Challenges in measuring winter precipitation : Advances in combining microwave remote sensing and surface observations

Globally, snow influences Earth and its ecosystems in several ways by having a significant impact on, e.g., climate and weather, Earth radiation balance, hydrology, and societal infrastructures. In mountainous regions and at high latitudes snowfall is vital in providing freshwater resources by accumulating water within the snowpack and releasing the water during the warm summer season. Snowfall also has an impact on transportation services, both in aviation and road maintenance. Remote sensing instrumentation, such as radars and radiometers, provide the needed temporal and spatial coverage for monitoring precipitation globally and on regional scales. In microwave remote sensing, the quantitative precipitation estimation is based on the assumed relations between the electromagnetic and physical properties of hydrometeors. To determine these relations for solid winter precipitation is challenging. Snow particles have an irregular structure, and their properties evolve continuously due to microphysical processes that take place aloft. Hence also the scattering properties, which are dependent on the size, shape, and dielectric permittivity of the hydrometeors, are changing. In this thesis, the microphysical properties of snowfall are studied with ground-based measurements, and the changes in prevailing snow particle characteristics are linked to remote sensing observations. Detailed ground observations from heavily rimed snow particles to openstructured low-density snowflakes are shown to be connected to collocated triple-frequency signatures. As a part of this work, two methods are implemented to retrieve mass estimates for an ensemble of snow particles combining observations of a video-disdrometer and a precipitation gauge. The changes in the retrieved mass-dimensional relations are shown to correspond to microphysical growth processes. The dependence of the C-band weather radar observations on the microphysical properties of snow is investigated and parametrized. The results apply to improve the accuracy of the radar-based snowfall estimation, and the developed methodology also provides uncertainties of the estimates. Furthermore, the created data set is utilized to validate space-borne snowfall measurements. This work demonstrates that the C-band weather radar signal propagating through a low melting layer can significantly be attenuated by the melting snow particles. The expected modeled attenuation is parametrized according to microphysical properties of snow at the top of the melting layer

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

How dual-polarization radar observations can be used to verify model representation of secondary ice

In this paper it is discussed how dual-polarization radar observations can be used to verify model representations of secondary ice production. An event where enhanced specific differential phase, K-dp, signatures in snow occur at the altitudes where temperatures lie in the range between -8 and -3 degrees C is investigated. By combining radar and surface-based precipitation observations it is shown that these dual-polarization radar signatures are most likely caused by ice with concentrations exceeding those expected from primary ice parameterizations. It is also shown that the newly formed ice particles readily aggregate, which may explain why K-dp values seem to be capped at 0.2-0.3 degrees/km for a Cband radar. For the event of interest, multiple high-resolution (1km) Weather Research and Forecasting (WRF) model simulations are conducted. When the default versions of the Morrison microphysics schemes were used, the simulated number concentration of frozen hydrometeors is much lower than observed and the simulated ice particle concentrations are comparable with values expected from primary ice parameterizations. Higher ice concentrations, which exceed values expected from primary ice parameterizations, were simulated when adhoc thresholds for rain and cloud water mixing ratio in the Hallett-Mossop part of the Morrison scheme were removed. These results suggest that the parameterization of secondary ice production in operational weather prediction models needs to be revisited and that dual-polarization radar observations, in conjunction with ancillary observations, can be used to verify them.Peer reviewe

Retrieval of Snow Water Equivalent by the Precipitation Imaging Package (PIP) in the Northern Great Lakes

Performance of the Precipitation Imaging Package (PIP) for estimating the snow water equivalent (SWE) is evaluated through a comparative study with the collocated National Oceanic and Atmospheric Administration National Weather Service snow stake field measurements. The PIP together with a vertically pointing radar, a weighing bucket gauge, and a laser-optical disdrometer was deployed at the NWS Marquette, Michigan, office building for a long-term field study supported by the National Aeronautics and Space Administration's Global Precipitation Measurement mission Ground Validation program. The site was also equipped with a weather station. During the 2017/18 winter, the PIP functioned nearly uninterrupted at frigid temperatures accumulating 2345.8 mm of geometric snow depth over a total of 499 h. This long record consists of 30 events, and the PIP-retrieved and snow stake field measured SWE differed less than 15% in every event. Two of the major events with the longest duration and the highest accumulation are examined in detail. The particle mass with a given diameter was much lower during a shallow, colder, uniform lake-effect event than in the deep, less cold, and variable synoptic event. This study demonstrated that the PIP is a robust instrument for operational use, and is reliable for deriving the bulk properties of falling snow.Peer reviewe

