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At least 55 records · Page 3

California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change

The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term “snow deluge” and use anomalously high snowpack in California’s Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California’s 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges—which we define as the 20 y April 1 SWE event—are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.

Marshall, Adrienne M.↗

Evaluation of long-term Northern Hemisphere snow water equivalent products

Nine gridded Northern Hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Intercomparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS-2; the European Centre for Medium-Range Weather Forecasts (ECMWF) interim land surface reanalysis – ERA-Interim/Land and ERA5; the NASA Modern-Era Retrospective Analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2); the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) stand-alone passive microwave retrievals (NASA AMSR-E SWE versions 1.0 and 2.0) which do not utilize surface snow observations. Evaluation included validation against independent snow course measurements from Russia, Finland, and Canada and product intercomparison through the calculation of spatial and temporal correlations in SWE anomalies. The stand-alone passive microwave SWE products (AMSR-E v1.0 and v2.0 SWE) exhibit low spatial and temporal correlations to other products and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides performance comparable to the reanalysis-based products; RMSE over Finland and Russia for all but the AMSR-E products is ∼50 mm or less, with the exception of ERA-Interim/Land over Russia. Using a seven-dataset ensemble that excluded the stand-alone passive microwave products reduced the RMSE by 10 mm (20 %) and increased the correlation from 0.67 to 0.78 compared to any individual product. The overall performance of the best multiproduct combinations is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.

Northern Hemisphere snow water equivalent (SWE) pr↗

Microwave Signatures of Melting/Refreezing Snow: Observations and Modeling Using Dense Medium Radiative Transfer Theory

Microwave brightness temperatures of snow covered terrains can be modeled by means of the Dense Radiative Transfer Medium Theory (DMRT). In a dense medium, such as snow, the assumption of independent scattering is no longer valid and the scattering of correlated scatterers must be considered. In the DMRT, this is done considering a pair distribution function of the particles position. In the electromagnetic model, the snowpack is simulated as a homogeneous layer having effective permittivity and albedo calculated through the DMRT. In order to account for clustering of snow crystals, a model of cohesive particles can be applied, where the cohesion between the particles is described by means of a dimensionless parameters called stickiness (z), representing a measure of the inversion of the attraction of the particles. The lower the z the higher the stickiness. In this study, microwave signatures of melting and refreezing cycles of seasonal snowpacks at high altitudes are studied by means of both experimental and modeling tools. Radiometric data were collected 24 hours per day by the University of Michigan Tower Mounted Radiometer System (TMRS). The brightness temperatures collected by means of the TMRS are simulated by means of a multi-layer electromagnetic model based on the dense medium theory with the inputs to the model derived from the data collected at the snow pits and from the meteorological station. The paper is structured as follows: in the first Section the temperature profiles recorded by the meteorological station and the snow pit data are presented and analyzed; in the second Section, the characteristics of the radiometric system used to collect the brightness temperatures are reported together with the temporal behavior of the recorded brightness temperatures; in the successive Section the multi-layer DMRT-based electromagnetic model is described; in the fourth Section the comparison between modeled and measured brightness temperatures is discussed. We dedicate the last Section to the conclusions and future works.

Tedesco, Marco↗

LANDSAT-D investigations in snow hydrology

Snow reflectance in all 6 TM reflective bands, i.e., 1, 2, 3, 4, 5, and 7 was simulated using a delta-Eddington model. Snow reflectance in bands 4, 5, and 7 appear sensitive to grain size. It appears that the TM filters resemble a ""square-wave'' closely enough that a square-wave can be assumed in calculations. Integrated band reflectance over the actual response functions was calculated using sensor data supplied by Santa Barbara Research Center. Differences between integrating over the actual response functions and the equivalent square wave were negligible. Tables are given which show (1) sensor saturation radiance as a percentage of the solar constant, integrated through the band response function; (2) comparisons of integrations through the sensor response function with integrations over the equivalent square wave; and (3) calculations of integrated reflectance for snow over all reflective TM bands, and water and ice clouds with thickness of 1 mm water equivalent over TM bands 5 and 7. These calculations look encouraging for snow/cloud discrimination with TM bands 5 and 7.

