Analysis of NAM Forecast Wind Shear for Dissipation of Mesoscale Convective Systems

Transcription

1 Analysis of NAM Forecast Wind Shear for Dissipation of Mesoscale Convective Systems MATTHEW P. HOFFMAN Meteorology Program, Iowa State University, Ames, IA Mentor: David Flory Department of Geological and Atmospheric Sciences Iowa State University, Ames, IA ABSTRACT Forecasting the dissipation of mesoscale convective systems (MCS) continues to be a difficult weather phenomenon to forecast. Analysis of observed MCSs has found that deep layer shear significantly drops before dissipation. In this study, MCSs were collected from the months of May through August for the years of 2007 through 2009 in the Midwest. The 40 km WRF-NMM was used to verify the existence of each observed MCS based on three hour precipitation. Mean wind shear was calculated for low level, mid-layer, and deep layer shear for various stages of the MCS lifetime. The resulting data was analyzed for a significant drop in the average wind shear within the nine hours preceding dissipation, and statistical analysis was used to determine if the drop off in mean wind shear was significant. Results show that the drop off in mean wind shear in a deep layer is in fact present in the model along with a drop off in wind shear in the low level layer, but the drop off is greater in magnitude for mean deep layer wind shear. 1. Introduction The mesoscale convective system (MCS) continues to be a complex weather phenomenon to forecast. MCSs can bring with them heavy rains and severe weather such as high winds, hail, and tornadoes. There has been a large amount of research done on MCS structure (Rotunno et al. 1988, Weisman and Rotunno 1988) to analysis of observed data like soundings, profiler data, and also numerical model output to understand more about the lifecycle of an MCS and its interaction with its environment (Congilio et al. 2007, Cohen et al. 2007, Congilio et al. 2006, and Gale et al. 2002). Before the lifecycle of an MCS could be determined, an MCS had to be characterized. Cohen et al. (2007), Congilio et al. (2006), and Congilio et al. (2007) looked for systems that were 100 km in length, lasted for at least five hours, and had nearly a continuous quasi-linear or bowed leading edge of at least 35 dbz reflectivity. By utilizing observed MCSs, Cohen et al. (2007) and Congilio et al. (2007) defined an MCS lifecycle in three distinct groupings. Initiation was defined as the existence of the initial cells prior to the development of an MCS. The Mature stage occurred when the MCS had strengthening or quasi-steady high reflectivity of 50 dbz or higher within a continuous line of greater than 35 dbz

