOKNC10.020.130.240.01 Omak Airport, WAOMK0.490.310.500.19 North Bend, OROTH1.090.970.830.30 Owyhee, NVOWYN20.060.000.020.01 Rome, ORP880.010.000.010.00 Pendleton, ORPDT0.06 0.010.03 Prairie City, ORPRCO30.830.891.370.00 Riddle, ORRDLO30.010.050.130.10 Redmond Roberts, ORRDM0.00 0.10 Glide, ORSRSO30.290.330.490.10 Goldendale, WASSPW11.161.211.171.40 Sexton Summit, ORSXT0.050.110.030.00 Williams, ORWLMO30.08 0.090.10 Yakima, WAYKM0.00 0.030.94 Avg0.470.450.500.35 Avg Bias1.321.271.42 Site Forecast and Observed Data “Wet” region sites“Dry” region sites

15. > 7 in/24h 5-6.9 in/24h 3-4.9 in/24h 0.16-2.9 in/24h Indicates “wet” region 0-0.15 in/24h 98 430 155 235 534 300 716 480 570 94 140 697 747 306 340 860 460 390 19 44 22 303 1360 524 257 52 350 370 216 233 12585 10 00 10 03 00 10 30 48 00 20 10 00 01 10 00 Observed precipitation (inchesX100) Observed precipitation at 50 evaluation sites

16. Comparison of QPF bias for forecasts with (“non- local”) and without (“control”) COSMIC data Control is best Minor difference Nonlocal is best Indicates “wet” region -02 46 -10 -57 00 24 26 44 12 29 05 -15 93 04 -14 -05 -25 -16 -29 -21 [QPF (non-local) – QPF (control)]/observed X100% * Numerical values represent difference between the two forecasts in inches, normalized by the total observed precipitation at that site. It is expressed as a percentage. *Color fill represents which forecast had smallest bias: -green: COSMIC data improved the forecast -red: Control run without COSMIC is still best -yellow: Differences were minor ***The COSMIC data improved the QPF at sites where the heaviest rain fell. NOLOCAL performs better than LOCAL.

17. Summary and Conclusions COSMIC GPS RO soundings successfully assimilated with NCEP regional GSI system using both local and nonlocal observation operators. Assimilation of COSMIC data improved regional analysis and prediction of the atmospheric river event: –Better fit to independent observations Nonlocal observation operator performs better than local observation operator: –Significantly reduces dry bias in precipitation forecast –improves QPF at sites where the heaviest rain fell More case studies are needed to substantiate the results.