Microphysical Properties of Snow and Their Link to Z(e)-S Relations during BAECC 2014

This study uses snow events from the Biogenic Aerosols-Effects on Clouds and Climate (BAECC) 2014 campaign to investigate the connection between properties of snow and radar observations. The general hydrodynamic theory is applied to video-disdrometer measurements to retrieve masses of falling ice particles. Errors associated with the observation geometry and the measured particle size distribution (PSD) are addressed by devising a simple correction procedure. The value of the correction factor is determined by comparison of the retrieved precipitation accumulation with weighing-gauge measurements. Derived mass-dimensional relations are represented in the power-law form m = a(m)D(m)(b). It is shown that the retrieved prefactor a(m) and exponent b(m) react to changes in prevailing microphysical processes. From the derived microphysical properties, event-specific relations between the equivalent reflectivity factor Z(e) and snowfall precipitation rate S (Z(e) = a(zs)S(zs)(b)) are determined. For the studied events, the prefactor of the Z(e)-S relation varied between 53 and 782 and the exponent was in the range of 1.19-1.61. The dependence of the factors a(zs) and b(zs) on the m(D) relation and PSD are investigated. The exponent of the Z(e)-S relation mainly depends on the exponent of the m(D) relation, whereas the prefactor a(zs) depends on both the intercept parameter N-0 of the PSD and the prefactors of the m(D) and nu(D) relations. Changes in a(zs) for a given N-0 are shown to be linked to changes in liquid water path, which can be considered to be a proxy for degree of riming.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

The Precipitation Imaging Package : Assessment of Microphysical and Bulk Characteristics of Snow

Remote-sensing observations are needed to estimate the regional and global impacts of snow. However, to retrieve accurate estimates of snow mass and rate, these observations require augmentation through additional information and assumptions about hydrometeor properties. The Precipitation Imaging Package (PIP) provides information about precipitation characteristics and can be utilized to improve estimates of snowfall rate and accumulation. Here, the goal is to demonstrate the quality and utility of two higher-order PIP-derived products: liquid water equivalent snow rate and an approximation of volume-weighted density called equivalent density. Accuracy of the PIP snow rate and equivalent density is obtained through intercomparison with established retrieval methods and through evaluation with colocated ground-based observations. The results confirm the ability of the PIP-derived products to quantify properties of snow rate and equivalent density, and demonstrate that the PIP produces physically realistic snow characteristics. When compared to the National Weather Service (NWS) snow field measurements of six-hourly accumulation, the PIP-derived accumulations were biased only +2.48% higher. Additionally, this work illustrates fundamentally different microphysical and bulk features of low and high snow-to-liquid ratio events, through assessment of observed particle size distributions, retrieved mass coefficients, and bulk properties. Importantly, this research establishes the role that PIP observations and higher-order products can serve for constraining microphysical assumptions in ground-based and spaceborne remotely sensed snowfall retrievals.Peer reviewe

Edistysaskelia lumi- ja räntäsateiden mittaamisessa yhdistämällä kaukokartoitus- ja pintahavaintoja

