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Explicit Forecasts of Hail Occurrence and Expected Hail Size Using the GEM–HAILCAST System with a Rainfall Filter JULIAN C. BRIMELOW AND GERHARD W. REUTER Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada (Manuscript received 4 March 2008, in final form 11 March 2009) ABSTRACT HAILCAST is a numerical model developed specifically to predict the size of the largest hail reaching the ground. It consists of a steady-state cloud model combined with a time-dependent hailstone growth model. The regional version of the Canadian Global Environmental Multiscale (GEM) model is used to provide prognostic model soundings that are used as input data for HAILCAST. A map of forecasted maximum hail size is thereby obtained. Because hail is typically accompanied by rain, it would be advantageous if the GEM–HAILCAST system were to predict the occurrence of hail only in those regions where the GEM model was predicting precipitation. Hence, the utility of applying a forecast rainfall mask from the GEM model to restrict hail forecasts to those areas where rainfall is forecast during a 12-h window centered on 0000 UTC was tested. The accumulated precipitation filter is objective and integrates both the thermodynamic and dynamic output from the GEM model over many time steps. To test the utility of applying the GEM forecast precipitation mask, the masking technique was applied to HAILCAST-predicted maximum hail size maps for the three Canadian prairie provinces between 1 June and 31 August 2000. Several case studies will be presented to illustrate the usefulness of adding the precipitation mask. Verification statistics confirm that applying the rainfall mask tends to slightly reduce the false alarm ratio while still identifying the majority of hail events within a special study area over southern Alberta. The performance of the precipitation masking technique was not as effective on severe hail days, especially when attempting to identify both the occurrence and location of severe hail swaths.
1. Introduction Spatial forecasts of the maximum expected hail size over the Canadian prairies have been available to forecasters at the prairie northern region (PNR) of the Meteorological Service of Canada (MSC) since 2003. The forecasts are generated using prognostic soundings from the Canadian Global Environmental Multiscale (GEM) model (Coˆte´ et al. 1998) as input for HAILCAST (Brimelow et al. 2002). This hail forecast system (GEM–HAILCAST) is capable of predicting the occurrence and size of hail over a typical severe thunderstorm watch area (Brimelow et al. 2006). While observations indicate that hail occurs in the presence of rain (e.g., Admirat et al. 1985), users have noticed that GEM–HAILCAST sometimes forecasts hail over areas where no deep, moist convection oc-
Corresponding author address: Gerhard Reuter, Dept. of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada. E-mail:
[email protected] DOI: 10.1175/2009WAF2222138.1 Ó 2009 American Meteorological Society
curred. To avoid this discrepancy, and to be consistent with the GEM model output, it would be advantageous for GEM–HAILCAST to predict the occurrence of hail only in those regions where the GEM model was predicting rain. In other words, the presence of forecast rainfall should be considered a ‘‘necessary condition’’ for the prediction of hail. To this end, we have introduced a rainfall mask to the GEM–HAILCAST system that ignores hail predictions in areas where rain is not forecast. In this paper we examine the skill of GEM– HAILCAST, with the rainfall mask, in forecasting the occurrence, location, and severity of hail. In particular, we investigate whether the addition of a rainfall mask reduces the false alarm ratio while maintaining an acceptable probability of detection for hail prediction. The rationale for applying this rainfall masking technique is that rainfall accumulation integrates both the thermodynamic and dynamic output from the GEM model. Moreover, by using the accumulated rainfall over a specified time interval, the convective parameterization scheme is integrated over many time steps and not just
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FIG. 1. Forecast domain of GEM–HAILCAST for the summer of 2000. SP is the location of the operational upper-air sounding site at Stony Plain. The city of Edmonton is located at XD, the city of Calgary at YC, and the radar site at R. The large rectangle indicates the outline of the ASA.
for a specific time as would be the case when applying a mask based on an instantaneous value of, for example, the vertical velocity at a fixed level. Schmeits et al. (2005) found that areas of model-predicted convective precipitation were strongly correlated with observed lightning. This finding supports our rationale for using the GEM forecast precipitation fields to identify areas of convective activity. Here, we will present our findings on the utility of this proposed methodology using forecast GEM soundings and precipitation for the summer of 2000.
