Helsinki University Of Technology Research Articles

Preface

The 12th International Workshop on Ship and Marine Hydrodynamics 28 August to 1 September 2023 Aalto University, Espoo, Finland Editors Eur.Ing. Dr. Spyros Hirdaris CEng FRINA, FSNAME Aalto University, Finland AND Professor Decheng Wan Shanghai Jiao Tong University, China The 12th International Workshop on Ship and Marine Hydrodynamics (IWSH 2023) was held from the 28th of August to the 1st of September 2023 at Aalto University, Finland. Aalto University was established in 2010 as a merger the Helsinki University of Technology (HUT), the Helsinki School of Economics and the University of Art and Design of Helsinki. Marine and Arctic Technology has been solid part of the well cherished traditions of HUT dating back to the 19th century.IWSH inaugurated by Prof. Zaojian Zou of Wuhan University of Technology in 1999. Early national events were organised by Wuhan University of Technology (2001,2003), Shanghai Jiao Tong University (2005), Jiangshu University of Science and Technology (2007) and Harbin Engineering University (2009).In 2011 Professor Decheng Wan organised the first international version of the workshop at Shanghai Jiao Tong University. Since then, the event has been organised by Seoul National University - South Korea (2013), the University of Strathclyde - UK (2015), the National Taiwan Ocean University (2017) and Hamburg University of Technology - Germany (2019). The 2021 edition of IWSH did not take place because of the COVID-19 pandemic. The “all-inclusive” nature of the 12th sequel of the workshop held in 2023 included scientific and social activities, fostering opportunities for networking.The workshop focused on publications of relevance to ship dynamics, hydrodynamics, wave mechanics, sea loads and responses, fluid structure interactions, ship resistance and propulsion, etc. Notably, emerging multiphysics methods with application on both ships and ocean engineering including renewable energies were evident. Six keynotes were delivered by Dr. Nuno Fonseca of SINTEF (Norway), Dr. Sime Malenica of Bureau Veritas (BV, France), Alessandro Iafrati of Istituto di iNgegneria del Mare (INM, Italy), Dr. Michel Visonneau of Centre National de la Recherche Scientifique (CNRS, France), Professor Stefano Brizzolara of Virginia Technological Institute and State University (USA) and Professor Yonghwan Kim of Seoul National University (South Korea), were invited to foster top level discussion on relevant subjects. As part of the workshop proceedings the main event sponsor, SINTEF Ocean (Norway) hosted a special session of their International Collaboration Network in Marine Technology.The oral technical program was organized in parallel sessions. In these proceedings, the papers are organized by major themes. The themes are: (1) Ship dynamics, (2) Wave mechanics, (3) Fluid Structure Interactions, (4) Ship motions and sea loads, (5) Ocean Engineering, (6) Ship resistance, (7) Marine propulsion.The workshop confirmed that the complexity of the subject as related to conventional and emerging technologies attracts a great deal of interest of relevance to engineering science and technological innovation. The field of ship and marine hydrodynamics remains more than relevant for the safety and sustainability of the ship and ocean engineering sectors. Dr. Spyros Hirdaris CEng FRINA FSNAME Aalto University, Espoo, Finland, September 2023 List of Sponsors, Reviewers acknowledgement, Aalto University’s IWSH 2023 local organizing committee, IWSH 2023 International Standing Committee, IWSH 2023 International Scientific Committee are available in this pdf.

Verification of the Ramberg-Osgood Material Model for the Fire Design of Steel Members

Abstract In this paper, the modified Ramberg-Osgood constitutive equations for calculating the stress-strain of steel at elevated temperatures using the parameters determined based on the transient state tensile test results achieved at the Helsinki University of Technology are verified. This is done by numerically comparing the global and local buckling capacities of I-shaped steel members incorporating the modified Ramberg-Osgood model along with the material model given in the fire section of Eurocode EN1993-1-2. For this purpose, a numerical model using the ABAQUS software was developed. Then, nonlinear analyses with imperfections (GMNIA) were performed to compare the buckling capacities of the steel columns and beams of four different hot-rolled cross-sections (IPE160, IPE180, HE100B, HE500B), made of steel grade S355, at three different temperatures (400°C, 500°C, 600°C). The results showed that adopting the modified Ramberg-Osgood model can lead to the same buckling capacities resulting when using the EN1993-1-2 material model for steel temperatures of less than 400°C. However, adopting this model for 600°C overestimates the buckling capacities in most cases.

Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach

Snow cover is highly critical for global water and energy cycles because of its wide areal extent, high reflectivity and good thermal insulation. Knowledge of snow conditions, e.g., snow water equivalent (SWE) and snow depth, is significant to hydrologic and climatologic processes. Spaceborne passive microwave (PMW) data, namely, brightness temperature (TB), have been in use for snow depth and SWE retrieval at the global scale since 1978. However, the sensitivity of TB to these parameters is complex due to snow metamorphism (e.g., snow grain size, GS), which limits the feasibility of many existing algorithms characterizing snow. This study presents a new methodology to retrieve snow depth over China by coupling a microwave snow emission model with a random forest (RF) machine learning (ML) technique. An effective GS value (effGS), a prior snowpack descriptor, was optimized utilizing the Helsinki University of Technology (HUT) model by minimizing the difference between AMSR2 observations (18.7 and 36.5 GHz) and HUT simulations. Five elaborately selected independent variables, including vertical polarized TB differences (TBD) between 18.7 and 36.5 GHz (TBD18.7V&36.5V), 10.65 and 36.5 GHz (TBD10.65V&36.5V), longitude, elevation and effGS, together with the target variable, snow depth, were applied to train the RF model, and then the 10-fold cross-validation (10-CV) approach was employed for performance validation using station data during the period from 2012 to 2018. The results indicated that (1) inclusion of effGS in RF greatly enhanced the overall performance in snow depth estimation; (2) the trained RF model performed better on a temporal scale than on a spatial scale, with unRMSEs of 1.81 cm and 3.17 cm, respectively; (3) specifically, the fitted RF algorithm partially addressed the overestimation in shallow (≤ 20 cm) snowpacks and underestimation in deep (> 20 cm) snow conditions when compared with the established RF algorithm based solely on predictor variables but without effGS. To evaluate the predictive power of the RF algorithm trained with samples in 2017 and 2018, spatially independent station measurements during the period from 2012 to 2016 and field survey data collected from January 2018 to March 2019 were used for validation. Additionally, the RF estimates were compared with two widely used satellite products (AMSR2 and GlobSnow-2). The validation results showed that RF estimates were closer to the in situ data than the other two satellite products. This study demonstrated the potential utility of combining the snow emission model with an ML approach to improve snow depth estimation.

Comparative approaches on urban planning: Alvar Aalto’s assistant from Italy — an interview with Vezio Nava

Vezio Nava (Fig. 1) was born in Rome, Italy, in 1936. He started studying architecture in Rome but graduated from the Helsinki University of Technology (formerly the Polytechnic, now Aalto Universi...

Advanced Nanosystems for Clinical Translation

Advanced Nanosystems for Clinical Translation

Evaluation of Brightness Temperature Sensitivity to Snowpack Physical Properties Using Coupled Snow Physics and Microwave Radiative Transfer Models

There are multiple existing microwave radiative transfer models (RTMs) to simulate the brightness temperature (Tb) of snowpacks. It is still challenging to have consistent Tb responses from RTMs due to individual physical formulations of the snowpack scattering process. This article examines three of the widely-used multi-layer RTMs: 1) the microwave emission model of layered snowpacks (MEMLS); 2) the dense media radiative transfer based on the quasi-crystalline approximation (QCA) of Mie scattering of densely packed sticky spheres (DMRT-QMS); and 3) the Helsinki University of Technology (HUT) model. Interestingly, these models yield slightly different Tb responses when driven by the same physical snowpack properties. Tb variations, dependent on the choice of RTMs, are then evaluated to improve the understanding of model differences in microwave emission from a snowpack. We first perform a sensitivity study of the Tb predictions from the three RTMs as a function of snow grain sizes, densities, and depths. While Tb from all three RTMs decreases with increasing snow grain sizes, it is found that a scaling factor is required to have the same amount of Tb attenuation for small grain sizes within the Rayleigh scattering regime. For larger grain sizes, however, a scaling coefficient is not enough to match the model outputs due to the different scattering assumptions of the RTMs. For a single snow layer with increasing snow depths and densities, all three RTMs exhibit Tb attenuations arising from the increase in path lengths and optical depths. Further evaluations are conducted by feeding the three RTMs with the output of a snow physics model driven by in situ weather forcing in a coupled simulation. Outputs of this coupled model include snowpack physical properties and Tbs. By using snow stratigraphy observations, another set of Tb simulation is also conducted with RTMs driven by in situ snowpit observations. The snow physics outputs from the coupled case are compared against in situ snow stratigraphy observations. And both Tb simulations are compared against ground-based microwave observations from the European Space Agency (ESA) Nordic Snow Radar Experiment (NoSREx) 2009–2012. For three consecutive years, the in situ driven Tbs have 21.0-K root-mean-squared error (RMSE) while the coupled simulations have 24.7-K RMSE. However, after isolating the dry snow period and excluding diurnal melting snow conditions, 12.2- and 6.3-K RMSEs are achieved from in situ and coupled cases, respectively, in the 2011 water year.

