Snow depth derived from Sentinel-1 compared to in-situ observations in northern Finland

Seasonal snow in the northern regions plays an important role providing water resources for both consumption and hydropower generation. Moreover, the snow changes in northern Finland during winter impact the local agriculture, vegetation, tourism and recreational activities. In this study we estimated snow depth using an empirical methodology applied to the dual-polarisation of the Sentinel-1 synthetic aperture radar (SAR) images and compared with in situ measurements collected by automatic weather stations (AWS) in northern Finland. We applied an adapted version of the empirical methodology developed by Lievens et al. (2019) to retrieve snow depth, using Sentinel-1 constellation between 2019 and 2022, and then compared to measurements from three automatic weather stations available over the same period. Overall, the Sentinel-1 snow depth retrievals were underestimated in comparison with the in-situ measurements from the automatic weather stations. We found slightly different patterns for the different years, and an overall correlation factor of 0.41, and a higher correlation in the 2020–2021 season (R=0.52). The high correlation between estimated and measured snow depth at the Inari Nellim location (R=0.81) reinforces the potential ability to derive snow changes in regions where in situ measurements of snow are currently lacking. Further investigation is still necessary to better understand how the physical properties of the snowpack influence the backscatter response over shallow snow regions

The Radiation, snow characteristics and albedo at summit (RASCALS) expedition report

The RASCALS expedition spent over three weeks at the Summit camp research station near the top of the Greenland Ice Sheet during polar summer 2010. During this time, detailed measurements of the physical and optical properties of Arctic perennial snow were carried out concurrently with snow albedo and reflectance measurements. Favorable weather conditions during the campaign enabled the collection of a large dataset on Arctic snow albedo and associated quantities for use in developing and validating remote sensing algorithms for snow albedo using satellites. This report provides a description of the measurements and conditions during the campaign.RASCALS-retkikunnan tehtävä oli tutkia Grönlannin mannerjäätikön lumen fysikaalisia ja optisia ominaisuuksia sekä Auringon valon vuorovaikutusta lumen kanssa. Retikunta vietti hieman yli kolme viikkoa mannerjäätikön keskellä sijaitsevalla Summit Camp-tutkimusasemalla tehden mittauksia. Sääolot suosivat kampanjaa, jonka seurauksena onnistuttiin keräämään laaja ja monipuolinen tietoaineisto mannerjäätikön lumen pintakerroksesta ja eritoten lumen heijastavuuden (albedon)käyttäytymisestä. Aineisto on hyödyllinen kehitettäessä ja varmennettaessa lumen albedon kaukokartoitusmenetelmiä satelliiteilla

Quantifying the amplified bias of PV system simulations due to uncertainties in solar radiation estimates

Solar radiation databases used for simulating PV systems are typically selected according to their annual bias in global horizontal irradiance (G(H)) because this bias propagates proportionally to plane-of-array irradiance (G(POA)) and module power (P-DC). However, the bias may get amplified through the simulations due to the impact of deviations in estimated irradiance on parts of the modeling chain depending on irradiance. This study quantifies these effects at 39 European locations by comparing simulations using satellite-based (SARAH) and reanalysis (COSMO-REA6 and ERAS) databases against simulations using station measurements. SARAH showed a stable bias through the simulations producing the best Pp c predictions in Central and South Europe, whereas the bias of reanalyses got substantially amplified because their deviations vary with atmospheric transmissivity due to an incorrect prediction of clouds. However, SARAH worsened at the northern locations covered by the product (55-65 degrees N) underestimating both G(POA) and P-DC. On the contrary, ERAS not only covers latitudes above 65 degrees but it also obtained the least biased P-DC estimations between 55 and 65 degrees N, which supports its use as a complement of satellite-based databases in high latitudes. The most significant amplifications occurred through the transposition model ranging from +/- 1% up to +/- 6%. Their magnitude increased linearly with the inclination angle, and they are related to the incorrect estimation of beam and diffuse irradiance. The bias increased around + 1% in the PV module model because the PV conversion efficiency depends on irradiance directly, and indirectly via module temperature. The amplification of the bias was similar and occasionally greater than the bias in annual G(H), so databases with the smallest bias in G(H) may not always provide the least biased PV simulations.Peer reviewe