2. Observational data We use the same dataset used by Brimelow et al. (2006), whose research focused on hailstorm activity over the Alberta study area (ASA; see Fig. 1) during the summer of 2000. The ASA encompasses the climatologically preferred region for hailstorms in Canada (Etkin and Brun 1999), and 2000 had the most complete and reliable set of hail reports. A total of 533 hail reports for the period 1 June– 31 August 2000 were used to verify the accuracy of the HAILCAST hail forecasts. Of these, 332 (or 62%) were surface hail reports obtained from the Alberta Agriculture Financial Services Corporation, 153 came from the Meteorological Service of Canada’s severe weather database, 28 came from Weather Modification Inc., and
20 came from the U.S. Storm Prediction Center’s severe weather database. A hail day was classified as severe when the reported hail diameter was 2 cm or larger. According to the surface reports, 61 (66%) of the 92 days in the dataset were identified as hail days. Of these, 44 were classified as severe hail days. To avoid the problems associated with relying on a sparse surface observation network to report hail, we used radar data to infer the presence and location of hailstorms. The advantage of weather radar is that it detects all precipitating cells within its viewing area. Radar reflectivity data for the period 1 June–31 August 2000 were collected by the Olds-Didsbury, Alberta, radar (51.718N, 114.118W; elevation 1024 m) in the ASA (Fig. 1). Volume scans were performed at 5-min intervals throughout the summer. Lenning et al. (1998) found that the use of radar-derived vertically integrated liquid (VIL) water content was useful for indicating the presence of hail in Florida. Brimelow et al. (2004) made comparisons with VIL data and observed hail sizes from surface reports for the ASA area. Their comparison showed that hail diameters larger than or equal to 2 cm were associated with VIL values larger than or equal to 25 kg m22. VIL values larger than or equal to 10 kg m22 were indicative of hail. Using the guidelines of Brimelow et al. (2004), a day in the ASA was classified as a probable hail day if the VIL at one or more pixels (;1 km2)
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was larger than or equal to 10 kg m22 between 2000 and 0500 UTC. If the VIL exceeded 25 kg m22 at two or more contiguous pixels (;3 km2), the day was classified as a severe hail day (with hail diameter 2 cm or larger). An example of a VIL map for the ASA is shown in Fig. 3 of Brimelow et al. (2006). Because of the scarcity of surface hail observations and incomplete radar coverage over the prairie provinces of Canada, we used lightning strike data from the Canadian Lightning Detection Network (Burrows et al. 2002) to identify areas of thunderstorm activity outside the ASA. Including the lightning data aided us in identifying those areas that received convective rain for the purpose of comparison with the forecasted precipitation and hail fields.
3. Model data a. HAILCAST HAILCAST consists of a steady-state cloud model linked to a hail growth model. The cloud model is initialized with a vertical profile of ambient temperature, humidity, and wind. These data are used to compute vertical profiles of liquid water content, updraft velocity, and in-cloud temperature that are representative of the hail growth environment close to the updraft’s nearadiabatic core. The time-dependent hail model then uses output from the cloud model to simulate the growth of hail in the updraft. A drizzle-sized hail embryo is introduced at cloud base and then grows by either wet or dry growth. Allowance is made for the melting of the hailstone as it descends below the in-cloud freezing level. During wet growth (melting), excess accreted water (meltwater) on the surface of the stone is shed. The reader is referred to Brimelow et al. (2002) for more detailed information on HAILCAST.
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hail diameter obtained by adding perturbations of 21.08C, 20.58C, 08C, 10.58C, and 11.08C to both the forecast surface temperature and dewpoint. HAILCAST was run for each combination of temperature and dewpoint, resulting in 25 hail diameter forecasts. The ensemble diameter was determined by calculating the arithmetic mean of all 25 forecast hail diameters. The GEM model is used operationally on a daily basis by the Canadian Meteorological Centre. In recent years there has been significant fine-tuning of the data assimilation procedures and model parameterization schemes to improve the accuracy and reliability of the quantitative precipitation forecasts. In general, the GEM model system has become skillful in predicting the observed distribution of precipitation (e.g., Erfani et al. 2003; Mailhot et al. 2006). Appendix A describes the skill of the 24-km resolution GEM system for the summer of 2000. The 3-hourly rainfall totals from the 1200 UTC run of the 24-km GEM model were used to produce rainfall maps for each day between 1800 and 0600 UTC. The 12-h window was used to identify the total-area forecast to be affected by precipitation rather than attempting to identify individual rainfall elements. By extending the time window beyond the nowcasting range of 6 h, we can avoid some of the spinup problems often associated with convective rainfall (Fowle and Roebber 2003; Gallus and Segal 2004). The 3-hourly total forecast accumulated precipitation produced by the 1200 UTC 24-km GEM model run was available on a 0.58 3 0.58 grid during the summer of 2000.
