The influence of radiosonde observations on the sharpness and altitude of the tropopause

The shape, sharpness and altitude of the extratropical tropopause (TP) is strongly linked to the position and the strength of the subtropical and polar jet streams that determine the weather in the midlatitudes. However, current numerical weather prediction models fail to correctly represent the sharpness of the TP (i.e., the gradients of wind and temperature). In this study, we address the question if and how the assimilation of radiosonde observations influences the TP representation and whether it acts to sharpen or smooth near near-tropopause gradient.

We investigate the influence by comparing temperature, Brunt-Väisälä frequency (N²) and wind profiles of the observations (y), the model background (x_b) and the analysis (x_a) in tropopause-relative coordinates.

In total, we analyse more than 9000 radiosondes that were assimilated by the European Centre for Medium-Range Weather Forecast’s Integrated Forecast System (ECMWF IFS) over Canada, the Northern Atlantic and Europe during a one-month period in fall 2016. To test whether the diagnosed influence is caused by the assimilated radiosondes, we conducted a data denial experiment that excluded 500 radiosondes that were launched in the framework of the North Atlantic Waveguide and Downstream EXperiment (NAWDEX) field campaign. In observation space, we investigate the departures (i.e., the differences between y, x_b and x_a) in the control run (CTR) with all radiosondes considered and the denial run (DEN) without the NAWDEX radiosondes.

The observed minimum temperature at the TP is overestimated in the background forecast (warm bias, ~1 K). Above, in a layer 0.5-2 km, the temperature is underestimated (~0.5 K). Consequently, the sharpness of the TP which is diagnosed by the maximum of N² is also underestimated. We show that data assimilation is able to improve the temperature and to slightly strengthen the TP in the analysis, particularly in situations where the observed and model TP altitude fairly agree. In the data denial experiment we show that this influence exists in the CTR, but not in the DEN run, and thus can be attributed to the assimilation of the radiosonde data.

Regarding wind, we find an underestimation of the maximum wind at and below the TP (0.5-1 m s^-1) and demonstrate that the assimilation of radiosonde winds is able to improve the wind profile across the TP. The bias and the positive influence are found to be stronger in situation of strong wind, i.e., the jet stream.

Although data assimilation is able to improve wind and temperature gradients across the tropopause by pulling the background closer to the observations, the individual analysis profiles still underestimate the sharpness of the tropopause. The misrepresented TP in models may impact the quality of weather and climate projections.