The Use of Moist Potential Vorticity Vector as Diagnostic Variable of Rainfall Events in Tanzania

The work of this paper is a first step of the new paradigm, to use the Moist Potential Vorticity Vector (MPVV) as a diagnostic variable of rainfall events in Tanzania. The paper aims at computing and assessing the usefulness of MPVV in the diagnosis of rainfall events that occurred on 08th and 09th May 2017 over different regions in Tanzania. The relative contributions of horizontal, vertical components and the magnitude of MPVV on diagnosis of rainfall events are assessed. Hourly dynamic and thermodynamic variables of wind speed, temperature, atmospheric pressure and relative humidity from the numerical output generated by the Weather Research and Forecasting (WRF) Model, running at Tanzania Meteorological Agency (TMA) are used in computation of MPVV. The computed MPVV is then compared with WRF model forecasts and observed rainfall. It is found that in most parts of the country, particularly over coastal areas and North-Eastern Highlands, MPVV exhibited positive values in the lower troposphere (925hPa) and (850hPa) indicating local instability possibly associated with topographic effects, and continent/ocean contrast. MPVV is mostly positive with slightly negative values indicating instabilities (due to possible convective instability). Moreover, MPVV provides remarkably accurate tracking of the locations received rainfall, suggesting its potential use as a dynamic diagnostic variable of rainfall events in Tanzania.

Comments on “Incorporating the Effects of Moisture into a Dynamical Parameter: Moist Vorticity and Moist Divergence”

In a recent article, Qian et al. introduced the quantities moist vorticity and moist divergence to diagnose locations of heavy rain. These quantities are constructed by multiplying the relative vorticity and divergence by relative humidity to the power k, where k = 10 in their article. Their approach is similar to that for the previously constructed quantity generalized moist potential vorticity. This comment critiques the approach of Qian et al., demonstrating that the moist vorticity, moist divergence, and by extension generalized moist potential vorticity are flawed mathematically and meteorologically. Raising relative humidity to the 10th power is poorly justified and is based on a single case study at a single time. No meteorological evidence is presented for why areas of moist vorticity and moist divergence should overlap with regions of 24-h accumulated rainfall. All three quantities have not been verified against the output of precipitation directly from the model nor is the approach of combining meteorological quantities into a single parameter appropriate in an ingredients-based forecasting approach. Researchers and forecasters are advised to plot the model precipitation directly and employ an ingredients-based approach, rather than rely on these flawed quantities.

Incorporating the Effects of Moisture into a Dynamical Parameter: Moist Vorticity and Moist Divergence

Properly including moisture effects into a dynamical parameter can significantly increase the parameter’s ability to diagnose heavy rain locations. The relative humidity–based weighting approach used to extend the moist potential vorticity (MPV) to the generalized moist potential vorticity (GMPV) is analyzed and demonstrates such an improvement. Following the same approach, two new diagnostic parameters, moist vorticity (MV) and moist divergence (MD), have been proposed in this study by incorporating moisture effects into the traditional vorticity and divergence. A regional heavy rain event that occurred along the Yangtze River on 1 July 1991 is used as a case study, and 41 daily regional heavy rain events during the notorious flooding year of 1998 in eastern China are used for a systematic evaluation. Results show that after the moisture effects were properly incorporated, the improved ability of all three parameters to capture a heavy rain area is significant (statistically at the 99% confidence level): the GMPV is improved over the MPV by 194%, the MD over the divergence by 60%, and the MV over the vorticity by 34% in terms of the threat score (TS). The average TS is 0.270 for the MD, 0.262 for the MV, and 0.188 for the GMPV. Application of the MV and MD to assess heavy rain potential is not intended to replace a complete, multiscale forecasting methodology; however, the results from this study suggest that the MV and MD could be used to postprocess a model forecast to potentially improve heavy rain location predictions.