Forecast Skill of Great Plains Low-Level Jet and Its Correlation to MCS Precipitation

1. WRF forecast skill of the great plains low-level jet and its correlation to forecast skill of mesoscale convective system precipitation Brian Squitieri Graduate research assistant Iowa state university

2. Project motivation • LLJ is a primary instigator for nocturnal mesoscale convective systems (MCSs) (Mitchell at al. 1995, French and Parker 2010). • MCSs are a primary source of summer precip across the central US (Jirak and Cotton 2007, Coniglio et al. 2010) • *Better forecasts of the LLJ could lead to better forecasts of MCS activity* • Better forecasts may assist farmers (Schaffer et al. 2011) and emergency managers (Jirak and Cotton 2007)

3. Project Motivation • Low-level jet (LLJ) is a narrow current of air btw 300-2000 m AGL with a relative wind maximum (Bonner and Paegle 1970, Mitchell 1995, Whiteman et al. 1997). • Source of upward vertical motion, conducive for convection at nose of LLJ or terminus (Chen and Kpaeyeh 1993, Augustine and Caracena 1994, Mitchell et al. 1995, Higgins et al. 1997, Jirak and Cotton 2007, Schumacher and Johnson 2009, Coniglio et al. 2010). • One of the most heavily studied atmospheric phenomena

4. Background • Bonner (1968): First to establish a LLJ climatology and ranking system for LLJ magnitudes • Southerly LLJs most common in summer months in plains states • LLJ criterion (Criterion I: Peak=12ms-16 ms-1, Criterion II: Peak=16ms-18ms-1, Criterion III: Peak=20ms-110ms-1) • Bonner and Paegle (1970): • Found that surface terrain heating contributed to diurnal meridional geostrophic wind maximum • Diurnal Vg maximum provided background flow for LLJ to develop upon

5. Background • Ucellinni and Johnson (1979): Determined LLJ development could be obtained from synoptic influences • Mass divergence associated with exit region of upper jet leads to rising motion • By mass continuity, LLJ develops. • Stronger synoptic setups resulted in stronger LLJs • Uccellinni(1980): Classified LLJs based on synoptic environment • LLJs were produced by the inertial oscillation (sfc heating and terrain influences) • Strong lee cyclogenesis, upper level jet streaks, and jet coupling allowed for powerful LLJs to develop

6. Background • Numerous, more recent studies attempted to investigate the LLJ (Mitchell et al. 1995, Whiteman et al. 1997, Song et al. 2005) • Many previous works empirically identified relationships between LLJ and other phenomena (boundary layer, convective processes) • No LLJ works have quantified the relationships btw LLJ and other atmospheric features (particularly in a forecast setting) • Can we forecast the LLJ and with accuracy? How can that be done?

7. Research Questions • Would higher 3D spatial simulations of LLJ and MCS activity improve forecasts? • Could such forecasts be validated? • LLJ observations are very limited (0000 and 1200 UTC radiosondes and few vertical profilers available) • Quality of vertical profilers (including high powered) is questionable [ruc.noaa.gov/ruc13_docs/RUC13ppt.htm] • What could we use in place of in-situ data? • *Would correlations in forecast skill between LLJ parameters and MCSs become apparent?* • *Which LLJ parameters would be most important*

8. Data and Output • STAGE IV 4km gridded data (6-hrly) for MCS precipitation observations • 00-hr 13 km RUC Analysis for LLJ parameters and other atmospheric features • 12 km NAM data used to establish initial and lateral boundary conditions for high resolution modeling • Weather Research and Forecasting model (WRF) with WRF advanced research core (ARW)

9. WRF-ARW Setup • 4 km horizontal grid spacing with 50 specified eta levels (28 concentrated below 850 hPa, at 5 hPa intervals) • 36 hr forecasts at 3 hr intervals (1200 UTC prior to LLJ/MCS episode of interest) • 6 runs for each of 20 cases (120 runs total) • WSM6 and Thompson Microphysics (MP) schemes employed • Under each MP scheme, 3 planetary boundary layer (PBL) schemes were selected (MYJ, MYNN, YSU) Note: Computer power/memory issues served as a limit Time steps were also lowered significantly

10. Methodology • 6 hrly STAGEIV data re-gridded to the WRF and 6-hr accumprecip calculated in the WRF • Equitable Threat Score (ETS) used to determine skill of WRF precipitation forecasts with .254 mm (0.01 inch) threshold • ETS calculated over subdomain (filter out non-MCS induced precipitation) • Time intervals included 0000-0600 UTC, 0300-0900 UTC, 0600-1200 UTC

11. Methodology • Mean Absolute Error used to determine forecast skill of WRF LLJ properties • LLJ was defined as the 65th percentile of 200-2000 m (100 m interval) total wind magnitude • Negative v-component (northerly winds) filtered out • Other LLJ variables filtered using above method (direction and magnitude of total, geostrophic and ageostrophic wind, potential temperature, atmospheric water vapor content) • MAE times were 0300 UTC, 0600 UTC and 0900 UTC

12. Methodology • MAEs for non-LLJ variables • 700 hPa positive temperature advection • mass convergence • horizontal moisture flux convergence • mixed layer convective available potential energy • mixed layer convective inhibition • Shear (0-1 km, 0-3 km and 0-6 km) • Frontogenesis (sfc and 850 hPa) • Calculated by determining lat/lon of MCS and summing grid points + or – 500 km lat/lon • Composites (shown later) were constructed in the same manner as Cotton et al. (1989)

