# Hailstorm Detection by Satellite Microwave Radiometers
## 4 Validation  
To validate the performances of the hail detection model described by eq. 4 (hereafter MWCC-Hail) twelve years of mean AMSU-derived hail occurrences have been used. Each MHS overpass was combined with hail occurrences at the ground within 1\({}^{\circ}\) grid box lat/lon and represents the total number of hailstorms over the entire spatial domain. The analysis is based on 12-year data March through September, i.e., the period of peak hailstorm occurrence.  
The MWCC-Hail is applied to all available MHS data for the whole period, not just to those co-located with the storm reports, which represent a very small subsample of all possible satellite measurements. Thus, the MWCC-Hail model-derived hail will cover different time and space domains with respect to those we observe from the ground.  
An inspection of the plates (a) and (b) in Figure 9 demonstrates the similarities between the MWCC-Hail and the SPC storm reports. The majority of hail occurrences are located in the Central US with a clear coverage of the Eastern US. Even though the MWCC-Hail tends to underestimate the number of events with a maximum number of occurrences around 40 with respect to more than 60 as reported by the storm reports, the area of maxima is well reproduced.  
Figure 9: 2000–2011 annual mean distribution of hail events from the (**a**) the MWCC hail detection model and (**b**) surface storm reports; (**c**) scatter plot of the correlation between the two datasets.  
The overall performance of MWCC-Hail quantified in the scatterplot of Figure 9c confirms on one hand the underestimation with respect to hail reports while at the same time the high correlation coefficient (0.79) indicates notable retrieval capabilities.  
The seasonal distribution of the number of events and their location from MWCC-Hail and SPC storm reports are displayed in Figure 10. Although the hail occurrences are generally underestimated, MWCC-Hail shows high sensitivity in identifying the location of maxima and their areal extent fitting the seasonality of the events. In fact, the peak in May-June is detected as well as the minimum in August-September. Note that the missing retrievals on the Eastern CONUS in May-June are associated with hail events characterized by small hail diameters (5196 data with 2.2 cm and 1418 with 4.1 cm) often corresponding to probability values under the minimum threshold (0.36). However, the statistical comparison (Figure 11) shows a good agreement between retrieved and observed data with correlation values ranging between 0.60 in September and 0.75 in May.  
Figure 10: Twelve-year (2000–2011) mean number of hail events over CONUS for the months of March, May, June and September from MWCC-Hail retrievals (**left**) and official storm reports (**right**).  
As an overall consideration on the validation results, note that while the model MWCC-Hail shows high capability in detecting hail events and their areal distribution it performs worse when small or very small hail diameters dominate the observed hailstorm. Additionally, potential overestimates can be registered during the winter season when the background conditions, often controlled by frozen soils, may affect the retrievals. This issue deserves further studies.  
Finally, the intrinsic flexibility of the probability-based approach reinforces the potential of MWCC-Hail both in identifying a variety of hail size distributions and fit similar PMW frequencies from other satellite instruments, as demonstrated in Section 5.