CLASSIFICATION OF MESOSCALE SNOW BANDS IN THE NORTHEAST UNITED STATES. Norman Shippee Plymouth State University Overview Objectives Background Data and methodology Results I. II. III. IV. I. II. V. VI. VII. Synoptic Composites Predictors from composite analysis Conclusions Suggestions for future.
Download ReportTranscript CLASSIFICATION OF MESOSCALE SNOW BANDS IN THE NORTHEAST UNITED STATES. Norman Shippee Plymouth State University Overview Objectives Background Data and methodology Results I. II. III. IV. I. II. V. VI. VII. Synoptic Composites Predictors from composite analysis Conclusions Suggestions for future.
CLASSIFICATION OF MESOSCALE SNOW BANDS IN THE NORTHEAST UNITED STATES. Norman Shippee Plymouth State University Overview Objectives Background Data and methodology Results I. II. III. IV. I. II. V. VI. VII. Synoptic Composites Predictors from composite analysis Conclusions Suggestions for future work Questions 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. Objectives 1. Develop a climatology of snow banding events in the domain based upon banding type Objectives 1. 2. 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 Objectives 1. 2. 3. 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 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) 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 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 Background – Snowfall Forecasting Current methods describe the two-fold approach to forecasting snowfall depth Snow amount conversion 10 inches of snow to 1 inch of QPF (10:1) QPF Background – Snowfall Forecasting Current methods describe the two-fold approach to forecasting snowfall depth Snow amount conversion 10 inches of snow to 1 inch of QPF (10:1) QPF 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 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 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. 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 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 Background – Banding Classes Single Banded example: Background – Banding Classes Single Banded example: 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 Background – Banding Classes Multi-banded example: 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 Background - Domain of Study Data & Methodology Needed a starting point: Satellite data Storms needed to be of “synoptic scale” on the satellite image Satellite images used to ID potential storms during winter months using the NCDC GIBBS Satellite Archive At least 1000 km in one dimension (Holton 2006) This method did not identify banding events, just potential storms 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 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 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 hr break) hours of snowfall (2 hr minimum, allow for 1 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 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) 1898 Data Points 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 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 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 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 Objectives 1. 2. 3. 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 Results Results - Climatology Type Number of Cases Percentage of Cases [%] Non-Banded 20 57% Single Banded 9 26% Multi-Banded 6 17% Totals: 35 100% 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 Results - Climatology 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. 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 Results - Climatology Single banded events: Multi-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. 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 Objectives 1. 2. 3. 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 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 Composites – Non-Banded Storms Sea Level Pressure for non-banded events Composites – Non-Banded Storms 700mb Omega for non-banded events Composites – Non-Banded Storms 500 hPa height for non-banded events Composites – Non-Banded Storms 250 hPa zonal wind for non-banded events Composites – Single Banded Storms L Sea Level Pressure for single banded events Composites – Single Banded Storms 700 hPa omega for single banded events Composites – Single Banded Storms 500 hPa height for single banded events Composites – Single Banded Storms 250 hPa zonal wind for single-banded events Composites – Multi-Banded Storms Sea Level Pressure for multi-banded events Composites – Single Banded Storms 700 hPa omega for multi-banded events Composites – Multi-Banded Storms 500 hPa height for multi-banded events Composites – Multi-Banded Storms 250 hPa zonal wind for multi-banded events Single Banded Multi-Banded Non-Banded Comparison of Composites Center of rising motion: Single banded and Multi Banded cases suggest the storm is still developing Non-banded cases are vertically stacked Sea level pressure: Single and Multi banded events have very different orientation of isobars, creating different flow over the domain Comparison of Composites Single Banded Multi-Banded Non-Banded Comparison of Composites 500 hPa trough location suggests that single and multi banded events have sufficient amounts of positive vorticity advection (cyclonic vorticity advection) 250 hPa jet cores put the single banded and multibanded composite lows in a favorable area for upper level support Non-banded events are in an area unfavorable to storm development Objectives 1. 