Internal moisture excess of residential buildings in Finland

In Finland, the indoor air conditions of 171 single-family buildings and 49 apartments in multi-family buildings have been studied in large research projects in collaboration with Tampere University of Technology and Helsinki University of Technology. This article deals with the results of moisture excess studied in these single-family buildings and apartments. Continuous field measurements up to 28 months enable to include two consecutive heating seasons in the analysis of the internal moisture excess. The higher 10% critical level during the cold period (Te ≤ 5°C) was between 3.4 and 4.9 g/m3 in Finnish heavyweight and lightweight single-family buildings and between 2.4 and 3.6 g/m3 in apartments of multi-family buildings. During the warm period (Te ≥ 15°C), the corresponding values were between 0.3 and 2.6 g/m3 for single-family buildings and 0.5 and 1.5 g/m3 for apartments. The number of occupants and the airtightness of different groups of external wall did not have influence on the average values of moisture excess. Slightly better ventilation air change rates were measured in apartments of multi-family buildings, where also the air change rates from supply–exhaust ventilation systems generally fulfilled the Finnish guideline value. The design values of moisture classes in EN ISO 13788:2012 were found difficult to apply for Finnish residential buildings. The upper limit value of humidity class 2 of EN ISO 13788:2012 is suitable for apartments in Finnish multi-family buildings, but too low for Finnish lightweight and heavyweight single-family buildings. Applying the design values of Finnish guidelines would be justified for the studied buildings almost at the full range of outdoor temperatures.

Benchmarking construction industry, company and project performance by participants’ evaluation

Purpose The construction industry needs effective methods for gathering and utilizing performance information on industry, company and individual project levels. Such benchmarking (BM) information can be collected from project participants’ bi-directional evaluations of each other’s performance during construction projects. The purpose of this paper is to present the method and discuss the features of a multi-company project evaluation system as a BM tool. Also the accumulated results, based on the extensive BM database in the Finnish construction industry, are investigated. Design/methodology/approach The web-based project evaluation tool was established in 2007 as a joint research project between the Helsinki University of Technology, construction organizations and main associations widely representing the construction industry in Finland. The evaluation method is based on soft, subjective measures, such as client satisfaction and project participants’ satisfaction. The empirical observations of the study are based on over 5,500 evaluations during which the BM system was used in the Finnish construction industry. Findings The use of the evaluation is presented as a multi-level BM tool to evaluate the performance of different discipline groups on project, company and industry levels. The Finnish case shows an example of the industry-level performance BMs of project consultants, main contractors, sub-contractors and architects/designers. The BM categories include project management, staff, collaboration and project goal accomplishment. According to the results, the participants are satisfied with each other’s performance. However, main development targets in the Finnish industry are related to risk management and managing design. Also, the development of sub-contractors’ selection procedures will contribute to the performance of the industry. Research limitations/implications The industry-level feedback is linked to the Finnish construction industry. Similar studies in other countries would enable international comparisons. Practical implications Client satisfaction and project participants’ satisfaction have been identified as one of the key factors affecting project success. With the help of mutual project evaluation, different BMs enable organizations to monitor their performance and to improve their operations in various areas. They also make it possible to position an organization’s performance in comparison to the competitors and help to perceive black spots in the process on project level. As a common system for the parties in the industry, the project evaluation BM system promotes quality improvement, customer orientation and collaboration between participants. Social implications Project evaluation between the project participants’ enhance collaboration and mutual learning. Originality/value Multi-company project evaluation is a new and promising topic in performance measurement which holds potential for performance improvement through the utilization of network information and team dynamics. The study presents its wider utility as the basis of national, company and project BM.

