Exploring Snowfall Variability through the High-Latitude Measurement of Snowfall (HiLaMS) Field Campaign

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.Exploring Snowfall Variability through the High-Latitude Measurement of Snowfall (HiLaMS) Field CampaignpublishedVersio

Ice clouds in satellite observations and climate models

This thesis concerns the microphysical properties of clouds made up of ice particles, called ice clouds. Ice clouds are strong modulators of the outgoing longwave radiation and incoming shortwave radiation, yet our knowledge on several key ice cloud properties, which govern the magnitude and sign of the net contribution to the Earth’s atmospheric radiation budget, is inadequate. For instance, currently climate models are far from consensus on the magnitude and spatial distribution of ice water path (IWP), a vital radiative property of ice clouds, and the main property of concern in this thesis. The large spread amongst the models in terms of IWP is mostly due to the lack of constraints from observations on ice cloud properties. The lacking constraints reflect the major difficulties faced in observing global ice cloud properties.In-situ measurements provide useful sources of information on ice clouds, but are far from adequate due to the sparseness of measurements. Cloud ice observations from satellites provides a global view and is the most useful source of information. However, measurements from satellites also carry large uncertainties and are notoriously difficult to use for model evaluation, due to a mismatch on how IWP is defined in the models compared to what is actually observed. Not one satellite instrument can measure ice particle information from the entire ice cloud column, as desired from the model point of view. Satellite observations of IWP depend for the most part on the wavelength spectrum the instrument measures in, hence the instruments measure related, but different information on clouds.A study addressing the satellite observed and modeled IWP is presented in the first appended article: Eliasson et al. [2011]. Large differences between climate models are observations, especially in areas with frequent deep convection, were reported and discussed. The second appended article is a first evaluation study of cloud parameters, such as IWP, in the EC-Earth climate model using satellite A-Train observations. The model captures large-scale features for the most part but has problems related to ice water content and cloud fraction. This is strongly linked to the treatment of precipitation.The thesis contains introductory chapters on ice clouds; their formation, radiative importance, and representation in climate models. This is followed by a more in depth chapter on the observational data. The different satellite techniques are then discussed following a radiation physics and radiative transfer background section.Godkänd; 2011; 20110824 (ysko); LICENTIATSEMINARIUM Ämnesområde: Rymdteknik/Space Engineering Examinator: Professor Stefan Buehler, Institutionen för system- och rymdteknik, Luleå tekniska universitet Diskutant: Dr. sc. ETH Helmholtz Young Investigators Group Leader Corinna Hoose, Karlsruhe Institute of Technology, Germany Tid: Måndag den 24 oktober 2011 kl 10.00 Plats: Kiruna Rymdcampus, Luleå tekniska universite

An Extrapolation Technique of Cloud Characteristics Using Tropical Cloud Regimes

This thesis tests a technique based on objectively identified tropical cloud regimes, in which some cloud characteristics are extrapolated from a single site in the tropics to the entire tropics. Information on cloud top pressure, cloud optical thickness and total cloud cover from 1985-2000 has been derived from the ISCCP D1 data set and has been used to create maps of tropical cloud regimes and maps of total cloud cover over the tropics. The distribution and characteristics of the tropical cloud regimes has been discussed after which total cloud cover values were extrapolated to the cloud regimes over the tropics. After a qualitative and quantitative assessment was used to evaluate the success of the extrapolating method, it was found that the method worked especially well for time averaged extrapolated data sets using the median values of total cloud cover values.I detta magisterexamensarbete testas en metod som baseras på objektivt framtagna molnregimer, där några molnegenskaper extrapoleras från en plats i tropikerna till resten av tropikerna. Informationen om molntoppstrycket, molnens optiska djup och det totala molntäcket från 1985-2000 har hämtats från ISCCP D1 data set och har använts till att skapa kartor för tropiska molnregimer och för det totala molntäcket över tropikerna. Distributionen och egenskaperna av de tropiska molnregimerna har diskuterats och användes sedan för att extrapolera det totala molntäcket över tropikerna. En kvalitativ och kvantitativ undersökning användes för att utvärdera framgångarna med extrapoleringsmetoden. Det framkom att metoden fungerade särskilt bra för extrapolerade data set med median totala molntäcksvärden över längre tidsperioder

