Multiscale assimilation of Advanced Microwave Scanning Radiometer-EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado

Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR-E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high-elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower-elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse-scale AMSR-E SWE and fine-scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR-E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root-mean-square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR-E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR-E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near-complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

Estimating Regional Snow Line Elevation Using Public Webcam Images

Snow cover is of high relevance for the Earth’s climate system, and its variability plays a key role in alpine hydrology, ecology, and socioeconomic systems. Measurements obtained by optical satellite remote sensing are an essential source for quantifying snow cover variability from a local to global scale. However, the temporal resolution of such measurements is often affected by persistent cloud coverage, limiting the application of high resolution snow cover mapping. In this study, we derive the regional snow line elevation in an alpine catchment area using public webcams. We compare our results to the snow line information derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 snow cover products and find our results to be in good agreement therewith. Between October 2017 and the end of June 2018, snow lines derived from webcams lie on average 55.8 m below and 33.7 m above MODIS snow lines using a normalized-difference snow index (NDSI) of 0.4 and 0.1, respectively, and are on average 53.1 m below snow lines derived from Sentinel-2. We further analyze the superior temporal resolution of webcam-based snow cover information and demonstrate its effectiveness in filling temporal gaps in satellite-based measurements caused by cloud cover. Our findings show the ability of webcam-based snow line elevation retrieval to complement and improve satellite-based measurements

Understanding past and future changes in northern Fennoscandian snow cover.

In this project, a combination of field measurements, remote sensing data and regional climate model outputs were used to study recent and projected future changes in Northern Fennoscandian snow cover. The research questions considered in this thesis are: What are the uncertainties in remote sensing and climate modelling datasets used in snow studies? How has snow cover been changing since the 1960s and how will it change over the next century, at a regional level over Northern Fennoscandia? Field measurements were made over two field seasons in the Khibiny Mountains in Arctic Russia. This ground data was used to gain an understanding of snow cover behaviour in the Western Mountain Regions (WMR) of the Kola Peninsula and to ground-truth 500 m resolution satellite data (MODIS: Moderate Resolution Imaging Spectroradiometer) snow products. The overall root mean square error (RMSE) for both MODIS instruments was found to be less than 10 %. The ground-truthed MODIS snow product was then used with station data to analyse past changes in snow cover in the WMR over the past 16 years. Though there is high inter-annual and spatial variability in the long-term snow cover trends in the WMR, overall, the duration of the snow cover season has increased at lower elevations and decreased at higher elevations. Field measurements and MODIS data were used in the sensitivity analysis of the Weather Research and Forecasting (WRF) regional climate model. Twelve experiments with different physics parameterisations were run over the first field season, and a statistical scores evaluation was undertaken to determine the optimised parameter setup for modelling snow in the region. Three CMIP5 (Coupled Model Intercomparison Project 5) models were used to force WRF in historical (1990 - 1999) and two future climate (2090 - 2099) emission scenarios over Northern Fennoscandia. Outputs from the historical runs were compared to data from 10 stations across Northern Fennoscandia in order to further validate WRF. WRF makes excellent temperature estimates, with a mean bias in the yearly mean temperature outputs of the runs of -1.89 °C. The precipitation outputs are less accurate with values often higher than observations, especially for extreme precipitation events (CMIP5 ‘ensemble’ mean RMSE of 24.0 mm for 20 + mm precipitation events). Finally, the future runs were compared to historical runs to study projected future changes in temperature, precipitation, snowfall and snow cover. The three models give a range of different future predictions for regional climate change over Northern Fennoscandia. However, all CMIP5 models agree that in both emission scenarios mean snow cover duration will be lower over 2090 to 2099 than it was between 1990 and 1999. Importantly, changes in temperature, precipitation and snowfall are all higher, and snow cover is most impacted, in the higher emission scenario. RCP 8.5 consistently sees a higher decrease in solid precipitation than RCP 4.5 at all stations, and for all models and seasons, for example. Thus, aiming to reduce greenhouse gas emissions is still crucial to reducing anthropogenic impact on Northern Fennoscandian snow.Funded by NERC PhD studentship NE/L002507/

