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Forecasting in a Changing Climate Harold E. Brooks NOAA/National Severe Storms Laboratory (Thanks to Andy Dean, Dave Stensrud, Tara Jensen, J J Gourley,

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Presentation on theme: "Forecasting in a Changing Climate Harold E. Brooks NOAA/National Severe Storms Laboratory (Thanks to Andy Dean, Dave Stensrud, Tara Jensen, J J Gourley,"— Presentation transcript:

1 Forecasting in a Changing Climate Harold E. Brooks NOAA/National Severe Storms Laboratory (Thanks to Andy Dean, Dave Stensrud, Tara Jensen, J J Gourley, Jerry Brotzge)

2 Observations Forecasting begins with observations New and experimental radars –Dual-polarization (NEXRAD 2010-2012) –Phased array Multi-sensor analyses

3 Radar-estimated rainfall Dual-Pol Reflectivity Overestimate (hail contamination)

4 R(Gauge) N=1299 R(Z) Non-polarimetric R(K DP ) R(syn) R(Z,Z DR |BC,HCA)R(Z,Z DR |JPOLE) R(Z,Z DR |BC)R(Z,Z DR |JPOLE,HCA) NB: 0.00 RMSE: 0.48 mm CORR: 1.00 NB: -0.09 RMSE: 6.51 mm CORR: 0.83 NB: -0.17 RMSE: 5.46 mm CORR: 0.88 NB: -0.31 RMSE: 4.95 mm CORR: 0.91 NB: -0.13 RMSE: 5.49 mm CORR: 0.89 NB: -0.25 RMSE: 5.81 mm CORR: 0.87 NB: -0.10 RMSE: 5.66 mm CORR: 0.89 NB: -0.27 RMSE: 5.40 mm CORR: 0.88

5 Rainfall algorithms evaluated with long-term bias removed R(syn) and R(Z,ZDR|BC,H CA) hit peak All polarimetric algs better than R(Z) Hydrolgic Evaluation – TS Erin (187 mm/3 hr)*

6 3-Yr Hydrologic Evaluation Open circles show results with long- term bias removed Best Skill Increasing Skill As is Bias Corrected Simple Complex

7 Oklahoma City Area Tornadoes 5:47 PM 10 May 2010

8 Questions Questions ???

9 Forecasting User-based evaluation tools 0-48 hour ensembles –Information content Future ensembles –Very high resolution –Radar input –Run often for a few hours

10 Example of Object-Oriented Verification Method 12 hour Forecast Valid: 11 May 2010 - 00 UTC Fcst Object #1 Matched Observed Objects HRRR – Simulated Composite Reflectivity NSSL – Q2 Composite Reflectivity Objects from DTC Model Evaluation Tool Method for Object Based Diagnostic Evaluation Fcst Object #2

11 Experimental QPF from Ensembles Mean Processed Max Obs

12 Convective-scale Warn-on-Forecast Vision Probabilistic tornado guidance: Forecast looks on track, storm circulation (hook echo) is tracking along centerline of highest tornadic probabilities Radar and Initial Forecast at 2100 CSTRadar at 2130 CST: Accurate Forecast Most Likely Tornad o Path T=2120 CST T=2150 T=2130 T=2140 70% 50% 30% T=2200 CST Developing thunderstorm Most Likely Tornad o Path T=2120 CST T=2150 T=2130 T=2140 T=2200 CST An ensemble of storm-scale NWP models predict the path of a potentially tornadic supercell during the next 1 hour. The ensemble is used to create probabilistic tornado guidance. 70% 50% 30% Stensrud et al. 2009 (October BAMS)

13 Thompson and Wicker 29 May 2004 Data Impact Phased Array vs 88D PAR 20 Minutes (20 Volumes) WSR-88D 20 Minutes (5 Volumes)

14 Generating the ensemble

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16 Challenges Rapid and accurate data quality control –Ordinary vs. extraordinary Model error Sensitivity to errors in environmental conditions Assimilation method to use? Ensemble methods for convective-scale


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