2 reflectivity. Dissipation was characterized by significantly weakening or shrinking areas of high reflectivity or loss of system organization and associated areas of high reflectivity without any re-intensification. Gale et al. (2002) went a step further to define dissipation of an MCS to be when all convective or heavy stratiform echoes of greater than 35 dbz were gone leaving only areas of stratiform precipitation with echoes of at most 35 dbz. Congilio et al. (2007) also looked at proximity soundings for hundreds of events and found that for forecasting MCSs, forecasters would need to utilize the integration of the parameters from soundings over a large depth of the convective layer. They concluded the mean vertical wind shear over a deep layer, such as 0-10 km is a better discriminator between a mature and dissipating MCS rather than lower level shear. The statistical differences were very large and have been confirmed by Cohen et al. (2007) and their research. Cohen et al. (2007) observed that 0-10km shear takes into account both low level and upper level shear and is a better judge of MCS intensity than either low level or upper level shear by themselves. Cohen et al. (2007) also found that by looking at 0-10 km shear they could find the best environment for the MCS to produce severe winds. While these studies looked at observed MCSs, this study s objective is to analyze how significant mean layer wind shear is through a low, middle, and deep layer from WRF-NMM (NAM) output based on observed MCS events. In this paper, I hypothesize that the North American Mesoscale model (NAM) will fail to significantly predict MCS dissipation based on the observed reduction of mean deep layer wind shear or show a drop off in wind shear as the MCS gets closer to dissipation. 2. Data and Method Observed MCSs that had either spent the majority of their lifetime or initiated in the Midwest were collected using the radar composites provided by the UCAR (University Corporation for Atmospheric Research) Image Archive. The most recent version of the NAM, the 40 km WRF-NMM, which became operational on June 22, 2006, was utilized for this project. MCSs were collected from the months of May to August for the years of 2007, 2008, and MCSs were defined by the following requirements derived from previous work by Gale et al. (2002), Cohen et al. (2007), and Congilio et al. (2007): 1) Continuous line at least 100 km in length, 2) Lifetime of at least five hours, and 3) Leading edge of the continuous, quasi-linear line having reflectivity values of greater than 35 dbz. Once an MCS was identified, the time, in Zulu time, and the general location were collected for five different stages of the MCS lifetime. Initiation was defined by when the initial convection first appeared that would eventually result in creating an MCS. MCS stage was defined by the criteria shown above. The MCS was considered Mature when the MCS became most organized and had embedded reflectivity values within the leading edge of 50 dbz or greater. The next stage was the end of the Mature stage, which occurred when the MCS lost reflectivity values of 50dBZ or greater within the leading edge of the MCS. Finally, the Dissipation stage occurred when all convective and/or heavy stratiform precipitation of reflectivity that was greater than 35 dbz was no longer present, and when, at most, all that remained was a disorganized area of light, stratiform precipitation of reflectivity values that were 35 dbz or less. Over the span of those twelve months where data was collected, 129 MCSs were found. The next step was to verify that the model was seeing the MCS based on three hour precipitation data. Model data was retrieved from Iowa State University s mtarchive data server. The 12Z run of the 40 km WRF-NMM model from the day prior to the initiation of the observed MCSs were analyzed, and an MCS was either accepted or rejected based on the following criteria: 1) MCS initiated within six hours of the observed MCS, 2) MCS s initiation occurred approximately in the same vicinity of the observed MCS locale, 3) MCS was at least 100 km in length, and 4) Leading edge had

3 precipitation rates of at least four inches over three hours. Wind shear data was recorded for six different stages of the model MCS lifetime. Those stages were Initiation or when the first precipitation formed, Mature when the MCS had precipitation rates of at least four inches over three hours, 9 Hours before dissipation, 6 Hours before dissipation, 3 Hours before dissipation, and Dissipation, which occurred when precipitation rates were less than four inches over three hours. Of the 129 observed MCSs, 56 MCSs were verified by the 40 km WRF-NMM. The values of mean wind shear in knots, hour in the model run, and time of day, in Zulu time, were recorded for the three levels in the atmosphere. Pressure levels where substituted for heights in determining the three layers. Low level shear was defined by the wind shear between 1000mb and 850mb. Mid-layer shear was the wind shear between 1000 mb and 500 mb, and deep layer shear was the wind shear between 1000mb and 300mb. For each stage in the lifetime of the MCS, the average wind shear was calculated for each of the levels at approximately 100 km in front of the leading edge of the model MCS, which encompassed the length of the model MCS. The direction in which average wind shear was taken was determined by the direction out in front of the model MCS propagation. MCSs were then divided into categories based on the time of day that they initiated either 0-6Z or 15-21Z. The 12Z run of the WRF-NMM from the day before the observed MCS initiated was taken for all cases. In order to compare multiple runs of the WRF-NMM, MCSs that initiated the earliest between 15-21Z the day before were identified and wind shear data was collected by the 12Z model run for the day of initiation of the observed MCS. 3. Statistical Analysis Data analysis for this study was divided into three parts. The first part was analyzing the low level, mid-layer and deep layer mean wind shear for all 56 MCS events. The second part was dividing those 56 MCS events into MCSs that initiated early between 15-21Z or initiated late between 0-6Z. There were 31 late cases and 25 early cases. Finally, all of the early cases were used in analyzing the wind shear data based on both the 12Z model run the day of initiation and then the 12Z model run the day before initiation. For all three of these parts the analysis was essentially the same. The wind shear data for the low level, mid-layer and deep layer shear were turned into box plots in an effort to view a significant drop in wind shear qualitatively (Fig. 1-9). In order to view what combination of time periods and shear layers had a statistical drop in mean wind shear and what was the magnitude of that drop, a paired t- test was performed via Paired T test t = d d x σ n which was the mean of the wind shear difference over the quantity of the standard deviation of the wind shear differences over the square root of the number of cases. In order to get the mean wind shear difference, the wind shear difference had to be found for each case d d x = W d W x (1) (2) by subtracting each mean wind shear value at dissipation (subscript d in Eq. 2) from the three, six, or nine hour (subscript x in Eq. 2) wind shear value and the mean was found. The test resulted in a p-value and a mean difference of wind shear for each combination of layers and times (Appendix A). A smaller p-value meant that it was more likely that there was a drop or difference in mean wind shear between two time periods, specifically dissipation minus either the nine, six, or three hour time period. When a p- value was deemed to be of a higher significance, the comparison of the mean differences could be examined to detail the magnitude of the drop off or difference in shear between dissipation and one of the three time periods. A color coded chart showing the significance of the p-value is available in Table A1.