4. Case studies In this section we present four case studies that demonstrate how applying the rain mask affected the hail forecasts generated by GEM–HAILCAST.
b. 24-km GEM model
a. 4 July 2000
To capture the prestorm environment on each day between 1 June and 31 August 2000, we used prognostic upper-air soundings produced by the 1200 UTC run of the 24-km resolution GEM model. A total of 1400 gridded binary format (GRIB) soundings were generated for 0000 UTC at 0.58 intervals between 498 and 608N and 1208 and 908W. The prognostic profiles comprise temperature, moisture, and horizontal wind data. HAILCAST was run at each grid point, and the resulting field of hail diameters was then contoured to create spatial maps of the maximum expected hail size. We used the same forecast hail diameters that were used by Brimelow et al. (2006). Specifically, the forecast hail diameters represent the ensemble average maximum
A cutoff low at 500 mb over the southern Canadian Rockies triggered widespread and severe thunderstorm activity over central and southern Alberta. Figure 2a indicates that the GEM–HAILCAST forecast the potential of hail over central and south-central Alberta at 0000 UTC, with severe hail forecast over portions of central Alberta. The potential for hail was also predicted in a narrow band extending through central and southeastern Saskatchewan. The forecast location of the hail was in close agreement with observations, with eight of the nine severe hail reports located within 50 km of areas forecast to be at risk of severe hail. The forecast maximum diameters of 3–4 cm over east-central Alberta were also in good
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FIG. 2. Maps of the Canadian prairie provinces depicting the contours of HAILCAST forecasted hail size (cm) and the 24-km GEMforecasted rainfall accumulation for (a)–(c) 4 Jul and (d)–(f) 5 Jul 2000. (a) Forecasted hail diameter for 0000 UTC 4 Jul 2000. (b) Forecasted rainfall accumulation from 1800 UTC 3 Jul to 0600 UTC 4 Jul 2000. (c) Forecasted hail diameter for 0000 UTC 4 Jul 2000 after applying the accumulated rainfall mask. (d) Forecasted hail diameter for 0000 UTC 5 Jul 2000. (e) Forecasted rainfall accumulation from 1800 UTC 4 Jul to 0600 UTC 5 Jul 2000. (f) Forecasted hail diameter for 0000 UTC 5 Jul 2000 after applying the accumulated rainfall mask. In all six panels, red dots show the locations of hail reports, while black dots show observed lightning strikes between 1800 and 0600 UTC.
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agreement with the observed maximum hail sizes (4 cm). The forecast for severe hail over southeastern Saskatchewan was confirmed by radar observations of several strong thunderstorms (maximum reflectivity 55 dBZ) over southeastern Saskatchewan that developed along a surface trough after 0000 UTC (Brimelow et al. 2006). Despite the extensive lightning activity observed over southern Manitoba and North Dakota, associated with a large mesoscale convective complex, no reports of hail were received from these locations. Figure 2b shows that GEM forecast precipitation over central and south-central Alberta, with the heaviest amounts (.20 mm) expected over the foothills of the Rocky Mountains west of the ASA. GEM also predicted precipitation over northern and extreme southeastern Saskatchewan, as well as over central and southern Manitoba. Apart from a small area of lightning over north-central Alberta, no lightning strikes were observed within the large area of precipitation predicted by the GEM model over northern Saskatchewan and central Manitoba. Thus, the lightning data suggest that the rain over these areas was predominantly nonconvective. In fact, GEM–HAILCAST only predicted the possibility of hail along the southern periphery of the large rain area (the existence of which was confirmed by surface reports and satellite imagery), which coincided with the area of thunderstorm activity. The areas forecast to be at risk of hail after applying the precipitation mask (i.e., excluding areas outside the 0.2-mm isohyet) are indicated in Fig. 2c. Of note is that the hail over central Alberta and portions of the ASA was still forecast correctly (allowing an offset of 50 km from the observed hail locations). However, while GEM–HAILCAST hail had previously correctly forecast the occurrence of hail over southeastern Alberta, the masked GEM–HAILCAST map failed to forecast rain and hail within 50 km of the hail report in question. Applying the mask reduced the size of the area erroneously forecast to receive hail over central and extreme southeastern Saskatchewan, while still correctly predicting the location of hail as inferred from radar. Nonsevere hail was still correctly forecast to occur over extreme southwestern Manitoba after applying the precipitation mask.