Aalto University Undergraduate Centre. Protected Alvar Aalto Building Awarded for Accessibility After Renovation.

The main building of the former Helsinki University of Technology (TKK) designed by Alvar Aalto is part of the cultural heritage in Finland. The building underwent a major renovation in 2011-2015 and has now become an awarded Undergraduate Centre for the modern interdisciplinary education of Aalto University. This paper presents how the architectural masterpiece from the 1960's was renovated and updated into a modern and accessible university building. Particular attention was paid for entering the building by wheelchairs, prams and pushchairs. The successful renovation was awarded in 2015 by the 'Esteetön Suomi -palkinto' (Accessible Finland Award), given every two years as a mark of recognition to activities or locations implementing the principles of accessibility and Universal Design for all on a broad scale and in a nationally significant way.

Implications of boreal forest stand characteristics for X-band SAR flood mapping accuracy

Abstract Synthetic aperture radar (SAR) enables the mapping of flooding over large areas, regardless of cloud and weather conditions. Simplified classification approaches based on threshold levels of backscattering intensity are typical in operational flood monitoring. Backscattering intensity from floods over open non-forested areas is typically lower, whereas backscatter from forest floods is higher compared to non-flooded areas. However, distinction of flooded areas from non-flooded surface in semi-forested areas with low canopy closure (CC) or low tree height (TH) is expected to be difficult due to confounding effects of the different scattering mechanisms. The aim of this study was to investigate X-band SAR backscattering in flooded boreal forests with varying TH and CC, and to quantify in which cases floods would be difficult to detect with typical threshold-based classification methods. To further understand the SAR signal behavior in flooded forests, the total backscatter was modeled using the HUT (Helsinki University of Technology) semi-empirical forest backscattering model. HH-polarized Cosmo Sky-Med acquisitions from four different locations in Finland were analyzed against airborne LiDAR based forest data, ground observations and a high resolution digital elevation model. Floods were well detected in open areas and dense forests. However, as hypothesized, when TH was higher than zero but lower than 4–5 m, or when CC was higher than zero but lower than 15–20%, the detection was less successful. TH was found to have slightly more influence on the capability of X-band SAR to detect floods than CC. In our four test areas, namely Kittila, Kolari, Pudasjarvi and Evo, 85.3%, 89.1%, 82.7% and 73.9% of the total floods were detected, respectively, by a simple threshold value method. The model was able to successfully estimate the different backscattering components of the flooded forests in Kittila and Pudasjarvi, where the number of observations from all forest conditions was sufficient.

Brightness temperature simulation of snow cover based on snow grain size evolution usingin situdata

Snow depth parameter inversion from passive microwave remote sensing is of great significance to hydrological process and climate systems. The Helsinki University of Technology (HUT) model is a commonly used snow emission model. Snow grain size (SGS) is one of the important input parameters, but SGS is difficult to obtain in broad areas. The time series of SGS are first evolved by an SGS evolution model (Jordan 91) using in situ data. A good linear relationship between the effective SGS in HUT and the evolution SGS was found. Then brightness temperature simulations are performed based on the effective SGS and evolution SGS. The results showed that the biases of the simulated brightness temperatures based on the effective SGS and evolution SGS were −6.5 and −3.6 K , respectively, for 18.7 GHz and −4.2 and −4.0 K for 36.5 GHz. Furthermore, the model is performed in six pixels with different land use/cover type in other areas. The results showed that the simulated brightness temperatures based on the evolution SGS were consistent with those from the satellite. Consequently, evolution SGS appears to be a simple method to obtain an appropriate SGS for the HUT model.