Quality control of global solar radiation data with satellite-based products

Several quality control (QC) procedures are available to detect errors in ground records of solar radiation, mainly range tests, model comparison and graphical analysis, but most of them are ineffective in detecting common problems that generate errors within the physical and statistical acceptance ranges. Herein, we present a novel QC method to detect small deviations from the real irradiance profile. The proposed method compares ground records with estimates from three independent radiation products, mainly satellite-based datasets, and flags periods of consecutive days where the daily deviation of the three products differs from the historical values for that time of the year and region. The confidence intervals of historical values are obtained using robust statistics and errors are subsequently detected with a window function that goes along the whole time series. The method is supplemented with a graphical analysis tool to ease the detection of false alarms. The proposed QC was validated in a dataset of 313 ground stations. Faulty records were detected in 31 stations, even though the dataset had passed the Baseline Surface Radiation Network (BSRN) range tests. The graphical analysis tool facilitated the identification of the most likely causes of these errors, which were classified into operational errors (snow over the sensor, soiling, shading, time shifts, large errors) and equipment errors (miscalibration and sensor replacements), and it also eased the detection of false alarms (16 stations). These results prove that our QC method can overcome the limitations of existing QC tests by detecting common errors that create small deviations in the records and by providing a graphical analysis tool that facilitates and accelerates the inspection of flagged values.Peer reviewe

Effect of small-scale snow surface roughness on snow albedo and reflectance

The primary goal of this paper is to present a model of snow surface albedo accounting for small-scale surface roughness effects. The model is based on photon recollision probability, and it can be combined with existing bulk volume albedo models, such as Two-streAm Radiative Trans-fEr in Snow (TARTES). The model is fed with in situ measurements of surface roughness from plate profile and laser scanner data, and it is evaluated by comparing the computed albedos with observations. It provides closer results to empirical values than volume-scattering-based albedo simulations alone. The impact of surface roughness on albedo increases with the progress of the melting season and is larger for larger solar zenith angles. In absolute terms, small-scale surface roughness can decrease the total albedo by up to about 0.1. As regards the bidirectional reflectance factor (BRF), it is found that surface roughness increases backward scattering especially for large solar zenith angle values

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994-2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm(-2)) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.An international team of researchers finds high potential for improving climate projections by a more comprehensive treatment of largely ignored Arctic vegetation types, underscoring the importance of Arctic energy exchange measuring stations.Peer reviewe

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types

Estimation of the Bidirectional Reflectance Distribution Function of Boreal Forest Using C-band Synthetic Aperture Radar

Albedo is known to be an important factor in climate research, and albedo is influenced by the bidirectional reflectance distribution function (BRDF). This thesis seeks to improve the accuracy of albedos calculated for boreal forests by studying methods with which BRDF could be estimated using C-band synthetic aperture radars. BRDF is usually calculated from optical data, which is vulnerable to cloud contamination. This estimation method could remove cloud contamination effects and improve albedo accuracy. The study involves a three-way analysis, comparing ASAR data to BRDFs calculated from optical satellite data from SPOT-4 and ground truth LAI data. The study area is at Tähtelä, near the town of Sodankylä in Northern Finland. The study shows a relation between ASAR data and the BRDF model results. SPOT-4 derived BRDF has a better correlation to ASAR data. Based on the analysis, estimation formulae are developed for calculating the BRDF of boreal forest from ASAR data. The estimation method is then applied to full scale satellite images of the Sodankylä area and surroundings. The results are inconclusive due to incomplete data concerning the natural conditions of the large scale area. However, the results are promising and this study is likely one of the first of this type of BRDF research.Maanpinnan kirkkaus eli albedo on merkittävä tekijä ilmaston ja sen muutoksen tutkimuksessa. Eräs siihen vaikuttavista tekijöistä on pinnan heijastuksen eri valaisuja katselukulmista määräävä BRDF-funktio. Se määritetään tavallisesti optisella kaukokartoituksella, joka kuitenkin on herkkä pilvien aiheuttamille virheille. Tämä diplomityö tutkii mahdollisuutta estimoida optisesti määritettyä BRDF:ä boreaalisissa metsissä käyttämällä C-kaistan SAR-satelliittitutkaa. C-kaistan satelliittitutka ei reagoi pilviin, joten estimoinnilla voitaisiin saavuttaa tarkempia tuloksia albedon laskennassa pilvien aiheuttamien virheiden vähentyessä. Diplomityössä vertaillaan satelliittitutkan mittaustuloksia sekä SPOT-satelliitin optisesta datasta laskettuun BRDF:n sekä maan pinnalla suoritettujen LAI-mittausten pohjalta laskettuun BRDF:n. Tutkimusalueena on Lapin ilmatieteellisen tutkimuskeskuksen alue Tähtelässä Sodankylän lähellä. Estimointi osoittautuu toimivaksi pienen mittakaavan koealueen metsissä. Tuloksissa näkyy korrelaatio satelliittitutkan mittausten ja optisen BRDF:n välillä. Tulosten perusteella lasketaan lineaarisen regression avulla estimointikaavat satelliittitutkan mittauksista laskettavan näkyviin valon ja lähi-infrapuna-alueen BRDF:lle boreaalimetsissä. Estimointikaavoja testataan myös täysimittaisilla satelliittikuvilla. Ympäristöolosuhteista ja puutteellisesta luonnon tilaan liittyvästä tiedosta johtuen täysimittaisten satelliittikuvien kokeilusta ei voida vetää johtopäätöksiä estimointimetodin toimivuudesta suurilla metsäalueilla. Diplomityö on tiettävästi ensimmäisiä tämäntyyppisestä estimoinnista tehtyjä tutkimuksia

