B6 - New data assimilation approaches to better predict tropical convection

Project leaders: PD Dr. Tijana Janjic-Pfander, Prof. Dr. Corinna Hoose, Prof. Dr. Peter Knippertz

Other researchers: Yvonne Ruckstuhl (PostDoc), Hyunju Jung (PhD student), Maurus Borne (PhD student - Aeolus), Martin Weissmann (member), Tobias Selz (PostDoc), Tatsiana Bardachova (Master student), Beata Czajka (Master student)

Forecasting precipitation in the tropics remains a large challenge for numerical weather prediction (NWP) today. The practical predictability realized with operational systems appears to be much further away from theoretical intrinsic predictability than at higher latitudes. Amongst the many reasons for this are the large analysis errors at synoptic to planetary scales in the tropics, which at least partly stem from the introduction of unphysical features through data assimilation (DA) systems optimized for the extratropics. These features may then further amplify through the coupling of tropical waves with convection, which occurs through wave-induced modification of environmental conditions for convection on one hand and wave triggering by convective latent and radiative heating on the other hand, as investigated in Project C2. During Phase 1, fundamental research was conducted on designing DA algorithms that can preserve pre-defined physical properties. In particular, preservation of mass, positivity, energy, and enstrophy were examined for atmospheric state estimation. Constraining or not constraining the analysis ensemble with the physical properties, had a profound effect on ensemble forecasts as well as representation of uncertainty in idealized experiments.

Phase 2 will build on these results and expand them to experiments with the full-blown NWP Icosahedral Non- hydrostatic (ICON) model. In order to specifically concentrate on the atmospheric dynamics most relevant for low latitudes, simulations will be run at high resolution in a tropical channel configuration with zonally symmetric sea-surface temperatures (SSTs) and no continents. Particular attention will be paid to the coupling of convection (and thus rainfall) with tropical waves. This coupling is suspected to provide a potential source of predictability on synoptic to planetary scales, but current state-of-the-art NWP models struggle to realistically represent the involved multi-scale interactions. We will investigate several potential reasons for this, using our idealized tropical channel experiments: (a) resolution: do we need grid spacings fine enough to explicitly represent deep moist convection? Are there other effects of resolution on the convection-wave coupling? (b) Clouds: how important is a detailed and realistic treatment of uncertain microphysical and cloud-radiative processes for the interaction with the waves, e.g., through modifications to vertical profiles of heating and divergence? (c) Data assimilation: how can we best constrain model forecasts in the tropics given the complex behavior of wave-convection couplings? How can we avoid unrealistic projections onto the wave structures and spurious convection? In order to answer these questions, we combine approaches from DA, cloud physics, and atmospheric dynamics. Ensemble DA experiments will be based on virtual observations taken from a high-resolution nature run using the ICON model and different physical constraints (e.g., energy conservation) will be applied. The characteristics of wave features (e.g. propagation, intensity, vertical structure) and their influence on convection will be extracted using filtering tools based on research in Project C2. Finally the role of clouds will be investigated through targeted sensitivity experiments built on insight into the most important microphysical controls (e.g., ice nucleation) from Project B1. In the long run, this kind of interdisciplinary approach can be extended to more realistic settings and thus has potential to improve practical predictability at low latitudes.

In August 2018, the Aeolus satellite carrying the first UV Doppler lidar in space (ALADIN) was successfully launched. The particular gap that Aeolus is closing in the global observing system is measurements of winds in cloud free regions and thus we expect Aeolus to substantially improve analysis fields and subsequently predictions of synoptic- to planetary-scale wave phenomena in the Tropics. The main aim of this PhD project is to analysis the impact of Aeolus measurements on the prediction of tropical waves and their connections to rainfall (and possible dust). To reach this aim, the following concrete objectives need to be met:

1. Assess the quality of Aeolus winds at different vertical levels in comparison with aircraft measurements (lidar and dropsondes, possibly drones) during a planned field campaign in Cape Verde in June-July 2020 and deduce possible reasons for the quality of the retrievals. This part will be done in close collaboration with the Aeolus team at DLR, who is responsible for the aircraft measurements.
2. Compute wave disturbances for the campaign period from satellite and model data. This will use wave tools from the project C2. Assess how well the waves are represented in European Centre for Medium-Range Weather Forecasts (ECMWF) and German Weather Service (DWD) analysis and forecast data relative to the satellite and aircraft measurements. Identify most interesting days for a case study.
3. Evaluate data denial experiments for the campaign and entire Aeolus period. For the shorter campaign period further targeted experiments will be conducted to test the impact of cycling or to restrict the denial to certain geographical regions (e.g. upstream Africa).
4. Investigate to what extent improvements in analysis data carry through to forecasts, both of the waves themselves and also of the associated rainfall (and possibly dust). This aspect could be extended to tropical cyclogenesis in collaboration with the C3 project.