Dozier, J.↗

Snow-eater heat waves of the western United States

Abrupt snowmelt, triggered by rain-on-snow events or "snow-eater heat waves," can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850-2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates, improving water management support tools.

Rhoades, Alan M↗

Theoretical and experimental studies of microwave radiation from a natural snow field

The brightness temperature of a natural snow field in northern Europe was studied theortically and experimentally at 5, 12, and 37 GHz for satellite remote sensing applications. A snow model consisting of ice spheres covered by a water shell was used in calculation, taking into account scattering and absorption. The brightness temperature of a natural snow field as a function of view angle was measured from a tower in 1978 and 1979. The measured brightness temperature curves can be fitted with calculated ones by assuming reasonable values for the wetness and the particle size of snow. Experimental results also show that relatively small changes in the snow conditions cause large changes in the brightness temperature. In order to obtain a more controlled situation, experiments were continued in 1980 using a measuring site covered with aluminum sheets and determining the wetness and the particle size in addition to the density and physical temperature.

Tiuri, M.↗

LANDSAT-D investigations in snow hydrology

The sample LANDSAT-4 TM tape (7 bands) of NE Arkansas/Tennessee area was received and displayed. Snow reflectance in all 6 TM reflective bands, i.e. 1, 2, 3, 4, 5, and 7 was simulated, using Wiscombe and Warren's (1980) delta-Eddington model. Snow reflectance in bands 4, 5, and 7 appear sensitive to grain size. One of the objectives is to interpret surface optical grain size of snow, for spectral extension of albedo. While TM data of the study area are not received, simulation results are encouraging. It also appears that the TM filters resemble a "square-wave" closely enough to permit assuming a square-wave in calculations. Integrated band reflectance over the actual response functions was simulated, using sensor data supplied by Santa Barbara Research Center. Differences between integrating over the actual response functions and the equivalent square wave were negligible.

Dozier, J.↗

Theoretical models for microwave snow response and applications to remote sensing

Two theoretical models were developed to characterize snowpacks: (1) a random medium with a variance, a horizontal correlation length, and a vertical correlation length; and (2) a homogeneous dielectric containing discrete scatters. Theoretical results were matched with experimental data collected from snow ice fields. For snow ice fields the horizontal correlation length is no less than the vertical correlation length signifying a more laminar structure. The correspondence between the continuum random medium and the discrete spherical scatter model can be verified when the vertical correlation length is equal to the horizontal correlation length. The vertically polarized backscattering cross section sigma (sub vv) is always greater than the horizontally polarized backscattering cross section sigma (sub hh) for half space scattering media and may become smaller for a two layer model. In order to account for diurnal change exhibited by snow fields in both the active and passive remote sensing cases, a three layer model with a thin top layer caused by solar illumination must be used.

Kong, J. A.↗

Reanalysis Surface Mass Balance of the Greenland Ice Sheet along K-transect (2000-2014)

Accurate estimates of surface mass balance over the Greenland ice sheet (GrIS) would contribute to understanding the cause of recent changes and would help to better estimate the future contribution of the GrIS to sea-level rise. Given the limitations of in-situ measurement, modeling, and remote sensing, it is critical to explore the opportunity to merge the available data to better characterize the spatial and temporal variation of the GrIS surface mass balance (SMB). This work utilizes a particle batch smoother data assimilation technique that yields SMB estimates that benefit from the snow model Crocus and a 16-day albedo product derived from satellite remote sensing data. Comparison of the results against in-situ SMB measurements shows that the assimilation of the albedo product reduces the root mean square error (RMSE) of the posterior estimates of SMB by 51% and reduces bias by 95%.