Arktisen alueen heijastavuuden määritys optisen alueen satelliitti-instrumenttien mittauksista ja tulosten varmentaminen

The topic of this dissertation is the study of Arctic snow and ice albedo based on satellite observations. Surface albedo timeseries based on data from the AVHRR instrument family were produced, validated, and analyzed as part of the work. Of note was the production of a 28-year (1982-2009) dataset on global surface albedo from homogenized satellite data, the longest such timeseries to date.In conjunction with the dataset validation, new methods were developed to improve the reliability of the calculated dataset quality estimate. Specifically, a new method for numerically assessing the representativeness of ground truth observations at the scale of the satellite field of view was introduced. The final part of this dissertation deals with the application of the 28-year surface albedo dataset in the calculation of sea ice albedo trends over the Arctic Ocean. The results showed a clear negative trend in both the mean composite open water-sea ice albedo and the mean albedo of the remaining sea ice zone. This decrease was linked to decreasing ice concentrations across the ice zone, increased air temperatures and lengthened melt seasons. The results are significant for investigations of the surface energy budget of the Arctic. The results of this dissertation are of use in the development and validation work of both satellite-based surface albedo datasets and climate models. The created datasets are also useful in observation-based tracking of climate change in the Arctic.Väitöskirjassa tutkittiin Arktisen lumen ja jään heijastavuuden eli albedon määrittämistä satelliittihavainnoista. Työssä on tuotettu AVHRR-radiometrien mittauksiin perustuvia aikasarjoja sekä operatiiviseen käyttöön että ilmastonmuutostutkimukseen. Aikasarjojen laatu on selvitetty maan pinnan referenssimittauksiin verraten. Työssä on myös kehitetty menetelmiä parantamaan aikasarjoille laskettujen laatuarvioiden luotettavuutta. Maan pinnan referenssimittausten vertautuvuutta satelliittimittauksiin voidaan työn tulosten perusteella arvioida entistä paremmin. Väitöskirjan viimeisenä osana on sovellettu työn aiempana osana kehitettyä 28 vuoden aikasarjaa Pohjoisen Jäämeren merijään albedotrendien selvittämiseen. Työssä havaittiin merijäävyöhykkeen keskimääräisen albedon olevan kesäkuukausina laskusuunnassa koko tutkimusjaksolla 1982-2009. Vastaava laskusuunta havaittiin myös jäljelläolevan jääpeitteen albedossa. Tuloksilla on merkitystä Arktisen alueen säteilytaseen, ja yleisemmin alueen ilmaston tutkimukselle. Väitöskirjan tuloksia voidaan hyödyntää satelliittipohjaisten albedoaikasarjojen ja ilmastomallien varmennus- ja kehitystyössä sekä havaintoihin perustuvassa ilmaston muutosten seurannassa