CLASSIFICATION OF MESOSCALE SNOW BANDS IN THE NORTHEAST UNITED STATES.

1. CLASSIFICATION OF MESOSCALE SNOW BANDS IN THE NORTHEAST UNITED STATES. Norman Shippee Plymouth State University

2. Overview • Objectives • Background • Data and methodology • Results • Synoptic Composites • Predictors from composite analysis • Conclusions • Suggestions for future work • Questions

3. Objectives • Classify types of mesoscale snow banding in winter storms occurring in the Northeast. • Utilize independent predictors, such as topographic data and local meteorological features, to predict the type and location of mesoscale banding (if any) before it occurs.

4. Objectives • Develop a climatology of snow banding events in the domain based upon banding type

5. Objectives • Develop a climatology of snow banding events in the domain based upon banding type • Create composites to attempt to distinguish synoptic differences between banding classes

6. Objectives • Develop a climatology of snow banding events in the domain based upon banding type • Create composites to attempt to distinguish synoptic differences between banding classes • Use predictors to attempt to predict the type of band and location such as: • Existence of a Coastal Front • Cross-shore θ gradient • Cross-shore topographic slope • Topographic height • Frontogenesis

7. Background • Areas of heavier precipitation embedded in winter storms. • These areas are localized, and result from mesoscale banding. • Can cause extreme rates of snowfall in a small area (1-3 inches per hour or more)

8. Background • Many studies on mesoscale snow-banding have been conducted • Mesoscale snow-banding is often handled poorly by conventional numerical modeling • Beyond 12 hours, forecasts of predictors for mesoscale banding are degraded

9. Background • Mesoscale banding occurs in approximately 85% of cyclones impacting the Northeast in the winter months • Important to understand and predict where and when banding might occur, along with how much snow might fall

10. Background – Snowfall Forecasting • Current methods describe the two-fold approach to forecasting snowfall depth • QPF  Snow amount conversion • 10 inches of snow to 1 inch of QPF (10:1)

11. Background – Snowfall Forecasting • Current methods describe the two-fold approach to forecasting snowfall depth • QPF  Snow amount conversion • 10 inches of snow to 1 inch of QPF (10:1) • Problems exist with this method • Rule was developed in 1888 • “Rule of thumb” • Misses range of density changes of snowfall • Could be seen as a hindrance to snowfall forecasts

12. Background - Ingredients • Primary ingredients needed for heavy, banded snowfall are: • Frontogenesis • Weak moist symmetric stability • Moisture • Cloud microphysics (less important) • Variables often are coupled in studies

13. Background – Model Forecasts • Both the GFS and the NAM do a poor job predicting mesoscale bands of heavy QPF (Jurewicz and Evans 2009). • Models often provide a good forecast of the potential predictors for mesoscale snow banding events up to 12 hours prior to onset. • Beyond 12 hours, model forecasts of the predictors is also degraded.

14. Background – Observing Using Radar • When viewing radar images, most mesoscale banding reflectivity returns fall within a range of reflectivities • 25-35 dBZ • Over 35 dBZ, there is potential for observation of bright banding

15. Background – Banding Classes • Banding classification scheme adopted from a paper by Novak et al (2004) • Single Band: 20-100 km in width, >250 km in length, >30 dBZ for 2 hr minimum

16. Background – Banding Classes • Single Banded example:

17. Background – Banding Classes • Single Banded example:

18. Background – Banding Classes • Banding classification scheme was adopted from a paper by Novak et al (2004) • Single Band: 20-100 km in width, >250 km in length, >30 dBZ for 2 hr minimum • Multi-Band: >3 finescale bands (5-20 in width) with periodic spacing and intensities >10 dBZ over background reflectivity

19. Background – Banding Classes • Multi-banded example:

20. Background – Banding Classes • Banding classification scheme was adopted from a paper by Novak et al (2004) • Single Band: 20-100 km in width, >250 km in length, >30 dBZ for 2 hr minimum • Multi-Band: >3 finescale bands (5-20 in width) with periodic spacing and intensities >10 dBZ over background reflectivity • Non-Banded: Meets no criteria; disorganized

21. Background - Domain of Study

22. Data & Methodology • Needed a starting point: Satellite data • Satellite images used to ID potential storms during winter months using the NCDC GIBBS Satellite Archive • Storms needed to be of “synoptic scale” on the satellite image • At least 1000 km in one dimension (Holton 2006) • This method did not identify banding events, just potential storms

23. Data & Methodology • Satellite time resolution was important • Storm could not make large jumps into or out of study domain • Minimum temporal resolution available: 3 hr • Typical synoptic storm can cover ~1200 km per day (Pidwirny 2006) • ~150 km in 3 hrs • Reasonable for the study domain size

