Unsupervised classification of vertical profiles of dual polarization radar variables

Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an unsupervised classification method. The method is based on k-means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase. For rain events, the classification is applied to radar profiles truncated at the melting layer top. For the snowfall cases, the surface air temperature is used as an additional input parameter. The proposed unsupervised classification was applied to 3.5 years of data collected by the Finnish Meteorological Institute Ikaalinen radar. The vertical profiles of radar variables were computed above the University of Helsinki Hyytiala station, located 64 km east of the radar. Using these data, we show that the profiles of radar variables can be grouped into 10 and 16 classes for rainfall and snowfall events, respectively. These classes seem to capture most important snow growth and ice cloud processes. Using this classification, the main features of the precipitation formation processes, as observed in Finland, are presented.Peer reviewe

Towards the connection between snow microphysics and melting layer : insights from multifrequency and dual-polarization radar observations during BAECC

In stratiform rainfall, the melting layer (ML) is often visible in radar observations as an enhanced reflectivity band, the so-called bright band. Despite the ongoing debate on the exact microphysical processes taking place in the ML and on how they translate into radar measurements, both model simulations and observations indicate that the radar-measured ML properties are influenced by snow microphysical processes that take place above it. There is still, however, a lack of comprehensive observations to link the two. To advance our knowledge of precipitation formation in ice clouds and provide new insights into radar signatures of snow growth processes, we have investigated this link This study is divided into two parts. Firstly, surface-based snowfall measurements are used to develop a new method for identifying rimed and unrimed snow from X- and Ka-band Doppler radar observations. Secondly, this classification is used in combination with multifrequency and dual-polarization radar observations collected during the Biogenic Aerosols - Effects on Clouds and Climate (BAECC) experiment in 2014 to investigate the impact of precipitation intensity, aggregation, riming and dendritic growth on the ML properties. The results show that the radar-observed ML properties are highly related to the precipitation intensity. The previously reported bright band "sagging" is mainly connected to the increase in precipitation intensity. Ice particle riming plays a secondary role. In moderate to heavy rainfall, riming may cause additional bright band sagging, while in light precipitation the sagging is associated with unrimed snow. The correlation between ML properties and dual-polarization radar signatures in the snow region above appears to be arising through the connection of the radar signatures and ML properties to the precipitation intensity. In addition to advancing our knowledge of the link between ML properties and snow processes, the presented analysis demonstrates how multifrequency Doppler radar observations can be used to get a more detailed view of cloud processes and establish a link to precipitation formation.Peer reviewe

Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland

In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiala are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). The density of snow is derived from measurements of particle fall velocity and PSD, provided by a particle video imager, and weighing gauge measurements of precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. A relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies, but it is observed to vary significantly from one winter to the other. From these observations, characteristic mass-dimensional relations of snow are retrieved. For snow rates more than 0.2 mm h(-1), a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed.Peer reviewe

Ensemble mean density and its connection to other microphysical properties of falling snow

Naturally occurring snowflakes represent a variety of shapes, sizes, fall velocities and degrees of riming, which makes their representation challenging in numerical models and remote sensing retrievals. In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiälä are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). A new generation video imager, the Particle Imaging Package (PIP), was employed for this study. The simple open structure, robust design, easy maintenance and low cost of the instrument make it attractive for studying winter precipitation microphysics. The density of snow is derived from measurements of particle fall velocity and PSD, provided by the PIP, and weighing gauge measurements of liquid water equivalent precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. The results show that a single camera video imager such as the PIP can succesfully be utilized for such studies. A relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies, but it is observed to vary significantly from one winter to the other. From these observations, characteristic mass-dimensional relations of snow are retrieved. For snow rates more than 0.2 mm/h, a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed

Snowfall microphysics in surface-based and radar observations

Snow has an important impact on hydrology, agriculture, climate and weather, infrastructure and different forms of both aerial and land transportation. The accumulation and properties of snow are inherently connected to the microphysical processes through which the falling ice particles grow. Furthermore, snow processes affect rainfall as well, since the vast majority of rain events originate as melted snow. For monitoring precipitation, the spatial coverage and resolution of radar instrumentation are unmatched. The quality of quantitative precipitation estimation using radars depends on our ability to establish meaningful relations between microphysical and electromagnetic scattering properties of hydrometeors. Especially for snow particles, these properties are diverse and the relations between them complex involving prominent uncertainties and knowledge gaps. Furthermore, the properties are constantly evolving as the falling particles undergo series of microphysical processes including growth from vapour, aggregation and riming. This dissertation work addresses these knowledge gaps by parametrizing microphysical properties of falling snow using ground based measurements, investigating the links between the properties and ice processes, and further studying their manifestations in collocated and off-site radar observations. A novel method is introduced for retrieving ensemble mean density of falling snow using a video disdrometer and a precipitation gauge. These retrievals are used in identifying triple frequency radar signatures of rimed particles and low density aggregates, and to develop a method for retrieving rime mass fraction. Based on the rime mass fraction retrievals, the effect of riming to snowfall is quantified. Using multifrequency Doppler radar and scanning C band radar observations we show that the downward streching of melting layer is linked primarily to precipitation intensity and secondarily to riming. Machine learning methods are employed in objectively documenting and automatically detecting known polarimetric fingerprints of ice microphysical processes in vertical profiles of radar variables. Automated ice process detection is anticipated to open the door for adaptive radar retrieval methods of snowfall rate.Lumella on merkittäviä vaikutuksia hydrologiaan, maanviljelykseen, ilmastoon, liikenteeseen ja rakennettuun ympäristöön. Satavan lumen määrä ja ominaisuudet liittyvät olennaisesti niihin fysikaalisiin prosesseihin, joiden myötä lumipartikkelit syntyvät ja kasvavat tai muulla tavoin muuntuvat. Myös vesisade on tropiikin ulkopuolella useimmiten sulanutta lunta. Lumiprosessien parempi tuntemus ja havaitseminen ovatkin keskeisiä tekijöitä sateen säätutkamittausten kehittämiseksi ja lumisateen tarkemmaksi kuvaamiseksi ilmakehämalleissa. Erityisesti säätutkahavainnontekoon lumisateessa liittyy suuria epävarmuustekijöitä johtuen lumen sirontaominaisuuksien kirjavasta vaihtelusta sen fysikaalisten ominasuuksien, kuten koko- ja muotojakauman, sekä tiheyden ja pintarakenteen mukaan. Väitöskirjassa satavan lumen ominaisuuksia on tutkittu maan pinnalla olevilla havaintolaitteilla. Lumipartikkeleiden tiheyden määrittämiseksi kehitettiin uusi suurnopeusvideomittalaiteen ja sademittarin yhdistelmää hyödyntävä menetelmä. Tutkimuksissa saatiin uutta tietoa sekä lumen fysikaalisten ominaisuuksien keskinäisistä riippuvuussuhteista että niiden liittymisestä lumiprosesseihin ja näiden yhdistämisestä tutkahavaintoihin. Tiheydenmääritysmenetelmän avulla määritettiin satavan lumen huurtumisen ja yhteentakertumisen tunnusomaiset jäljet kolmitaajuustutkamittauksissa, sekä kehitettiin menetelmä huurtumisen massaosuuden määrittämiseksi. Osoitimme että huurtuminen selittää Suomessa 5-40% satavan lumen massasta, ja että se on yhteydessä myös tutkalla havaittavan sulamiskerroksen paksuuntumiseen. Vielä vahvempi yhteys löytyi kerroksen paksuuntumisen ja sateen voimakkuuden väliltä. Jotta lumiprosessien tunnettuja vaikutuksia sateeseen voitaisiin hyödyntää tosiaikaisissa säätutkamittauksissa, on niiden tunnusomaiset merkit kyettävä tunnistamaan tutkahavainnoista automaattisesti. Osoitimme tavanomaisten koneoppimismenetelmien olevan lupaavia työkaluja tähän tarkoitukseen