RADIATIVE FLUXES AND ALBEDO FEEDBACK IN POLAR REGIONS

The Arctic is experiencing an unprecedented increase in surface temperature and decrease in sea ice extent. Discussion as to the causes that contribute to the Arctic warming is still ongoing. The ice-albedo feedback has been proposed as a possible mechanism for polar amplification of such warming. It states that more open water leads to more solar heat absorption, which results in more ice melting and more open water. In order to study this relationship there is a need for accurate information on the solar heat input into the Arctic Oceans. I have developed and improved inference schemes for shortwave radiative fluxes that respond to the needs of Polar Regions utilizing most recent information on atmospheric and surface states. A Moderate Resolution Imaging Spectroradiometer (MODIS) approach has been optimized for Polar Regions and implemented at 1° for 2002-2010 and at 5-km for 2007. A methodology was developed to derive solar fluxes from the Advanced Very High Resolution Radiometer (AVHRR) and implemented at 0.5° for 1983-2006. Evaluation against ground measurements over land and ocean at high latitudes shows that the MODIS shortwave flux estimates are in best agreement with ground observations as compared to other available satellite and model products, with a bias of -3.6 Wm-2 and standard deviation of 23 Wm-2 at a daily time scale. The AVHRR estimates agree with ground observations with a bias of -4.7 Wm-2 and a standard deviation of 41 Wm-2 at a daily time scale. The ice-albedo feedback was evaluated by computing the solar heating into the Arctic Ocean using the improved satellite flux estimates. A growth at a rate of 2 %/year in the trend of solar heating for 2003-09 was found at a 75 % confidence level; the trend for 1984-2002 was only 0.2 %/year at a 99 % confidence level. The ice retreat is correlated to the solar energy into the ocean at 0.7 at a 75 % confidence level. An increase in the open water fraction resulted in a maximum 300 % positive anomaly in solar heating in 2007 located where the maximum sea ice retreat is

A Review of Global Satellite-Derived Snow Products

Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation. Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow

Empirical approach to satellite snow detection

Lumipeitteellä on huomattava vaikutus säähän, ilmastoon, luontoon ja yhteiskuntaan. Pelkästään sääasemilla tehtävät lumihavainnot (lumen syvyys ja maanpinnan laatu) eivät anna kattavaa kuvaa lumen peittävyydestä tai muista lumipeitteen ominaisuuksista. Sääasemien tuottamia havaintoja voidaan täydentää satelliiteista tehtävillä havainnoilla. Geostationaariset sääsatelliitit tuottavat havaintoja tihein välein, mutta havaintoresoluutio on heikko monilla alueilla, joilla esiintyy kausittaista lunta. Polaariradoilla sääsatelliittien havaintoresoluutio on napa-alueiden läheisyydessä huomattavasti parempi, mutta silloinkaan satelliitit eivät tuota jatkuvaa havaintopeittoa. Tiheimmän havaintoresoluution tuottavat sääsatelliittiradiometrit, jotka toimivat optisilla aallonpituuksilla (näkyvä valo ja infrapuna). Lumipeitteen kaukokartoitusta satelliiteista vaikeuttavat lumipeitteen oman vaihtelun lisäksi pinnan ominaisuuksien vaihtelu (kasvillisuus, vesistöt, topografia) ja valaistusolojen vaihtelu. Epävarma ja osittain puutteellinen tieto pinnan ja kasvipeitteen ominaisuuksista vaikeuttaa luotettavan automaattisen analyyttisen lumentunnistusmenetelmän kehittämistä ja siksi empiirinen lähestymistapa saattaa olla toimivin vaihtoehto automaattista lumentunnistusmenetelmää kehitettäessä. Tässä työssä esitellään kaksi EUMETSATin osittain rahoittamassa H SAFissa kehitettyä lumituotetta ja niissä käytetyt empiiristä lähestymistapaa soveltaen kehitetyt algoritmit. Geostationaarinen MSG/SEVIRI H31 lumituote on saatavilla vuodesta 2008 alkaen ja polaarituote Metop/AVHRR H32 vuodesta 2015 alkaen. Lisäksi esitellään pintahavaintoihin perustuvat validointitulokset, jotka osoittavat tuotteiden saavuttavan määritellyt tavoitteet.Snow cover plays a significant role in the weather and climate system, ecosystems and many human activities, such as traffic. Weather station snow observations (snow depth and state of the ground) do not provide highresolution continental or global snow coverage data. The satellite observations complement in situ observations from weather stations. Geostationary weather satellites provide observations at high temporal resolution, but the spatial resolution is low, especially in polar regions. Polarorbiting weather satellites provide better spatial resolution in polar regions with limited temporal resolution. The best detection resolution is provided by optical and infra-red radiometers onboard weather satellites. Snow cover in itself is highly variable. Also, the variability of the surface properties (such as vegetation, water bodies, topography) and changing light conditions make satellite snow detection challenging. Much of this variability is in subpixel scales, and this uncertainty creates additional challenges for the development of snow detection methods. Thus, an empirical approach may be the most practical option when developing algorithms for automatic snow detection. In this work, which is a part of the EUMETSAT-funded H SAF project, two new empirically developed snow extent products for the EUMETSAT weather satellites are presented. The geostationary MSG/SEVIRI H32 snow product has been in operational production since 2008. The polar product Metop/AVHRR H32 is available since 2015. In addition, validation results based on weather station snow observations between 2015 and 2019 are presented. The results show that both products achieve the requirements set by the H SAF