b. 5 July 2000 A broad upper-air trough was present over the western Canadian prairies and British Columbia, with a cutoff low and associated surface low located over northern Alberta. GEM–HAILCAST predicted hail that was 1–1.5 cm in diameter over much of central Alberta (Fig. 2d). In addition, severe hail was forecast over southeastern and far eastern Saskatchewan, as well
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as over extreme southwestern Manitoba. Isolated, weak thunderstorms were observed over central Alberta. Widespread thunderstorms were observed over northern Saskatchewan and Alberta. A line of thunderstorms that developed ahead of an advancing positive vorticity maximum located over south Saskatchewan at 0000 UTC produced several severe hailstorms (with hail as large as golf balls reported) over the far eastern portions of Saskatchewan and extreme western Manitoba. Thunderstorms formed after 0000 UTC over far southwestern Saskatchewan, although no reports of hail were received from these storms. No reports of hail were received from Alberta on this day. Apart from the erroneous forecast of hailstorms over central Alberta, the forecasts for severe hail over eastern Saskatchewan and western Manitoba were in relatively good agreement with the observations. However, the GEM–HAILCAST system predicted the hail to occur about 80 km farther to the east than was observed. This timing error is most likely because the GEM model was moving the positive vorticity maximum through too quickly. GEM did not forecast any rain over the ASA between 1800 and 0600 UTC (Fig. 2e). Rain was, however, forecast over far west-central and portions of northern Alberta, far eastern and southwestern Saskatchewan, and over much of northern Saskatchewan and Manitoba. After applying the rainfall mask, the large area erroneously forecast to receive hail over central Alberta was excluded, while the correct forecast of severe hail over eastern Saskatchewan was maintained (Fig. 2f). However, the hail over extreme western Manitoba would have been missed.
c. 21 July 2000 On this day the upper-air circulation over Alberta was dominated by an upper-air ridge. However, GEM forecast high humidity values near the ground and unstable temperature profiles with minimal convective inhibition (CIN). Consequently, GEM–HAILCAST forecast a large area of severe hail over south-central Alberta and southwestern Saskatchewan (Fig. 3a). The 0000 UTC Stony Plain, Alberta, sounding indicated that there was in fact a subsidence inversion (between 700 and 600 mb) above the lifted condensation level (775 mb). In the absence of strong lift, the inversion was strong enough to suppress convection and, as indicated by the lightning data, thunderstorms actually developed along a surface trough line over far western Saskatchewan and along the Alberta–Saskatchewan border. Isolated reports of hail were received from southwestern Saskatchewan and far east-central Alberta (with a report of severe hail over far southwestern Saskatchewan), which agreed with the hail forecast in those locations.
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FIG. 3. Maps of the Canadian prairie provinces depicting contours of HAILCAST-forecasted hail size (cm) and 24-km GEM-forecasted rainfall accumulation for (a)–(c) 21 Jul and (d)–(f) 22 Jul 2000. (a) Forecasted hail diameter for 0000 UTC 21 Jul 2000. (b) Forecasted rainfall accumulation from 1800 UTC 21 Jul to 2200 UTC 4 Jul 2000. (c) Forecasted hail diameter for 0000 UTC 21 Jul 2000 after applying the accumulated rainfall mask. (d) Forecasted hail diameter for 0000 UTC 22 Jul 2000. (e) Forecasted rainfall accumulation from 1800 UTC 21 Jul to 0600 UTC 22 Jul 2000. (f) Forecasted hail diameter for 0000 UTC 22 Jul 2000 after applying the accumulated rainfall mask. In all six panels, red dots show the locations of hail reports, while black dots show observed lightning strikes between 1800 and 0600 UTC.
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The GEM model did not predict precipitation over the majority of central and southern Alberta (Fig. 3b). While the location of the forecast precipitation over northern and western Saskatchewan corresponded closely to the area of thunderstorm activity, the model predicted the rain over southern Saskatchewan to fall farther eastward than was observed. Inspection of forecast fields from GEM indicated that the reason for this is that the model had moved the surface trough through too rapidly, resulting in convection being triggered farther to the east. After applying the rainfall mask, a very large area that would have previously been erroneously forecast to be at risk of hailstorms was excluded (Fig. 3c). However, hail was no longer forecast over far southwestern Saskatchewan, and the severe hail observed there would have been missed. Nevertheless, forecasters trying to decide whether the potential of severe hailstorms would actually be realized over central Alberta on this day would have probably benefited by using the hail forecast maps in combination with the GEM model forecast precipitation fields. Further, by being able to discount the likelihood of severe hailstorms over Alberta, forecasters would have then been able to focus their attention on identifying the area at risk of severe hail over southwestern Saskatchewan, where a trigger mechanism was in place.