4 4. Results a. Analysis of All Cases i) Low Level Wind Shear Based on all 56 cases collected, the mean low level wind shear does show a drop off at all three time periods leading up to dissipation (Fig. 1). P-values for all three differences are considered highly significant meaning a drop off can be concluded (Table A2). This also can be seen by the mean difference being less than zero for each time difference. The difference decreased as it got closer to dissipation with p- values slightly increasing (Table A2). The box plots of mean low level wind shear show a decrease from nine hours before dissipation to dissipation itself (Fig. 1). Based on the IQR (Inter-quartile Range) based on the highly significant p-values and mean differences of wind shear. ii) Mid-Layer Wind Shear Mid-layer wind shear showed larger p- values for all times compared to low level and deep layer wind shear (Table A2). The D-9 p- value was considered non-significant and the mean difference was very small being less than negative one (Table A2). Starting at D-6, p- values did drop leading them to be considered in the significant category with the mean difference of wind shear getting marginally larger at D-6 and then falling back again at D-3 (Table A 2). The mid-layer box plot shows (Fig. 2) much more of a steady state of the IQRs with a slight decrease after six hours before dissipation. Based on this evidence, the midlayer wind shear does not appear to exhibit a mean wind shear drop off before dissipation. Figure 1. Mean low level wind shear (850mb- 1000m at each stage of the MCS lifetime for all 56 MCS cases. Figure 2. Mean mid-layer wind shear (500mb- of each box, the drop off appears to be most pronounced going from the nine hour to six hour period before dissipation as seen in Fig. 1, and that corresponds to the largest difference occurring between the D-9 and D-6 mean wind shear differences (Table A2). D-9, D-6, and D-3 are referring to the time frames of dissipation minus 9 hours, 6 hours, and 3 hours before dissipation, respectively, and are used to get the mean wind shear difference and p-values from the paired t-test (Appendix A). In this study, the low level wind shear showed at least a modest drop off, especially 1000m at each stage of the MCS lifetime for all 56 MCS cases. iii) Deep Layer Wind Shear The p-values are highly significant with a mean difference much larger than either low level or mid-layer wind shear (Table A2). An interesting side note, which occurs sporadically throughout the data, can be seen by the values between D-9 to D-6 (Table A2). As opposed to D-9, the mean difference at D-6 is smaller, yet the p-value for D-6 is a bit smaller. This suggests that among the different cases there is

