scholarly journals seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

2018 ◽  
Vol 10 (1) ◽  
pp. 235-249 ◽  
Author(s):  
Cristian Lussana ◽  
Tuomo Saloranta ◽  
Thomas Skaugen ◽  
Jan Magnusson ◽  
Ole Einar Tveito ◽  
...  
Keyword(s):  
High Resolution ◽  
Daily Precipitation ◽  
Spatial Scales ◽  
National Level ◽  
Calibration Procedure ◽  
Geographical Information ◽  
Optimal Interpolation ◽  
Small Scale ◽  
Meteorological Forcing ◽  
Atmospheric Sciences

Abstract. The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall–runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957–2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html.

scholarly journals Evaluation of <i>seNorge2</i>, a conventional climatological datasets for snow- and hydrological modeling in Norway

2017 ◽  
Author(s):  
Cristian Lussana ◽  
Tuomo Saloranta ◽  
Thomas Skaugen ◽  
Jan Magnussson ◽  
Ole Einar Tveito ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Interpolation ◽  
Hydrological Modeling ◽  
National Level ◽  
Precipitation Amount ◽  
Optimal Interpolation ◽  
Small Scale ◽  
Mean Temperature ◽  
Meteorological Input ◽  
Grid Points

Abstract. The conventional climate datasets based on observations only are a widely used source of information for climate and hydrology. On the Norwegian mainland, the seNorge datasets of daily mean temperature and total precipitation amount constitute a valuable meteorological input for snow- and hydrological simulations which are routinely conducted over such a complex and heterogeneous terrain. A new seNorge version (seNorge2) has been released recently and to support operational applications for civil protection purposes, it must be updated daily and presented on a high-resolution grid (1 km of grid spacing). The archive goes back to 1957. The seNorge2 statistical interpolation schemes can provide high-resolution fields for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The statistical schemes build upon classical spatial interpolation methods, such as Optimal Interpolation and successive-correction schemes, and introduce original approaches. For both temperature and precipitation, the spatial interpolation exploits the concept of (spatial) scale-separation and the first-guess field is derived from the observed data. Furthermore, the geographical coordinates and the elevation are used as complementary information. The evaluation of the seNorge2 products is presented both from a general point of view, through systematic cross-validations, and specifically as the meteorological input in the operational model chains used for snow- and hydrological simulations. The seNorge snow model is used for simulation of snow fields and the DDD (Distance Distribution Dynamics) rainfall-runoff model is the hydrological model used. The evaluation points out important information for the future seNorge2 developments: the daily mean temperature fields constitute an accurate and precise dataset, on average the predicted temperature is an unbiased estimate of the actual temperature and its precision (at grid points) varies between 0.8 °C and 2.4 °C; the daily precipitation fields provide a reasonable estimate of the actual precipitation, the cross-validation shows that on average the precision of the estimates (at grid points) is about &amp;pm;20 %, though a systematic underestimation of precipitation occurs in data-sparse areas and for intense precipitation. Both the seNorge snow and the DDD models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The dataset described in this article is available for public download at http://doi.org/10.5281/zenodo.845733.

scholarly journals In situ quantification of supraglacial cryoconite morphodynamics using time-lapse imaging: an example from Svalbard

2011 ◽  
Vol 57 (204) ◽  
pp. 651-657 ◽  
Author(s):  
Tristram D.L. Irvine-Fynn ◽  
Jonathan W. Bridge ◽  
Andrew J. Hodson
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Time Series Data ◽  
Time Lapse ◽  
Random Motion ◽  
Biologically Active ◽  
Series Data ◽  
Small Scale ◽  
Meteorological Forcing ◽  
Ice Surface

