The High-Latitude Measurement of Snowfall (HiLaMS) campaign explored variability in snowfall properties and processes at meteorologically distinct field sites located in Haukeliseter, Norway, and Kiruna, Sweden, during the winters of 2016/17 and 2017/18, respectively. Campaign activities were founded upon the sensitivities of a low-cost, core instrumentation suite consisting of Micro Rain Radar, Precipitation Imaging Package, and Multi-Angle Snow Camera. These instruments are highly portable to remote field sites and, considered together, provide a unique and complementary set of snowfall observations including snowflake habit, particle size distributions, fall speeds, surface snowfall accumulations, and vertical profiles of radar moments and snow water content. These snow-specific parameters, used in combination with existing observations from the field sites such as snow gauge accumulations and ambient weather conditions, allow for advanced studies of snowfall processes. HiLaMS observations were used to 1) successfully develop a combined radar and in situ microphysical property retrieval scheme to estimate both surface snowfall accumulation and the vertical profile of snow water content, 2) identify the predominant snowfall regimes at Haukeliseter and Kiruna and characterize associated macrophysical and microphysical properties, snowfall production, and meteorological conditions, and 3) identify biases in the HARMONIE-AROME numerical weather prediction model for forecasts of snowfall accumulations and vertical profiles of snow water content for the distinct snowfall regimes observed at the mountainous Haukeliseter site. HiLaMS activities and results suggest value in the deployment of this enhanced snow observing instrumentation suite to new and diverse high-latitude locations that may be underrepresented in climate and weather process studies.

Meteorological forecast and climate models require good knowledge of the microphysical properties of hydrometeors and the atmospheric snow and ice crystals in clouds. For instance, their size, cross-sectional area, shape, mass, and fall speed. Especially shape is an important parameter in that it strongly affects the scattering properties of ice particles, and consequently their response to remote sensing techniques. The fall speed and mass of ice particles are other important parameters both for numerical forecast models and for the representation of snow and ice clouds in climate models. In the case of fall speed, it is responsible for the rate of removal of ice from these models. The particle mass is a key quantity that connects the cloud microphysical properties to radiative properties. Using an empirical relationship between the dimensionless Reynolds and Best numbers, fall speed and mass can be derived from each other if particle size and cross-sectional area are also known.

In this work, ground-based in-situ measurements of snow particle microphysical properties are used to analyse mass as a function of shape and the other properties particle size, cross-sectional area, and fall speed. The measurements for this study were done in Kiruna, Sweden during snowfall seasons of 2014 to 2019 and using the ground-based in-situ instrument Dual Ice Crystal Imager (D-ICI), which takes high-resolution side- and top-view images of natural hydrometeors. From these images, particle size (maximum dimension), cross-sectional area, and fall speed of individual particles are determined. The particles are shape classified according to the scheme presented in our previous work, in which particles sort into 15 different shape groups depending on their shape and morphology. Particle masses of individual ice particles are estimated from measured particle size, cross-sectional area, and fall speed. The selected dataset covers sizes from about 0.1 mm to 3.2 mm, fall speeds from 0.1 m s−1 to 1.6 m s−1, and masses from close to 0.2 μg to 320 μg. In our previous work, the fall speed relationships between particle size and cross-sectional area were studied. In this work, the same dataset is used to determine the particle mass, and consequently, the mass relationships between particle size, cross-sectional area, and fall speed are studied for these 15 shape groups. Furthermore, the mass relationships presented in this study are compared with the previous studies.

For certain crystal habits, in particular columnar shapes, the maximum dimension is unsuitable for determining Reynolds number. Using a selection of columns, for which the simple geometry allows the verification of an empirical Best-number-to-Reynolds-number relationship, we show that Reynolds number and fall speed are more closely related to the diameter of the basal facet than the maximum dimension. The agreement with the empirical relationship is further improved using a modified Best number, a function of an area ratio based on the falling particle seen in the vertical direction.