Greenland Ice Sheet↗

New Developments for Physically-based Falling Snow Retrievals over Land in Preparation for GPM

The NASA Global Precipitation Measurement mission (GPM) concept centers on deploying a Core spacecraft carrying a dual-frequency precipitation radar and a microwave radiometric imager with channels from 10 to 183 GHz to serve as a precipitation physics observatory and a calibration reference to unify a constellation of dedicated and operational passive microwave sensors. Because of the extended orbit of the Core (plus or minus 65 deg) and the enhanced dual frequency radar and high frequency radiometer, GPM will be able to sense falling snow precipitation and light rain over land. Accordingly, GPM has partnered with the Canadian CloudSat/CALIPSO Validation Project (C3VP) to obtain observations to provide one of several important ground-based validation data sets around which the falling snow models and retrieval algorithms can be further developed and tested. In this work we compare and correlate the long time series (Nov.'06 - March '07) measurements of precipitation rate from parsivels to the passive (89, 150, 183 plus or minus 1, plus or minus 3, plus or minus 7 GHz) observations of NOAA's AMSU-B radiometer. We separate the comparisons into categories of no precipitation, liquid rain and falling snow precipitation. We found that there are similar TBs (especially at 89 and 150 GHz) for cases with falling snow and for non-precipitating cases. The comparisons indicate that surface emissivity contributions to the satellite observed TB over land can add uncertainty in detecting and estimating falling snow. The newest results show that by computing brightness temperatures based on CARE radiosonde data and a rough estimate of surface emissivity show that the cloud ice scattering signal in the AMSU-B data is detected. That is the differences in computed TB and AMSU-B TB for precipitating and non-precipitating cases are unique such that the precipitating and non-precipitating cases can be identified. These results require that the radiosonde releases are within an hour of the AMSU-B data. Forest fraction, snow cover, and measured emissivities were combined to calculate the surface emissivities.

Jackson, Gail S.↗

Seasonal Glacier and Snow Loading in Svalbard Recovered from Geodetic Observations

We processed time series from seven Global Navigation Satellite System (GNSS) stations and one Very Long Baseline Interferometry (VLBI) station in Svalbard. The goal was to capture the seasonal vertical displacements caused by elastic response of variable mass load due to ice and snow accumulation. We found that estimates of the annual signal in different GNSS solutions disagree by more than 3 mm which makes geophysical interpretation of raw GNSS time series problematic. To overcome this problem, we have used an enhanced Common Mode (CM) filtering technique. The time series are differentiated by the time series from remote station BJOS with known mass loading signals removed a priori. Using this technique, we have achieved a substantial reduction of the differences between the GNSS solutions. We have computed mass loading time series from a regional Climatic Mass Balance (CMB) and snow model that provides the amount of water equivalent at a 1 km resolution with a time step of 7 days. We found that the entire vertical loading signal is present in data of two totally independent techniques at a statistically significant level of 95%. This allowed us to conclude that the remaining errors in vertical signal derived from the CMB model are less than 0.2 mm at that significance level. Refining the land water storage loading model with a CMB model resulted in a reduction of the annual amplitude from 2:1 mm to 1:1 mm in the CM filtered time series, while it had only a marginal impact on raw time series. This provides a strong evidence that CM filtering is essential for revealing local periodic signals when a millimetre level of accuracy is required.

H P Kierulf↗

The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currentlyconfigured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean throughthe accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two(vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection_divergence, blowingsnow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim,and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus "median" daily snowfall dataset from these reanalyses.The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate_strong correlations and root mean squared errors of 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

Petty, Alek A.↗

A clear-sky spectral solar radiation model for snow-covered mountainous terrain

A clear-sky spectral solar radiation model for direct and diffuse fluxes, combined with topographic calculations from digital terrain data, computes either incident, net, or reflected solar radiation at any point on a snow surface in mountainous terrain. The radiation may be integrated over any wavelength range from 250 to 5000 nm, or over any time step. Atmospheric attenuation parameters are ozone, water vapor, the Angstrom turbidity coefficient and exponent, and the absorptance to reflectance ratio of the atmospheric aerosols. The model derives these, from measurements which may contain both systematic and random errors, by finding the least squares solution to an overdetermined set of nonlinear equations. For calculations over a specified area, it employs table look-up procedures, so that computation speed for the spectral model approaches that for a lumped model. Thus it may be useful as part of a snow surface energy budget calculation over a drainage basin.