AbstractThere is growing recognition of the significance of biologically active supraglacial dust (cryoconite) for glacial mass balance and ecology. Nonetheless, the processes controlling the distribution, transport and fate of cryoconite particles in the glacial system remain somewhat poorly understood. Here, using a 216 hour time series of plot-scale (0.04 m2) images, we quantify the small-scale dynamics of cryoconite on Longyearbreen, Svalbard. We show significant fluctuations in the apparent cryoconite area and dispersion of cryoconite over the plot, within the 9 day period of observations. However, the net movement of cryoconite across the ice surface averaged only 5.3 mm d−1. High-resolution measurements of cryoconite granule motion showed constant, random motion but weak correlation with meteorological forcing factors and no directional trends for individual particle movement. The high-resolution time-series data suggest that there is no significant net transport of dispersed cryoconite material across glacier surfaces. The areal coverage and motion of particles within and between cryoconite holes appears to be a product of differential melting leading to changes in plot-scale microtopography, local meltwater flow dynamics and weather-dependent events. These subtle processes of cryoconite redistribution may be significant for supraglacial albedo and have bearing on the surface energy balance at the glacier scale.

scholarly journals Probabilistic description of ice-supersaturated layers in low resolution profiles of relative humidity

2010 ◽  
Vol 10 (2) ◽  
pp. 2357-2395 ◽  
Author(s):  
N. C. Dickson ◽  
K. M. Gierens ◽  
H. L. Rogers ◽  
R. L. Jones
Keyword(s):  
High Resolution ◽  
Relative Humidity ◽  
Climate Models ◽  
Numerical Models ◽  
Spatial Scales ◽  
Error Function ◽  
Global Distribution ◽  
Small Scale ◽  
Vertical Resolution ◽  
Average Relative Humidity

Abstract. The global observation, assimilation and prediction in numerical models of ice super-saturated (ISS) regions (ISSR) are crucial if the climate impact of aircraft condensations trails (contrails) is to be fully understood, and if, for example, contrail formation is to be avoided through aircraft operational measures. A robust assessment of the global distribution of ISSR will further this debate, and ISS event occurrence, frequency and spatial scales have recently attracted significant attention. The mean horizontal path length through ISSR as observed by MOZAIC aircraft is 150 km (±250 km). The average vertical thickness of ISS layers is 600–800 m (±575 m) but layers ranging from 25 m to 3000 m have been observed, with up to one third of ISS layers thought to be less than 100 m deep. Given their small scales compared to typical atmospheric model grid sizes, statistical representations of the spatial scales of ISSR are required, in both horizontal and vertical dimensions, if global occurrence of ISSR is to be adequately represented in climate models. This paper uses radiosonde launches made by the UK Meteorological Office, from the British Isles, Gibraltar, St. Helena and the Falkland Islands between January 2002 and December 2006, to investigate the probabilistic occurrence of ISSR. Specifically each radiosonde profile is divided into 50- and 100-hPa pressure layers, to emulate the coarse vertical resolution of some atmospheric models. Then the high resolution observations contained within each thick pressure layer are used to calculate an average relative humidity and an ISS fraction for each individual thick pressure layer. These relative humidity pressure layer descriptions are then linked through a probability function to produce an s-shaped curve describing the ISS fraction in any average relative humidity pressure layer. An empirical investigation has shown that this one curve is statistically valid for mid-latitude locations, irrespective of season and altitude, however, pressure layer depth is an important variable. Using this empirical understanding of the s-shaped relationship a mathematical model was developed to represent the ISS fraction within any arbitrary thick pressure layer. Here the statistical distributions of actual high resolution RHi observations in any thick pressure layer, along with an error function, are used to mathematically describe the s-shape. Two models were developed to represent both 50- and 100-hPa pressure layers with each reconstructing their respective s-shapes within 8–10% of the empirical curves. These new models can be used, to represent the small scale structures of ISS events, in modelled data where only low vertical resolution is available. This will be useful in understanding, and improving the global distribution, both observed and forecasted, of ice super-saturation.

scholarly journals Selection of foraging and commuting habitats of the greater horseshoe bat, revealed by high-resolution GPS-tracking

2020 ◽  
Author(s):  
Emyo Fujioka ◽  
Koki Yoshimura ◽  
Tomohiro Ujino ◽  
Ken Yoda ◽  
Dai Fukui ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Great Influence ◽  
Movement Patterns ◽  
Gps Tracking ◽  
Small Scale ◽  
Auditory Information ◽  
Natural Forests ◽  
In The Wild ◽  
Horseshoe Bat

