Jordan G. Powers Kevin W. Manning. Mesoscale and Microscale Meteorology Laboratory National Center for Atmospheric Research Boulder, Colorado, USA

Transcription

1 Jordan G. Powers Kevin W. Manning Mesoscale and Microscale Meteorology Laboratory National Center for Atmospheric Research Boulder, Colorado, USA

2 Background : Model for Prediction Across Scales = Global atmospheric model In public release since 2013: Version 4.0 by Sept Current Study: Initial Comparison of and Testing: 5-day forecasts over Antarctica Setup: approximates setup, but not indentical Caveats w/use of Cost, physics limitations, performance not a replacement for anytime soon

3 Background : Numerical weather prediction model engineered to operate from the global scale to the cloud scale -A = -Atmosphere system jointly developed by NCAR and Los Alamos National Laboratory (LANL) NCAR: -A primary development and support See : Grid

4 Background : Run at NCAR for real-time NWP and research Ex: Support of NOAA/SPC Hazardous Weather Testbed Spring Experiment Global Uniform Grid Current : Global mesh w/regional refinements No standalone limited-area grids System Components Global Variable Resolution Grid Infrastructure Atmosphere -A Ocean -O Ice -I -I not connected to -A -I being linked to a different atmospheric model by LANL in CESM

5 : Global Cloud Scale Moore, OK Tornado 20 May km/3-km Variable Mesh 500 mb Relative Vorticity 01 UTC 18 May 11 UTC 21 May 2013 (22 h) 3-km reflectivity 70-hr fcst 22 UTC 20 May Obsv d composite reflectivity Mesh spacing contours: 40, 30, 20, 12, 8 & 4 km

6 Model Setups 30-km/10-m grids 60-km/15-km variable global mesh 60 vertical levels 46 vertical levels Data assimilation No DA GFS first guess GFS first-guess Domains Antarctic Mesh 30 km 10 km Terrain height (m) 15-km Antarctic refinement

7 Note: Model Physics Forecast Review (i) has a narrow set of options taken from (ii) versions not the latest 1) Subjective Forecast Analyses Review of runs since October ) Statistical Verification AWS Surface Verifications: T, Wind speed, Pressure Statistics: Bias, RMSE, Correlation Verification Periods (i) 20 Oct 31 Dec 2015 (ii) 8 Feb 31 Mar 2016

8 Case Review: 0000 UTC 8 April 2016 Forecast Hours shown: 24, 72, 120 Sfc and 500 mb analyses

9 & 24-hr Forecasts SLP and 3-Hr Precip 0000 UTC 9 Apr 2016 (8 Apr 0000 UTC Init) 970 L 965 mb L mb Contour interval= 4 mb Forecasts aligned at 24h

10 & 72-hr Forecasts SLP and 3-Hr Precip 0000 UTC 11 Apr 2016 (8 Apr 0000 UTC Init) 942 mb 940 mb x Forecasts similar, but shows an extended trough and suggests secondary development (x) Contour interval= 4 mb

11 & 120-hr Forecasts SLP and 3-Hr Precip 0000 UTC 13 Apr 2016 (8 Apr 0000 UTC Init) 959 mb 952 mb 942 mb : Two lows, with a significant (959) center in western Ross Sea Contour interval= 4 mb

12 AMPS Analysis SLP 0000 UTC 13 Apr h 120 h Contour interval= 4 mb

13 & 120-hr Forecasts 500 mb 0000 UTC 13 Apr 2016 (8 Apr 0000 UTC Init) 4605 m 4554 m 4636 m : Two circulation centers

14 AMPS Analysis 500 mb GHT, Wind 0000 UTC 13 Apr h 120 h

15 Surface Verifications Verifications performed with AWS obs at sites Oct. Dec and Feb.- Mar periods Variables: Temperature, Pressure, Wind speed To do: Upper-air verification comparisons

16 McMurdo Temperature Oct. Dec. Feb. Mar. Obs Bias: = -2.8C = -4.0 C RMSE: = = 4.0 C 5.1 C Correlation: =.89 =.86 : Increased cold bias & RMSE

17 South Pole Temperature Oct. Dec. Feb. Mar. Obs Bias: = 3.7 C = 3.4 C RMSE: = 4.6 C = 4.9 C Correlation: =.94 =.91 : Decreased warm bias Oct. Dec.

18 McMurdo Pressure Oct. Dec. Feb. Mar. Obs } 1 mb Bias: = 1.0 mb = 1.0 mb RMSE: = 2.7 mb = 2.6 mb Correlation: =.96 =.97 & : Similar, small biases

19 South Pole Wind Speed Oct. Dec. Feb. Mar. Obs Bias: = 0.5 ms -1 = -1.6 ms -1 RMSE: = 1.7 ms -1 = 2.3 ms -1 : Greater bias (negative) and RMSE

20 Temperature Bias Oct. Dec. Feb. Mar. Oct. Dec: slightly better Feb. Mar: better

21 Wind Speed Bias Oct. Dec. Feb. Mar. Oct. Dec: slightly better / comparable

22 Summary Test Forecasts Subjective reviews: and daily forecasts consistent through about 72 hrs, but can diverge for longer lead times daily operation reliable No strange behavior in No big performance dropoff with Surface verifications: overall better than statistically Station results vary with parameter/season To do: Upper-air verification & additional periods

23 Summary (cont d) Testing and Implementation in AMPS: Caveats Setups not identical: Compute and model constraints resolution coarser than : 15 km v. 10 km physics limited significantly more expensive than Estimate: 6X computational cost of for a 10-km Antarctic mesh Further testing and evaluation necessary

24 End

25 Model Physics Physics (i) Uses a narrow set of physics options taken from (ii) versions not the latest Shared Package Areas LSM Noah ( V3.3.1, V3.7.1) Cu Kain-Fritsch ( V3.5, V3.7.1) LW rad RRTMG LW ( V3.4.1,, V3.7.1) Surface layer (Eta) ( V3.5,, V3.7.1) Different Package Areas PBL : YSU : MYJ Microphysics : WSM-6 : WSM-5 SW rad : RRTMG : Goddard

26 Why Voronoi Grids? Lat-Lon Grid (UM, Global ) Lat-Lon Grid Issues Poor scaling on computers with large numbers O( ) of processors because of necessity of a polar filter 50 km 3 km mesh Local refinement is limited to nesting or problematic coordinate transformations Advantages of Grid over Lat-Lon Grid scales well on MPP architectures (no poles) Voronoi Grid () offers flexible local refinement (variable resolution grids)