Dozier, J.↗

Comparison of Local Scale Measured and Modeled Brightness Temperatures and Snow Parameters from the CLPX 2003 by Means of a Dense Medium Radiative Transfer Theory Model

Microwave remote sensing offers distinct advantages for observing the cryosphere. Solar illumination is not required, and spatial and temporal coverage are excellent from polar-orbiting satellites. Passive microwave measurements are sensitive to the two most useful physical quantities for many hydrological applications: physical temperature and water content/state. Sensitivity to the latter is a direct result of the microwave sensitivity to the dielectric properties of natural media, including snow, ice, soil (frozen or thawed), and vegetation. These considerations are factors motivating the development of future cryospheric satellite remote sensing missions, continuing and improving on a 26-year microwave measurement legacy. Perhaps the biggest issues regarding the use of such satellite measurements involve how to relate parameter values at spatial scales as small as a hectare to observations with sensor footprints that may be up to 25 x 25 km. The NASA Cold-land Processes Field Experiment (CLPX) generated a dataset designed to enhance understanding of such scaling issues. CLPX observations were made in February (dry snow) and March (wet snow), 2003 in Colorado, USA, at scales ranging from plot scale to 25 x 25 km satellite footprints. Of interest here are passive microwave observations from ground-based, airborne, and satellite sensors, as well as meteorological and snowpack measurements that will enable studies of the effects of spatial heterogeneity of surface conditions on the observations. Prior to performing such scaling studies, an evaluation of snowpack forward modelling at the plot scale (least heterogeneous scale) is in order. This is the focus of this paper. Many forward models of snow signatures (brightness temperatures) have been developed over the years. It is now recognized that a dense medium radiative transfer (DMRT) treatment represents a high degree of physical fidelity for snow modeling, yet dense medium models are particularly sensitive to snowpack structural parameters such as grain size, density, and depth---parameters that may vary substantially within a snowpack. Microwave radiometric data and snow pit measurements collected at the Local-Scale Observation Site (LSOS) during the third Intensive Observation Period (IOP3) of the CLPX have been used to test the capabilities of a DMRT model using the Quasi Crystalline Approximation with Coherent Potential (QCA-CP). The radiometric measurements were made by the University of Tokyo s Ground Based Microwave Radiometer-7 (GBMR-7) system. We evaluate the degree to which a DMRT-based model can accurately reproduce the GBMR-7 brightness temperatures at different frequencies and incidence angles.

Tedescol, Marco↗

Understanding Uncertainty of Snow Radiative Transfer Modeling Within a Mixed Deciduous and Evergreen Forest

Satellite-based passive microwave observations provide the best available continuous observational estimates of global snow water storage due to their broad geographic footprint and low sensitivity to clouds and precipitation. However, these observations are subject to substantial uncertainty due to the complex radiative properties of snow and from interference in forested areas. Physical radiative transfer models can be leveraged to improve the fidelity of these observations and as a data-assimilation tool. In this article, the Dense Media Radiative Transfer model with Multiple Layers (DMRT-ML) is used to simulate snow brightness temperatures from data collected from snow pits excavated during a two-day-long field study performed a temperate forest in the Northeast United States. The simulations are evaluated against surface-based radiometer observations collected at the snow pits. The DMRT-ML is configured with varying complexity to determine the snowpack characteristics most essential toward simulating brightness temperature within a temperate forest with complicated snow stratigraphy. In general, the single-layer configurations were not sufficiently complex to accurately simulate snow brightness temperature without significant tuning. The most accurate simulation was a two-layer configuration with a prescribed ice layer separating the snow layers. This simulation had a root-mean-square error <;15 K for the 37-GHz frequency. More complicated snowpack stratigraphy configurations did not substantively improve the results over the two-layer model configuration. The DMRT-ML was also used to examine differences between redundant datasets of density and grain size. It was determined that similar snow data collection and radiative transfer model configuration techniques are critical to ensure cross-study comparability.

