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- Achberger, C, et al.
(författare)
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Estimation of local near-surface wind conditions - a comparison of WASP and regression based techniques
- 2002
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Ingår i: Meteorological Applications. - 1350-4827. ; 9:2, s. 211-221
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Tidskriftsartikel (refereegranskat)abstract
- This study compares the performance of different models used to assess the local wind near-surface conditions at an agricultural site in Scania, southern Sweden. The methods are: (a) the WASP model (Wind Analysis and Application Program), (b) separate linear regressions of the two wind vector components, (c) a regression model based on vector correlation, and (d) linear regression of scalar wind. Each method was tested with three different data sets over nine months: wind measurements from the nearby Sturup airport SYNOP station, 10 m surface wind and surface geostrophic wind produced by the operational Swedish Mesoscale Analysis system (Mesan). The wind climate estimations were compared with observed winds at the field site, with respect to mean wind speed, wind direction, wind speed frequency distribution and the relative frequency of winds above 6 m s(-1). All models performed reasonably well with data from Sturup and Mesan surface wind, but gave less reliable results with the Mesan geostrophic data. The estimated frequency of winds above 6 m s(-1) was in general lower than the observed frequency. Overall, best results were obtained with WASP in combination with measurements from Sturup.
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- Bhowmik, Avit Kumar, et al.
(författare)
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Representativeness impacts on accuracy and precision of climate spatial interpolation in data-scarce regions
- 2014
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Ingår i: Meteorological Applications. - : Wiley-Blackwell. - 1350-4827 .- 1469-8080. ; 22, s. 368-377
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Tidskriftsartikel (refereegranskat)abstract
- Data scarcity is a major scientific challenge for accuracy and precision of the spatial interpolation of climatic fields, especially in climate-stressed developing countries. Methodologies have been suggested for coping with data scarcity but data have rarely been checked for their representativeness of corresponding climatic fields. This study proved that satisfactory accuracy and precision can be ensured in spatial interpolation if data are satisfactorily representative of corresponding climatic fields despite their scarcity. The influence of number and representativeness of climate data on accuracy and precision of their spatial interpolation has been investigated and compared. Two precipitation and temperature indices were computed for a long time series in Bangladesh, which is a data-scarce region. The representativeness was quantified by dispersion in the data and the accuracy and precision of spatial interpolation were computed by four commonly used error statistics derived through cross-validation. The precipitation data showed very little and sometimes null representativeness whereas the temperature data showed very high representativeness of the corresponding fields. Consequently, precipitation data denoted scarcity but the temperature data denoted sufficiency regarding the required number of data for ensuring satisfactory accuracy and precision for spatial interpolation. It was also found that with the available data, accurate and precise precipitation surfaces can be produced only for representative synoptic spatial scales whereas such temperature surfaces can be generated for the regional scale of Bangladesh. It is highly recommended that the rain-gauge network of Bangladesh be increased or redistributed for computing representative regional precipitation surfaces.
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- Breinl, Korbinian, et al.
(författare)
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Simulating daily precipitation and temperature: a weather generation framework for assessing hydrometeorological hazards
- 2015
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Ingår i: Meteorological Applications. - : Wiley. - 1350-4827 .- 1469-8080. ; 22:3, s. 334-347
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Tidskriftsartikel (refereegranskat)abstract
- Stochastic weather generators simulate synthetic weather data while maintaining statistical properties of the observations. A new semi-parametric algorithm for multi-site precipitation has been published recently by Breinl et al. (2013), who used a univariate Markov process to simulate precipitation occurrence at multiple sites for two small rain gauge networks. Precipitation amounts were simulated in a two-step process by first resampling observations and then sampling and reshuffling of parametric precipitation amounts. In the present study, the precipitation model by Breinl et al. (2013, J. Hydrol. 498: 23–35) is implemented in a weather generation framework for daily precipitation and temperature. It is extended to a considerably larger gauge station network of 19 stations and further improved to reduce the duplication of historical records in the simulation. Autoregressive-moving-average models (ARMA) are used to simulate mean daily temperature at three sites. Power transformations reduce the bias of simulated temperature extremes. Precipitation amounts are simulated by means of hybrid distributions consisting of a Weibull distribution for low precipitation amounts and a generalized Pareto distribution (GPD) for moderate and extreme precipitation amounts. The proposed weather generator is particularly suitable for assessing hydrometeorological hazards such as flooding as it reproduces the spatial variability of precipitation very well and can generate unobserved extremes.
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- Hannachi, Abdelwaheb
(författare)
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Intermittency, autoregression and censoring : a first-order AR model for daily precipitation
- 2014
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Ingår i: Meteorological Applications. - : Wiley. - 1350-4827 .- 1469-8080. ; 21:2, s. 384-397
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Tidskriftsartikel (refereegranskat)abstract
- Daily precipitation is investigated in this study in terms of simple first order autoregressive models. The methodology is based on combining theory from censored processes with continuous autoregressive models to model intermittent phenomena. The choice of short-memory autoregressive models is corroborated further by recent findings on scaling properties of daily precipitation records. The theory and application to synthetic models are presented. The methodology is then applied to Northern Ireland Armagh Observatory daily precipitation for the period 1950-2001 for each month. Both zero- and non zero-mean processes are considered. The analysis indicates that the model parameters do capture seasonality where, for example, the autocorrelation co-efficient is larger in winter, compared to in the summer. This is arguably a reflection of the stronger effect of large-scale processes on rainfall in winter compared to summer. Interestingly, the parameters of the zero- and non zero-mean processes are found to be quite similar, reflecting the symmetric nature of the truncated processes in the midlatitude and extratropics. It is suggested, in particular, that the process mean can be used as a measure to quantify dryness or wetness of a given region. Ways of model improvement, including power transformation, based on the square root, to represent extremes using exploratory quantile-quantile plots better are also discussed. Copyright (c) 2012 Royal Meteorological Society
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- He, M. S., et al.