Understanding what happens to hydrometeors, such as atmospheric snow particles (ice crystals, snow crystals, and snowflakes) in clouds is crucial for improving meteorolog-ical forecast and climate models. Consequently, improved predictions of the precipitation amount reaching the ground (snowfall) require accurate knowledge of the microphysical properties of ice crystals, such as their size, cross-sectional area, shape, fall speed, and mass. In particular, the shape is an important parameter. It strongly influences the scattering properties of these ice particles. Snowfall has long been monitored by ground-based instruments, but instruments that can simultaneously measure all microphysical properties are still scarce. Accurate knowledge of microphysical properties is essential to achieve more realistic parameterizations in atmospheric models. Also, this knowledge is required for increasing accuracy of different remote sensing applications such as cloud and precipitation retrievals from passive and active measurements from satellites. Questions of particular interest are whether microphysical properties of precipitating snow particles show notably different characteristics depending on location, for instance at high-latitudes and what parame-terizations best describe these microphysical properties. How particle shape affects other properties, such as fall speed and mass, is also important. The particle shape is an important parameter, not only for the investigation of growth processes but also because of its importance for optical remote sensing retrievals of cloud properties and snow albedo. Therefore, studying snow microphysical properties and how they depend on particle shape is crucial to ensure accurate cloud parameterizations in climate and forecast models, and to the understanding of precipitation in cold climates.In this thesis ground-based in-situ measurements carried out in Kiruna, Sweden, are presented. Natural snow, ice crystals, and other hydrometeors covering particle sizes from 0.05 to 4 mm have been classified. Measurements have been taken during the snow-fall season from the beginning of November to the middle of May from 2014 to 2019. A ground-based in-situ instrument, Dual Ice Crystal Imager (D-ICI), which takes high-resolution side- and top-view images of hydrometeors was used. Particle size (maximum dimension), cross-sectional area, area ratio, aspect ratio, fall speed and mass of individual particles have been determined. A novel shape classification, where each particle shape is sorted into different shape groups, has been proposed, comprising a total of 135 unique shapes, including 34 new snow crystal shapes found in Kiruna. The main contributions of this thesis will enhance the improvement in the under-standing of precipitation in a cold climate. An updated snow crystal shape classification is presented and a different shape classification method is proposed. The new snow mea-surements and parameterizations studied in this work for different snow crystal shapes will be useful for climate and forecast models. These parameterizations include rela-tionships between particle size, cross-sectional area, fall speed and mass as a function of shape. The measured data shows a wide spread; however, binning the data according to size or cross-sectional area has improved correlations leading to more reliable parameteri-zations of fall speed versus size or cross-sectional area. Vertically orientated particles fall faster on average, but most particles for which orientation can be defined fall horizontally. The particle mass has been determined from measured particle size, cross-sectional area, and fall speed. When binning the data, the fall speed vs mass, mass vs particle size, and mass vs cross-sectional area relationships also show a high correlation. The relationships presented in this thesis have been compared with the results shown in previous studies.

Improved snowfall predictions require accurate knowledge of the properties of ice crystals and snow particles, such as their size, cross-sectional area, shape, and fall speed. In particular, the shape is an important parameter as it strongly influences the scattering properties of these ice particles, and thus their response to remote sensing techniques such as radar measurements. The fall speed of ice particles is a critical parameter for the representation of ice clouds and snow in atmospheric numerical models, as it determines the rate of removal of ice from the modelled clouds. They are also required for snowfall predictions alongside other properties such as ice particle size, cross-sectional area, and shape. For example, shape is important as it strongly influences the scattering properties of these ice particles, and thus their response to remote sensing techniques.

This work analyses fall speed as a function of shape and other properties using ground-based in-situ measurements. The measurements for this study were done in Kiruna, Sweden during the snowfall seasons of 2014 to 2019, using the ground-based in-situ instrument Dual Ice Crystal Imager (D-ICI). The resulting data consist of high-resolution images of falling hydrometeors from two viewing geometries that are used to determine size (maximum dimension), cross-sectional area, area ratio, orientation, and the fall speed of individual particles. The selected dataset covers sizes from about 0.06 to 3.2 mm and fall speeds from 0.06 to 1.6 m s−1.

The particles are shape-classified into 15 different shape groups depending on their shape and morphology. For these 15 shape groups relationships are studied, firstly, between size and cross-sectional area, then between fall speed and size or cross-sectional area. The data show in general low correlations to fitted fall-speed relationships due to large spread observed in fall speed. After binning the data according to size or cross-sectional area, correlations improve and we can report reliable parameterizations of fall speed vs. size or cross-sectional area for part of the shapes. The effects of orientation and area ratio on the fall speed are also studied, and measurements show that vertically orientated particles fall faster on average. However, most particles for which orientation can be defined fall horizontally.

