Press release 2021-02-02 at 13:30
A researcher of the Finnish Environment Institute, Jussi Tiira, has studied the impact of snow growth processes on snowfall, and further their manifestation in radar observations. The results are expected to improve quantitative snowfall estimation with radars and the representation of snow processes in numerical weather and climate models. Jussi Tiira is defending his thesis at the University of Helsinki on Friday, 5 February.
Measuring snowfall intensity accurately from radar observations is a big challenge since the large variability in snow properties affects the measurements.
”Quantitative snowfall estimation with weather radars suffers from large uncertainties if the shapes, densities and surface structure of target ice particles are unknown. These microphysical properties have an important impact on radar reflectivity. Snow microphysics and its manifestation in radar observations are a key focus in my thesis”, says Jussi Tiira.
Detailed knowledge on the scattering of radar microwave pulses from the target snow particles is needed for accurate snowfall intensity measurements from the radar. The scattering properties, however, are deeply linked to the greatly varying microphysical properties of snow. This is why we need to know what type of snow is falling in order to make accurate measurements.
Improved knowledge on snow growth
The research advanced our understanding especially on riming and aggregation of ice particles and how they affect snow properties. The two processes affect especially snow density; riming increases it and may eventually result in graupel, while snow aggregates can grow large and fluffy.
A new method was introduced for retrieving the density of falling snow using a combination of high frame rate video disdrometer and a precipitation gauge. The method was then applied for estimating the effect of riming on snow. The results show that, depending on the ambient conditions, riming is responsible for 5 to 40 percent of snow mass.
The same density retrieval method was used for investigating the manifestation of riming and aggregation in radar measurements, and how the density is linked to other properties of falling snow. The results can be used for improving the representation of snow processes in numerical models and quantitative snowfall estimation using radars. A perquisite for this is, however, that the radar fingerprints of snow processes can be detected automatically. For this purpose, a machine learning method is proposed in the thesis.
The research was enabled by the versatile, state of the art measurement setup at the University of Helsinki measurement station Hyytiälä in Juupajoki.
In addition to standard meteorological measurements such as of wind and temperature, various instruments for measuring clouds and precipitation are deployed on the measurement site. These include radars and other remote sensing instruments, and probes providing in situ measurements of precipitation particle properties at the surface.
Further information
Jussi Tiira, M.Sc., researcher, Finnish Environment Institute SYKE.
tel. +358 295 252 125, firstname.lastname@syke.fi
Jussi Tiira is defending his thesis on 5 February 2021 at 12.15 in the Faculty of Science, University of Helsinki. The defence of the dissertation will be in English.
Address: University of Helsinki, Physicum building, room E204, Gustaf Hällströmin katu 2, Helsinki. It will be possible to watch a video stream of the event (zoom). Code: 200637
Events of the University of Helsinki
Dissertation: Snowfall microphysics in surface-based and radar observations