Ice clouds in satellite observations and climate models

Ice clouds have an important role in climate. They are strong modulators of the outgoing longwave radiation and the incoming shortwave radiation and are an integral part of the hydrological cycle. However, our knowledge about them is inadequate. Climate models are far from consensus on the magnitude and spatial distribution of several cloud parameters, including the column integrated cloud ice amount, called Ice Water Path (IWP). The lack of adequate constraints from observations is a main contributor to the non-consensus. Cloud ice retrievals from satellite measurements are an important source of observations, since they are global and continuous. However, they carry large uncertainties since different sensors are sensitive to different aspects of clouds, and because clouds are largely inhomogeneous with complicated microphysical properties. Satellite observations are also notoriously difficult to use for model evaluation, due to a mismatch on how cloud parameters are defined in the models compared to what is actually observed. No satellite instrument can measure information from the entire cloud column, as desired from the model point of view. This thesis mainly concerns IWP, which is one of the key cloud parameters. By measuring clouds using different techniques at different wavelengths, the IWP retrievals are sensitive to different parts of the ice particle size distribution, and different depths in the cloud. A main aim of the PhD project is to assess the agreement of datasets based on different techniques and how they may be complementary. This investigation of IWP in observations and models starts by a comparison study of monthly averaged IWP from a climate perspective. The study shows that the differences in IWP within a group of models, and compared to observations are up to an order of magnitude. This confirmed results from previous studies, but in this study, large differences in the spatial distribution of IWP are also identified. The spatial distributions of modelled IWP indicate that they are in disagreement on where the Tropical convective regions are and how much IWP is found there in relation to the global averaged IWP. However, the observational datasets also differ by up to an order of magnitude and the uncertainties for the monthly averaged observations are almost intangibly large. This prompted a new study comparing strictly collocated observations to each other. By doing so, large uncertainties caused by spatially and temporally averaging data were removed. DARDAR, with IWP retrievals based on a combination of Radar and Lidar measurements, is regarded as the best dataset of IWP, and was therefore chosen as the reference dataset. This study determines that DARDAR has a relatively low uncertainty of between 20% to 50%. The validity ranges of the other datasets, i.e., the IWP values where data are trustworthy, are determined by comparing to DARDAR IWP. Once established for each dataset, the systematic and random errors of each dataset are quantified. It is shown that retrievals based on solar reflectance measurements are sensitive to the largest range of IWP values, from ∼30 gm-2 to ∼7000 gm-2, and have random uncertainties less than a factor of two throughout most of this range. To analyse the uncertainties further, the collocated measurements are assessed separately in different types of cloudy scenarios. It is shown that large uncertainties are attributed to the assumed cloud phase and the choice of IWP parameterisations. Further in depth studies on models were carried out using the EC-Earth climate model. A validation study of several upper tropospheric parameters showed that the model captures most large-scale features but has problems with clouds. This led to another study comparing the modelled evolution of several atmospheric variables before and after deep convection events to that of observations. A follow-up study analyses the impacts of clouds on upper tropospheric humidity (UTH) retrievals depending on if they are based on microwave or infrared measurements. By these cross-dataset comparisons we are closer to understanding how to utilise datasets that normally are not comparable due to their different sensitivities.Godkänd; 2013; 20121112 (saleli); DISPUTATION Ämne: Rymdteknik/Space Technology Opponent: Assistant professor Tristan L’Ecuyer, Dept of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, USA Ordförande: Professor Stefan Buehler, Institutionen för system- och rymdteknik, Luleå tekniska universitet Tid: Torsdag den 31 januari 2013, kl 10.00 Plats: Aula, Svenska institutet för rymdfysik, Kirun

On the Temperature Dependence of the Cloud Ice Particle Effective Radius—A Satellite Perspective

Cloud ice particle effective radius in atmospheric models is usually parametrized. A widely‐used parametrization comprises a strong dependence on the temperature. Utilizing available satellite‐based estimates of both cloud ice particle effective radius and cloud‐top temperature we evaluate if a similar temperature‐dependence exists in these observations. We find that for very low cloud‐top temperatures the modeled cloud ice particle effective radius generally agrees on average with satellite observations. For high sub‐zero temperatures however, the modeled cloud ice particle effective radius becomes very large, which is not seen in the satellite observations. We conclude that the investigated parametrization for the cloud ice particle effective radius, and parametrizations with a similar temperature dependence, likely produce systematic biases at the cloud top. Supporting previous studies, our findings suggest that the vertical structure of clouds should be taken into account as factor in potential future updates of the parametrizations for cloud ice particle effective radius.Plain Language Summary: Atmospheric models are often used to diagnose and predict the atmospheric state including clouds. One very important property of clouds that consist of ice particles is the cloud ice particle effective radius. This ice effective radius is based on assumptions about the size and shapes of the ice particles in clouds, and thus parametrized, and is one of the important variables needed for calculating the effect of clouds on electromagnetic radiation, in particular on the solar radiation that enters the Earth's atmosphere. In our study we found that the parametrized ice effective radius agrees well on average and global scale with the ice effective radius inferred from satellite observations for cold clouds. However, we also found that for warmer ice clouds the parametrized ice effective radius is much higher than in satellite observations. Our study suggests that parametrizations of the ice effective radius used in atmospheric models show potential for improvements.Key Points: Comparisons of modeled cloud ice particle effective radius with satellite observations are presented. For very low cloud temperatures the modeled cloud ice particle effective radius agrees on average with satellite observations. Modeled large cloud ice particle effective radii for high sub‐zero temperatures are not found in satellite observations.European Space Agency http://dx.doi.org/10.13039/501100000844https://doi.org/10.5281/zenodo.7445152https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V002http://doi.org/10.5067/MODIS/MYD06_L2.NRT.06

