# Hailstorm Detection by Satellite Microwave Radiometers
## 2 Data Selection
### The MicroWave Cloud Classification (MWCC) Method
In this case, the three water vapor channels measure quite similar TBs because their weighting functions change their typical distribution being flat on the cloud region characterized by large ice. At the edges of deep convections, the MWCC classifies as moderate (CO2, yellow) and shallow (CO1, light green) convection the rest of the storm. This is due to the temperature weighting functions of the channels at 186 and 190 GHz, which span the middle and lower atmosphere capturing the scattering by smaller frozen hydmeteors near the main core, which increases with frequency. The TB variation according to the three convective cloud types observed over South Dakota is provided in Figure 4. For shallow and moderate convection (Figure 4a,b) the only TB variations are registered in the channels at 186 and 190 GHz but the perturbation of signal at 184 GHz oscillates in a narrow range since the water vapor absorption generally masks cloud tops below the weighting function distribution. For shallow convections, the signal variation is lower than 15% while for convective clouds in the mid troposphere values go up to 20%. Figure 3: Hailstorm over South Dakota on 23 July 2010 2254 UTC. (**a**) Microwave Humidity Sounder (MHS) channel 2 TBs (K); (**b**) MWCC cloud classification. Colors in b) identify deep (CO3) to shallow (CO1) convection (red to light green) and high (ST3) to low (ST1) stratiform clouds (dark green to cyan). Figure 4: Distribution of the brightness temperature variations \(TB_{i}^{\text{sur}}\) as a function of MWCC convective cloud classification (CO) during the hailstorm over South Dakota, 23 July 2010 2254 UTC. (**a**) CO1, (**b**) CO2, and (**c**) CO3. Red brackets enclose the hail signatures inducing strong TB variations in the three water vapor channels. During a deep convective event the weighting functions of the three water vapor channels are distorted by the convection tower because the scattering by frozen aggregates perturbs considerably the radiation field. Thus, all frequencies contribute to the identification of cloud features and the MWCC method can help to delimit the cloud areas where the signal is strongly extinguished by hailstones reaching the convective top. From the analysis of the diagram in Figure 4c a region (red brackets) where the signal variations reach a maximum can be seen. This is due to the scattering by large hail which significantly depresses TBs at all frequencies. Therefore, if no-hail is detected, each channel preserves its own dynamics where most prominent oscillations characterize high frequencies (10-50% and 20-40% for \(TB^{\text{sur}}_{190}\) and \(TB^{\text{sur}}_{186}\), respectively) and lower values characterize \(TB^{\text{sur}}_{184}\). On the contrary, if hail is detected, the TB depression rapidly increases till 60% at 190 GHz, 53% at 186 GHz, and 48% at 184 GHz. Notably, the scattering by hail is well-distinguished with respect to the whole signal distribution, in particular at \(TB^{\text{sur}}_{184}\) (blue lines) where values are generally distributed under the 30% threshold increasing to 50% when hail is observed. This suggests new criteria for isolating hail signatures and calibrate a new method for hail detection. The high potential of the MWCC method in selecting deep convection when coupled with the sensitivity of \(TB^{\text{sur}}_{184}\) allow to identify out-of-range signals normally included in the CO3 cloud type. Therefore, by extracting these signals a sub-database for calibrating the hail detection model can be realized. This is the main strategy for powering a probabilistic model used to describe the relationship between the radiometric impact of hail sizes and the net effect of TB reductions.