Validation of Fast Approximation for Cb Top Height Diagnosis

To avoid hazards associated with deep convective clouds, airplanes must fly above or around their tops. Therefore, accurate diagnosis of deep convective cloud top height is crucial in aviation meteorology.

This work employs an established operational method that involves comparing infrared satellite brightness temperature (BT) with a calculated parcel curve temperature. The intersection of BT and the parcel curve corresponds to a theoretical cloud top pressure (CTP) level, which is directly related to altitude (flight level) in the standard atmosphere.

Typically, the parcel curve is calculated iteratively from surface temperature and dewpoint, but this process can be computationally intensive for large datasets. In the first phase of this study, inspired by previous work on non-iterative calculations of moist adiabats, we found that the best approximation for moist adiabats is 5th-degree polynomial, with variable coefficients which are all functions of the wet bulb potential temperature. These coefficients can also be approximated with 4th-degree polynomials. In this approximation, a total of 6 polynomials (comprising 30 coefficients) must be evaluated, rendering it computationally very efficient. This represents a novel approach, as previous non-iterative approximations of moist adiabats employed a total of 200 coefficients and a different methodology to model the changing shape of moist adiabats.

In the second phase of the study the developed approximation was implemented in an operational environment (Visual Weather visualization software). For the method to perform effectively for elevated convection, the temperature and dewpoint of the most unstable layer should be used for calculating the moist adiabat. For this purpose, the layer with the maximum equivalent potential temperature is assumed to be the most unstable layer. Additionally, it is important to note that this method's validity is limited to convective clouds, as other cloud types lack the updraft required for temperatures to follow moist adiabats.

The aim of the third phase of this study is the validation of the automated cloud top height (CTH) diagnostic method. This validation can be challenging due to the lack of ground truth CTH data. This presentation will demonstrate the performance of our method across various convective situations and convective cloud top ranges (e.g., 6000-15000 m) by comparing the calculated CTH with radar vertical cross-sections, which are taken as ground truth. We also compared it with other similar products, such as the NWC SAF CTTH and radar ECHO TOPS products. Based on the analysis of all considered cases, we can conclude that our new method demonstrates very good performance.