Research Methods for Working with Helsinki Testbed Data

1. Research Methods for Working with Helsinki Testbed Data Including Class Project Ideas!!!!

2. Synoptic and Mesoscale Analysis Describe weather patterns, structures, evolutions. Get at processes responsible for structures and observed weather.

3. Nonclassical Cold-Frontal Structure Caused by Dry Subcloud Air in Northern Utah during IPEX David M. Schultz and Robert J. Trapp CIMMS and NSSL, Norman, Oklahoma October 2003 Monthly Weather Review and Manuscript in Preparation

4. • Oasis (NSSL4) Map of Utah

5. NSSL4 time series • temperature drops nearly 8°C in 8 minutes • pressure rises 20 minutes before temperature drops • wind changes direction in concert with pressure rise • RH increases after frontal passage • RH decreases and temperature rises two hours after frontal passage

7. IPX8 North to south station time series IPX2 SNH CFO PVU rate of temperature drop decreases as front moves south, although total temperature drop is nearly constant

8. Snowbasin time series temperature drop occurs earlier with height postfrontal temperature rise decreases with height

9. Temp change as a function of height

10. orographically unfavorable orographically favorable Precipitation decreases linearly with height below cloud base. Precipitation is nearly constant above cloud base. Orographic influences are greatest above cloud base.

12. Summary • Forward-sloping cloud with mammatus and superadiabatic layer underneath indicates importance of subcloud sublimation. • Cooling aloft precedes that at surface • Pressure trough precedes front at surface • Destabilization of prefrontal environment • Dry subcloud air promotes strong cooling

13. Types of Potential Testbed Projects • Case study of sea-breeze • Case study of fronts or severe weather • Case study of air-quality episode

15. Climatology and Composites(and a little bit of statistics) Describe long-term weather (climate) patterns. Composites (average) represent the typical pattern associated with the weather phenomenon in question Regression models are used to predict relevant observational quantities for forecasting.

16. Intraseasonal Variability of the North American Monsoon in Arizona (Will it Boomer Sooner or Later?) Pamela Heinselman Dissertation Seminar 14 October 2003

17. Bursts & Breaks Today’s weather • Forecast Challenges: • Where will storms initiate over elevated terrain? • Will storms develop over the mountains only, or over Phoenix as well? Central Mountains

18. Goals Advance our understanding of the intraseasonal variability of diurnal storm development and atmospheric environment in Arizona during the NAM • 1. Do storms tend to initiate and evolve repeatedly over similar regions? • 2. What environmental conditions are related to diurnal storm development? • 3. How do storm development, Phoenix soundings, and synoptic-scale flow evolve on a daily basis?

19. Data: July – August 1997 & 1999 Central Mountains Radar Rawinsonde

20. 1. Do storms tend to initiate and evolve repeatedly over similar regions? • Composite radar reflectivity mosaics • JulyAugust 1997 & 1999 WSR-88D reflectivity data from Phoenix and Flagstaff mapped to 1-km Cartesian grid every 10 min ( 112/124 days) • 1-km digitized terrain data • Variability in storm development is investigated subjectively by observing the diurnal evolution of hourly composite radar reflectivity mosaics • Illustrate similarity in regions where storms tend to develop by calculating diurnal relative frequencies of radar reflectivity  25 dBZ for days comprising each pattern

21. 1. Do storms tend to initiate and evolve repeatedly over similar regions? • YES! • Reflectivity Regimes include: • Dry (DR) • Eastern Mountain (EMR) • Central–Eastern Mountain (CEMR) • Central–Eastern and Sonoran Desert (CEMSR) • Non-Diurnal (NDR) • North-moving (11 events or 46%) • East-moving (7 events or 29%) • West-moving (6 events or 25%) • Unclassified (UNC)

22. Eastern Mountain Relative frequency of reflectivity  25 dBZ 1820 UTC (1113 LST) 2200 UTC (1517 LST) N=11 or 9 % % 0204 UTC (1921 LST) 0608 UTC (23 01 LST) July−August 1997 & 1999

23. Central–Eastern Mountain Relative frequency of reflectivity  25 dBZ 1820 UTC (1113 LST) 2200 UTC (1517 LST) N=39 or 31.5 % % 0204 UTC (1921 LST) 0608 UTC (23 01 LST) July−August 1997 & 1999

24. % Central–Eastern Mountain & Sonoran Relative frequency of reflectivity  25 dBZ 1820 UTC (1113 LST) 2200 UTC (1517 LST) N=20 or 16 % 0204 UTC (1921 LST) 0608 UTC (23 01 LST) July−August 1997 & 1999

25. % Non-Diurnal Relative frequency of reflectivity  25 dBZ 1820 UTC (1113 LST) 2200 UTC (1517 LST) N=24 or 16 % 0204 UTC (1921 LST) 0608 UTC (23 01 LST) July−August 1997 & 1999