24. Data & Methodology • Analyzed storms from the winter months (November to March) for the years of 2004 to 2009 • Storms used in this study met 24-hr snowfall warning criteria for the most southern parts of the study domain • 8 inches in 24 hours at 2 or more stations

26. Data & Methodology • After utilizing satellite images to ID potential storms, NCDC snowfall reports and decoded surface METAR reports were analyzed for 2 things • Total 24-hr snowfall depth change • Consecutive hours of snowfall (2 hr minimum, allow for 1 hr break)

27. Data & Methodology – Gridding Data • Necessary to create a gridded domain for the study to utilize numerical model data and radar data • The gridded area was defined as roughly Pennsylvania to Maine and rotated 45 degrees to align with the axis of the Appalachian Mountains

28. Data & Methodology – Gridding Data • Data were gridded on a 20 km by 20 km square grid extending from Western Pennsylvania northeastward to Maine • Grid spacing chosen in order to capture the short axis scale of a small single-band event • ~20 KM (Novak et al 2004)

29. 1898 Data Points

30. Data & Methodology - Classification • Banding identification was performed by radar imagery using WSI NOWRAD generated from the NCAR archives • Storms were classified into three classes described before: • Single-Banded • Multi-Banded • Non-Banded

31. Data & Methodology • Gridding topography data • Used the ETOPO1 dataset with horizontal resolution of 1arc-minute • Data were gridded to the 20 km spacing using a series of scripts • Greatest topographic height within a 12.071 km radial sweep of each point was assigned to the corresponding grid point

32. Data & Methodology • NCDC Radar data were also downscaled from high resolution data to the 20 X 20 km grid • Needed for regression analysis • Used maximum value within a box around each grid point to eliminate overlap • Different than the topographic height interpolation

33. Summary of Data Sources • Topographic data : NGDC ETOPO1 dataset • Satellite images: GIBBS satellite archive, available online at http://www.ncdc.noaa.gov/gibbs. • Radar data: National Climatic Data Center (NCDC) repository of NEXRAD radar data and WSI NOWRAD imagery from UCAR • Numerical model data: NARR-A 25 km resolution dataset. • Snowfall data: METAR and NCDC reports

34. Objectives • Develop a climatology of snow banding events in the domain based upon banding type • Create composites to attempt to distinguish synoptic differences between banding classes • Use predictors to attempt to predict the type of band and location such as: • Existence of a Coastal Front • Cross-shore θ gradient • Cross-shore topographic slope • Topographic height • Frontogenesis

35. Results

36. Results - Climatology

37. Results - Climatology • Single banding events dominate all events observed in the study period with 20 out of 35 (57%) of all cases • Multi-banding events were observed in 9 out of 35 cases (26%) • Non-banded storms comprised 6 out of 35 cases (17%) • Overall, 83% of cases exhibited a type of banding

38. Results - Climatology

39. Results - Climatology • Single banded events: • More dominant overall • There is a steady progression of more single banded events in the colder months, to less single banded events in the warmer months.

40. Results - Climatology • Single banded events: • More dominant overall • There is a steady progression of more single banded events in the colder months, to less single banded events in the warmer months. • Multi-banded events: • Similar progression • A peak of total number of events in the months of January and February

41. Results - Climatology • Single banded events: • More dominant overall • There is a steady progression of more single banded events in the colder months, to less single banded events in the warmer months. • Multi-banded events: • Similar progression • A peak of total number of events in the months of January and February • Non-banded events: • Least dominant • Smallest number of total events; one or two over the 5 year period in each month; none in November

42. Objectives • Develop a climatology of snow banding events in the domain based upon banding type • Create composites to attempt to distinguish synoptic differences between banding classes • Use predictors to attempt to predict the type of band and location such as: • Existence of a Coastal Front • Cross-shore θ gradient • Cross-shore topographic slope • Topographic height • Frontogenesis

43. Synoptic Composites • Synoptic composites were constructed using the NCEP/NCAR Reanalysis to analyze the potential differences in synoptic setup based upon the type of banding produced by each storm • The three classes for composite analysis: • Single banded • Multi-banded • Non-banded

44. Composites – Non-Banded Storms Sea Level Pressure for non-banded events

45. Composites – Non-Banded Storms 700mb Omega for non-banded events

46. Composites – Non-Banded Storms 500 hPa height for non-banded events

47. Composites – Non-Banded Storms 250 hPa zonal wind for non-banded events

48. Composites – Single Banded Storms L Sea Level Pressure for single banded events

49. Composites – Single Banded Storms 700 hPa omega for single banded events

50. Composites – Single Banded Storms 500 hPa height for single banded events