Theodore Letcher↗

The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part II: Initial Testing Using Radar, Radiometer and In Situ Observations

In this study, two different particle models describing the structure and electromagnetic properties of snow are developed and evaluated for potential use in satellite combined radar-radiometer precipitation estimation algorithms. In the first model, snow particles are assumed to be homogeneous ice-air spheres with single-scattering properties derived from Mie theory. In the second model, snow particles are created by simulating the self-collection of pristine ice crystals into aggregate particles of different sizes, using different numbers and habits of the collected component crystals. Single-scattering properties of the resulting nonspherical snow particles are determined using the discrete dipole approximation. The size-distribution-integrated scattering properties of the spherical and nonspherical snow particles are incorporated into a dual-wavelength radar profiling algorithm that is applied to 14- and 34-GHz observations of stratiform precipitation from the ER-2 aircraft-borne High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) radar. The retrieved ice precipitation profiles are then input to a forward radiative transfer calculation in an attempt to simulate coincident radiance observations from the Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR). Much greater consistency between the simulated and observed CoSMIR radiances is obtained using estimated profiles that are based upon the nonspherical crystal/aggregate snow particle model. Despite this greater consistency, there remain some discrepancies between the higher moments of the HIWRAP-retrieved precipitation size distributions and in situ distributions derived from microphysics probe observations obtained from Citation aircraft underflights of the ER-2. These discrepancies can only be eliminated if a subset of lower-density crystal/aggregate snow particles is assumed in the radar algorithm and in the interpretation of the in situ data.

Ice particles; Radiances; Radiative transfer; Airc↗

Microwave Signatures of Snow on Sea Ice: Modeling

Accurate knowledge of snow-depth distribution over sea ice is critical for polar climate studies. Current snow-depth-over-sea-ice retrieval algorithms do not sufficiently account for variations in snow and ice physical properties that can affect the accuracy of retrievals. For this reason, airborne microwave observations were coordinated with ground-based measurements of snow depth and snow properties in the vicinity of Barrow, AK, in March 2003. In this paper, the effects of snowpack properties and ice conditions on microwave signatures are examined using detailed surface-based measurements and airborne observations in conjunction with a thermal microwave-emission model. A comparison of the Microwave Emission Model of Layered Snowpacks (MEMLS) simulations with detailed snowpack and ice data from stakes along the Elson Lagoon and the Beaufort Sea and ra- 'diometer data taken from low-level flights using a Polarimetric Scanning Radiometer (PSR-A) shows that MEMLS can be used to simulate snow on sea ice and is a useful tool for understanding the limitations of the snow-depth algorithm. Analysis of radiance data taken over the Elson Lagoon and the Beaufort Sea using MEMLS suggests that the radiometric differences between the two locations are due to the differences in sea-ice emissivity. Furthermore, measured brightness temperatures suggest that the current snow-depth retrieval algorithm is sufficient for areas of smooth first-year sea ice, whereas new algorithm coefficients are needed for rough first-year sea ice. Snowpack grain size and density remain an unresolved issue for snow-depth retrievals using passive-microwave radiances.

Powell, D. C.↗

Snow Physics and Meltwater Hydrology of the SSiB Model Employed for Climate Simulation Studies with GEOS 2 GCM

Present-day climate models produce large climate drifts that interfere with the climate signals simulated in modelling studies. The simplifying assumptions of the physical parameterization of snow and ice processes lead to large biases in the annual cycles of surface temperature, evapotranspiration, and the water budget, which in turn causes erroneous land-atmosphere interactions. Since land processes are vital for climate prediction, and snow and snowmelt processes have been shown to affect Indian monsoons and North American rainfall and hydrology, special attention is now being given to cold land processes and their influence on the simulated annual cycle in GCMs. The snow model of the SSiB land-surface model being used at Goddard has evolved from a unified single snow-soil layer interacting with a deep soil layer through a force-restore procedure to a two-layer snow model atop a ground layer separated by a snow-ground interface. When the snow cover is deep, force-restore occurs within the snow layers. However, several other simplifying assumptions such as homogeneous snow cover, an empirical depth related surface albedo, snowmelt and melt-freeze in the diurnal cycles, and neglect of latent heat of soil freezing and thawing still remain as nagging problems. Several important influences of these assumptions will be discussed with the goal of improving them to better simulate the snowmelt and meltwater hydrology. Nevertheless, the current snow model (Mocko and Sud, 2000, submitted) better simulates cold land processes as compared to the original SSiB. This was confirmed against observations of soil moisture, runoff, and snow cover in global GSWP (Sud and Mocko, 1999) and point-scale Valdai simulations over seasonal snow regions. New results from the current snow model SSiB from the 10-year PILPS 2e intercomparison in northern Scandinavia will be presented.

Mocko, David M.↗