Author(s):
Azimi, Shima; Dariane, Alireza B.; Modanesi, Sara; Bauer-Marschallinger, Bernhard; Bindlish, Rajat; Wagner, Wolfgang; Massari, Christian
Publication title: Journal of Hydrology
2020
| Volume: 581
2020
Abstract:
In runoff generation process, soil moisture plays an important role as it controls the magnitude of the flood events in response to the rainfall input… In runoff generation process, soil moisture plays an important role as it controls the magnitude of the flood events in response to the rainfall inputs. In this study, we investigated the ability of a new era of satellite soil moisture retrievals to improve the Soil & Water Assessment Tool (SWAT) daily discharge simulations via soil moisture data assimilation for two small ( more
Author(s):
Röhrs, J.; Gusdal, Y.; Rikardsen, E.S.U.; Durán Moro, M.; Brændshøi, J.; Kristensen, N.M.; Fritzner, S.; Wang, K.; Sperrevik, A.K.; Idžanović, M.; Lavergne, T.; Debernard, J.B.; Christensen, K.H.
Publication title: Geoscientific Model Development
2023
| Volume: 16 | Issue: 18
2023
Abstract:
An operational ocean and sea ice forecast model, Barents-2.5, is implemented for short-term forecasting at the coast off northern Norway, the Barents … An operational ocean and sea ice forecast model, Barents-2.5, is implemented for short-term forecasting at the coast off northern Norway, the Barents Sea, and the waters around Svalbard. Primary forecast parameters are sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model also provides input data for drift modeling of pollutants, icebergs, and search-and-rescue applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an ensemble prediction system with 24 daily realizations of the model state. SIC, SST, and in situ hydrography are constrained through the ensemble Kalman filter (EnKF) data assimilation scheme executed in daily forecast cycles with a lead time up to 66gh. Here, we present the model setup and validation in terms of SIC, SST, in situ hydrography, and ocean and ice velocities. In addition to the model's forecast capabilities for SIC and SST, the performance of the ensemble in representing the model's uncertainty and the performance of the EnKF in constraining the model state are discussed. © 2023 Johannes Röhrs et al. more
Author(s):
Mayer, J.; Bugliaro, L.; Mayer, B.; Piontek, D.; Voigt, C.
Publication title: Atmospheric Measurement Techniques
2024
| Volume: 17 | Issue: 13
2024
Abstract:
A comprehensive understanding of the cloud thermodynamic phase is crucial for assessing the cloud radiative effect and is a prerequisite for remote se… A comprehensive understanding of the cloud thermodynamic phase is crucial for assessing the cloud radiative effect and is a prerequisite for remote sensing retrievals of microphysical cloud properties. While previous algorithms mainly detected ice and liquid phases, there is now a growing awareness for the need to further distinguish between warm liquid, supercooled and mixed-phase clouds. To address this need, we introduce a novel method named ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI), which enables cloud detection and the determination of cloud-top phase using SEVIRI (Spinning Enhanced Visible and Infrared Imager), the geostationary passive imager aboard Meteosat Second Generation. ProPS discriminates between clear sky, optically thin ice (TI) cloud, optically thick ice (IC) cloud, mixed-phase (MP) cloud, supercooled liquid (SC) cloud and warm liquid (LQ) cloud. Our method uses a Bayesian approach based on the cloud mask and cloud phase from the lidar-radar cloud product DARDAR (liDAR/raDAR). The validation of ProPS using 6 months of independent DARDAR data shows promising results: the daytime algorithm successfully detects 93% of clouds and 86% of clear-sky pixels. In addition, for phase determination, ProPS accurately classifies 91% of IC, 78% of TI, 52% of MP, 58% of SC and 86% of LQ clouds, providing a significant improvement in accurate cloud-top phase discrimination compared to traditional retrieval methods. © Copyright: more
Author(s):
Su, Chun-Hsu; Eizenberg, Nathan; Steinle, Peter; Jakob, Dörte; Fox-Hughes, Paul; White, Christopher J.; Rennie, Susan; Franklin, Charmaine; Dharssi, Imtiaz; Zhu, Hongyan
Publication title: Geoscientific Model Development
2019
| Volume: 12 | Issue: 5
2019
Abstract:
Abstract. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis… Abstract. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand, and Southeast Asia. The production of the reanalysis with approximately 12 km horizontal resolution – BARRA-R – is well underway with completion expected in 2019. This paper describes the numerical weather forecast model, the data assimilation methods, the forcing and observational data used to produce BARRA-R, and analyses results from the 2003–2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed, surface pressure, and precipitation. Comparing against the global reanalyses ERA-Interim and MERRA-2, BARRA-R scores lower root mean square errors when evaluated against (point-scale) 2 m temperature, 10 m wind speed, and surface pressure observations. It also shows reduced biases in daily 2 m temperature maximum and minimum at 5 km resolution and a higher frequency of very heavy precipitation days at 5 and 25 km resolution when compared to gridded satellite and gauge analyses. Some issues with BARRA-R are also identified: biases in 10 m wind, lower precipitation than observed over the tropical oceans, and higher precipitation over regions with higher elevations in south Asia and New Zealand. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale ( more
Author(s):
Bulgin, Claire E.; Embury, Owen; Maidment, Ross I.; Merchant, Christopher J.
