ML20080M195
| ML20080M195 | |
| Person / Time | |
|---|---|
| Issue date: | 03/23/2020 |
| From: | Ahijevych D, Powers J, Prein A, Schwartz C, Sobash R Office of Nuclear Regulatory Research, National Center for Atmospheric Research |
| To: | Office of Nuclear Regulatory Research |
| T. Aird | |
| Shared Package | |
| ML20080M135 | List:
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| References | |
| Download: ML20080M195 (20) | |
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This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsor ed by the National Science Foundation under Cooperative Agreement No. 1852977.
Photo by @KenGeiger How well can Kilometer
-Scale Models Capture Recent Intense Precipitation Events?
5th Annual Probabilistic Flood Hazard Assessment Workshop, Feb. 19, 2020 Andreas F. Prein, D Ahijevych, J Powers, R Sobash, C Schwartz National Center for Atmospheric Research
Correct representation of:
Spatial structures Intensities Time evolution Convective outbreak Model Observation
Step Improvement in Simulating Intense Rainfall Storms x = 4 km x = 12 km (K-F scheme) x = 1 km
Deep convection in atmospheric models 100 km 100 km GCM grid spacing (~100 x 100 km)
- Deep convection is sub-gridscale process
- Needs cumulus parameterization When do we start to resolve deep convection?
~4 km horizontal grid spacing (Weisman et al. 1997) 16 times more grid cells 625 times more grid cells
Resolution of State-Of-The-Art Climate Models
Resolution of State-Of-The-Art Climate Models
Resolution of State-Of-The-Art Climate Models
Resolution of State-Of-The-Art Climate Models
Resolution of State-Of-The-Art Climate Models
NRC project NR. 31310019S0015 Convection-Permitting Modeling for Intense Precipitation Processes Probable Maximum Precipitation (PMP)
Does not allow quantification of uncertainties in hazard estimates in either a physical or a risk sense.
Convection-Permitting Models Can they facilitate a more physically-based probabilistic flood risk assessments?
Intense Precipitation Events in Eastern CONUS Daily, 1-in-5-yr precipitation amount for 3646 stations for the period of 1950-2010 Kunkel et al. 2012 Evaluation in Four Regions
Convection-Permitting Model Simulations Dataset x
Elements Period Region References NCAR Real-time Ensemble 3 km 10-member ensemble forecasts 5/1/2015-12/31/2017 CONUS Schwartz et al. (2014, 2015a, 2015b),
Romine et al. (2014)
NCAR MPEX Ensemble 3 km &
1 km 10-member ensemble forecasts 5/15/2013-6/15/2013 Central /
eastern U.S.
Schwartz et al. (2017)
NCAR Severe Weather Study 3 km &
1 km Deterministic forecasts; 500 cases 2010-2017 Central /
eastern U.S.
Sobash et al. (2019),
Schwartz et al. (2019) 10,570 36-hour WRF simulations/forecasts at 3-km horizontal grid spacing (1.8 mi) 810 36-hour simulations at x=1 km (0.6 mi)
Are Intense Precipitation Events Harder to Simulate?
Overlap []
Equitable Threat Score (ETS) [km]
Simulated Storm Observed Storm Equitable Threat Score (ETS) 1 0
Equitable Threat Score (ETS)
Observed Precipitation Rate [mm/d]
>= 5 mm/d
>= 20 mm/d
>= 50 mm/d Thresholds Southern U.S.
Model skill increases with intensity of event
Case Selection l Top 20 Events in Each Region Top 20 Events in Appalachia Region
Lagrangian Evaluation Framework Simulation has to capture:
Track Movement speed Size evolution Precipitation volume Peak accumulation West Virginia Flooding of 2016
Storm Speed West Virginia Flooding of 2016 Storm Size Observed Accumulation Strom Tracks Intense Rainfall Intensity vs. Elevation Precipitation Volume Peak Accumulation Peak Displacement
West Virginia Flooding of 2016 Observed Precipitation Best Peak Accumulation Best Volume l 1 km Best Peak Location Worst Overall Simulation Large spread due to initial condition perturbations 3 km and 1 km results are comparable 3 km seem to have too much rainfall on lee-side
Tropical Storm Bill l June 2015 Observed Precipitation Best Simulation Peak Accumulation Precipitation Volume Peak Displacement
Next Steps Uncertainty Source Setting Horizontal grid spacing (x) 3 km, 1 km (1.8 mi, 0.6 mi)
Precipitation observations Stage-IV (Crosson et al. 1996, Fulton et al. 1998)
Mosaic WSR-88D (Zhang and Gourley 2018)
PRISM (Daly et al. 1994, 2002, 2008)
Newman (Newman et al 2015)
Initial Conditions Ensemble datasets to be used reflect initial condition perturbations
- Assessment of model performance based on ensemble of intense events
- Quantification of systematic model biases
- Analyses of uncertainty sources to model performance
- Conceptual framework to use CPM simulations in Monte Carlo rainfall-runoff simulations
- Convection-permitting models can capture recently observed intense rainfall events east of the Continental Divide
- Predictability increases with rarity of event
- Sensitivity to initial condition perturbations is large
- 3 km and 1 km simulations show comparable results Summary and Conclusions prein@ucar.edu This work is sponsored by NRC under the Interagency Agreement Number 31310019S0015