5 less of a standard deviation at the D-6 time period and the wind shear data points have less of a spread. This is evident in the deep layer box plot when comparing the nine hours IQR to the smaller six hours IQR (Fig. 3). Figure 3. Mean deep layer wind shear (300mb- 1000m at each stage of the MCS lifetime for all 56 MCS cases. The D-3 p-value is marginally significant with a much smaller mean difference to be noted. The largest drop off of wind shear occurred between six hours to three hours before dissipation (Table A2). This can also be seen based on the box plots and their change in IQR (Fig. 3). Based on the larger mean differences and the greater drop off it does appear that the deep layer does, in fact, not only show a drop off in mean wind shear but is easier to distinguish when compared to low level or mid layer shear. This agrees with Congilio et al. (2007) that stated deep layer wind shear is a better discriminator between mature and dissipated MCSs. both early and late initiation cases (Table A3, A4). Something to note in considering the low level wind shear was the change in the p-values for the D-3 time period going from early to late initiation (Table A3, A4). For the early initiation cases, the p-value is marginally significant with a very minimal mean difference, so both the likelihood and magnitude of a drop off is minimal (Table A3). However, when looking at the D-3 time period for late initiation cases, the p-value is highly significant with a larger drop (Table A4). When comparing the change of mean wind shear differences between the three time periods before dissipation, the drop off in wind shear seems quite gradual. Examining the box plot (Fig. 4 for late initiation cases, there is a definite drop off between the three hour and dissipation time periods based on the drop of their IQRs. D-3 is also where we see a highly significant p-value (Table A4). b. Initiation Difference Cases Based on Time of Day MCSs were divided based on whether they initiated early between 12Z to15z in the model run or late in the model run between 0 to 6Z. For low level wind shear, the early initiation times favored a greater potential of drop offs in wind shear and also with a greater magnitude of a drop off. The p-value is smallest and highly significant for the early initiation D-9 time period (Table A3). D-9 also has the largest mean difference in wind shear of any time period of Figure 4. Mean low level wind shear for MCS cases where initiation occurred between 15-21Z Early and 0-6Z Late.

6 Mid-layer wind shear did perform somewhat better for late initiation cases. The p-values went from non-significant to marginally significant for the late cases (Table A3, A4) with an increase of the mean difference as well. However, based on these higher p-values, midlayer wind shear really cannot be attributed to distinguishing greater drop offs in wind shear between early and late initiation. By examining the box plots for mid-layer shear, there is no clear drops offs of the IQRs leading up to dissipation (Fig. 5). smaller and have larger mean differences implying that the early initiation MCSs show greater drop offs in mean wind shear leading up to dissipation (Table A3). The box plots show a very nice downward trend and drop of IQRs for early initiation cases in comparison to late initiation cases where there is a drop off before three hours, but then there is an increase of the IQRs going from three hours to dissipation (Fig. 6). Interestingly, for both early and late initiating cases we see the indications of the mean wind shear showing less of a spread between D-9 and D-6 (Table A3, A4). No other layer consistently showed this in the data that was collected. Figure 5. Mean mid-layer wind shear for MCS cases where initiation occurred between 15-21Z Early and 0-6Z Late. Deep layer wind shear proved to be the best layer for observing a drop off in wind shear especially for the D-9 and D-6 time periods for both early and late cases (Table A3). P-values are highly significant for both early and late initiation cases during D-9 and D-6 (Table A3, A4). However, the early initiation p-values are Figure 6. Mean deep layer wind shear for MCS cases where initiation occurred between 15-21Z Early and 0-6Z Late. Based on the analysis of data between early and late initiation cases there seems to be a