Prototyping an improved PPS cloud detection for the Arctic polar night

under noll grader och snö och regn är vanligt förekommande, vilket bl.a. skapar hala vägar och behov avA new Polar Platform Systems (PPS) Cloud Mask (CM) test sequence is required for improving cloud detection during Arctic winter conditions. This study introduces a test sequence, called Ice Night Sea (INS), that to a greater extent successfully detects clouds over ice surfaces and which is less sensitive to cloud free misclassification.The test sequence uses a combination of Numerical Weather Prediction (NWP) fields and Advanced Very High Resolution Radiometer (AVHRR) satellite data. Only the infrared (IR) AVHRR channels can be exploited during night conditions. Training target data from winter 2001-2002, collected over a large area north of the Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska, were used to assess the general atmospheric state of the Arctic and to perform a qualitative validation of CM test sequences. Results clearly show that the atmospheric conditions during Arctic winter severely hamper cloud detection efforts. Very cold surface temperatures and immense surface temperature inversions lead to a diminished separability between surfaces and clouds. One particular problem is that the IR brightness temperatures for the shortest wavelength (3.7μm - henceforth T37) are strongly affected by noise. The use of an IR noise filter was shown to improve results significantly. In addition, the problem of misclassifying cracks in the pack ice as Cirrus clouds was basically solved by using a dedicated filter using the local variance of T37.Using an inverse version of a typical daytime Cirrus test (based on just two IR channels and normally applied successfully outside the Arctic region), it is shown that we can detect a substantial part of the warmsemi-transparent clouds commonly found in the Arctic. Running the test sequences on training target data revealed an improvement in correct cloud free target classification of around 30% but only a marginal improvement for cloudy training targets. However, visual inspection of results obtained for about 50 scenes covering a large part of the Arctic region in January 2007 clearly indicated improvements also for the cloudy portion of the scenes. The INS CM test sequence awaits a more rigorous and quantitative validation, e.g. based on comparisons with CLOUDSAT/CALIPSO satellite data sets

Shape Dependence of Falling Snow Crystals' Microphysical Properties Using an Updated Shape Classification

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 mu m 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

Global Cloudiness and Cloud Top Information from AVHRR in the 42-Year CLARA-A3 Climate Data Record Covering the Period 1979–2020

This paper investigates the quality of global cloud fraction and cloud-top height products provided by the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from the AVHRR data (CLARA-A3) climate data record (CDR) produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). Compared with with CALIPSO–CALIOP cloud lidar data and six other cloud CDRs, including the predecessor CLARA-A2, CLARA-A3 has improved cloud detection, especially over ocean surfaces, and improved geographical variation and cloud detection efficiency. In addition, CLARA-A3 exhibits remarkable improvements in the accuracy of its global cloud-top height measurements. For example, in tropical regions, previous underestimations for high-level clouds are reduced by more than 2 km. By taking advantage of more realistic descriptions of global cloudiness, this study attempted to estimate trends in the observable fraction of low-level clouds, acknowledging their importance in producing a net climate cooling effect. The results were generally inconclusive in the tropics, mainly due to the interference of El Nino modes during the period under study. However, the analysis found small negative trends over oceanic surfaces outside the core tropical region. Further studies are needed to verify the significance of these results

Shape dependence of snow crystal fall speed

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. 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. Fall speed is 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 analyzes fall speed as a function of particle size (maximum dimension), cross-sectional area, and shape 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 particle 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 3.2mm and fall speeds from 0.06 to 1.6 m s(-1). Relationships between particle size, cross-sectional area, and fall speed are studied for different shapes. 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. particle size or cross-sectional area for part of the shapes. For most of these shapes, the fall speed is better correlated with cross-sectional area than with particle size. The effects of orientation and area ratio on the fall speed are also studied, and measurements show that vertically oriented particles fall faster on average. However, most particles for which orientation can be defined fall horizontally