Abstract Background Echolocating bats make a series of decisions to select their flight routes based on auditory information obtained by sonar; accumulations of these flight routes are represented as daily movement patterns. However, there is still a lack of a unified understanding of continuous movements of echolocating bats in the wild from small to large spatial scales (i.e., from meters to tens of kilometers). Methods In this study, we investigated nightly flight paths of the Japanese greater horseshoe bat, Rhinolophus ferrumequinum nippon , using high-resolution GPS data loggers. Our aim was to identify foraging and commuting behavior based on the observed movement patterns and to investigate the relationship between these movement types and specific habitats. Results We found that the majority of tagged bats alternated between foraging and commuting behavior throughout the tracking period, and one individual moved 23.6 km from its roost. The distance between two successive foraging sites was on average 332 ± 398 m (mean ± standard deviation), and half of all foraging periods lasted less than 3 min. An analysis of habitat preferences revealed that the bats preferred conifer plantation and natural forests as pathways and feeding habitats. Particularly, they locally traveled along forest roads at flight speeds significantly higher than those flown over other habitats. Conclusions Our findings suggest the forests and their structures have a great influence on the nocturnal behavior of the greater horseshoe bats. Although this study has a descriptive character due to a relatively small number of tagged individuals, it was possible to elucidate the small-scale interactions between wild Rhinolophus bats and their environment using the latest high-resolution GPS technology, which will allow us to give new insights into the foraging ecology of echolocating bats in the wild.

scholarly journals Predictive power of a shallow landslide model in a high resolution landscape: dissecting the effects of forest roads

2013 ◽  
Vol 10 (7) ◽  
pp. 9761-9798 ◽  
Author(s):  
D. Penna ◽  
M. Borga ◽  
G. T. Aronica ◽  
G. Brigandì ◽  
P. Tarolli
Keyword(s):  
High Resolution ◽  
Predictive Power ◽  
Spatial Scales ◽  
Model Performance ◽  
Shallow Landslide ◽  
Small Scale ◽  
Forest Roads ◽  
Forest Road ◽  
The Impact ◽  
Landslide Model

Abstract. This work evaluates the predictive power of the quasi-dynamic shallow landslide model QD-SLaM to simulate shallow landslide locations in a small-scale Mediterranean landscape: the Giampilieri catchment located in Sicily (Italy). The catchment was impacted by a sequence of high-intensity storms over the years 2007–2009. The effect of high resolution Digital Terrain Models (DTMs) on the quality of model predictions is tested by considering four DTM resolutions: 2 m, 4 m, 10 m and 20 m. Moreover, the impact of the dense forest road network on the model performance is evaluated by considering separately road-related landslides and natural landslides. The landslide model does not incorporate the description of road-related failures. The model predictive power is shown to be DTM-resolution dependent. When assessed over the sample of mapped natural landslides, better model performances are reported for 4 m and 10 m DTM resolution, thus highlighting the fact that higher DTM resolution does not necessarily mean better model performances. Model performances over road-related failures are, as expected, lower than for the other cases. These findings show that shallow landslide predictive power can benefit from increasing DTM resolution only when the model is able to describe the physical processes emerging at the smaller spatial scales resolved by the digital topography. Model results show also that the combined use of high DTM resolution and a model capable to deal with road-related processes may lead to substantially better performances in landscapes where forest roads are a significant factor of slope stability.

scholarly journals Foraging and commuting habitats of the greater horseshoe bat, revealed by high-resolution GPS-tracking

2020 ◽  
Author(s):  
Emyo Fujioka ◽  
Koki Yoshimura ◽  
Tomohiro Ujino ◽  
Ken Yoda ◽  
Dai Fukui ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Great Influence ◽  
Movement Patterns ◽  
Gps Tracking ◽  
Small Scale ◽  
Auditory Information ◽  
Natural Forests ◽  
In The Wild ◽  
Horseshoe Bat