13. Methodology • R2 value employed for determining correlation in LLJ/MCS forecast skills • Helland (1982) and many others urged caution in using R2 outright • Significance of R2 value is relative • Pairing with p-value a good method for significant R2 values • Study the scatterplots

14. Methodology • LLJs were classified based on synoptic environment • All cases were a nearly even distribution of dynamic LLJs (synoptic events or type C), Inertial oscillation driven events(type A) • Determined subjectively via streamline analysis and magnitude of the winds at 900 mb(10-15 ms-1) and 200 mb(15-30 ms-1) along with convergence/divergence coupling for 0600 UTC. • http://eamex.iastate.edu/publication/Type_classification_of_LLJ.pdf

15. TYPE C Case • 24 May 2007 Case (0600 UTC) Left: 900 mb plot with streamlines and filled wind magnitude contours at 10 and 15 ms-1. Right: Same as left but at 200 mb for contours of 15 and 30 ms-1, respectively.

16. Red contours are negative divergence (10-5 s-1) at 900 mb. Blue contours are positive divergence at 200 mb.

17. TYPE A Case • 09 August 2010 Case (0600 UTC) • Left: 900 mb plot with streamlines and filled wind magnitude contours at 10 and 15 ms-1. Right: Same as left but at 200 mb for contours of 15 and 30 ms-1, respectively.

18. Red contours are negative divergence (10-5 s-1) at 900 mb. Blue contours are positive divergence at 200 mb.

19. LLJ RESULTS (RUC ANALYSIS)

20. LLJ behavior • LLJ peak wind slopes with height (case example 24 May 2007 at 0900 UTC via RUC Analysis)

21. 200 m

22. 600 m

23. 1000 m

24. 1400 m

25. Model Moist Bias Check Model moist biases often a concern (Mason 1989; Hammil 1999; Mesinger 2008 Weight given to the model Models considered to have a moist bias when bias ≥ 1.0 Moist biases were not common in ETS evaluations (for all runs at all time intervals)

26. Positive Correlations in forecast skill (between llj parameters and mcs precipitation) Geostrophic Wind Direction

27. Positive Correlations in forecast skill (between llj parameters and mcs precipitation) R2 values always positive, but negative slopes desired Greater MCS precip ETS with lesser LLJ variable MAE

28. RUC Analyses Composites

29. WSM6 MYJ WRF Composites

30. RUC Analysis Thompson MYJ Thompson MYNN Thompson YSU WSM6 MYJ WSM6 MYNN WSM6 YSU

31. RUC Analysis Thompson MYJ Thompson MYNN Thompson YSU WSM6 MYJ WSM6 MYNN WSM6 YSU

32. Observed Thompson MYJ Thompson MYNN Thompson YSU WSM6 MYJ WSM6 MYNN WSM6 YSU

33. Geostrophic wind and the LLJ • Diurnal Geostrophic wind establishes orientation and structure of the LLJ • Geostrophic wind magnitude forecast skill and MCS precip forecast skill did not correlate • Relation to structure of diurnal geostrophic wind field and nocturnal precipitation (Augustine and Carcarena 1994) • Correlations help quantify the LLJ/MCS relationship • Correlations and composites show that MCS precip is dependent on geostrophic wind fields • Relationship persevere independent of LLJ magnitude/type in forecasts

34. Positive Correlations in forecast skill (between llj parameters and mcs precipitation) Ageostrophic Wind Direction

35. Positive Correlations in forecast skill (between llj parameters and mcs precipitation)

36. 2100 UTC

37. 0000 UTC

38. 0300 UTC

39. 0600 UTC

40. 0900 UTC

41. 1200 UTC

42. AGeostrophicwind and the LLJ • The Ageostrophic component induces divergent flow • Effect most prevalent at LLJ terminus • Veering of LLJ winds reduces convergence, weakening the MCS (noted in Coniglio et al. 2010) • Noted in nearly all LLJ cases in both the RUC and WRF runs • Ageostrophic direction correlations not as significant overall as geostrophic • LLJs in varying synoptic conditions with different forcing mechanisms may be the cause for lack of correlations

43. Positive Correlations in forecast skill (between llj parameters and mcs precipitation) Potential Temperature

44. Positive Correlations in forecast skill (between llj parameters and mcs precipitation)

45. RUC Analyses Composites

46. WSM6 MYJ WRF composites

47. RUC Analysis Thompson MYJ Thompson MYNN Thompson YSU WSM6 MYJ WSM6 MYNN WSM6 YSU

48. RUC Analysis Thompson MYJ Thompson MYNN Thompson YSU WSM6 MYJ WSM6 MYNN WSM6 YSU

49. Observed Thompson MYJ Thompson MYNN Thompson YSU WSM6 MYJ WSM6 MYNN WSM6 YSU

50. Thermodynamics and the LLJ • Early thermodynamic state of parcels in LLJ have greatest impact on early/mature MCS precip prediction • Developing LLJ advects air mass favorable for convection northward • Minimal convective inhibition early on allows for sfc based convective initiation in many cases • Still, no correlations existed between forecast skills of stability parameters and MCS precip • Thompson WRF runs had higher MLCIN and less precip