Accurate predictions of snowfall require good knowledge of the microphysical properties of the snow ice crystals and particles. Shape is an important parameter as it strongly influences the scattering properties of the ice particles, and thus their response to remote sensing techniques such as radar measurements. The fall speed of ice particles is another important parameter for both numerical forecast models as well as representation of ice clouds and snow in climate models, as it is responsible for the rate of removal of ice from these models.

We describe a new ground-based in situ instrument, the Dual Ice Crystal Imager (D-ICI), to determine snow ice crystal properties and fall speed simultaneously. The instrument takes two high-resolution pictures of the same falling ice particle from two different viewing directions. Both cameras use a microscope-like setup resulting in an image pixel resolution of approximately 4 µm pixel−1. One viewing direction is horizontal and is used to determine fall speed by means of a double exposure. For this purpose, two bright flashes of a light-emitting diode behind the camera illuminate the falling ice particle and create this double exposure, and the vertical displacement of the particle provides its fall speed. The other viewing direction is close-to-vertical and is used to provide size and shape information from single-exposure images. This viewing geometry is chosen instead of a horizontal one because shape and size of ice particles as viewed in the vertical direction are more relevant than these properties viewed horizontally, as the vertical fall speed is more strongly influenced by the vertically viewed properties. In addition, a comparison with remote sensing instruments that mostly have a vertical or close-to-vertical viewing geometry is favoured when the particle properties are measured in the same direction.

The instrument has been tested in Kiruna, northern Sweden (67.8∘ N, 20.4∘ E). Measurements are demonstrated with images from different snow events, and the determined snow ice crystal properties are presented.

We present ground-based in situ snow measurements in Kiruna, Sweden, using the ground-based in situ instrument Dual Ice Crystal Imager (D-ICI). D-ICI records dual high-resolution images from above and from the side of falling natural snow crystals and other hydrometeors with particle sizes ranging from 50 µm to 4 mm. The images are from multiple snowfall seasons during the winters of 2014/2015 to 2018/2019, which span from the beginning of November to the middle of May. From our images, the microphysical properties of individual particles, such as particle size, cross-sectional area, area ratio, aspect ratio, and shape, can be determined. We present an updated classification scheme, which comprises a total of 135 unique shapes, including 34 new snow crystal shapes. This is useful for other studies that are using previous shape classification schemes, in particular the widely used Magono–Lee classification. To facilitate the study of the shape dependence of the microphysical properties, we further sort these individual particle shapes into 15 different shape groups. Relationships between the microphysical properties are determined for each of these shape groups.

The energy requirements of the planetary exploration spacecrafts constrain the lifetime of the missions, their mobility and capabilities, and the number of instruments onboard. They are limiting factors in planetary exploration. Several missions to the surface of Mars have proven the feasibility and success of solar panels as energy source. The analysis of the exergy efficiency of the solar radiation has been carried out successfully on Earth, however, to date, there is not an extensive research regarding the thermodynamic exergy efficiency of in-situ renewable energy sources on Mars. In this paper, we analyse the obtainable energy (exergy) from solar radiation under Martian conditions. For this analysis we have used the surface environmental variables on Mars measured in-situ by the Rover Environmental Monitoring Station onboard the Curiosity rover and from satellite by the Thermal Emission Spectrometer instrument onboard the Mars Global Surveyor satellite mission. We evaluate the exergy efficiency from solar radiation on a global spatial scale using orbital data for a Martian year; and in a one single location in Mars (the Gale crater) but with an appreciable temporal resolution (1 h). Also, we analyse the wind energy as an alternative source of energy for Mars exploration and compare the results with those obtained on Earth. We study the viability of solar and wind energy station for the future exploration of Mars, showing that a small square solar cell of 0.30 m length could maintain a meteorological station on Mars. We conclude that the low density of the atmosphere of Mars is responsible of the low thermal exergy efficiency of solar panels. It also makes the use of wind energy uneffective. Finally, we provide insights for the development of new solar cells on Mars.

The Graduate School of Space Technology