2. 3. 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 Potential Predictors from Composites From the composite analysis, there were some potential predictors determined Certain patterns (single banded cases) suggested the existence of a coastal front in the storms producing banding Predictors from Composites The following predictors were tested in this study: Existence of a Coastal Front Cross-shore θ gradient Cross-shore topographic slope Topographic height Cross-shore frontogenetic convergence Role of the Coastal Front Type Cases w/ CF Cases w/ no CF % of total with CF % of total without CF Single-Banded 11 9 31% 26% Multi-Banded 3 6 9% 17% Non-Banded 2 4 6% 11% Totals 16 19 46% 54% Results from Testing Predictors Highest rate of coastal front occurrence is with single-banded cases In the presence of a coastal front, single-banded cases dominate Also Multi- dominate overall and Non-banded events are more likely to occur without a coastal front present Suggests coastal front is more likely to develop in single-banded cases than in multi- or non- banded cases Best Subsets Regression In order to determine the best predictors of the linear reflectivity values, a best subsets regression analysis was completed for each of the banding cases Three variables were used in this analysis: Cross-shore potential temperature gradient (THTA) Cross-shore topographic slope (SLOPE) Cross-shore frontogenetic convergence (FRNT) Results for single-banded cases are shown Best subsets for Single-Banded Events Best Subsets Regression Each case (single-, multi-, and non-banded) showed similar results for the analysis Multi- and Non-banded cases showed higher R-squared values, though lacked in the number of cases Possible issues that resulted in the poor results will be discussed at the end Nonetheless, linear regression was attempted for each case Linear Regression Once again, results from single-banded cases will be shown The best-subsets regression suggested the use of all three variables would generate the best linear regression equation for prediction of banding Linear Regression Linear Regression The null hypothesis for all banding types was that the linear reflectivity (Z) is not a function of the three tested variables Single- and Multi-banded events suggest there is a small probability that the results of the regression analysis hold some predictive ability It was still important to attempt to use the linear regression equations to predict the occurrence of banding Testing the Linear Regression Equation Results of the LR Equation Test Results were unexpected All grid points produced a value greater than 30 dBZ, suggesting the entire domain would be experiencing banding These variables alone are not enough to work in this case. Conclusions There are significant synoptic differences between each of the banding cases Composite analysis showed differences in structure and potentially age of the storm The coastal front has some influence on banding type Conclusions More variables are needed to predict the occurrence and location of banding events Regression analysis performed poorly in all cases, suggesting the need for changes Suggestions for Future Work Largest source of error: Grid resolution Need to increase the grid resolution in the study 20 X 20 km to coarse to resolve small scale features that influence banding Create subset grids Based on RDA location or geographic location Increase number of grid points in each subset grid Inter-comparison of radar data from multiple RDA locations Parallax error Differences in event classification Suggestions for Future Work Other statistical tests F-test and T-test Bias and residuals Stratification of frontogenesis levels to increase vertical resolution of the banding structure Determine formation the vertical layer most associated with band Suggestions for Future Work Utilize additional statistical methods Classification and Regression Tree (CART) models and Principle Component Analyses (PCA) Different proxy for banding occurrence or snowfall depth Determine the best threshold values for predictors Predictability of each banding type QPF Higher resolution model data NARR to coarse NAM-WRF or RUC Limited by the availability of the model data for the time period studied Acknowledgements Deepest thanks to my thesis committee: Dr. Sam Miller, Dr. James Koermer, and Mr. Toby Fusco Thanks to all of the faculty here: Dr. Eric Hoffman, Dr. Lourdes Aviles, Dr. Joseph Zabransky, and Mr. Brendon Hoch Thanks to my family and friends, fellow graduate students, and my fiancée Katie References Abbe, C., 1888: Appendix 46. 1887 Annual report of the Chief Signal Officer of the Army under the direction of Brigadier-General A. 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