Seasonal variability of temperature profiles of vegetative and traditional gravel-ballasted roofs: A case study for Lebanon

2017 ◽  
Vol 151 ◽  
pp. 358-364 ◽  
Author(s):  
Jessica Koura ◽  
Rima Manneh ◽  
Rafik Belarbi ◽  
Vanessa El Khoury ◽  
Makram El Bachawati
Keyword(s):  
Seasonal Variability ◽  
Temperature Profiles

scholarly journals Postprocessing ensemble forecasts of vertical temperature profiles

2020 ◽  
Vol 6 (1) ◽  
pp. 45-60
Author(s):  
David Schoenach ◽  
Thorsten Simon ◽  
Georg Johann Mayr
Keyword(s):  
Rank Order ◽  
Probability Distributions ◽  
Ensemble Prediction ◽  
Order Structure ◽  
Temperature Profiles ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Atmospheric Observations ◽  
Prediction Systems

Abstract. Weather forecasts from ensemble prediction systems (EPS) are improved by statistical models trained on past EPS forecasts and their atmospheric observations. Recently these corrections have moved from being univariate to multivariate. The focus has been on (quasi-)horizontal atmospheric variables. This paper extends the correction methods to EPS forecasts of vertical profiles in two steps. First univariate distributional regression methods correct the probability distributions separately at each vertical level. In the second step copula coupling re-installs the dependence among neighboring levels by using the rank order structure of the EPS forecasts. The method is applied to EPS data from the European Centre for Medium-Range Weather Forecasts (ECMWF) at model levels interpolated to four locations in Germany, from which radiosondes are released to measure profiles of temperature and other variables four times a day. A winter case study and a summer case study, respectively, exemplify that univariate postprocessing fails to preserve stable layers, which are crucial for many atmospheric processes. Quantile resampling and a resampling that preserves the relative distance between individual EPS members improve the calibration of the raw forecasts of the temperature profiles as shown by rank histograms. They also improve the multivariate metrics of energy score and variogram score and retain the stable layers. Improvements take place over all times of the day and all seasons. They are largest within the atmospheric boundary layer and for shorter lead times.

scholarly journals Does spatial and seasonal variability in fleshy-fruited trees affect fruit availability? A case study in gallery forests of Central Brazil

2021 ◽  
Vol 35 (3) ◽  
pp. 456-465
Author(s):  
Adriano Antonio Brito Darosci ◽  
Frederico Scherr Caldeira Takahashi ◽  
Carolyn Elinore Barnes Proença ◽  
Lucia Helena Soares-Silva ◽  
Cássia Beatriz Rodrigues Munhoz
Keyword(s):  
Seasonal Variability ◽  
Fruit Availability ◽  
Central Brazil ◽  
Gallery Forests

Seasonal variability in the palatability of freshwater macrophytes: a case study

Hydrobiologia ◽  
2006 ◽  
Vol 570 (1) ◽  
pp. 89-93 ◽  
Author(s):  
A. Elger ◽  
M. H. Barrat-Segretain ◽  
N. J. Willby
Keyword(s):  
Seasonal Variability ◽  
Freshwater Macrophytes

scholarly journals Seasonal variability in ice crystal properties at NorthGRIP: a case study around 301 m depth

2003 ◽  
Vol 37 ◽  
pp. 119-122 ◽  
Author(s):  
Anders Svensson ◽  
Pauli Baadsager ◽  
Asbjørn Persson ◽  
Christine Schøtt Hvidberg ◽  
Marie-Louise Siggaard-Andersen
Keyword(s):  
Seasonal Variability ◽  
Ice Core ◽  
Ice Crystal ◽  
Axis Orientation ◽  
Annual Cycles ◽  
Average Value ◽  
The North ◽  
Chemical Impurities ◽  
Crystal Properties

AbstractThe aim of this case study is to quantify the seasonal variability in crystal properties and to discuss the reason for the variability. A continuous 1.10 m long vertical thin-section profile covering approximately five annual cycles has been obtained from the North Greenland Icecore Project (NorthGRIP) ice core at around 301 m depth. The crystal outline and the c-axis orientation of more than 13000 crystals in the profile have been measured on a new Australian automated ice-crystal analyzer. In 2.5 cm resolution we observe a strong seasonal variability in crystal areas of >30%deviation from the average value of 6.7 mm2. Each year, a band of smaller crystals is observed in ice deposited during spring. The area distribution function is found to be close to a lognormal distribution. The crystal areas are compared to the concentration of chemical impurities in the ice; at a 5 cm resolution, the best correlation is found with the concentration of Ca2+. Our results show no seasonal variability of the average c-axis orientation of ice crystals.

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