Globally, snow influences Earth and its ecosystems in several ways by having a significant impact on, e.g., climate and weather, Earth radiation balance, hydrology, and societal infrastructures. In mountainous regions and at high latitudes snowfall is vital in providing freshwater resources by accumulating water within the snowpack and releasing the water during the warm summer season. Snowfall also has an impact on transportation services, both in aviation and road maintenance. Remote sensing instrumentation, such as radars and radiometers, provide the needed temporal and spatial coverage for monitoring precipitation globally and on regional scales. In microwave remote sensing, the quantitative precipitation estimation is based on the assumed relations between the electromagnetic and physical properties of hydrometeors. To determine these relations for solid winter precipitation is challenging. Snow particles have an irregular structure, and their properties evolve continuously due to microphysical processes that take place aloft. Hence also the scattering properties, which are dependent on the size, shape, and dielectric permittivity of the hydrometeors, are changing. In this thesis, the microphysical properties of snowfall are studied with ground-based measurements, and the changes in prevailing snow particle characteristics are linked to remote sensing observations. Detailed ground observations from heavily rimed snow particles to open-structured low-density snowflakes are shown to be connected to collocated triple-frequency signatures. As a part of this work, two methods are implemented to retrieve mass estimates for an ensemble of snow particles combining observations of a video-disdrometer and a precipitation gauge. The changes in the retrieved mass-dimensional relations are shown to correspond to microphysical growth processes. The dependence of the C-band weather radar observations on the microphysical properties of snow is investigated and parametrized. The results apply to improve the accuracy of the radar-based snowfall estimation, and the developed methodology also provides uncertainties of the estimates. Furthermore, the created data set is utilized to validate space-borne snowfall measurements. This work demonstrates that the C-band weather radar signal propagating through a low melting layer can significantly be attenuated by the melting snow particles. The expected modeled attenuation is parametrized according to microphysical properties of snow at the top of the melting layer.Lumi vaikuttaa maapallon ilmastoon, ekosysteemeihin, säteilytasapainoon ja hydrologiseen kiertoon. Vuoristoisilla alueilla sekä pohjoisessa lumisade kerryttää elintärkeitä makean veden varantoja, ja se voi vaikeuttaa sekä maa- että lentoliikennettä. Kaukokartoitusinstrumentit, kuten tutkat ja radiometrit, mahdollistavat sateen havainnoinin maailmanlaajuisesti suurella paikallisella ja ajallisella tarkkuudella. Mikroaaltokaukokartoituksessa sateen mittaaminen perustuu havaitun sadepisaran tai lumihiutaleen sähkömagneettisten ja fysikaalisten ominaisuuksien välisen riippuvuuden mallintamiseen. Lumi-ja räntäsateesta kertyvän sademäärän arvioiminen on haastavaa. Lumikiteet ja -hiutaleet ovat muodoltaan epäsäännöllisiä ja niiden ominaisuudet muotoutuvat jatkuvasti ilmakehässä eri mikrofysikaalisissa prosesseissa. Näin ollen, sirontaominaisuudet, jotka ovat riippuvaisia kohteen koosta, muodosta ja sähköisestä permittiivisyydestä, muuttuvat ja vaihtelevat hetkestä ja tilanteesta toiseen. Tässä työssä lumisateen mikrofysikaalisia ominaisuuksia on tutkittu maan pinnalla olevilla laitteilla, ja havaitut muutokset ominaisuuksissa on yhdistetty kaukokartoitusmittauksiin. Tarkkoja havaintoja erilaisista lumihiutaleista, niin vahvasti huurtuneista lumirakeista kuin avoimen rakenteen omaavista lumihiutaleista, on yhdistetty kolmitaajuustutkamittauksissa havaittujen ominaisuuksien kanssa.Työssä on toteutettu kaksi eri menetelmää lumihiutaleiden massan tai tiheyden määrittämiseen yhdistäen automaattisia disdrometri- ja sademittarihavainnointoja. Lumihiutaleiden massa-koko -suhteen muutokset on osoitettu olevan yhteydessä ilmakehässä havaittaviin lumihiutaleen kasvutapoihin. Näitä massa-koko -suhdemuutoksia on tutkimuksessa hyödynnetty myös C-taajuusalueen tutkahavaintojen tulkinnassa, ja tutkahavaintojen riippuvuutta lumisateen mikrofysikaalisien ominaisuuksien osalta on tutkittu ja parametrisoitu. Tulosten avulla lumisateen määrää voidaan säätutkalla arvioida tarkemmin ja määrittää virherajat arvioidulle sademäärälle. Tutkimuksessa luotua aineistoa on edelleen käytetty satelliittipohjaisen lumisadetuotteen validoinnissa. Tässä työssä on myös osoitettu, että lähellä maanpintaa olevan sulamiskerroksen aiheuttava vaimennus C-taajuusalueella toimivalle säätutkalle on merkittävä. Vaimennuksen suuruus on mallinnuksen avulla yhdistetty lumen mikrofysikaalisiin ominaisuuksiin sulamiskerroksen yläpuolella