TABLE 1. Skill scores calculated for predicting the occurrence of hail or severe hail within the ASA for the summer of 2000. Skill scores listed in the A column are calculated without the rainfall mask, while skill scores in the M column are calculated with the rainfall mask. All hail days (%)
POD FAR CSI
Severe hail days (%)
A
M
A
M
93 45 53
89 34 61
79 45 48
71 41 48
After applying the rainfall mask, hail was forecast to be limited to the foothills of southwestern Alberta, extreme southeastern Saskatchewan, and northern Manitoba (Fig. 3f). As a result, the large area of severe hail incorrectly forecast by GEM–HAILCAST over central and south-central Alberta would have been excluded, while rain and hail were correctly forecast in the immediate vicinity of the hail reports received from westcentral Alberta and the southwestern ASA.
5. Verification statistics
d. 22 July 2000
a. Forecasting the occurrence of a hail day within the ASA
The axis of an upper-air ridge was located over the Continental Divide at 0000 UTC 23 July, but a rapidly advancing upper-air trough resulted in heights falling by 60 gpm by 1200 UTC 23 July. GEM–HAILCAST predicted a large area at risk of hail over central and southcentral Alberta, with hail as large at 5 cm in diameter being forecast over the northern ASA (Fig. 3d). In contrast, the GEM model did not predict precipitation between 1800 and 0600 UTC over most of Alberta (Fig. 3e), with rain forecast to be limited to the foothills of Alberta (including the extreme western portions of the ASA), extreme southeastern Saskatchewan, and southwestern and northern Manitoba. Lightning data show that thunderstorms formed after 0000 UTC along the foothills of the Rocky Mountains of southwestern Alberta, as well as along a line extending from southeastern Saskatchewan through central and northern Manitoba. A report of severe hail was received from west-central Alberta, while nonsevere hail (grape sized) was reported in the southwestern corner of the ASA. The C-band radar at Olds-Didsbury indicated that severe hail may have been associated with the hailstorms over the far southwestern ASA. No reports of hail were received from the storms over Saskatchewan and Manitoba.
The utility of masking hail maps using rainfall data to forecast the occurrence of a hail day or a nonhail day within the ASA was quantified using the probability of detection (POD), false alarm ratio (FAR), and the critical success index (CSI). Table 1 summarizes the skill scores for the 92-day period, with and without the GEM-predicted rainfall mask. The performance statistics indicate that applying the forecast precipitation mask improved the forecasting of hail occurrence over the ASA. The FAR was reduced from 0.45 to 0.34, while the POD decreased only slightly from 0.93 to 0.89. The CSI for the masked forecasts was higher than that scored for the unmasked maps (0.61 versus 0.53). The skill of GEM–HAILCAST when forecasting the occurrence of severe hail over the ASA was less impressive (POD 5 0.79, FAR 5 0.45, and CSI 5 0.48). The lower forecast skill when forecasting the occurrence of severe hail over the ASA using the rainfall masks was also evident (POD 5 0.71, FAR 5 0.41, and CSI 5 0.48). These data suggest that applying the rainfall mask did reduce the FAR slightly, but at the expense of a lower POD. Consequently, the CSI for the masked forecasts was the same as that obtained without applying the precipitation mask.
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TABLE 2. Skill scores calculated for predicting the occurrence and location of hail or severe hail within the ASA for the summer of 2000. Skill scores listed in the A column are calculated without the rainfall mask, while skill scores in the M column are calculated with the rainfall mask. All hail days (%)
POD FAR CSI
TABLE 3. Skill scores calculated for predicting the occurrence and location of hail within the ASA for days on which hail was forecasted or was observed. No mask
Severe hail days (%)
A
M
A
M
87 54 43
75 37 52
66 57 35
50 53 32
b. Forecasting the spatial distribution of hail within the ASA The performance statistics used to quantify the skill of the masked hail maps at predicting both the occurrence and spatial distribution of hail within the ASA were calculated using a method very similar to that employed by Brimelow et al. (2006) and Weiss et al. (1980). The only difference is that our method required that both hail and precipitation had to be forecast in the vicinity of the hail report in order to qualify as a hit. Brimelow et al. (2006) provided a detailed description of the verification technique, so in this paper only a summary of the verification procedure is provided in appendix B. Table 2 summarizes the skill scores for all 92 days with and without the rainfall mask. The skill scores for predicting the spatial distribution of hail within the ASA were lower then those obtained when GEM– HAILCAST was required to predict only the occurrence of hail within the entire ASA. The performance statistics (Table 2) indicate that after applying the forecast precipitation mask to the hail maps, GEM–HAILCAST still displayed skill when forecasting the occurrence and location of hail. Specifically, the FAR was reduced from 0.54 to 0.37. However, the POD of 0.75 achieved with the rainfall mask was also lower than that scored without the rainfall mask (0.87). Nevertheless, the CSI for the masked forecasts was higher than that scored for the unmasked maps (i.e., 0.53 versus 0.43). The statistics in Table 2 suggest that applying the rainfall mask was of limited utility when trying to forecast both the occurrence and location of severe hail. While these data suggest that applying the rainfall mask did reduce the FAR slightly from 0.57 to 0.53, although this came at the expense of a lower POD (0.50 versus 0.66). The CSI of 0.32 for the masked forecasts was slightly lower than that obtained without applying the precipitation mask (0.35).