Publication title: Remote Sensing
2022
| Volume: 14 | Issue: 9
2022
Abstract:
Cloud detection is a necessary step in the generation of land surface temperature (LST) climate data records (CDRs) and affects data quality and uncer… Cloud detection is a necessary step in the generation of land surface temperature (LST) climate data records (CDRs) and affects data quality and uncertainty. We present here a sensor-independent Bayesian cloud detection algorithm and show that it is suitable for use in the production of LST CDRs. We evaluate the performance of the cloud detection with reference to two manually masked datasets for the Advanced Along-Track Scanning Radiometer (AATSR) and find a 7.9% increase in the hit rate and 4.9% decrease in the false alarm rate when compared to the operational cloud mask. We then apply the algorithm to four instruments aboard polar-orbiting satellites, which together can produce a global, 25-year LST CDR: the second Along-Track Scanning Radiometer (ATSR-2), AATSR, the Moderate Resolution Spectroradiometer (MODIS Terra) and the Sea and Land Surface Temperature Radiometer (SLSTR-A). The Bayesian cloud detection hit rate is assessed with respect to in situ ceilometer measurements for periods of overlap between sensors. The consistency of the hit rate is assessed between sensors, with mean differences in the cloud hit rate of 4.5% for ATSR-2 vs. AATSR, 4.9% for AATSR vs. MODIS, and 2.5% for MODIS vs. SLSTR-A. This is important because consistent cloud detection performance is needed for the observational stability of a CDR. The application of a sensor-independent cloud detection scheme in the production of CDRs is thus shown to be a viable approach to achieving LST observational stability over time. more
Author(s):
Nygard Riise, Heine; Moe Nygård, Magnus; Lupton Aarseth, Bjørn; Dobler, Andreas; Berge, Erik
Publication title: Solar Energy
2024
| Volume: 282
2024
Abstract:
Estimated solar irradiances from CAMS, PVGIS SARAH-2, Solargis, Meteonorm, PVGIS ERA5, and NASA POWER are benchmarked against measurements conducted a… Estimated solar irradiances from CAMS, PVGIS SARAH-2, Solargis, Meteonorm, PVGIS ERA5, and NASA POWER are benchmarked against measurements conducted at 34 ground stations in Norway at latitudes between 58 and 76°N. We find that the data products that mainly rely on high-resolution, geostationary satellite images, i.e., CAMS, PVGIS SARAH-2, and Solargis, have higher accuracy with lower relative Mean Absolute Error (rMAE) and relative Mean Bias Error. By dividing the stations in distinct categories, such as above 65°N, snow-affected and horizon-shaded, challenges with irradiance estimation that are common in Norway and at high latitudes in general are highlighted and discussed. The accuracy of the data products is dependent on latitude, and by excluding stations above 65°N, the median rMAE of the different data products improves 3.2 – 9.4 %abs compared to the median rMAE when including all stations, depending on data product. Similarly, by excluding snow-affected stations, the median rMAE improves 1.9 – 8.1 %abs, depending on data product. The improvement in rMAE by excluding snow-affected stations is partially related to the difficulty of separating snow on the ground from cloud cover in satellite images. This difficulty is illustrated by concrete examples of irradiance time series from clear sky days when the ground is covered in snow. Although the performance of the data products is dependent on the categorization of stations, i.e., latitude, snow conditions, and local topography, the relative performance between the products is maintained regardless of sub-division. more
Author(s):
Lind, P.; Belušić, D.; Christensen, O.B.; Dobler, A.; Kjellström, E.; Landgren, O.; Lindstedt, D.; Matte, D.; Pedersen, R.A.; Toivonen, E.; Wang, F.
Publication title: Climate Dynamics
2020
| Volume: 55 | Issue: 7-8
2020
Abstract:
Convection-permitting climate models have shown superior performance in simulating important aspects of the precipitation climate including extremes a… Convection-permitting climate models have shown superior performance in simulating important aspects of the precipitation climate including extremes and also to give partly different climate change signals compared to coarser-scale models. Here, we present the first long-term (1998–2018) simulation with a regional convection-permitting climate model for Fenno-Scandinavia. We use the HARMONIE-Climate (HCLIM) model on two nested grids; one covering Europe at 12 km resolution (HCLIM12) using parameterized convection, and one covering Fenno-Scandinavia with 3 km resolution (HCLIM3) with explicit deep convection. HCLIM12 uses lateral boundaries from ERA-Interim reanalysis. Model results are evaluated against reanalysis and various observational data sets, some at high resolutions. HCLIM3 strongly improves the representation of precipitation compared to HCLIM12, most evident through reduced “drizzle” and increased occurrence of higher intensity events as well as improved timing and amplitude of the diurnal cycle. This is the case even though the model exhibits a cold bias in near-surface temperature, particularly for daily maximum temperatures in summer. Simulated winter precipitation is biased high, primarily over complex terrain. Considerable undercatchment in observations may partly explain the wet bias. Examining instead the relative occurrence of snowfall versus rain, which is sensitive to variance in topographic heights it is shown that HCLIM3 provides added value compared to HCLIM12 also for winter precipitation. These results, indicating clear benefits of convection-permitting models, are encouraging motivating further exploration of added value in this region, and provide a valuable basis for impact studies. © 2020, The Author(s). more
Author(s):
Kakoulaki, G.; Gonzalez Sanchez, R.; Gracia Amillo, A.; Szabo, S.; De Felice, M.; Farinosi, F.; De Felice, L.; Bisselink, B.; Seliger, R.; Kougias, I.; Jaeger-Waldau, A.