Arctic Snow Microstructure Experiment for the development of snow emission modelling

Abstract. The Arctic Snow Microstructure Experiment (ASMEx) took place in Sodankylä, Finland in the winters of 2013–2014 and 2014–2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macro- and microstructure observations. Radiometric measurements of the microwave emission of snow on reflector and absorber bases were made at frequencies 18.7, 21.0, 36.5, 89.0, and 150.0 GHz, for both horizontal and vertical polarizations. Two measurement configurations were used for radiometric measurements: a reflecting surface and an absorbing base beneath the snow slabs. Simulations of brightness temperatures using two microwave emission models, the Helsinki University of Technology (HUT) snow emission model and Microwave Emission Model of Layered Snowpacks (MEMLS), were compared to observed brightness temperatures. RMSE and bias were calculated; with the RMSE and bias values being smallest upon an absorbing base at vertical polarization. Simulations overestimated the brightness temperatures on absorbing base cases at horizontal polarization. With the other experimental conditions, the biases were small, with the exception of the HUT model 36.5 GHz simulation, which produced an underestimation for the reflector base cases. This experiment provides a solid framework for future research on the extinction of microwave radiation inside snow.

Snow Depth Inversion Using the Localized HUT Model Based on FY-3B MWRI Data in the Farmland of Heilongjiang Province, China

Snow depth parameter inversion in the farmland using passive microwave remote sensing is of great significance to the agricultural production in Northeast China. Firstly, the Helsinki University of Technology (HUT) snow emission model was validated in the farmland based on microwave radiation imager (MWRI) onboard FengYun-3B satellite (FY-3B). The results showed that there was a big difference between the brightness temperature of HUT model simulation and MWRI for 18.7 GHz horizontal polarization (18.7 H) and 36.5 GHz horizontal polarization (36.5 H). To improve HUT model, the empirical parameter in the model was localized. Then the localized HUT (LHUT) model was built, where the extinction coefficient was calculated by the new extinction coefficient formula. Next, LHUT model was validated based on MWRI data and compared with HUT model. The results showed that LHUT underestimates slightly the brightness temperature with 0.91 and 4.19 K for 18.7 and 36.5 H respectively, and LHUT is superior to HUT model. Finally, the genetic algorithm (GA) was used to invert snow depth based on LHUT. The results showed that snow depth was underestimated with 6.79 cm based on LHUT. The inverted snow depth based on LHUT model is in better agreement with the measured snow depth.

Differences Between the HUT Snow Emission Model and MEMLS and Their Effects on Brightness Temperature Simulation

Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer Helsinki University of Technology (HUT) model and the microwave emission model of layered snowpacks (MEMLS). By comparing the mathematical formulation side by side, three major differences were identified: 1) by assuming that the scattered intensity is mostly (96%) in the forward direction, the HUT model simplifies the radiative transfer equation in $4\pi$ space into two one-flux equations, whereas MEMLS uses a two-flux theory; 2) the HUT scattering coefficient is much larger than the one of MEMLS; and 3) MEMLS considers the trapped radiation inside snow due to internal reflection by a six-flux model, which is not included in HUT. Simulation experiments indicate that the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of one-flux simplification and the trapped radiation still result in different $T_{B}$ simulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankyla, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that the HUT model tends to underestimate $T_{B}$ for deep snow. MEMLS with the physically based improved Born approximation performed best among the models, with a bias of −1.4 K and a root-mean-square error of 11.0 K.

Plot Scale Passive Microwave Measurements and Modeling of Layered Snow Using the Multi-layered HUT Model