c. Daily verification statistics of the spatial distribution of hail The hail verification statistics presented so far have been summary values for all 92 days; thus, statistical
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With mask
POD
FAR
CSI
POD
FAR
CSI
100 85 100 100 100 100 100 100 100 100 93 95 100 100 100 100 100 100 100 100 100 100 100 78 100 93 100 100 100 67 67 100 100 100 100 50 100
80 8 87 8 0 50 5 52 44 24 7 0 5 42 64 46 8 68 19 23 60 0 56 0 0 0 12 9 5 0 0 40 0 52 11 60 27
20 79 13 92 100 50 95 48 56 76 87 95 95 58 36 54 92 32 81 77 40 100 44 78 100 93 88 91 95 67 67 60 100 48 89 29 73
100 85 100 100 100 100 100 100 100 89 93 95 92 100 100 100 78 67 100 20 100 80 100 78 94 93 100 100 100 44 72 100 100 100 100 50 100
80 8 67 10 0 56 0 0 0 39 7 0 10 14 40 50 20 50 19 0 50 0 0 0 0 0 12 15 5 0 0 27 0 50 0 60 13
20 79 33 90 100 44 100 100 100 57 87 95 84 86 60 50 65 40 81 20 50 80 100 78 94 93 88 85 95 44 72 73 100 50 100 29 88
results on particular days are lost, being lumped into one final set of POD, FAR, and CSI values. It is useful to examine the verification for each day separately in order to identify how many occasions applying the mask resulted in superior (or inferior) hail forecasts. Table 3 compares the daily POD, FAR, and CSI values for forecasts with and without the rainfall mask for those days on which hail was forecast or observed. Adding the rainfall mask lowered the POD on 8 days (30 June; 6, 10, 12, 15, 20, and 25 July; and 10 August). The FAR showed improvements on 13 days. As expected, for some days the CSI was improved by adding the constraints of the rainfall mask, while on others there was a small decrease. Table 4 compares the daily POD, FAR, and CSI values for those days on which severe hail was forecast
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TABLE 4. Skill scores calculated for predicting the occurrence and location of severe hail within the ASA for days on which severe hail was forecasted or was observed. No mask
21 Jun 28 Jun 30 Jun 1 Jul 4 Jul 6 Jul 12 Jul 14 Jul 16 Jul 20 Jul 23 Jul 31 Jul 4 Aug 6 Aug 11 Aug 12 Aug 13 Aug
With mask
POD
FAR
CSI
POD
FAR
CSI
100 100 100 50 50 50 100 100 100 100 67 50 100 100 40 100 100
38 86 14 33 0 43 56 19 60 17 0 63 0 0 0 67 21
62 14 86 40 50 36 44 81 40 83 67 27 100 100 40 33 79
100 100 67 50 50 50 50 100 100 100 67 25 100 100 40 100 100
17 67 0 33 0 67 50 13 57 0 0 83 0 0 0 73 21
83 33 67 40 50 25 33 87 43 100 67 11 100 100 40 27 79
or observed. Adding the rainfall mask reduced POD values on 30 June and 31 July. The CSI values were improved on 6 days (21 and 28 June; and 14, 16, and 20 July), but were lowered on 5 days (30 June; 6, 12, and 31 July; and 12 August). Clearly, the additional gain of using a rainfall mask was minimal for the case of forecasting the occurrence and location of severe hail. It would be beneficial for users of GEM–HAILCAST if they knew a priori whether or not there was a tendency for the rainfall mask to improve hail forecasts under certain synoptic situations. To this end we inspected the upper-air and surface maps for days when applying the rainfall mask either improved or degraded the hail forecasts. Our analysis found that on approximately 75% of those days when applying the hail maps improved the forecast skill, the synoptic-scale forcing was considered to be significant. On the other hand, on days when the forecast skill was degraded by applying the mask, there was a tendency for the forcing to be weaker (60% of days). However, there were also a high percentage of days (40%) when the synoptic-scale forcing was significant and applying the forecast mask degraded the forecast. On these days the primary reason for forecast degradation occurred because of relatively small errors in the placement of the precipitation, resulting in no hail being forecast in areas where hail had originally been correctly forecast. These findings support the hypothesis that it is typically easier to forecast the timing and location of convection when the forcing or trigger mechanism is clearly defined (e.g., Fowle and Roebber 2003; Anthes 1986).
