scholarly journals Present temperature, precipitation and rain-on-snow climate in Svalbard

2020 ◽  
Author(s):  
Siiri Wickström ◽  
Marius O. Jonassen ◽  
John Cassano ◽  
Timo Vihma ◽  
Jørn Kristiansen
Keyword(s):  
Weather Prediction ◽  
Open Water ◽  
Low Pressure ◽  
Numerical Weather Prediction Model ◽  
The Arctic ◽  
Pressure Center ◽  
Climate Conditions ◽  
Temperature And Precipitation ◽  
Pressure Centre ◽  
Local Variability

<p>Potentially high-impact warm and wet winter conditions have become increasingly common in recent decades in the arctic archipelago of Svalbard. In this study, we document present 2m temperature, precipitation and rain-on-snow (ROS) climate conditions in Svalbard and relate them to different atmospheric circulation (AC) types. For this purpose, we utilise a set of observations together with output from the high resolution numerical weather prediction model AROME-Arctic. We find that 2m median temperatures vary the most across AC types in winter and spring, and the least in summer. Southerly and southwesterly flow is associated with 10th percentile 2m temperatures above freezing in all seasons. In terms of precipitation, we find the highest amounts and intensities with onshore flow over open water. Sea ice appears to play a strong role in the local variability in both 2m temperature and precipitation. ROS is a frequent phenomenon in the study period, in particular below 250 m ASL. In winter, ROS only occurs with AC types from the southerly sector or during the passage of a low pressure centre or trough. Most of these events occur during southwesterly flow, with a low pressure center west of Svalbard.</p><p> </p>

scholarly journals Synoptic climatological approach associated with three recent summer heatwaves in the Canadian Arctic

2020 ◽  
Vol 11 (S1) ◽  
pp. 233-250 ◽  
Author(s):  
Farahnaz Fazel-Rastgar
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Arctic Region ◽  
Low Pressure ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
The North ◽  
Pressure Centre ◽  
Temperature Records ◽  
Arctic Temperature

Abstract The observed unusually high temperatures in the Arctic during recent decades can be related to the Arctic sea ice declines in summer 2007, 2012 and 2016. Arctic dipole formation has been associated with all three heatwaves of 2007, 2012 and 2016 in the Canadian Arctic. Here, the differences in weather patterns are investigated and compared with normal climatological mean (1981–2010) structures. This study examines the high-resolution datasets from the North American Regional Reanalysis model. During the study periods, the north of Alaska has been affected by the low-pressure tongue. The maximum difference between Greenland high-pressure centre and Alaska low-pressure tongue for the summers of 2012, 2016 and 2007 are 8 hPa, 7 hPa and 6 hPa, respectively, corresponding and matching to the maximum summer surface Canadian Arctic temperature records. During anomalous summer heatwaves, low-level wind, temperatures, total clouds (%) and downward radiation flux at the surface are dramatically changed. This study shows the surface albedo has been reduced over most parts of the Canadian Arctic Ocean during the mentioned heatwaves (∼5–40%), with a higher change (specifically in the eastern Canadian Arctic region) during summer 2012 in comparison with summer 2016 and summer 2007, agreeing with the maximum surface temperature and sea ice decline records.

In search of operational snow model structures for the future – comparing four snow models for 17 catchments in Norway

2018 ◽  
Vol 49 (6) ◽  
pp. 1929-1945 ◽  
Author(s):  
Thomas Skaugen ◽  
Hanneke Luijting ◽  
Tuomo Saloranta ◽  
Dagrun Vikhamar-Schuler ◽  
Karsten Müller
Keyword(s):  
Energy Balance ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Threshold Temperature ◽  
Temperature Index ◽  
Ungauged Sites ◽  
Temperature And Precipitation ◽  
Snow Conditions ◽  
Model Structures