26. 2. What synoptic-scale conditions are related to each reflectivity regime? • NEXT: • Composite 500 mb maps

27. Dry Regime 500-mb Geopotential Height 500-mb Specific Humidity • Composite maps from CDC website, constructed using NCEP reanalysis data (N=13) • Pattern similar to breaks and pre-monsoon conditions

28. Eastern Mountain Regime 500-mb Geopotential Height 500-mb Specific Humidity • Composite maps from CDC website, constructed using NCEP reanalysis data (N=11) • Pattern similar to monsoon boundary (Adang and Gall 1989)

29. Central–Eastern Mountain Regime 500-mb Geopotential Heights 500-mb Specific Humidity • Composite maps from CDC website, constructed using NCEP reanalysis data (N=39) • Westward expansion of subtropical anticyclone / meridional moist axis

30. Central–Eastern Mountain & Sonoran Regime 500-mb Geopotential Heights 500-mb Specific Humidity • Composite maps from CDC website, constructed using NCEP reanalysis data (N=20) • Subtropical ridge builds northwestward southeasterly flow • More moist at 500 mb

31. Non-Diurnal Regime 500-mb Geopotential Heights 500-mb Specific Humidity • Composite maps from CDC website, constructed using NCEP reanalysis data (N=24) • Numerous shortwave troughs, not seen in composites • Meridional moist axis extends farther west and north

32. 2.6 Synoptic and Mesoscale Influences on West Texas Dryline Development and Associated Convection Christopher Weiss Texas Tech University, Lubbock, TX David Schultz National Severe Storm Laboratory/CIMMS Norman, OK

33. West Texas Mesonet • West Texas Mesonet (WTM) has been steadily growing since its inception in 2002. • As of early October, a total of 49 stations are operational across the Texas Panhandle. • Now possible to perform multi-year climatological analysis of features routinely observed in West Texas, including drylines.

34. Our Understanding of Dryline Structure and Propagation Vertical Mixing of Heat/Momentum + Terrain Slope Synoptic-Scale Forcing Land-Use / Soil Moisture Gradients “Internal” Solenoidal Circulations

35. Our Understanding of Dryline Structure and Propagation Vertical Mixing of Heat/Momentum + Terrain Slope Synoptic-Scale Forcing Land-Use / Soil Moisture Gradients “Internal” Solenoidal Circulations

36. GOALS: To resolve the dependency of dryline intensity on the background synoptic pattern To identify pertinent synoptic and mesoscale forcing factors for dryline convection initiation and mode Our Understanding of Dryline Structure and Propagation Synoptic-Scale Forcing

37. Dryline Case Selection Period of study April-June 2004-2005 Domain WTM A dryline case satisfied the following criteria: • An eastward directed dewpoint-gradient (DTd)at 1800 LT • DTd exceeded 1 oC, corresponding to a constant mixing ratio at stations MORT and PADU (different elevation) • No contribution to DTd from convective outflow or a frontal boundary • DTd increased between 0700 LT and 1800 LT • A deceleration in eastward propagation / acceleration of westward propagation was evident near and after 1800 LT • Most of the dewpoint gradient (per regional observations) was contained within the WTM domain (subjective) PADU MORT

38. Method • 64 dryline cases identified • Cases ranked by intensity (DTd) • Upper quartile of cases classified as “strong” (16) • Lower quartile of cases classified as “weak” (16) • Synoptic composites generated using data from the NCAR/NCEP Reanalysis (available at http://www.cdc.noaa.gov)

39. Dryline Intensity vs. Confluence(all cases, WTM domain scale) • Clear correlation between • WTM-scale dryline intensity • and confluence • However, significant • outliers exist. Conclusion: • Confluence within scale of WTM domain width • Variations in duration/strength of confluence • Other processes involved in forcing (Schultz et al. 2006)

40. 500 mb Geopotential Height WEAK STRONG (Schultz et al. 2006)

41. Sea Level Pressure WEAK STRONG (Schultz et al. 2006)

42. Dryline Convection • Logistic regression (stepwise selection) employed to find pertinent forcing for convection initiation and mode. • Potential regressors collected from: Logit Function (Ryan 1997) WTM PADU MORT

43. More Potential Regressors NCEP/NCAR Reanalysis WTM Domain Gridpoint Locations

44. Regression Models(12 total, 6 at position “E”, 6 at position “W”)

45. Results

46. Results • As expected, lower tropospheric specific humidity is a prominent • factor in generation of moist convection.

47. Results • As expected, stronger zonal momentum figures prominently in the • occurrence of dryline-associated tornadic storms.

48. Results • Generally, large low-mid tropospheric lapse rates favor LFC • attainment near initiation point, and severity of convective • development downstream.

49. Results • Deeper-layer (T850-T500) and shallower-layer (T700-T500) lapse rates • do explain separate variance occasionally (Griesinger and Weiss, 1.5).

50. Results 5) Dryline “strength” significant in determining intensity of resultant convection.