. The excavation of snow layers was used to compare observed and modeled plot-scale brightness temperature (TB) values for 19 and 37 GHz, with regard to snow water equivalent (SWE), snow type, grain size, and layered structure, for 3 land cover types acquired near Churchill, Manitoba, Canada in March of 2010. In situ snow geophysical measurements were input to the Helsinki University of Technology (HUT) multi-layer snow emission model, and performance was characterized by RMSE and MBE. Emission scattering from depth hoar was disproportionate to its SWE contribution when compared to other snow types, and it also masked observed scattering contributions from upper snow layers. The simulated and observed TB diverged above 130 mm SWE, as the model did not capture scattering extinction or snowpack emission. These may impact the initial effective grain size optimization process if applied to the GlobSnow SWE assimilation technique. Grain size is optimized to fit emission simulations to TB from satellite observations; the model's continued sensitivity to SWE above observed signal saturation may be attributed to the existing snowpack. These inaccuracies may then be carried forward in the assimilation process, through variance calculations used to weight the contributions of assimilation modules, leading to less accurate SWE retrievals.Résumé. L’excavation des couches de neige a été utilisée pour comparer les valeurs observées et modélisées de la température de brillance (TB) à 19 et 37 GHz à l’échelle de la parcelle, en lien avec l’équivalent en eau de la neige (SWE), le type de neige, la taille des grains, et la structure en couches, pour trois types de couverture terrestre acquises près de Churchill au Manitoba (Canada) en mars 2010. Les mesures géophysiques in situ de neige ont été utilisées en entrées du modèle HUT (Helsinki University of Technology), un modèle multicouche d’émission de la neige, et la performance a été caractérisée par l’écart moyen quadratique (RMSE) et l’erreur de justesse moyenne (MBE). La diffusion de l’émission du givre de profondeur était disproportionnée par rapport à sa contribution au SWE en comparaison avec d’autres types de neige et a aussi masqué les contributions observées de la diffusion des couches de neige supérieures. Les valeurs de TB simulées et observées ont divergé au-dessus de 130 mm de SWE, parce que le modèle n’a pas représenté l’extinction de la diffusion ou l’émission du manteau neigeux. Cela peut affecter le processus d’optimisation initiale de la taille effective des grains, s’il est appliqué à la technique d’assimilation GlobSnow SWE. La taille des grains est optimisée pour ajuster les simulations d’émission aux TB provenant des observations par satellite; la sensibilité du modèle au SWE au-dessus de la saturation du signal observé peut être attribuée au manteau neigeux existant. Ces inexactitudes peuvent ensuite être reportées dans le processus d’assimilation, par des calculs de la variance utilisés pour pondérer les contributions des modules d’assimilation ce qui conduit à des résultats moins précis d’estimation du SWE.

What to Do with Alvar Aalto?

In Finland the influence of the Alvar Aalto has been very strong. It is not easy to say, what to do with Alvar Aalto and his buildings? Let’s take three cases: Library in Vyborg was built just before the Second World War. The former Finnish town was after the war one part of the Soviet Union. During the soviet era the building was in very bad condition. During the last 20 years it has been renovated. Now it looks like it was in the 30es. Some details remind the soviet renovations. Should we have a building like this in his earlier presentation or should we see also the history of the building? Sunila area in Kotka was built by one wood company in the middle of the 20thcentury. In the 60es the company sold the buildings. The flats are small, in the flats there are toilets but not showers. The situation especially in the 70es was miserable. In the last decades the Pro Sunila society has developed the area and the flats (for example two small flats together as a big one with bigger showers etc.). How we can develop an area? Nano laboratory building in Otaniemi was built in 60es as wood laboratory of the Helsinki University of Technology. There are many very fine architectural details in the building. For about eight years ago the building was renovated as nano laboratory. How to renovate a laboratory building, when you should in the same time use renovation, conservation and build high tech laboratory?

Simulating seasonally and spatially varying snow cover brightness temperature using HUT snow emission model and retrieval of a microwave effective grain size

The Helsinki University of Technology (HUT) snow emission model forms the basis of the European Space Agency's GlobSnow snow water equivalent (SWE) product (Takala et al., 2011). The model applies a semi-empirical radiative transfer calculation to account for the interaction of the snow medium with microwaves; separate components are applied to account for vegetation, the atmosphere, and emission from the ground surface. For the retrieval of SWE, an innovative method is used to account for spatial and temporal variability in snow conditions by retrieving an effective parameter describing the scattering behavior of microwaves in the snow (a proxy indicator of the microwave effective snow grain size). In this study, the influence of differing snow conditions, as well as varying land cover, on the retrieved microwave effective snow grain size was analyzed. Passive microwave measurements were acquired using tower-based, mobile sled-based and airborne radiometers in a mixed forest environment near Sodankylä, Finland. Forward simulations at the tower site over an entire winter period showed that the use of an empirical relation to modify the classical in situ measured grain size produced HUT model bias errors less than 6K on average at 19 and 37GHz from a ~20–80cm deep boreal snowpack. Model simulations for airborne and sled-based observations showed that using a simplified 2-layer representation of the snowpack improves simulation biases and RMSE, although modification of the measured grain size was again necessary to achieve these results, regardless of the layering configuration. The microwave effective grain size retrieved from HUT predictions was closely related to a simple average grain size measured in situ, both in terms of magnitude and temporal trend. This is an important finding as the retrieval scheme of Takala et al. (2011) relies on the microwave effective grain size to retain a degree of physical basis for values to be generally applicable over larger areas, which is challenging because this free parameter also accounts for errors unrelated to the effective snow grain size (i.e. vegetation, soil conditions, scene heterogeneity, etc.).