7 clearer and more significant drop off in mean wind shear for early initiating MCSs within the model, especially in the deep layer. c. Model Run Differences of MCS Cases From all 25 early initiation cases, meaning initiation times between 12-15Z in the model run, wind shear data was collected and compared from the 12Z model run the day before initiation to the 12Z model run the day of initiation. The low level wind shear had nonsignificant p-values for all three time periods for the day before initiation (Table A5). The mean differences were higher for the day of initiation time periods and both the D-9 and D-6 time periods had highly significant p-values (Table A6). The box plots as well show that low level wind shear had a greater drop off in wind shear for the model run the day of initiation (Fig. 7). Figure 7. Mean low level wind shear for the early MCS cases from either the 12Z model run of the day before initiation or 12Z model run of the day of initiation. The mid-layer mean wind shear showed more of a drop off leading up to dissipation based on the day of initiation model run. P- values went from non-significant for the day before to non-significant for D-9, marginally significant for D-6, and significant for D-3 (Table A5, A6). The lowest mean difference of wind shear was for the D-9 time period with a max difference at D-6 and with D-3 being slightly lower despite a lower p-value (Table A6). Once again there was less spread of the wind shear due to the decrease in the standard deviation, which can be seen in the box plots (Fig. 8). Based on a comparison of the box plots, a more visible drop off can be seen in the day of initiation model run plot (Fig. 8. Figure 8. Mean mid-layer wind shear for the early MCS cases from either the 12Z model run of the day before initiation or 12Z model run of the day of initiation.

8 The deep layer wind shear between the day before and day of initiation model runs both have highly significant p-values for the D-9 and D-6 times with both D-3 values being nonsignificant (Table A5, A6). The mean difference is highest for the day before initiation model run at D-9 and D-6 (Table A5). However, for D-9, the p-value actually decreases from the day before to the day of model run indicating there is less of a spread at nine hours on the day of initiation model run (Table A5, A6). For D-6, the mean difference decreases from the day before to the day of model run while its p-value increases somewhat showing a decrease in the drop off (Table A5, A6). Examining the box plots, both have a decent looking drop off in wind shear leading up to dissipation (Fig. 9). There is also shrinking of the IQR and range of the nine hour time period from the day before to the day of model run associated with that decrease in the p-value (Fig. 9). Figure 9. Mean deep layer wind shear for the early MCS cases from either the 12Z model run of the day before initiation or 12Z model run of the day of initiation. Overall, the model run from the day of initiation of the observed MCSs appears to show the better drop off in mean wind shear as a whole based on a decrease in p-values and increase in the mean differences. The only outlier in this judgment is the D-9 and D-6 deep layer mean wind shear. However, both show significant mean differences in wind shear and highly significant p-values. 5. Conclusions Based on the data and results of this study, the 40 km WRF-NMM does show a statistically significant drop in the layer mean wind shear based on the reduction of deep layer wind shear in agreement with observed MCSs. The MCS cases were analyzed by looking at all the cases, initiation differences based on time of day, and then differences based on different model runs. Deep layer wind shear was shown to have the most highly significant p-values for all three and the largest mean differences of mean wind shear than the other two layers. The mid-layer showed the least amount of statistically significant decreases in mean layer wind shear leading up to dissipation. Low level mean wind shear did do surprisingly well in showing a drop off of wind shear, especially when examining all the cases. Many times throughout the study the low level mean wind shear had smaller p-values than deep layer shear. This meant that low level mean wind shear was more likely to have a drop off of wind shear for that time. Despite its lower p- values at times than the deep layer wind shear p- values, the mean differences for low level wind shear were never greater than deep layer wind shear, which means the magnitude of the decrease was smaller. This is further reasoning to why deep layer shear seems to be the better in observing a drop off of wind shear ahead of dissipation. Overall, the early initiating MCSs show more drop offs of wind shear with greater magnitudes than do late initiating MCSs. The model run the day of initiation tended to show more drop offs with mixed results as far as the magnitude of those drop offs. Each time besides the D-9 and D-6 time periods, p-values