Abstract Background Echolocating bats make a series of decisions to select their flight routes based on auditory information obtained by sonar; accumulations of these flight routes are represented as daily movement patterns. However, there is still a lack of a unified understanding of continuous movements of echolocating bats in the wild from small to large spatial scales (i.e., from meters to tens of kilometers). In this study, we investigated nightly flight paths of the Japanese greater horseshoe bat, Rhinolophus ferrumequinum nippon, using high-resolution GPS data loggers. Our aim was to identify foraging and commuting behavior based on the observed movement patterns and to investigate the relationship between these movement types and specific habitats.Results We found that the majority of tagged bats alternated between foraging and commuting behavior throughout the tracking period, and one individual moved 23.6 km from its roost. The bats usually left a stay point in the opposite direction from which they entered it, indicating that almost all of the stay sites were on their way to another destination. The distance between two successive foraging sites was on average 332 ± 398 m (mean ± standard deviation), and half of all foraging periods lasted less than 3 min. An analysis of habitat use revealed that the bats used conifer plantation and natural forests as pathways and feeding habitats.Conclusions Our findings suggest that the structure of the forests have a great influence on the nocturnal behavior of the greater horseshoe bats. Although this study has a descriptive character due to a relatively small number of tagged individuals, it was possible to elucidate the small-scale interactions between wild Rhinolophus bats and their environment using the latest high-resolution GPS technology, which will allow us to give new insights into the foraging ecology of echolocating bats in the wild.

scholarly journals Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

2020 ◽  
Vol 24 (6) ◽  
pp. 2951-2962
Author(s):  
Suwash Chandra Acharya ◽  
Rory Nathan ◽  
Quan J. Wang ◽  
Chun-Hsu Su ◽  
Nathan Eizenberg
Keyword(s):  
High Resolution ◽  
Spatial Scale ◽  
Temporal Variability ◽  
Daily Precipitation ◽  
Spatial Scales ◽  
Daily Rainfall ◽  
Spatial Displacement ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
Spatial Evaluation

Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.

A downscaling framework for precipitation nowcasting by merging radar retrievals at different scales and resolutions

2021 ◽  
Author(s):  
Roberto Deidda ◽  
Stefano Farris ◽  
Maria Grazia Badas ◽  
Marino Marrocu ◽  
Luca Massidda ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Weather Radar ◽  
Early Warning Systems ◽  
Small Scale ◽  
Time Step ◽  
Radar Network ◽  
X Band

&lt;p&gt;Convective rainfall events represent one of the most critical issues in urban areas, where numerical weather prediction models are affected by a large uncertainty related to the short temporal and spatial scales involved, thus making early warning systems ineffective. Conversely, radar-based nowcasting models may be a useful tool to guarantee short-term forecasts, through the extrapolation of most recent properties in observed precipitation fields, for lead times ranging from minutes to few hours.&lt;/p&gt;&lt;p&gt;In this study we develop a procedure for merging relevant information from two radar products with different resolutions and scales: (i) high-resolution observations retrieved by an X-band weather radar in a small domain (the metropolitan area of Cagliari, located in Sardinia, Italy), and (ii) the mosaic data provided by the Italian Civil Protection national radar network (the whole region of Sardinia). Specifically, we here adapt some STEPS procedures to merge the large-scale advection from the latter radar network, and the small-scale statistical properties for the former X-band weather radar. We thus combine the corresponding forecasts preserving the higher resolution scale. In details, for each time step we (i) evaluate the power spectra of the two forecasts (ii) merge the two spectra taking the power of the large (small) frequencies from the high (low) resolution data spectrum and (iii) achieve optimal downscaling by reconstructing the high-resolution nowcast from the blend of the two spectra.&lt;/p&gt;

scholarly journals Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

2019 ◽  
Author(s):  
Suwash Chandra Acharya ◽  
Rory Nathan ◽  
Quan J. Wang ◽  
Chun-Hsu Su ◽  
Nathan Eizenberg
Keyword(s):  
High Resolution ◽  
Spatial Scale ◽  
Temporal Variability ◽  
Daily Precipitation ◽  
Spatial Scales ◽  
Daily Rainfall ◽  
Spatial Displacement ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
Spatial Evaluation

Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the Fractions Skill Score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA are found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75 % quantile (90 % quantile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.

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