Publication title: Renewable and Sustainable Energy Reviews
2023
| Volume: 171
2023
Abstract:
Achieving carbon-neutrality is increasing the demand of renewable electricity which is raising the competition for land and associated acquisition cos… Achieving carbon-neutrality is increasing the demand of renewable electricity which is raising the competition for land and associated acquisition costs. Installation of floating photovoltaic (FPV) on existing hydropower reservoirs offers one solution to limited land availability while providing solar electricity, leveraging water bodies, and reducing water evaporation losses. This work assesses the potential electricity output of FPVs at regional and national levels on 337 hydropower reservoirs in the EU27 considering four scenarios and two types of floaters. Evaporation, water losses and water savings due to FPVs installation are also estimated using climatic parameters for the year 2018. The reservoirs' total water losses are estimated at 9380 mcm. The installation of FPVs of equal installed capacity as the hydropower plants, has the potential to generate 42.31 TWh covering 2.3% of the total reservoir area. In this case, up to 557 mcm could be saved by installing FPV. The FPVs' multiple benefits and the potential offered by existing hydropower reservoirs are compatible with the EU's goals for net zero emissions and more autonomy from imported fossil fuels and energy transformation. more
Author(s):
Zampieri, L.; Goessling, H.F.; Jung, T.
Publication title: Geophysical Research Letters
2018
| Volume: 45 | Issue: 18
2018
Abstract:
With retreating sea ice and increasing human activities in the Arctic come a growing need for reliable sea ice forecasts up to months ahead. We exploi… With retreating sea ice and increasing human activities in the Arctic come a growing need for reliable sea ice forecasts up to months ahead. We exploit the subseasonal-to-seasonal prediction database and provide the first thorough assessment of the skill of operational forecast systems in predicting the location of the Arctic sea ice edge on these time scales. We find large differences in skill between the systems, with some showing a lack of predictive skill even at short weather time scales and the best producing skillful forecasts more than 1.5 months ahead. This highlights that the area of subseasonal prediction in the Arctic is in an early stage but also that the prospects are bright, especially for late summer forecasts. To fully exploit this potential, it is argued that it will be imperative to reduce systematic model errors and develop advanced data assimilation capacity. ©2018. The Authors. more
Author(s):
Urraca, Ruben; Trentmann, Jörg; Pfeifroth, Uwe; Gobron, Nadine
Publication title: Remote Sensing of Environment
2024
| Volume: 315
2024
Abstract:
Satellite products provide the best way to monitor the solar radiation reaching the Earth’s surface on a global scale. However, their capability to mo… Satellite products provide the best way to monitor the solar radiation reaching the Earth’s surface on a global scale. However, their capability to monitor solar radiation trends needs to be constantly evaluated. This depends on their temporal stability and the accurate representation of all processes driving solar radiation. This study evaluates these aspects by comparing and cross-comparing different solar radiation products (ERA5, CAMS-RAD 4.6, SARAH-3, CLARA-A3, CERES-EBAF 4.2) against in-situ measurements over Europe. All products show a moderate positive bias over Europe but strong differences in their root mean squared deviation (RMSD) related to their different cloud transmittance models. Geostationary-based products (SARAH-3, CAMS-RAD 4.6) provide the smallest RMSD closely followed by CLARA-A3, whereas ERA5 shows a large RMSD due to random errors in cloud transmittance. All products show an increase in surface solar radiation, or brightening, over the last 40 years over Europe, but the magnitude of the trends and their spatiotemporal variability differ between products. Despite finding temporal inhomogeneities in some products, the different trends are mostly due to different aerosol modeling approaches implemented by each product. Both SARAH-3 (+2.3 W/m2/decade, 2001–22) and CERES-EBAF 4.2 (+2.2 W/m2/decade, 2001–22) provide the most consistent trends compared to in-situ data, showing that after stabilizing in the late 2000s, brightening is particularly recovering in Western Europe. In-situ measurements show a reduction of aerosol optical depth from 2001 to 2022 that has been accentuated in the last 10 years, particularly in Western Europe. This would be consistent with the hypothesis that brightening recovery is driven by an aerosol reduction, though other analyses suggest that clouds also play a role in this recovery. More work is needed to understand the contribution of aerosols to solar radiation trends and the exact aerosol effects represented by each solar radiation product. more