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Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System
No full textMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed
Changing freshwater contributions to the Arctic : A 90-year trend analysis (1981-2070)
No full textThe pan-Arctic domain is undergoing some of Earth's most rapid and significant changes resulting from anthropogenic and climate-induced alteration of freshwater distribution. Changes in terrestrial freshwater discharge entering the Arctic Basin from pan-Arctic watersheds significantly impact oceanic circulation and sea ice dynamics. Historical streamflow records in high-latitude basins are often discontinuous (seasonal or with large temporal gaps) or sparse (poor spatial coverage), however, making trends from observed records difficult to quantify. Our objectives were to generate a more continuous 90-year record (1981-2070) of spatially distributed freshwater flux for the Arctic Basin (all Arctic draining rivers, including the Yukon), suitable for forcing ocean models, and to analyze the changing simulated trends in freshwater discharge across the domain. We established these data as valid during the historical period (1971-2015) and then used projected futures (preserving uncertainty by running a coupled climate-hydrologic ensemble) to analyze long-term (2021-2070) trends for major Arctic draining rivers. When compared to historic trends reported in the literature, we find that trends are projected to nearly double by 2070, with river discharge to the Arctic Basin increasing by 22% (on average) by 2070. We also find a significant trend toward earlier onset of spring freshet and a general flattening of the average annual hydrograph, with a trend toward decreasing seasonality of Arctic freshwater discharge with climate change and regulation combined. The coupled climate-hydrologic ensemble was then used to force an ocean circulation model to simulate freshwater content and thermohaline circulation. This research provides the marine research community with a daily time series of historic and projected freshwater discharge suitable for forcing sea ice and ocean models. Although important, this work is only a first step in mapping the impacts of climate change on the pan-Arctic region
Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach
No full textUnder a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas
Convection-permitting modeling with regional climate models : Latest developments and next steps
No full textApproximately 10 years ago, convection-permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1-4 km) decadal-long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal (similar to 1 km; similar to 1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs. This article is categorized under: Climate Models and Modeling > Earth System Model
From local measures to regional impacts : Modelling changes in nutrient loads to the Baltic Sea
No full textValidation metrics of homogenization techniques on artificially inhomogenized monthly temperature networks in Sweden and Slovenia (1950-2005)
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Radiative effects of reduced aerosol emissions during the COVID-19 pandemic and the future recovery
No full textSMHI Gridded Climatology
No full textA gridded dataset (SMHI Gridded Climatology - SMHIGridClim) has been produced forthe years 1961 - 2018 over an area covering the Nordic countries on a grid with 2.5 kmhorizontal resolution. The variables considered are the two meter temperature and twometer relative humidity on 1, 3 or 6 hour resolution, varying over the time periodcovered, the daily minimum and maximum temperatures, the daily precipitation and thedaily snow depth. The gridding was done using optimal interpolation with the gridppopen source software from the Norwegian Meteorological Institute.Observations for the analysis are provided by the Swedish, Finish and Norwegianmeteorological institutes, and the ECMWF. The ECA&D observation data set (e.g. usedfor the gridded E-OBS dataset) was considered for inclusion but was left out because ofcomplications with time stamps and accumulation periods varying between countries andperiods. Quality check of the observations was performed using the open source softwareTITAN, also developed at the Norwegian Meteorological Institute.The first guess to the optimal interpolation was given by statistically downscaledforecasts from the UERRA-HARMONIE reanalysis at 11 km horizontal resolution. Thedownscaling was done to fit the output from the operational MEPS NWP system at 2.5km with a daily and yearly variation in the downscaling parameters.The quality of the SMHIGridClim dataset, in terms of annual mean RMSE, was shown tobe similar to that of gridded datasets covering the other Nordic countries; “seNorge”from Norway and the dataset “FMI_ClimGrid” from Finland.Ett klimatologiskt griddat datasett (SMHI Gridded Climatology - SMHIGridClim) hartagits fram för åren 1961 – 2018. Data täcker de nordiska länderna med en horisontellupplösning av 2,5 km. Variablerna som tagits fram är lufttemperatur och relativluftfuktighet vid 2m höjd med en upplösning av1,3 eller 6 timmar beroende av tidsperiod,samt dygnsupplöst min- och maxtemperatur, nederbörd och snödjup. Datasetet ärframtaget med optimal interpolation av stationsdata genom analysverktyget gridpp, somär en öppet tillgänglig programvara från Norska Meteorologiska Institutet.Observationer till analysen har erhållits från de svenska, norska och finskameteorologiska instituten, samt ECMWF. En ansats gjordes också att användaobservationer från datasetet ECA&D från KNMI, men på grund av svårigheter med atttidsstämplarna för data från olika länder inte överensstämde, uteslöts datasetet uranalysen. Kvalitetskontroll av observationerna gjordes med programvaran TITAN, somäven den finns tillgänglig från och utvecklats av Norska Meteorologiska Institutet.Som en första gissning till interpolationen användes statistiskt nerskalade prognosfält(från 11 km till 2,5 km upplösning) från UERRA-HARMONIE. Nerskalningen gjordesmot fält från den operationella numeriska väderprognosmodellen MEPS. Anpassningengjordes med nedskalningsparametrar som varierar över året och dygnet.Kvalitén hos ”SMHIGridClim med avseende på genomsnittligt RMSE är liknande densom tagits fram för griddade data för andra nordiska länderna med varierandeanalysmetoder; “seNorge” från Norge och “FMI_ClimGrid” från Finland