Abstract In order to use the best suited snow models to investigate snow conditions at ungauged sites and for a changed climate, we have tested four snow models for 17 catchments in Norway. The Crocus and seNorge models are gridded whereas the Distance Distribution Dynamics (DDD) model with its two versions, DDD_CX and DDD_EB, is catchment based. Crocus and DDD_EB use energy balance for estimating snowmelt and SeNorge and DDD_CX use temperature-index methods. SeNorge has calibrated the temperature-index against observed snowmelt, whereas DDD_CX has calibrated the temperature-index against runoff. The models use gridded temperature and precipitation at 1 h resolution for the period 2013–2016. Crocus needs additional forcing from a numerical weather prediction model, whereas DDD_EB calculates the energy-balance elements by using proxy models forced by temperature and precipitation. The threshold temperature for solid and liquid precipitation is common for all the models and equal to 0.5 °C. No corrections of precipitation or temperature are allowed. The snow simulations are validated against observed snow water equivalent (SWE) and against satellite derived snow covered area (SCA). SeNorge and DDD_EB perform best with respect to both SWE and SCA suggesting model structures suited for describing snow conditions at ungauged sites and for a changed climate.

scholarly journals The EUMETSAT sea ice climate record

2016 ◽  
Author(s):  
R. T. Tonboe ◽  
S. Eastwood ◽  
T. Lavergne ◽  
A. M. Sørensen ◽  
N. Rathmann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Open Water ◽  
The Arctic ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Microwave Imager

Abstract. An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of American microwave radiometer data from Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from 1978 to 2014 and updates and further developments are planned for the next phase of the project. The methodology is using: 1) numerical weather prediction (NWP) input to a radiative transfer model (RTM) for correction of the brightness temperatures for reduction of atmospheric noise, 2) dynamical algorithm tie-points to mitigate trends in residual atmospheric, sea ice and water emission characteristics and inter-sensor differences/biases, 3) and a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new algorithm has been developed to estimate the spatially and temporally varying sea ice concentration uncertainties. A comparison to sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate ice concentrations. The sea ice climate dataset is available for download at (www.osisaf.org) including documentation.

scholarly journals Evaluation of Arctic cloud products from the EUMETSAT Climate Monitoring Satellite Application Facility based on CALIPSO-CALIOP observations

2009 ◽  
Vol 9 (4) ◽  
pp. 16755-16810 ◽  
Author(s):  
K.-G. Karlsson ◽  
A. Dybbroe
Keyword(s):  
Weather Prediction ◽  
Lower Troposphere ◽  
Arctic Region ◽  
Numerical Weather Prediction Model ◽  
The Arctic ◽  
Cloud Type ◽  
Near Surface ◽  
Noaa Avhrr ◽  
Climate Monitoring ◽  
June July

Abstract. The performance of the three cloud products cloud fractional cover, cloud type and cloud top height, derived from NOAA AVHRR data and produced by the EUMETSAT Climate Monitoring Satellite Application Facility, has been evaluated in detail over the Arctic region for four months in 2007 using CALIPSO-CALIOP observations. The evaluation was based on 142 selected NOAA/Metop overpasses allowing almost 400 000 individual matchups between AVHRR pixels and CALIOP measurements distributed approximately equally over the studied months (June, July, August and December 2007). Results suggest that estimations of cloud amounts are very accurate during the polar summer season while a substantial loss of detected clouds occurs in the polar winter. Evaluation results for cloud type and cloud top products point at specific problems related to the existence of near isothermal conditions in the lower troposphere in the polar summer and the use of reference vertical temperature profiles from Numerical Weather Prediction model analyses. The latter are currently not detailed enough in describing true conditions relevant on the pixel scale. This concerns especially the description of near-surface temperature inversions which are often too weak leading to large errors in interpreted cloud top heights.

A Methodology for Optimizing Numerical Weather Prediction Models

2020 ◽  
Author(s):  
Rafaella - Eleni Sotiropoulou ◽  
Ioannis Stergiou ◽  
Efthimios Tagaris
Keyword(s):  
Numerical Weather Prediction ◽  
Land Surface ◽  
Prediction Models ◽  
Carbon Sink ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather ◽  
Temperature And Precipitation ◽  
Statistical Measures ◽  
Numerical Weather Prediction Models

