On the Role of Data Assimilation in the Prediction of Tropical Rainfall

Kurzfassung/Abstract

Precipitation forecasts in the tropics are poor due to large model and initial condition errors. However, it has been hypothesized that the coupling of tropical waves and convection offers a source of predictability, suggesting that capturing these waves accurately in the model and in initial conditions could lead to improved precipitation forecasts. In this work, we investigate whether the data assimilation (DA) algorithm, ensemble Kalman filter (EnKF), is fundamentally capable of recovering tropical waves and thereby providing initial conditions that lead to skillful precipitation forecasts. To capture the essence of tropical dynamics without contamination of land–sea contrasts, sea surface temperature gradients, and influences from the extratropics, we use a tropical aquachannel configuration at 13-km resolution with the Icosahedral Nonhydrostatic (ICON) numerical model. To isolate the role of the initial conditions provided by DA, we assume a perfect model. In our setup, Kelvin waves dominate over other wave types and primarily modulate precipitation. In addition, there is a rainfall event similar to a Madden–Julian oscillation (MJO), which is a planetary-scale disturbance coupled to convection. We show that, by assimilating wind observations, we can reduce the errors in the representation of the Kelvin waves sufficiently to provide accurate precipitation forecasts up to several weeks. Even the MJO-like rainfall event, which starts after a forecast lead time of 10 days, is captured by the forecast ensemble. We find that accurate initial conditions for humidity are important to slow down error growth in the tropics. As emphasized by several other studies, we conclude that wind observations are by far the most important input to achieve skillful tropical forecasts.

Ruckstuhl’s current affiliation: Mathematical Institute for Machine Learning and Data Science, KU Eichstätt-Ingolstadt, Ingolstadt, Germany.