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6. Summary and discussion The PNR of the Meteorological Service of Canada has the mandate to issue severe storm warnings to the public for hail of diameter larger than or equal to 2 cm. The HAILCAST model coupled with the operational GEM model output of prognostic soundings is used in the PNR as an objective tool to predict the likely occurrence of large hail for a given warning area. Since 2003, spatial maps of the forecasted maximum hail size (generated using GEM–HAILCAST) have been available to forecasters. Observations indicate that hail is almost always associated with rainfall. Therefore, it makes sense to add a constraint to the GEM–HAILCAST system to ‘‘mask out’’ any areas where hail is forecast but rain is not. In this study we used the GEM–HAILCAST system with a rainfall mask from the GEM model to identify those areas where both rainfall and hail are forecast. The total accumulated rain forecast by the 24-km GEM model between 1800 and 0600 UTC was used for this purpose. Forecast hail maps were prepared using prognostic soundings (valid for 0000 UTC) from the 1200 UTC run of the Canadian 24-km GEM model. Given that precipitation data are easily accessible from the GEM model, applying a precipitation mask is easy to implement to produce the hail forecast. To test the utility of the rainfall mask, the forecast skill before and after applying the precipitation mask was evaluated for the 92 days during the summer of 2000. The verification data suggest that applying the rainfall mask slightly reduced the FAR on days when the GEM predicted conditions that were unfavorable for rainfall. The masking technique was also found to be marginally useful for reducing the FAR when identifying both the occurrence and location of hail over the ASA. However, skill scores suggest that the 24-km GEM precipitation forecasts could not be used with confidence for all days. On some days, the rainfall mask improved the CSI values, while on other days the CSI was lowered. In particular, the rainfall mask helped little in improving the forecasting of severe hail (i.e., hail with a diameter of at least 2 cm). This finding is not surprising given that severe hailstorms cover only a very small portion of the forecast domain and that one is applying a strict criterion to the severe hail forecasts (i.e., the occurrence of both forecast hail and precipitation in close proximity to observed hail reports). Our research has demonstrated that applying the rainfall mask on days with significant forcing is more likely to improve the skill of the hail forecasts over the ASA. There are several avenues for potential further research. One obvious choice would be to repeat the analysis using a finer spatial resolution for the GEM model.
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It is conceivable that, for example, a 10-km model resolution would improve the precipitation forecasts, which would provide a much refined rainfall mask. In addition, the higher resolution would possibly improve the model proximity soundings, which are used as initial data for the HAILCAST model. A second extension of this study should be to focus on model-forecasted convective precipitation rather than total precipitation. The GEM model predicts the amount of precipitation stemming from both the implicit (convective) and explicit (stratiform) precipitation schemes. Because hail is formed in convective clouds rather than stratiform clouds, it is probably better to use a mask of convective rainfall rather than one of total rainfall. The methodology of using a filter to mask out hail is not restricted to precipitation fields. Attempts should be made to examine the use of CIN as a possible filter to block out areas of forecasted hail. Deep convection tends to occur in regions where CIN shows a local minimum (e.g., Weckwerth 2000; Burrows et al. 2005). Other viable options for improving the existing GEM–HAILCAST system would be to generate ensemble model hail forecasts at each grid point using soundings from either multiple numerical weather prediction (NWP) models or multiple soundings from individual ensemble members of a given NWP scheme. This would allow consideration to be given to the temperature, moisture, and winds for the entire profile, and not just the surface conditions, as is currently done. There may also be benefit in coupling probabilistic hail maps with a mask of convective rain probability. Either way, a detailed validation of probabilistic hail (precipitation) forecasts over a larger area than the ASA needs to be undertaken to explore the utility of making such a product available to forecasters.