(författare)
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A delayed modulation of solar ultraviolet radiation on the COVID-19 transmission reflects an incubation period
- 2022
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Ingår i: Meteorological Applications. - : Wiley. - 1350-4827 .- 1469-8080. ; 29:5
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Tidskriftsartikel (refereegranskat)abstract
- Laboratory experiments have revealed the meteorological sensitivity of the coronavirus disease 2019 (COVID-19) virus. However, no consensus has been reached about how outdoor meteorological conditions modulate the virus transmission as it is also constrained by non-meteorological conditions. Here, we identify the outbreak's evolution stage, constrained least by non-meteorological conditions, by searching the maximum correlation coefficient between the ultraviolet flux and the growth rate of cumulative confirmed cases at the country level. At this least-constrained stage, the cumulative cases count around 1300-3200, and the count's daily growth rate correlates with the ultraviolet flux and temperature significantly (correlation coefficients r = -0.54 +/- 0.09 and -0.39 +/- 0.10 at p<0.01$$ p, respectively), but not with precipitation, humidity, and wind. The ultraviolet correlation exhibits a delay of about 7 days, providing a meteorological measure of the incubation period. Our work reveals a seasonality of COVID-19 and a high risk of a pandemic resurgence in winter, implying a need for seasonal adaption in public policies.
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- Jansson, Christer, et al.
(författare)
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Heat balance of an asphalt surface : observations and physically-based simulations
- 2006
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Ingår i: Meteorological Applications. - : Wiley. - 1350-4827 .- 1469-8080. ; 13:2, s. 203-212
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Tidskriftsartikel (refereegranskat)abstract
- A physically-based 1-D beat and mass transfer model was tested to estimate the beat fluxes of an asphalt surface. The model was run for two summer months for a road test site in southwest Sweden. Parameters for thermal properties, surface runoff, radiation and turbulent transfer were obtained from a description of the road stratification and from the literature. Coefficients of determination (r(2)) 0.94, 0.93 and 0.97 were obtained when simulated results were compared with observations of net radiation, beat flow below the surface and surface temperature respectively, all with slope coefficients close to unity. In addition, simulation results elucidated the robe of water vapour transport through the asphalt-soil profile and its effect on the latent beat flow from the surface. Problems were identified with closure of the beat balance in measurements based on discrepancy between simulated and observed sensible heat flux.
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- Kurian, Nisha, et al.
(författare)
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A soft-computing ensemble approach (SEA) to forecast Indian summer monsoon rainfall
- 2017
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Ingår i: Meteorological Applications. - : Wiley-Blackwell. - 1350-4827 .- 1469-8080. ; 24:2, s. 308-314
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Tidskriftsartikel (refereegranskat)abstract
- Agriculture is the backbone of the Indian economy and contributes ∼16% of gross domestic product and about10% of total exports. Hence, accurate and timely forecasting of monthly Indian summer monsoon rainfall is very much in demand for economic planning and agricultural practices. Several methods and models, comprising dynamic and statistical models and combinations of the two, exist for monsoon forecasting. Here, a multi-model ensemble approach, combined within the artificial neural networking technique, was used to develop a soft-computing ensemble algorithm (SEA) to forecast the monthly and seasonal rainfall over the Indian subcontinent. Forecasts using January to May initial conditions along with observations during 1982–2014 were used to develop the model. The SEA compares well with observations
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- Merlone, A., et al.
(författare)
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The MeteoMet project - metrology for meteorology: challenges and results
- 2015
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Ingår i: Meteorological Applications. - : Wiley. - 1350-4827 .- 1469-8080. ; 22, s. 820-829
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Tidskriftsartikel (refereegranskat)abstract
- The study describes significant outcomes of the Metrology for Meteorology' project, MeteoMet, which is an attempt to bridge the meteorological and metrological communities. The concept of traceability, an idea used in both fields but with a subtle difference in meaning, is at the heart of the project. For meteorology, a traceable measurement is the one that can be traced back to a particular instrument, time and location. From a metrological perspective, traceability further implies that the measurement can be traced back to a primary realization of the quantity being measured in terms of the base units of the International System of Units, the SI. These two perspectives reflect long-standing differences in culture and practice and this project - and this study - represents only the first step towards better communication between the two communities. The 3 year MeteoMet project was funded by the European Metrology Research Program (EMRP) and involved 18 European National Metrological Institutes, 3 universities and 35 collaborating stakeholders including national meteorology organizations, research institutes, universities, associations and instrument companies. The project brought a metrological perspective to several long-standing measurement problems in meteorology and climatology, varying from conventional ground-based measurements to those made in the upper atmosphere. It included development and testing of novel instrumentation as well as improved calibration procedures and facilities, instrument intercomparison under realistic conditions and best practice dissemination. Additionally, the validation of historical temperature data series with respect to measurement uncertainties and a methodology for recalculation of the values were included.
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