Observation and Modeling of the Microwave Brightness Temperature of Snow-Covered Frozen Lakes and Wetlands

Small-scale variability in land cover influences both the snow cover and the microwave response of a snow-covered surface. Since low microwave frequencies penetrate below the snowpack, the differing dielectric properties of soil and water have a significant effect on passive microwave observations and therefore cause errors in the interpretation of snow parameters from satellite data. Here, the brightness temperature of snow- and ice-covered lakes and wetlands is studied using airborne and spaceborne microwave radiometer observations and modeling of brightness temperature from in situ measurements. We aim at assessing the validity of the multilayer Helsinki University of Technology (HUT) snow emission model on lake- and wetland-rich areas and at examining the error from omission of water bodies in the forward modeling of brightness temperature. The results indicate that the model can estimate brightness temperatures of lakes and wetlands with rms errors of 12-28 K and 9-16 K, respectively. The inclusion of lakes in the satellite-scale simulations reduces the simulation error in 52%-100% of the simulated areas at 18.7 and 36.5 GHz. The inclusion of wetlands further improves simulations, resulting in an rms error of satellite scenes of 4-5 K at 18.7 and 36.5 GHz (5-10 K without lakes and wetlands). However, the natural variability of brightness temperature over water bodies is not entirely captured particularly at 10.65 GHz. The inclusion of lakes and wetlands can be used to reduce errors in the forward model and thus increase the accuracy of snow parameters derived from satellite data.

Estimation of ice thickness on large northern lakes from AMSR-E brightness temperature measurements

An ice thickness retrieval algorithm utilizing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) was developed and applied to Great Bear Lake (GBL) and Great Slave Lake (GSL), Northwest Territories, Canada, for the period 2002–2009. The temporal evolution of vertically polarized AMSR-E brightness temperature (TB) at 18.7GHz was explored to estimate ice thickness between the late freeze-up (ice-on) and early break-up (melt-onset) periods. The sensitivity of AMSR-E TB at H and V polarizations to the seasonal evolution of ice thickness was examined statistically and with forward simulations of TB using the most recent version of the Helsinki University of Technology (HUT) model, which incorporates a freshwater ice layer. The strong relation found between TB at 18.7GHz V-pol and ice thickness was exploited for the development of a regression-based ice thickness retrieval algorithm. Simple linear regression equations allow for the estimation of ice thickness on a monthly basis from January to April; one, the Global equation, combines TB data for GBL and GSL, and two others, the Regional equations, use TB data for each lake alone. Estimated late-winter ice thicknesses on GBL were determined to be on average 5–10cm thicker than on GSL with the exception of ice season 2005–2006 when it was estimated to be 10cm thicker on GSL. For both lakes the 2004–2005 and 2008–2009 ice seasons experienced the thickest end-of-winter ice thicknesses, ranging from 130 to 134cm on GBL and GSL. The thinnest end-of-winter ice thicknesses were on average 120cm on GBL (2005–2006) and 123cm on GSL (2007–2008). Variability in air temperature, snowfall and subsequent redistribution by wind, and lake depth explain ice thickness variations within and between lakes over the seven winter seasons analyzed. Estimated ice thicknesses from AMSR-E compare well with coincident in situ measurements collected on GBL and GSL over a limited number of ice seasons and sites within the large passive microwave footprints (Mean Bias Error, MBE=6cm; Root Mean Square Error, RMSE=19cm).

Snow stratigraphic heterogeneity within ground‐based passive microwave radiometer footprints: Implications for emission modeling

Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size, and temperature) were used as inputs to the multilayer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (−0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations, and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical specific surface area to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested that the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed that a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%.