<p>Optimizing the performance of numerical weather prediction models is a very complicated process due to the numerous parameterization choices provided to the user. In addition, improving the predictability of one model’s variable (e.g., temperature) does not necessarily imply the improvement of another (e.g., precipitation). In this work the Technique of Preference by Similarity to the Ideal Solution (TOPSIS) is suggested as a method to optimize the performance of a numerical weather prediction model. TOPSIS provides the ability of using multiple statistical measures as ranking criteria for multiple forecasting variables. The Weather Research and Forecasting model (WRF) is used here for application of TOPSIS in order to optimize the model’s performance by the combined assessment of temperature and precipitation over Europe. Six ensembles optimize model’s physics performance (i.e., microphysics, planetary boundary layer, cumulus scheme, Long–and Short– wave and Land Surface schemes). The best performing option for each ensemble is selected by using multiple statistical criteria as input for the TOPSIS method, based on the integration of entropy weights. The method adopted here illustrates the importance of an integrated evaluation of weather prediction models’ performance and suggests a pathway for its improvement.</p><p>Acknowledgments LIFE CLIMATREE project “A novel approach for accounting & monitoring carbon sequestration of tree crops and their potential as carbon sink areas” (LIFE14 CCM/GR/000635).</p>

scholarly journals Evaluation of Arctic cloud products from the EUMETSAT Climate Monitoring Satellite Application Facility based on CALIPSO-CALIOP observations

2010 ◽  
Vol 10 (4) ◽  
pp. 1789-1807 ◽  
Author(s):  
K.-G. Karlsson ◽  
A. Dybbroe
Keyword(s):  
Weather Prediction ◽  
Lower Troposphere ◽  
Arctic Region ◽  
Numerical Weather Prediction Model ◽  
The Arctic ◽  
Cloud Type ◽  
Near Surface ◽  
Noaa Avhrr ◽  
Climate Monitoring ◽  
June July

Abstract. The performance of the three cloud products cloud fractional cover, cloud type and cloud top height, derived from NOAA AVHRR data and produced by the EUMETSAT Climate Monitoring Satellite Application Facility, has been evaluated in detail over the Arctic region for four months in 2007 using CALIPSO-CALIOP observations. The evaluation was based on 142 selected NOAA/Metop overpasses allowing almost 400 000 individual matchups between AVHRR pixels and CALIOP measurements distributed approximately equally over the studied months (June, July, August and December 2007). Results suggest that estimations of cloud amounts are very accurate during the polar summer season while a substantial loss of detected clouds occurs in the polar winter. Evaluation results for cloud type and cloud top products point at specific problems related to the existence of near isothermal conditions in the lower troposphere in the polar summer and the use of reference vertical temperature profiles from Numerical Weather Prediction model analyses. The latter are currently not detailed enough in describing true conditions relevant on the pixel scale. This concerns especially the description of near-surface temperature inversions which are often too weak leading to large errors in interpreted cloud top heights.

POLAR LOW DETECTION USING SOLAB SIOWS ARCTIC PORTAL

2017 ◽  
Author(s):  
Olga Mashtaler ◽  
Olga Mashtaler ◽  
Alexander Myasoedov ◽  
Alexander Myasoedov ◽  
Elizaveta Zabolotskikh ◽  
...  
Keyword(s):  
Model Development ◽  
Sharp Decrease ◽  
Open Water ◽  
The Arctic ◽  
Wind Fields ◽  
Polar Lows ◽  
Statistical Regularities ◽  
Meteorological Observations ◽  
Strong Winds ◽  
Synthetic Aperture Radars

The relevance of the polar lows (PLs) research is justified by their great destructive power and creation of threat to the safety of navigation in the high latitudes and along the Northern Sea Route. The most dangerous effects on maritime activities are strong winds, waves and icing. In addition, the study of the PLs acquires relevance due to the sharp decrease of the sea ice area in the Arctic in recent years and the emergence of areas of open water, suitable for the appearance and development of PLs. However, despite the importance of PLs, they are apparently not sufficiently studied. As there are no meteorological observations in the areas of their appearance, the main source of information about them are satellite observations. By using images on the SOLab SIOWS Arctic Portal from multiple satellites operating in the IR and visible ranges (e.g., MODIS and AVHRR), and using near-water wind fields from high resolution synthetic aperture radars (Sentine-1, ASAR) and low resolution scatterometers (ASCAT), we identify polar lows in various parts of the Arctic, revealing statistical regularities in the appearance of PLs, their distribution and intensity. Collected database of Pls and their characteristics will be used for further PLs forecasting model development.

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