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Haberreiter M.,University of Colorado at Boulder | Finsterle W.,Physikalisch Meteorologisches Observatorium Davos
Solar Physics | Year: 2010

Observations carried out with the Magneto-Optical Filter at Two Heights (MOTH) experiment show upward-traveling wave packets in magnetic regions with frequencies below the acoustic cut-off. We demonstrate that the frequency dependence of the observed travel times, i. e. the dispersion relation, shows significant differences in magnetic and non-magnetic regions. More importantly, at and above the layer where the Alfvén speed equals the sound speed we do not see the dispersion relation of the slow acoustic mode with a lowered cut-off frequency. Our comparisons with theoretical dispersion relations suggest that this is not the slow acoustic wave type for the upward low-frequency wave. From this we speculate that partial mode conversion from the fast acoustic to the fast magnetic wave might take place. © 2010 Springer Science+Business Media B.V. Source


Halios C.H.,National and Kapodistrian University of Athens | Helmis C.G.,National and Kapodistrian University of Athens | Flocas H.A.,National and Kapodistrian University of Athens | Nyeki S.,Physikalisch Meteorologisches Observatorium Davos | Assimakopoulos D.N.,National and Kapodistrian University of Athens
Meteorology and Atmospheric Physics | Year: 2012

Synoptic climatology relates the atmospheric circulation with the surface environment. The aim of this study is to examine the variability of the surface meteorological patterns, which are developing under different synoptic scale categories over a suburban area with complex topography. Multivariate Data Analysis techniques were performed to a data set with surface meteorological elements. Three principal components related to the thermodynamic status of the surface environment and the two components of the wind speed were found. The variability of the surface flows was related with atmospheric circulation categories by applying Correspondence Analysis. Similar surface thermodynamic fields develop under cyclonic categories, which are contrasted with the anti-cyclonic category. A strong, steady wind flow characterized by high shear values develops under the cyclonic Closed Low and the anticyclonic H-L categories, in contrast to the variable weak flow under the anticyclonic Open Anticyclone category. © 2012 Springer-Verlag. Source


Huttunen J.,Finnish Meteorological Institute | Huttunen J.,University of Eastern Finland | Kokkola H.,Finnish Meteorological Institute | Mielonen T.,Finnish Meteorological Institute | And 13 more authors.
Atmospheric Chemistry and Physics | Year: 2016

In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the look-up table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during the observation period. © Author(s) 2016. Source


Wacker S.,Physikalisch Meteorologisches Observatorium Davos | Grobner J.,Physikalisch Meteorologisches Observatorium Davos | Zysset C.,Physikalisch Meteorologisches Observatorium Davos | Zysset C.,Computer Controls AG | And 7 more authors.
Journal of Geophysical Research Atmospheres | Year: 2015

We present observations of total cloud cover and cloud type classification results from a sky camera network comprising four stations in Switzerland. In a comprehensive intercomparison study, records of total cloud cover from the sky camera, long-wave radiation observations, Meteosat, ceilometer, and visual observations were compared. Total cloud cover from the sky camera was in 65-85% of cases within ±1 okta with respect to the other methods. The sky camera overestimates cloudiness with respect to the other automatic techniques on average by up to 1.1 ± 2.8 oktas but underestimates it by 0.8 ± 1.9 oktas compared to the human observer. However, the bias depends on the cloudiness and therefore needs to be considered when records from various observational techniques are being homogenized. Cloud type classification was conducted using the k-Nearest Neighbor classifier in combination with a set of color and textural features. In addition, a radiative feature was introduced which improved the discrimination by up to 10%. The performance of the algorithm mainly depends on the atmospheric conditions, site-specific characteristics, the randomness of the selected images, and possible visual misclassifications: The mean success rate was 80-90% when the image only contained a single cloud class but dropped to 50-70% if the test images were completely randomly selected and multiple cloud classes occurred in the images. ©2015. The Authors. Source


Wacker S.,Physikalisch Meteorologisches Observatorium Davos | Wacker S.,University of Bern | Grobner J.,Physikalisch Meteorologisches Observatorium Davos | Nowak D.,Federal Office of Meteorology and Climatology MeteoSwiss | And 2 more authors.
Atmospheric Research | Year: 2011

This analysis presents radiative transfer calculations of surface downwelling long-wave and short-wave radiation and the corresponding cloud radiative effect of single-layered, completely overcast stratus situations (stratus nebulosus) at the Baseline Surface Radiation Network (BSRN) site Payerne. We found an excellent agreement of 0.6Wm-2 mean difference between modeled and observed downwelling long-wave radiation with a root mean squared error of 1.5Wm-2 for 30 carefully selected cases. The discrepancies between modeled and observed diffuse downwelling short-wave radiation are with 2.8±25.4Wm-2 considerably higher. The net cloud radiative effect of the 30 cases shows a pronounced diurnal variation determined by the diurnal cycle of the short-wave cloud effect and the nearly constant positive long-wave cloud effect. Mean net cloud effect ranges from 80±3Wm-2 (min.: 75Wm-2; max.: 85Wm-2) during nighttime in the absence of solar radiation to -197±74Wm-2 (min.: -373Wm-2; max.: -91Wm-2) around noon. Mean net cloud effect averaged over 24h is 18±20Wm-2 (min.: -28Wm-2; max.: +42Wm-2) for the 30 cases assuming a persistent, completely overcast stratus cloud. This implies that stratus nebulosus can have a substantial positive radiative effect during the winter half year at this site. © 2011 Elsevier B.V. Source

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