Investigating the Factors That Contribute to African Easterly Wave Intensity Forecast Uncertainty in the ECMWF Ensemble Prediction System

Abstract

Although there have been numerous studies documenting the processes/environments that lead to the intensification of African easterly waves (AEWs), only a few of these studies investigated the effect of those processes or the environment on the predictability of AEWs. Here, the large-scale modulation of AEW intensity predictability is evaluated using the 51-member ECMWF ensemble prediction system (EPS) during an active AEW period (July–September 2011–13). Forecasts are stratified based on the 72-h AEW intensity standard deviation (SD) to evaluate hypotheses for how different processes contribute to large forecast SD. While large and small SD forecasts are associated with similar baroclinic and barotropic energy conversions, forecasts with large SD are characterized by higher relative humidity values downstream of the AEW trough. These areas of higher humidity are also associated with higher precipitation and precipitation SD, suggesting that uncertainty associated with diabatic processes could be linked with large AEW intensity SD. Although water vapor is a strong function of longitude and phase of convectively coupled equatorial waves, the cases with large and small SD are characterized by similar longitude and wave phase, suggesting that AEWs occurring in certain locations or convectively coupled equatorial wave phases are not more or less predictable.

African Easterly Wave Evolution and Tropical Cyclogenesis in a Pre‐Helene (2006) Hindcast Using the Model for Prediction Across Scales‐Atmosphere (MPAS‐A)

“…Recall that during the afternoon of 9 September, a land MCS developed that interacted with the observed pre-Helene. Given the more moisture-enriched nature of the MPAS-GFS simulation favoring water-vapor distribution by convection and wave-MCS interactions, these higher boundary-layer net moisture-flux values reflect the more intense simulated AEW (shown in Figure 5) outcome described by Torn (2010) and Elless and Torn (2019). While not as high as the boundary-layer net moisture-flux values for the simulations (between −50 and −225 g kg −1 m s −1 on average), there is a notable and persistent increase of boundary-layer net moisture flux in ERA5 between 0600 UTC and 1200 UTC on 12 September 2006, just when the wave was declared a tropical depression.…”

“…For example, for African easterly waves (AEWs), short lead‐time forecasts are sensitive to the near profile of the AEW such that higher equivalent potential temperature from enhanced water vapor and/or temperature near the AEW results in a more intense forecast (Torn, 2010). At the same time, AEW intensity forecasts with low skill are characterized by higher water vapor downstream of the AEW trough (Elless & Torn, 2019). Conversely, dry air advection can inhibit AEW development, and a wrong representation of the interactions between dry air and deep convection results in an unrealistically strong vortex (Brammer et al., 2018).…”

Verification of operational numerical weather prediction model forecasts of precipitation using satellite rainfall estimates over Africa

“…The ECMWF and NCEP‐GFS are two of the most common products used in global and regional weather and climate forecasting, hydrology, public health and other applications (Elless & Torn, 2019; Karrouk, 2019; Kerns & Chen, 2014; Meynadier et al, 2010; Nikulin et al, 2012; Sajadi et al, 2020).…”

Numerical prediction of tropical cyclogenesis. Part II: Identification of large‐scale physical processes under the monsoon shear line synoptic pattern

“…Horizontal resolution of the TIGGE data (the finest being tens of kilometres) likely precludes the identification of these internal processes (Elless and Torn, 2018; 2019). We will therefore focus on the “external” or environmental processes.…”