9 were non-significant for the day before model run. Meanwhile for the day of model run, low level wind shear did much better at showing highly significant p-values and larger mean wind shear difference values. However, deep layer wind shear was highly significant for both model runs and greater mean differences were found for the day before model run. Also, although the mean difference went down for the D-9 time period, the p-value went down as well, which was a result of a lowering of the standard deviation and less spread in the wind shear values. Based on all the data, the most significant drop offs of wind shear are occurring before three hours before dissipation. The D-3 had the worst p-values consistently with the smallest mean differences of wind shear as well. Also, when calculating the difference of each of the time periods of mean differences of wind shear, it appears that in general the largest drop off of wind shear occurs more often between six hours and three hours before dissipation. Based on these results, the hypothesis that the 40 km WRF-NMM would not show a drop off in deep layer wind shear as the MCS nears dissipation has been proven false. The model did show a clear drop off in the mean wind shear, especially, in a deep layer of the atmosphere. Further research would include examining and comparing this methodology to other models such as a higher resolution NAM and the GFS model. Also, further statistical analysis could be done to further pinpoint the time frame leading up to dissipation where we see the greatest drop off in wind shear and possibly a more specific magnitude of the wind shear drop off. Throughout this study, a shrinking of the wind shear s standard deviation and spread was observed. It would be interesting to see what kind of relationship can be derived from that. 6. Acknowledgements I would like to thank Dave Flory for his mentorship and guidance on this project. I would also like to thank Dr. William Gallus for his guidance and Jon Hobbs, Adam Deppe, and Sho Kawazoe for their help with statistical analysis. REFERENCES Cohen, A. E., M. C. Coniglio, S. F. Corfidi, and S. J. Corfidi, 2007: Discrimination of mesoscale convective system environments using sounding observations. Wea, Forecasting, 22, Coniglio, M. C., H. E. Brooks, S. J. Weiss, and S. F. Corfidi, 2007: Forecasting the maintenance of quasi-linear mesoscale convective systems. Wea, Forecasting, 22, Gale, J. J., W. A. Gallus Jr., and K. A. Jungbluth, 2002: Toward improved prediction of mesoscale convective system dissipation.wea. Forecasting, 17, Conglio, M. C., H. Bardon, K. Virts, and S. J. Weiss, 2006b: Forecasting the maintenance of mesoscale convective systems. Preprints, 23d Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., CD-ROM, P2.3. Weisman, M. L., J. B. Klemp, R. Rotunno, 1988: Structure and Evolution of Numerically Simulated Squall Lines. J. Atmos. Sci., 45, Rotunno, R., J. B. Klemp, M. L. Weisman, 1988: A Theory for Strong, Long-Lived Squall Lines. J. Atmos. Sci., 45,

10 7. Appendix A Table A1. The table to the left gives a color code and ranges for the different statistical significance categories for the p-values. The smaller the p-value the more likely there is a drop off in the mean wind shear and the difference is not zero. Table A2. The above table shows the mean difference of wind shear between dissipation and three time periods before dissipation, as well as, a p-value for each of the three shear layers for all the MCS cases. P-value significance is color coded (See Table A1). Table A3. The above table shows the mean difference of wind shear between dissipation and three time periods before dissipation, as well as, a p-value for each of the three shear layers for all the early initiation (15-21Z) MCS cases. P-value significance is color coded (See Table A1). Table A4. The above table shows the mean difference of wind shear between dissipation and three time periods before dissipation, as well as, a p-value for each of the three shear layers for all the late initiation (0-6Z) MCS cases. P-value significance is color coded (See Table A1).

11 Table A5. The above table shows the mean difference of wind shear between dissipation and three time periods before dissipation, as well as, a p-value for each of the three shear layers for all the early initiation (15-21Z) MCS case provided from the 12Z model run the day before initiation. P-value significance is color coded (See Table A1). Table A6. The above table shows the mean difference of wind shear between dissipation and three time periods before dissipation, as well as, a p-value for each of the three shear layers for all the early initiation (15-21Z) MCS case provided from the 12Z model run the day of initiation. P-value significance is color coded (See Table A1).