rainfall totals were compared at 60 stations located over central and southern Alberta. In total, 300 point precipitation forecasts were used to quantify the skill of the 24-km GEM forecasts. The 5 days used to verify the precipitation forecasts over the ASA were active days for thunderstorms. Hail was reported by spotters on each of the 5 days, with a total of 26 hail reports received from within the ASA; most of the hail reports were from the northern third of the study area. The skill in forecasting rainfall was quantified in terms of the probability of detection, false alarm rate, and the critical success index (e.g., Marzban and Stumpf 1998). For a precipitation forecast to be considered a hit, the GEM model had to forecast rainfall at the location where rainfall was observed. The skill scores were POD 5 0.71, FAR 5 0.29, and CSI 5 0.55. These scores are encouraging in view of the fact that the rainfall on these days was predominately convective in origin. The relatively high skill scores in this study are consistent with the findings of Be´lair et al. (2000), who noted that the 24-km GEM model showed promising skill at predicting warm season precipitation over North America. Despite the skill of operational NWP models in predicting rainfall distribution and amounts, Fowle and Roebber (2003) state that the ‘‘specificity concerning the areal coverage of precipitation remains problematic and becomes increasingly challenging as the level of convective organization diminishes.’’ This limitation must be kept in mind when using the rainfall data from NWP models and, especially, when using models having coarser resolution.
Acknowledgments. The authors express their gratitude to Ron Goodson and Terry Krauss for their invaluable assistance in undertaking this research. Hail reports were obtained from the Alberta Severe Weather Management Society and the Alberta Agriculture Financial Services Corporation. The research was supported by the Canadian Foundation for Climate and Atmospheric Sciences.
Skill Score Calculations for Hail
APPENDIX A Skill of GEM Rain Forecasts To verify the spatial distribution of the rainfall forecasts over south-central Alberta, the following 5 days were selected: 10 June, 21 June, 30 June, 14 July, and 25 July 2000. For each day, the forecast and observed
APPENDIX B
The verification method used to verify the skill of forecasting both the occurrence and location of hail over the ASA was described by Brimelow et al. (2006). The Alberta Study Area (ASA) was divided into 25 blocks. Each block measured 0.58 3 0.58 (;1900 km2). A block was only counted as a forecast hail block if hail was forecast to cover at least 50% of the block, and if the forecast accumulated rainfall between 1800 and 0600 UTC from the GEM model in that block exceeded 0.2 mm. If the maximum VIL maps indicated hail in a given block, then that block was tagged as a hail block. To allow for timing errors in the forecast fields, and to compensate for the fact that convection typically covers small areas and the uncertainty in the location of hail reports, an observed hail block (OH) was deemed to verify the forecast occurrence of hail in all the blocks
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bordering that block. Observed hail blocks (along hailswaths) that occurred within one block of the forecast hail block(s) were deemed to be correctly forecast (CF). The POD was calculated by dividing the number of the observed hail blocks that were correctly forecast by the sum of the observed hail blocks: POD 5 åCF/åOH. The first step in calculating the FAR was to calculate the ‘‘good percentage area’’ (GPA; Weiss et al. 1980). The GPA was calculated by dividing the sum of all those forecast hail blocks that bordered the observed hail blocks by the sum of all of the blocks forecast to receive hail. The GPA represents the fraction of the area forecast to receive hail that was actually affected by hail. The FAR was then calculated by subtracting the GPA from a value of 1. The overall forecast skill was quantified in terms of the critical success index (CSI), where CSI 5 [(POD)21 1 (1 2 FAR)21 2 1]21. REFERENCES Admirat, P., G. G. Goyer, L. Wojtiw, E. A. Carte, D. Roos, and E. P. Lozowski, 1985: A comparative study of hail in Switzerland, Canada and South Africa. J. Climatol., 5, 35–51. Anthes, R. A., 1986: The general question of predictability. Mesoscale Meteorology and Forecasting, P. S. Ray, Ed., Amer. Meteor. Soc., 636–656. Be´lair, S., A. Me´thot, J. Mailhot, B. Bilodeau, A. Patoine, G. Pellerin, and J. Coˆte´, 2000: Operational implementation of the Fritsch–Chappell convective scheme in the 24-km Canadian regional model. Wea. Forecasting, 15, 257–274. Brimelow, J. C., G. W. Reuter, and E. P. Poolman, 2002: Modeling maximum hail size in Alberta thunderstorms. Wea. Forecasting, 17, 1048–1062. ——, ——, A. Bellon, and D. Hudak, 2004: A radar-based methodology for preparing a severe thunderstorm climatology in central Alberta. Atmos.–Ocean, 42, 13–22. ——, ——, R. Goodson, and T. W. Krauss, 2006: Spatial forecasts of maximum hail size using prognostic model soundings and HAILCAST. Wea. Forecasting, 21, 206–219.
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