Kahden vaiheenpalautusalgoritmin vertailu alimillimetriaaltoalueella

Diplomityössä on tutkittu mahdollisuutta käyttää iteratiivisia vaiheenpalautusalgoritmeja alimillimetriaaltoalueen antennimittauksissa. Työhän on valittu kaksi alan kirjallisuudessa esitettyä algoritmia ja ne on kirjoitettu Matlabin ohjauskielellä vertailua varten. Valitut algoritmit ovat Kalifornian yliopiston (UCLA) tutkimusryhmän iteratiiviseen Fourier-menetelmään perustuva tasolta-tasolle-diffraktioalgoritmi (PPD) ja Napolin sekä Salernon yliopistojen tutkimusryhmän käyttämä konjugaattigradienttialgoritmi (CGM). Algoritmit laskevat sähkökentän amplitudin sekä vaiheen antennin apetuurissa kahden amplitudi- tai intensiteettitasomittauksen perusteella. Molemmat mittaustasot sijaitsevat antennin lähikentässä tietyn etäisyyden päässä toisistaan. Lisäinformaationa PPD-algoritmi tarvitsee antennin apertuurin koon. Algoritmeja on vertailtu toisiinsa mallinnetun esimerkkikentän avulla 310 GHz:n taajuudella antennin apertuurin ollessa neliskulmainen ja kooltaan 40 lambda x 40 lambda. Algoritmien vertailussa on tutkittu niiden suppenevuutta, kahden mittaustason välimatkan merkitystä algoritmien suorituskykyyn sekä kohinan, näytteenottovälin kasvattamisen ja näytteenottoantennin paikkavirheiden vaikutusta. PPD-algoritmin havaittiin suppenevan nopeasti ja saavuttavan hyviä tuloksia tilanteissa, jossa seuraavat kriteerit täyttyivät: 1) mittaustasojen välisen etäisyyden tuli olla riittävä, 2) näytteenottovälin tuli olla <=lambda/2 ja 3) kohina ja mittausvirheet eivät saaneet olla liian merkittäviä. CGM-algoritmi havaittiin suppenevuudeltaan huomattavasti PPD-algoritmia hitaammaksi, mutta kestävämmäksi erilaisille muutoksille mittauksissa. CGM-algoritmin hitaus johtuu kulma-alueen vaiheittaisesta kasvattamisesta, joka estää iteraatiotuloksen ajautumisen paikalliseen minimiin globaalin ratkaisun sijasta. Diplomityötä varten mitattiin kaksi y-suuntaista leikkausta 310 GHz:n taajuudella hologrammiin perustuvassa kompaktin mittauspaikan hiljaisessa alueessa. Mittauksissa haluttiin selvittää algoritmien soveltuvuutta hiljaisen alueen analysointiin. Hologrammin halkaisija on 0,6 m ja yhdensuuntaiset mittaustasot sijaitsivat 1,8 m:n ja 2,02 m:n päässä hologrammista. Algoritmit eivät pystyneet laskemaan vaihetta oikein amplitudimittausten perusteella, sillä yksiulotteinen tasoaaltohajotelma ei pysty riittävällä tarkkuudella mallintamaan todellista kaksiulotteisesta hologrammista säteilevää kenttää

Validation of Microphysical Snow Models Using In Situ, Multifrequency, and Dual-Polarization Radar Measurements in Finland

As complex forward models for snow have become common in radar-based retrievals, there is a demand to validate these models in different environments. In this study, we perform a qualitative, general validation for nine different snow models that have been published and are available to users. The chosen models span a variety of different snow types, such as aggregates, rimed aggregates, melted aggregates, graupel, and single crystals, mainly because these particles are commonly observed in the Finnish climate. Fitted power law formulas for mass, fall velocity, aspect ratio, and area ratio are compared between the models and 5-year winter measurements in the Hyytiala forestry field station in Finland. We also compare the backscattering properties of the models to triple-frequency dual-polarization radar measurements during the Biogenic Aerosols Effects on Clouds and Climate campaign in 2014. We find that the denser models, regardless of the exact shapes, fit the in situ measurements best due to the prevalence of rime in the falling snow. However, when comparing also to the triple-frequency radar measurements at X, Ka, and W bands, and the linear depolarization ratio at Ka band, the physical snow models fit overall better than the empirical ones