Research

The Dynamic Meteorology group studies a wide range of topics all of which are related to better understanding the Earth's weather and climate. In our research we utilize observations, reanalysis data sets, and numerical models of the atmosphere and climate system. The overall aims of our research are to:

  • Develop in-depth physical understanding of meteorological phenomena, such as deep convection and high impact extra-tropical storms, and apply this knowledge to improve numerical weather prediction models and hence weather forecasts.
  • Develop ensemble prediction and algorithmic parameter estimation techniques together with open source software packages to permit further improvement of numerical weather forecast models.
  • Quantify how weather patterns  will change in the future.
  • Assess the accuracy of the current generation of coupled climate models, understand inter-model variations and factors that affect regional temperature changes.

Specific topics we study are:

Atmospheric deep convection is the mechanism behind showers of rain and many severe weather phenomena, such as thunderstorms, hurricanes, tornadoes and flash floods. It is also the main source of water vapour in the free-troposphere. The processes controlling the physics of deep convection need to be understood and parameterized in order to produce accurate weather forecasts and climate predictions. In our group, we study the fundamental, yet not properly understood, relationship between deep convection and lower tropospheric humidity. The mechanism behind this relationship has been studied extensively, yet models still struggle to represent it accurately.

We have analysed radiosonde soundings and precipitation data from satellites over the tropical oceans.  Based on this analysis we were able to suggest a new mechanism to explain the relationship between deep convection and lower tropospheric humidity. More specifically, after precipitation in dry areas robust lower tropospheric warm anomalies were observed, whereas in moist areas these warm anomalies were absent. Our results suggest that the warm anomalies may be the result of subsidence below a layer of strong evaporation of precipitation.

We have also performed idealised experiments with the Weather Research and Forecasing (WRF) model to study the temperature structures associated with evaporation of stratiform precipitation, which often occurs associated with deep convection. The simulations showed that evaporation of stratiform precipitation and resulting subsidence warming causes lower tropospheric warm anomalies, which are qualitatively similar to those found in our earlier observational study.

As lower tropospheric warm anomalies are known to inhibit deep convection, we believe that evaporation of precipitation is a key mechanism in controlling the formation of future deep convection and should be accounted for in both models and theories of convective phenomena.

Extra-tropical cyclones, often also called low pressure systems and mid-latitude cyclones, are the cause of most of the day-to-day variability in weather in the mid-latitudes. The over arching aim of this area of research is to fully understand the dynamics of these systems in our current climate, particularly the dynamics of extreme or unusual storms, and also identify how extra-tropical cyclones will change in the future as our climate becomes warmer.

We use OpenIFS, a state-of-the-art global numerical weather prediction model. Recently we have used OpenIFS to analyse the dynamics and evolution of two historical, unusual extra-tropical cyclones: windstorm Mauri and Hurricane Ophelia and to study how extra-tropical cyclone may change in the future.

Windstorm Mauri caused considerable damage in northern Finland on 22 September 1982 and, at the time, forecasters speculated that this windstorm was related to Hurricane Debby, a category four hurricane. Our OpenIFS simulations show that Hurricane Debby underwent extra-tropical transition which resulted in ridge building and an acceleration of the jet stream but ex-Debby did not re-intensify immediately. Instead ex-Debby travelled rapidly across the Atlantic as a diabatic Rossby wave-like feature. When ex-Debby approached the UK, it moved ahead of an upper-level trough and rapid re-intensification began. Ex-Debby then underwent 24 hours of rapid deepening before affecting northern Finland as storm Mauri.

Hurricane Ophelia was a category 3 hurricane which underwent extratropical transition and made landfall in Europe as an exceptionally strong cyclone in October 2017. We used specially developed software, consisting of a generalized omega equation and vorticity equation, to investigate the relative contribution of different physical processes (thermal advection, diabatic processes etc. ) to the transformation of Hurricane Ophelia to to an intense mid-latitude cyclone are studied. Vorticity advection, which is often considered an important forcing for the development of mid-latitude cyclones, played a small role in the re-intensification of Ophelia as an extratropical storm. Diabatic heating was the dominant forcing in both the tropical and extratropical phases of Ophelia. In particular diabatic heating from the convection scheme was the dominant forcing during the hurricane phase whereas diabatic heating from the microphysics scheme was more dominate during the extra-tropical phase.

We have also used OpenIFS to investigate how the characteristics and spatial structure of extra-tropical cyclones may change in the future as the climate warms. To do this, we have used OpenIFS configured as an aqua-planet and have run simulations where we uniformly warm the whole surface of the Planet. The results show that warming does not increase the median intensity of extra-tropical cyclones but that the strongest cyclones intensify. The amount of precipitation associated with the cyclones increases by almost 50% and the location of the precipitation moves further downstream away from the cyclone centre. These results indicate that the spatial structure of extra-tropical cyclones may change in the future as the climate.

The climate changes that we observe in the real world result from a combination of two factors. On one hand, climate is affected by changes in external forcing such as the atmospheric composition. On the other hand, the non-linear dynamics of the climate system generate substantial internal variability. It is our goal to better understand the effects of this duality on both the interpretation of observed climate changes and on the climate changes and variability that we may expect in the future. We are also interested in the energetics of climate change and climate variability.

As a recent example, we studied the effects of atmospheric circulation on monthly mean temperature trends in Finland in the years 1979-2018. We found that circulation trends have only had a very modest effect on the observed annual mean warming, but they have substantially modified the changes in some individual months. In particular, they explain the lack of observed warming in June and about a half of the very large warming in December. The temperatures that are nowadays observed in Finland are systematically warmer than they would have been under similar atmospheric circulation four decades ago, with an annual mean difference of about 2ºC.

Ensemble predictions (ENS) in meteorology are operationally applied to assess the day-to-day uncertainties of weather predictions, or "predict the predictability". These uncertainties are due to unavoidable small errors in the initial state and the model formulation. Over the past five or so years, our group has pioneered the use of ENS to assess uncertainties in model closure parameters. All geophysical prediction models contain such parameters and a handful of them are critically important for the forecast quality. To this end, we have developed algorithmic parameter estimation techniques, foremost the ensemble prediction and parameter estimation system (EPPES), which has proven to shift the effort of model tuning from the domain of human learning to the domain of expert assisted machine learning. In academia, running ensemble predictions is tedious due to the huge number of simulations needed to gauge the small but highly non-linear model parameter space. A recent innovation from our group is a software package called OpenEPS, which facilitates administration of the model runs, output file post-processing, and parameter estimation, and opens new opportunities to apply ENS outside operational centres.